wrappers.go

// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

// DO NOT EDIT
// This file was machine generated by github.com/tensorflow/tensorflow/tensorflow/go/genop/internal
//
// WARNING: This generation of wrapper function for TensorFlow ops is in an
// experimental state. The generated API can change without notice.

package op

import tf "github.com/tensorflow/tensorflow/tensorflow/go"

// optionalAttr is an intentionally un-exported type to hide
// details of how optional attributes to operations are implemented.
type optionalAttr map[string]interface{}

func makeOutputList(op *tf.Operation, start int, output string) ([]tf.Output, int, error) {
	size, err := op.OutputListSize(output)
	if err != nil {
		return nil, start, err
	}
	list := make([]tf.Output, size)
	for i := 0; i < size; i++ {
		list[i] = op.Output(start + i)
	}
	return list, start + size, nil
}

// Adds sparse updates to the variable referenced by `resource`.
//
// This operation computes
//
//     # Scalar indices
//     ref[indices, ...] += updates[...]
//
//     # Vector indices (for each i)
//     ref[indices[i], ...] += updates[i, ...]
//
//     # High rank indices (for each i, ..., j)
//     ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
//
// Duplicate entries are handled correctly: if multiple `indices` reference
// the same location, their contributions add.
//
// Requires `updates.shape = indices.shape + ref.shape[1:]`.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/ScatterAdd.png" alt>
// </div>
//
// Arguments:
//	resource: Should be from a `Variable` node.
//	indices: A tensor of indices into the first dimension of `ref`.
//	updates: A tensor of updated values to add to `ref`.
//
// Returns the created operation.
func ResourceScatterAdd(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ResourceScatterAdd",
		Input: []tf.Input{
			resource, indices, updates,
		},
	}
	return scope.AddOperation(opspec)
}

// Checks whether a resource handle-based variable has been initialized.
//
// Arguments:
//	resource: the input resource handle.
//
// Returns a scalar boolean which is true if the variable has been
// initialized.
func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "VarIsInitializedOp",
		Input: []tf.Input{
			resource,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Subtracts a value from the current value of a variable.
//
// Any ReadVariableOp which depends directly or indirectly on this assign is
// guaranteed to see the incremented value or a subsequent newer one.
//
// Outputs the incremented value, which can be used to totally order the
// increments to this variable.
//
// Arguments:
//	resource: handle to the resource in which to store the variable.
//	value: the value by which the variable will be incremented.
//
// Returns the created operation.
func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "AssignSubVariableOp",
		Input: []tf.Input{
			resource, value,
		},
	}
	return scope.AddOperation(opspec)
}

// Assigns a new value to a variable.
//
// Any ReadVariableOp with a control dependency on this op is guaranteed to return
// this value or a subsequent newer value of the variable.
//
// Arguments:
//	resource: handle to the resource in which to store the variable.
//	value: the value to set the new tensor to use.
//
// Returns the created operation.
func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "AssignVariableOp",
		Input: []tf.Input{
			resource, value,
		},
	}
	return scope.AddOperation(opspec)
}

// VarHandleOpAttr is an optional argument to VarHandleOp.
type VarHandleOpAttr func(optionalAttr)

// VarHandleOpContainer sets the optional container attribute to value.
//
// value: the container this variable is placed in.
// If not specified, defaults to ""
func VarHandleOpContainer(value string) VarHandleOpAttr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// VarHandleOpSharedName sets the optional shared_name attribute to value.
//
// value: the name by which this variable is referred to.
// If not specified, defaults to ""
func VarHandleOpSharedName(value string) VarHandleOpAttr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// Creates a handle to a Variable resource.
//
// Arguments:
//	dtype: the type of this variable. Must agree with the dtypes
// of all ops using this variable.
//	shape: The (possibly partially specified) shape of this variable.
func VarHandleOp(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...VarHandleOpAttr) (resource tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype, "shape": shape}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "VarHandleOp",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
//
// Arguments:
//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation,
// shape one of: `[d]`, `[b, d]`,  `[b, h, w, d]`.
//	inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape
//   same as `gradients`.
// min, max: Quantization interval, floats of shape `[d]`.
//
//
//
// Returns Backpropagated gradients w.r.t. inputs, shape same as
// `inputs`:
//   `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter, shape `[d]`:
// `sum_per_d(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter, shape `[d]`:
// `sum_per_d(gradients * (inputs > max))`.
func FakeQuantWithMinMaxVarsPerChannelGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FakeQuantWithMinMaxVarsPerChannelGradient",
		Input: []tf.Input{
			gradients, inputs, min, max,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
//
// and `max` to 'outputs' tensor of same shape as `inputs`.
//
// [min; max] is the clamping range for the 'inputs' data.  Op divides this range
// into 255 steps (total of 256 values), then replaces each 'inputs' value with the
// closest of the quantized step values.
//
// This operation has a gradient and thus allows for training `min` and `max` values.
func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output) (outputs tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FakeQuantWithMinMaxVars",
		Input: []tf.Input{
			inputs, min, max,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QuantizedInstanceNormAttr is an optional argument to QuantizedInstanceNorm.
type QuantizedInstanceNormAttr func(optionalAttr)

// QuantizedInstanceNormOutputRangeGiven sets the optional output_range_given attribute to value.
//
// value: If True, `given_y_min` and `given_y_min`
// and `given_y_max` are used as the output range. Otherwise,
// the implementation computes the output range.
// If not specified, defaults to false
func QuantizedInstanceNormOutputRangeGiven(value bool) QuantizedInstanceNormAttr {
	return func(m optionalAttr) {
		m["output_range_given"] = value
	}
}

// QuantizedInstanceNormGivenYMin sets the optional given_y_min attribute to value.
//
// value: Output in `y_min` if `output_range_given` is True.
// If not specified, defaults to 0
func QuantizedInstanceNormGivenYMin(value float32) QuantizedInstanceNormAttr {
	return func(m optionalAttr) {
		m["given_y_min"] = value
	}
}

// QuantizedInstanceNormGivenYMax sets the optional given_y_max attribute to value.
//
// value: Output in `y_max` if `output_range_given` is True.
// If not specified, defaults to 0
func QuantizedInstanceNormGivenYMax(value float32) QuantizedInstanceNormAttr {
	return func(m optionalAttr) {
		m["given_y_max"] = value
	}
}

// QuantizedInstanceNormVarianceEpsilon sets the optional variance_epsilon attribute to value.
//
// value: A small float number to avoid dividing by 0.
// If not specified, defaults to 1e-05
func QuantizedInstanceNormVarianceEpsilon(value float32) QuantizedInstanceNormAttr {
	return func(m optionalAttr) {
		m["variance_epsilon"] = value
	}
}

// QuantizedInstanceNormMinSeparation sets the optional min_separation attribute to value.
//
// value: Minimum value of `y_max - y_min`
// If not specified, defaults to 0.001
func QuantizedInstanceNormMinSeparation(value float32) QuantizedInstanceNormAttr {
	return func(m optionalAttr) {
		m["min_separation"] = value
	}
}

// Quantized Instance normalization.
//
// Arguments:
//	x: A 4D input Tensor.
//	x_min: The value represented by the lowest quantized input.
//	x_max: The value represented by the highest quantized input.
//
// Returns A 4D Tensor.The value represented by the lowest quantized output.The value represented by the highest quantized output.
func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf.Output, optional ...QuantizedInstanceNormAttr) (y tf.Output, y_min tf.Output, y_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizedInstanceNorm",
		Input: []tf.Input{
			x, x_min, x_max,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Concatenates quantized tensors along one dimension.
//
// Arguments:
//	concat_dim: 0-D.  The dimension along which to concatenate.  Must be in the
// range [0, rank(values)).
//	values: The `N` Tensors to concatenate. Their ranks and types must match,
// and their sizes must match in all dimensions except `concat_dim`.
//	input_mins: The minimum scalar values for each of the input tensors.
//	input_maxes: The maximum scalar values for each of the input tensors.
//
// Returns A `Tensor` with the concatenation of values stacked along the
// `concat_dim` dimension.  This tensor's shape matches that of `values` except
// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents.
func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "QuantizedConcat",
		Input: []tf.Input{
			concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes),
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// QuantizeAndDequantizeAttr is an optional argument to QuantizeAndDequantize.
type QuantizeAndDequantizeAttr func(optionalAttr)

// QuantizeAndDequantizeSignedInput sets the optional signed_input attribute to value.
// If not specified, defaults to true
func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr {
	return func(m optionalAttr) {
		m["signed_input"] = value
	}
}

// QuantizeAndDequantizeNumBits sets the optional num_bits attribute to value.
// If not specified, defaults to 8
func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr {
	return func(m optionalAttr) {
		m["num_bits"] = value
	}
}

// QuantizeAndDequantizeRangeGiven sets the optional range_given attribute to value.
// If not specified, defaults to false
func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr {
	return func(m optionalAttr) {
		m["range_given"] = value
	}
}

// QuantizeAndDequantizeInputMin sets the optional input_min attribute to value.
// If not specified, defaults to 0
func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr {
	return func(m optionalAttr) {
		m["input_min"] = value
	}
}

// QuantizeAndDequantizeInputMax sets the optional input_max attribute to value.
// If not specified, defaults to 0
func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr {
	return func(m optionalAttr) {
		m["input_max"] = value
	}
}

// Use QuantizeAndDequantizeV2 instead.
//
// DEPRECATED at GraphDef version 22: Replaced by QuantizeAndDequantizeV2
func QuantizeAndDequantize(scope *Scope, input tf.Output, optional ...QuantizeAndDequantizeAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizeAndDequantize",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// OneHotAttr is an optional argument to OneHot.
type OneHotAttr func(optionalAttr)

// OneHotAxis sets the optional axis attribute to value.
//
// value: The axis to fill (default: -1, a new inner-most axis).
// If not specified, defaults to -1
func OneHotAxis(value int64) OneHotAttr {
	return func(m optionalAttr) {
		m["axis"] = value
	}
}

// Returns a one-hot tensor.
//
// The locations represented by indices in `indices` take value `on_value`,
// while all other locations take value `off_value`.
//
// If the input `indices` is rank `N`, the output will have rank `N+1`,
// The new axis is created at dimension `axis` (default: the new axis is
// appended at the end).
//
// If `indices` is a scalar the output shape will be a vector of length `depth`.
//
// If `indices` is a vector of length `features`, the output shape will be:
// ```
//   features x depth if axis == -1
//   depth x features if axis == 0
// ```
//
// If `indices` is a matrix (batch) with shape `[batch, features]`,
// the output shape will be:
// ```
//   batch x features x depth if axis == -1
//   batch x depth x features if axis == 1
//   depth x batch x features if axis == 0
// ```
//
//
// Examples
// =========
//
// Suppose that
//
// ```
//   indices = [0, 2, -1, 1]
//   depth = 3
//   on_value = 5.0
//   off_value = 0.0
//   axis = -1
// ```
//
// Then output is `[4 x 3]`:
//
//     ```output =
//       [5.0 0.0 0.0]  // one_hot(0)
//       [0.0 0.0 5.0]  // one_hot(2)
//       [0.0 0.0 0.0]  // one_hot(-1)
//       [0.0 5.0 0.0]  // one_hot(1)
//     ```
//
// Suppose that
//
// ```
//   indices = [0, 2, -1, 1]
//   depth = 3
//   on_value = 0.0
//   off_value = 3.0
//   axis = 0
// ```
//
// Then output is `[3 x 4]`:
//
//     ```output =
//       [0.0 3.0 3.0 3.0]
//       [3.0 3.0 3.0 0.0]
//       [3.0 3.0 3.0 3.0]
//       [3.0 0.0 3.0 3.0]
//     //  ^                one_hot(0)
//     //      ^            one_hot(2)
//     //          ^        one_hot(-1)
//     //              ^    one_hot(1)
//     ```
// Suppose that
//
// ```
//   indices = [[0, 2], [1, -1]]
//   depth = 3
//   on_value = 1.0
//   off_value = 0.0
//   axis = -1
// ```
//
// Then output is `[2 x 2 x 3]`:
//
//     ```output =
//       [
//         [1.0, 0.0, 0.0]  // one_hot(0)
//         [0.0, 0.0, 1.0]  // one_hot(2)
//       ][
//         [0.0, 1.0, 0.0]  // one_hot(1)
//         [0.0, 0.0, 0.0]  // one_hot(-1)
//       ]```
//
// Arguments:
//	indices: A tensor of indices.
//	depth: A scalar defining the depth of the one hot dimension.
//	on_value: A scalar defining the value to fill in output when `indices[j] = i`.
//	off_value: A scalar defining the value to fill in output when `indices[j] != i`.
//
// Returns The one-hot tensor.
func OneHot(scope *Scope, indices tf.Output, depth tf.Output, on_value tf.Output, off_value tf.Output, optional ...OneHotAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "OneHot",
		Input: []tf.Input{
			indices, depth, on_value, off_value,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Bitcasts a tensor from one type to another without copying data.
//
// Given a tensor `input`, this operation returns a tensor that has the same buffer
// data as `input` with datatype `type`.
//
// If the input datatype `T` is larger than the output datatype `type` then the
// shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)].
//
// If `T` is smaller than `type`, the operator requires that the rightmost
// dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from
// [..., sizeof(`type`)/sizeof(`T`)] to [...].
//
// *NOTE*: Bitcast is implemented as a low-level cast, so machines with different
// endian orderings will give different results.
func Bitcast(scope *Scope, input tf.Output, type_ tf.DataType) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"type": type_}
	opspec := tf.OpSpec{
		Type: "Bitcast",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Extract `patches` from `images` and put them in the "depth" output dimension.
//
// Arguments:
//	images: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`.
//	ksizes: The size of the sliding window for each dimension of `images`.
//	strides: 1-D of length 4. How far the centers of two consecutive patches are in
// the images. Must be: `[1, stride_rows, stride_cols, 1]`.
//	rates: 1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the
// input stride, specifying how far two consecutive patch samples are in the
// input. Equivalent to extracting patches with
// `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by
// subsampling them spatially by a factor of `rates`.
//	padding: The type of padding algorithm to use.
//
// We specify the size-related attributes as:
//
// ```python
//       ksizes = [1, ksize_rows, ksize_cols, 1]
//       strides = [1, strides_rows, strides_cols, 1]
//       rates = [1, rates_rows, rates_cols, 1]
// ```
//
// Returns 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *
// ksize_cols * depth]` containing image patches with size
// `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension.
func ExtractImagePatches(scope *Scope, images tf.Output, ksizes []int64, strides []int64, rates []int64, padding string) (patches tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "rates": rates, "padding": padding}
	opspec := tf.OpSpec{
		Type: "ExtractImagePatches",
		Input: []tf.Input{
			images,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// DepthToSpace for tensors of type T.
//
// Rearranges data from depth into blocks of spatial data.
// This is the reverse transformation of SpaceToDepth. More specifically,
// this op outputs a copy of the input tensor where values from the `depth`
// dimension are moved in spatial blocks to the `height` and `width` dimensions.
// The attr `block_size` indicates the input block size and how the data is moved.
//
//   * Chunks of data of size `block_size * block_size` from depth are rearranged
//     into non-overlapping blocks of size `block_size x block_size`
//   * The width the output tensor is `input_depth * block_size`, whereas the
//     height is `input_height * block_size`.
//   * The depth of the input tensor must be divisible by
//     `block_size * block_size`.
//
// That is, assuming the input is in the shape:
// `[batch, height, width, depth]`,
// the shape of the output will be:
// `[batch, height*block_size, width*block_size, depth/(block_size*block_size)]`
//
// This operation requires that the input tensor be of rank 4, and that
// `block_size` be >=1 and that `block_size * block_size` be a divisor of the
// input depth.
//
// This operation is useful for resizing the activations between convolutions
// (but keeping all data), e.g. instead of pooling. It is also useful for training
// purely convolutional models.
//
// For example, given this input of shape `[1, 1, 1, 4]`, and a block size of 2:
//
// ```prettyprint
// x = [[[[1, 2, 3, 4]]]]
//
// ```
//
// This operation will output a tensor of shape `[1, 2, 2, 1]`:
//
// ```prettyprint
//    [[[[1], [2]],
//      [[3], [4]]]]
// ```
//
// Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`,
// the corresponding output will have 2x2 elements and will have a depth of
// 1 channel (1 = `4 / (block_size * block_size)`).
// The output element shape is `[2, 2, 1]`.
//
// For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g.
//
// ```prettyprint
// x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
// ```
//
// This operation, for block size of 2, will return the following tensor of shape
// `[1, 2, 2, 3]`
//
// ```prettyprint
//    [[[[1, 2, 3], [4, 5, 6]],
//      [[7, 8, 9], [10, 11, 12]]]]
//
// ```
//
// Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2:
//
// ```prettyprint
// x =  [[[[1, 2, 3, 4],
//        [5, 6, 7, 8]],
//       [[9, 10, 11, 12],
//        [13, 14, 15, 16]]]]
// ```
//
// the operator will return the following tensor of shape `[1 4 4 1]`:
//
// ```prettyprint
// x = [[ [1],   [2],  [5],  [6]],
//      [ [3],   [4],  [7],  [8]],
//      [ [9],  [10], [13],  [14]],
//      [ [11], [12], [15],  [16]]]
//
// ```
//
// Arguments:
//
//	block_size: The size of the spatial block, same as in Space2Depth.
func DepthToSpace(scope *Scope, input tf.Output, block_size int64) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"block_size": block_size}
	opspec := tf.OpSpec{
		Type: "DepthToSpace",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// BatchToSpace for N-D tensors of type T.
//
// This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of shape
// `block_shape + [batch]`, interleaves these blocks back into the grid defined by
// the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as
// the input.  The spatial dimensions of this intermediate result are then
// optionally cropped according to `crops` to produce the output.  This is the
// reverse of SpaceToBatch.  See below for a precise description.
//
// Arguments:
//	input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
// where spatial_shape has M dimensions.
//	block_shape: 1-D with shape `[M]`, all values must be >= 1.
//	crops: 2-D with shape `[M, 2]`, all values must be >= 0.
//   `crops[i] = [crop_start, crop_end]` specifies the amount to crop from input
//   dimension `i + 1`, which corresponds to spatial dimension `i`.  It is
//   required that
//   `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.
//
// This operation is equivalent to the following steps:
//
// 1. Reshape `input` to `reshaped` of shape:
//      [block_shape[0], ..., block_shape[M-1],
//       batch / prod(block_shape),
//       input_shape[1], ..., input_shape[N-1]]
//
// 2. Permute dimensions of `reshaped` to produce `permuted` of shape
//      [batch / prod(block_shape),
//
//       input_shape[1], block_shape[0],
//       ...,
//       input_shape[M], block_shape[M-1],
//
//       input_shape[M+1], ..., input_shape[N-1]]
//
// 3. Reshape `permuted` to produce `reshaped_permuted` of shape
//      [batch / prod(block_shape),
//
//       input_shape[1] * block_shape[0],
//       ...,
//       input_shape[M] * block_shape[M-1],
//
//       input_shape[M+1],
//       ...,
//       input_shape[N-1]]
//
// 4. Crop the start and end of dimensions `[1, ..., M]` of
//    `reshaped_permuted` according to `crops` to produce the output of shape:
//      [batch / prod(block_shape),
//
//       input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
//       ...,
//       input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
//
//       input_shape[M+1], ..., input_shape[N-1]]
//
// Some examples:
//
// (1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and
//     `crops = [[0, 0], [0, 0]]`:
//
// ```prettyprint
// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
// ```
//
// The output tensor has shape `[1, 2, 2, 1]` and value:
//
// ```prettyprint
// x = [[[[1], [2]], [[3], [4]]]]
// ```
//
// (2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and
//     `crops = [[0, 0], [0, 0]]`:
//
// ```prettyprint
// [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
// ```
//
// The output tensor has shape `[1, 2, 2, 3]` and value:
//
// ```prettyprint
// x = [[[[1, 2, 3], [4, 5, 6]],
//       [[7, 8, 9], [10, 11, 12]]]]
// ```
//
// (3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and
//     `crops = [[0, 0], [0, 0]]`:
//
// ```prettyprint
// x = [[[[1], [3]], [[9], [11]]],
//      [[[2], [4]], [[10], [12]]],
//      [[[5], [7]], [[13], [15]]],
//      [[[6], [8]], [[14], [16]]]]
// ```
//
// The output tensor has shape `[1, 4, 4, 1]` and value:
//
// ```prettyprint
// x = [[[1],   [2],  [3],  [4]],
//      [[5],   [6],  [7],  [8]],
//      [[9],  [10], [11],  [12]],
//      [[13], [14], [15],  [16]]]
// ```
//
// (4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and
//     `crops = [[0, 0], [2, 0]]`:
//
// ```prettyprint
// x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
//      [[[0], [2], [4]]], [[[0], [10], [12]]],
//      [[[0], [5], [7]]], [[[0], [13], [15]]],
//      [[[0], [6], [8]]], [[[0], [14], [16]]]]
// ```
//
// The output tensor has shape `[2, 2, 4, 1]` and value:
//
// ```prettyprint
// x = [[[[1],   [2],  [3],  [4]],
//       [[5],   [6],  [7],  [8]]],
//      [[[9],  [10], [11],  [12]],
//       [[13], [14], [15],  [16]]]]
// ```
func BatchToSpaceND(scope *Scope, input tf.Output, block_shape tf.Output, crops tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "BatchToSpaceND",
		Input: []tf.Input{
			input, block_shape, crops,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SpaceToBatch for 4-D tensors of type T.
//
// This is a legacy version of the more general SpaceToBatchND.
//
// Zero-pads and then rearranges (permutes) blocks of spatial data into batch.
// More specifically, this op outputs a copy of the input tensor where values from
// the `height` and `width` dimensions are moved to the `batch` dimension. After
// the zero-padding, both `height` and `width` of the input must be divisible by the
// block size.
//
// Arguments:
//	input: 4-D with shape `[batch, height, width, depth]`.
//	paddings: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
//   the padding of the input with zeros across the spatial dimensions as follows:
//
//       paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]
//
//   The effective spatial dimensions of the zero-padded input tensor will be:
//
//       height_pad = pad_top + height + pad_bottom
//       width_pad = pad_left + width + pad_right
//
// The attr `block_size` must be greater than one. It indicates the block size.
//
//   * Non-overlapping blocks of size `block_size x block size` in the height and
//     width dimensions are rearranged into the batch dimension at each location.
//   * The batch of the output tensor is `batch * block_size * block_size`.
//   * Both height_pad and width_pad must be divisible by block_size.
//
// The shape of the output will be:
//
//     [batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
//      depth]
//
// Some examples:
//
// (1) For the following input of shape `[1, 2, 2, 1]` and block_size of 2:
//
// ```prettyprint
// x = [[[[1], [2]], [[3], [4]]]]
// ```
//
// The output tensor has shape `[4, 1, 1, 1]` and value:
//
// ```prettyprint
// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
// ```
//
// (2) For the following input of shape `[1, 2, 2, 3]` and block_size of 2:
//
// ```prettyprint
// x = [[[[1, 2, 3], [4, 5, 6]],
//       [[7, 8, 9], [10, 11, 12]]]]
// ```
//
// The output tensor has shape `[4, 1, 1, 3]` and value:
//
// ```prettyprint
// [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
// ```
//
// (3) For the following input of shape `[1, 4, 4, 1]` and block_size of 2:
//
// ```prettyprint
// x = [[[[1],   [2],  [3],  [4]],
//       [[5],   [6],  [7],  [8]],
//       [[9],  [10], [11],  [12]],
//       [[13], [14], [15],  [16]]]]
// ```
//
// The output tensor has shape `[4, 2, 2, 1]` and value:
//
// ```prettyprint
// x = [[[[1], [3]], [[9], [11]]],
//      [[[2], [4]], [[10], [12]]],
//      [[[5], [7]], [[13], [15]]],
//      [[[6], [8]], [[14], [16]]]]
// ```
//
// (4) For the following input of shape `[2, 2, 4, 1]` and block_size of 2:
//
// ```prettyprint
// x = [[[[1],   [2],  [3],  [4]],
//       [[5],   [6],  [7],  [8]]],
//      [[[9],  [10], [11],  [12]],
//       [[13], [14], [15],  [16]]]]
// ```
//
// The output tensor has shape `[8, 1, 2, 1]` and value:
//
// ```prettyprint
// x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
//      [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
// ```
//
// Among others, this operation is useful for reducing atrous convolution into
// regular convolution.
//
func SpaceToBatch(scope *Scope, input tf.Output, paddings tf.Output, block_size int64) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"block_size": block_size}
	opspec := tf.OpSpec{
		Type: "SpaceToBatch",
		Input: []tf.Input{
			input, paddings,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QuantizeAndDequantizeV2Attr is an optional argument to QuantizeAndDequantizeV2.
type QuantizeAndDequantizeV2Attr func(optionalAttr)

// QuantizeAndDequantizeV2SignedInput sets the optional signed_input attribute to value.
//
// value: If the quantization is signed or unsigned.
// If not specified, defaults to true
func QuantizeAndDequantizeV2SignedInput(value bool) QuantizeAndDequantizeV2Attr {
	return func(m optionalAttr) {
		m["signed_input"] = value
	}
}

// QuantizeAndDequantizeV2NumBits sets the optional num_bits attribute to value.
//
// value: The bitwidth of the quantization.
// If not specified, defaults to 8
func QuantizeAndDequantizeV2NumBits(value int64) QuantizeAndDequantizeV2Attr {
	return func(m optionalAttr) {
		m["num_bits"] = value
	}
}

// QuantizeAndDequantizeV2RangeGiven sets the optional range_given attribute to value.
//
// value: If the range is given or should be computed from the tensor.
// If not specified, defaults to false
func QuantizeAndDequantizeV2RangeGiven(value bool) QuantizeAndDequantizeV2Attr {
	return func(m optionalAttr) {
		m["range_given"] = value
	}
}

// Quantizes then dequantizes a tensor.
//
// This op simulates the precision loss from the quantized forward pass by:
// 1. Quantizing the tensor to fixed point numbers, which should match the target
//    quantization method when it is used in inference.
// 2. Dequantizing it back to floating point numbers for the following ops, most
//    likely matmul.
//
// There are different ways to quantize. This version does not use the full range
// of the output type, choosing to elide the lowest possible value for symmetry
// (e.g., output range is -127 to 127, not -128 to 127 for signed 8 bit
// quantization), so that 0.0 maps to 0.
//
// To perform this op, we first find the range of values in our tensor. The range
// we use is always centered on 0, so we find m such that
//
// 1. m = max(abs(input_min), abs(input_max)) if range_given is true,
// 2. m = max(abs(min_elem(input)), abs(max_elem(input))) otherwise.
//
// Our input tensor range is then [-m, m].
//
// Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed].
// If signed_input is true, this is
//
//   [min_fixed, max_fixed ] =
//       [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1].
//
// Otherwise, if signed_input is false, the fixed-point range is
//
//   [min_fixed, max_fixed] = [0, (1 << num_bits) - 1].
//
// From this we compute our scaling factor, s:
//
//   s = (max_fixed - min_fixed) / (2 * m).
//
// Now we can quantize and dequantize the elements of our tensor.  An element e
// is transformed into e':
//
//   e' = (e * s).round_to_nearest() / s.
//
// Note that we have a different number of buckets in the signed vs. unsigned
// cases.  For example, if num_bits == 8, we get 254 buckets in the signed case
// vs. 255 in the unsigned case.
//
// For example, suppose num_bits = 8 and m = 1.  Then
//
//   [min_fixed, max_fixed] = [-127, 127], and
//   s = (127 + 127) / 2 = 127.
//
// Given the vector {-1, -0.5, 0, 0.3}, this is quantized to
// {-127, -63, 0, 38}, and dequantized to {-1, -63.0/127, 0, 38.0/127}.
//
// Arguments:
//	input: Tensor to quantize and then dequantize.
//	input_min: If range_given, this is the min of the range, otherwise this input
// will be ignored.
//	input_max: If range_given, this is the max of the range, otherwise this input
// will be ignored.
func QuantizeAndDequantizeV2(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, optional ...QuantizeAndDequantizeV2Attr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizeAndDequantizeV2",
		Input: []tf.Input{
			input, input_min, input_max,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SpaceToBatch for N-D tensors of type T.
//
// This operation divides "spatial" dimensions `[1, ..., M]` of the input into a
// grid of blocks of shape `block_shape`, and interleaves these blocks with the
// "batch" dimension (0) such that in the output, the spatial dimensions
// `[1, ..., M]` correspond to the position within the grid, and the batch
// dimension combines both the position within a spatial block and the original
// batch position.  Prior to division into blocks, the spatial dimensions of the
// input are optionally zero padded according to `paddings`.  See below for a
// precise description.
//
// Arguments:
//	input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
// where spatial_shape has `M` dimensions.
//	block_shape: 1-D with shape `[M]`, all values must be >= 1.
//	paddings: 2-D with shape `[M, 2]`, all values must be >= 0.
//   `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension
//   `i + 1`, which corresponds to spatial dimension `i`.  It is required that
//   `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`.
//
// This operation is equivalent to the following steps:
//
// 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the
//    input according to `paddings` to produce `padded` of shape `padded_shape`.
//
// 2. Reshape `padded` to `reshaped_padded` of shape:
//
//      [batch] +
//      [padded_shape[1] / block_shape[0],
//        block_shape[0],
//       ...,
//       padded_shape[M] / block_shape[M-1],
//       block_shape[M-1]] +
//      remaining_shape
//
// 3. Permute dimensions of `reshaped_padded` to produce
//    `permuted_reshaped_padded` of shape:
//
//      block_shape +
//      [batch] +
//      [padded_shape[1] / block_shape[0],
//       ...,
//       padded_shape[M] / block_shape[M-1]] +
//      remaining_shape
//
// 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch
//    dimension, producing an output tensor of shape:
//
//      [batch * prod(block_shape)] +
//      [padded_shape[1] / block_shape[0],
//       ...,
//       padded_shape[M] / block_shape[M-1]] +
//      remaining_shape
//
// Some examples:
//
// (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and
//     `paddings = [[0, 0], [0, 0]]`:
//
// ```prettyprint
// x = [[[[1], [2]], [[3], [4]]]]
// ```
//
// The output tensor has shape `[4, 1, 1, 1]` and value:
//
// ```prettyprint
// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
// ```
//
// (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and
//     `paddings = [[0, 0], [0, 0]]`:
//
// ```prettyprint
// x = [[[[1, 2, 3], [4, 5, 6]],
//       [[7, 8, 9], [10, 11, 12]]]]
// ```
//
// The output tensor has shape `[4, 1, 1, 3]` and value:
//
// ```prettyprint
// [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
// ```
//
// (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and
//     `paddings = [[0, 0], [0, 0]]`:
//
// ```prettyprint
// x = [[[[1],   [2],  [3],  [4]],
//       [[5],   [6],  [7],  [8]],
//       [[9],  [10], [11],  [12]],
//       [[13], [14], [15],  [16]]]]
// ```
//
// The output tensor has shape `[4, 2, 2, 1]` and value:
//
// ```prettyprint
// x = [[[[1], [3]], [[9], [11]]],
//      [[[2], [4]], [[10], [12]]],
//      [[[5], [7]], [[13], [15]]],
//      [[[6], [8]], [[14], [16]]]]
// ```
//
// (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and
//     paddings = `[[0, 0], [2, 0]]`:
//
// ```prettyprint
// x = [[[[1],   [2],  [3],  [4]],
//       [[5],   [6],  [7],  [8]]],
//      [[[9],  [10], [11],  [12]],
//       [[13], [14], [15],  [16]]]]
// ```
//
// The output tensor has shape `[8, 1, 3, 1]` and value:
//
// ```prettyprint
// x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
//      [[[0], [2], [4]]], [[[0], [10], [12]]],
//      [[[0], [5], [7]]], [[[0], [13], [15]]],
//      [[[0], [6], [8]]], [[[0], [14], [16]]]]
// ```
//
// Among others, this operation is useful for reducing atrous convolution into
// regular convolution.
func SpaceToBatchND(scope *Scope, input tf.Output, block_shape tf.Output, paddings tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SpaceToBatchND",
		Input: []tf.Input{
			input, block_shape, paddings,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ListDiffAttr is an optional argument to ListDiff.
type ListDiffAttr func(optionalAttr)

// ListDiffOutIdx sets the optional out_idx attribute to value.
// If not specified, defaults to DT_INT32
func ListDiffOutIdx(value tf.DataType) ListDiffAttr {
	return func(m optionalAttr) {
		m["out_idx"] = value
	}
}

// Computes the difference between two lists of numbers or strings.
//
// Given a list `x` and a list `y`, this operation returns a list `out` that
// represents all values that are in `x` but not in `y`. The returned list `out`
// is sorted in the same order that the numbers appear in `x` (duplicates are
// preserved). This operation also returns a list `idx` that represents the
// position of each `out` element in `x`. In other words:
//
// `out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]`
//
// For example, given this input:
//
// ```prettyprint
// x = [1, 2, 3, 4, 5, 6]
// y = [1, 3, 5]
// ```
//
// This operation would return:
//
// ```prettyprint
// out ==> [2, 4, 6]
// idx ==> [1, 3, 5]
// ```
//
// Arguments:
//	x: 1-D. Values to keep.
//	y: 1-D. Values to remove.
//
// Returns 1-D. Values present in `x` but not in `y`.1-D. Positions of `x` values preserved in `out`.
func ListDiff(scope *Scope, x tf.Output, y tf.Output, optional ...ListDiffAttr) (out tf.Output, idx tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ListDiff",
		Input: []tf.Input{
			x, y,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// SqueezeAttr is an optional argument to Squeeze.
type SqueezeAttr func(optionalAttr)

// SqueezeSqueezeDims sets the optional squeeze_dims attribute to value.
//
// value: If specified, only squeezes the dimensions listed. The dimension
// index starts at 0. It is an error to squeeze a dimension that is not 1.
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func SqueezeSqueezeDims(value []int64) SqueezeAttr {
	return func(m optionalAttr) {
		m["squeeze_dims"] = value
	}
}

// Removes dimensions of size 1 from the shape of a tensor.
//
// Given a tensor `input`, this operation returns a tensor of the same type with
// all dimensions of size 1 removed. If you don't want to remove all size 1
// dimensions, you can remove specific size 1 dimensions by specifying
// `squeeze_dims`.
//
// For example:
//
// ```prettyprint
// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
// shape(squeeze(t)) ==> [2, 3]
// ```
//
// Or, to remove specific size 1 dimensions:
//
// ```prettyprint
// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
// ```
//
// Arguments:
//	input: The `input` to squeeze.
//
// Returns Contains the same data as `input`, but has one or more dimensions of
// size 1 removed.
func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Squeeze",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// A placeholder op for a value that will be fed into the computation.
//
// N.B. This operation will fail with an error if it is executed. It is
// intended as a way to represent a value that will always be fed, and to
// provide attrs that enable the fed value to be checked at runtime.
//
// Arguments:
//	dtype: The type of elements in the tensor.
//	shape: The shape of the tensor. The shape can be any partially-specified
// shape.  To be unconstrained, pass in a shape with unknown rank.
//
// Returns A placeholder tensor that must be replaced using the feed mechanism.
func PlaceholderV2(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype, "shape": shape}
	opspec := tf.OpSpec{
		Type: "PlaceholderV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// PlaceholderAttr is an optional argument to Placeholder.
type PlaceholderAttr func(optionalAttr)

// PlaceholderShape sets the optional shape attribute to value.
//
// value: (Optional) The shape of the tensor. If the shape has 0 dimensions, the
// shape is unconstrained.
// If not specified, defaults to <>
func PlaceholderShape(value tf.Shape) PlaceholderAttr {
	return func(m optionalAttr) {
		m["shape"] = value
	}
}

// A placeholder op for a value that will be fed into the computation.
//
// N.B. This operation will fail with an error if it is executed. It is
// intended as a way to represent a value that will always be fed, and to
// provide attrs that enable the fed value to be checked at runtime.
//
// Arguments:
//	dtype: The type of elements in the tensor.
//
// Returns A placeholder tensor that must be replaced using the feed mechanism.
func Placeholder(scope *Scope, dtype tf.DataType, optional ...PlaceholderAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Placeholder",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Pads a tensor with mirrored values.
//
// This operation pads a `input` with mirrored values according to the `paddings`
// you specify. `paddings` is an integer tensor with shape `[n, 2]`, where n is
// the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
// how many values to add before the contents of `input` in that dimension, and
// `paddings[D, 1]` indicates how many values to add after the contents of `input`
// in that dimension. Both `paddings[D, 0]` and `paddings[D, 1]` must be no greater
// than `input.dim_size(D)` (or `input.dim_size(D) - 1`) if `copy_border` is true
// (if false, respectively).
//
// The padded size of each dimension D of the output is:
//
// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
//
// For example:
//
// ```prettyprint
// # 't' is [[1, 2, 3], [4, 5, 6]].
// # 'paddings' is [[1, 1]], [2, 2]].
// # 'mode' is SYMMETRIC.
// # rank of 't' is 2.
// pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2]
//                       [2, 1, 1, 2, 3, 3, 2]
//                       [5, 4, 4, 5, 6, 6, 5]
//                       [5, 4, 4, 5, 6, 6, 5]]
// ```
//
// Arguments:
//	input: The input tensor to be padded.
//	paddings: A two-column matrix specifying the padding sizes. The number of
// rows must be the same as the rank of `input`.
//	mode: Either `REFLECT` or `SYMMETRIC`. In reflect mode the padded regions
// do not include the borders, while in symmetric mode the padded regions
// do include the borders. For example, if `input` is `[1, 2, 3]` and `paddings`
// is `[0, 2]`, then the output is `[1, 2, 3, 2, 1]` in reflect mode, and
// it is `[1, 2, 3, 3, 2]` in symmetric mode.
//
// Returns The padded tensor.
func MirrorPad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"mode": mode}
	opspec := tf.OpSpec{
		Type: "MirrorPad",
		Input: []tf.Input{
			input, paddings,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Return the reduction indices for computing gradients of s0 op s1 with broadcast.
//
// This is typically used by gradient computations for a broadcasting operation.
func BroadcastGradientArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output, r1 tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "BroadcastGradientArgs",
		Input: []tf.Input{
			s0, s1,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Return the shape of s0 op s1 with broadcast.
//
// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the
// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors.
func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "BroadcastArgs",
		Input: []tf.Input{
			s0, s1,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns locations of true values in a boolean tensor.
//
// This operation returns the coordinates of true elements in `input`. The
// coordinates are returned in a 2-D tensor where the first dimension (rows)
// represents the number of true elements, and the second dimension (columns)
// represents the coordinates of the true elements. Keep in mind, the shape of
// the output tensor can vary depending on how many true values there are in
// `input`. Indices are output in row-major order.
//
// For example:
//
// ```prettyprint
// # 'input' tensor is [[True, False]
// #                    [True, False]]
// # 'input' has two true values, so output has two coordinates.
// # 'input' has rank of 2, so coordinates have two indices.
// where(input) ==> [[0, 0],
//                   [1, 0]]
//
// # `input` tensor is [[[True, False]
// #                     [True, False]]
// #                    [[False, True]
// #                     [False, True]]
// #                    [[False, False]
// #                     [False, True]]]
// # 'input' has 5 true values, so output has 5 coordinates.
// # 'input' has rank of 3, so coordinates have three indices.
// where(input) ==> [[0, 0, 0],
//                   [0, 1, 0],
//                   [1, 0, 1],
//                   [1, 1, 1],
//                   [2, 1, 1]]
// ```
func Where(scope *Scope, input tf.Output) (index tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Where",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the gradient of `Tile`.
//
// DEPRECATED at GraphDef version 3: TileGrad has been replaced with reduce_sum
//
// Since `Tile` takes an input and repeats the input `multiples` times
// along each dimension, `TileGrad` takes in `multiples` and aggregates
// each repeated tile of `input` into `output`.
func TileGrad(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TileGrad",
		Input: []tf.Input{
			input, multiples,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// StridedSliceGradAttr is an optional argument to StridedSliceGrad.
type StridedSliceGradAttr func(optionalAttr)

// StridedSliceGradBeginMask sets the optional begin_mask attribute to value.
// If not specified, defaults to 0
func StridedSliceGradBeginMask(value int64) StridedSliceGradAttr {
	return func(m optionalAttr) {
		m["begin_mask"] = value
	}
}

// StridedSliceGradEndMask sets the optional end_mask attribute to value.
// If not specified, defaults to 0
func StridedSliceGradEndMask(value int64) StridedSliceGradAttr {
	return func(m optionalAttr) {
		m["end_mask"] = value
	}
}

// StridedSliceGradEllipsisMask sets the optional ellipsis_mask attribute to value.
// If not specified, defaults to 0
func StridedSliceGradEllipsisMask(value int64) StridedSliceGradAttr {
	return func(m optionalAttr) {
		m["ellipsis_mask"] = value
	}
}

// StridedSliceGradNewAxisMask sets the optional new_axis_mask attribute to value.
// If not specified, defaults to 0
func StridedSliceGradNewAxisMask(value int64) StridedSliceGradAttr {
	return func(m optionalAttr) {
		m["new_axis_mask"] = value
	}
}

// StridedSliceGradShrinkAxisMask sets the optional shrink_axis_mask attribute to value.
// If not specified, defaults to 0
func StridedSliceGradShrinkAxisMask(value int64) StridedSliceGradAttr {
	return func(m optionalAttr) {
		m["shrink_axis_mask"] = value
	}
}

// Returns the gradient of `StridedSlice`.
//
// Since `StridedSlice` cuts out pieces of its `input` which is size
// `shape`, its gradient will have the same shape (which is passed here
// as `shape`). The gradient will be zero in any element that the slice
// does not select.
//
// Arguments are the same as StridedSliceGrad with the exception that
// `dy` is the input gradient to be propagated and `shape` is the
// shape of `StridedSlice`'s `input`.
func StridedSliceGrad(scope *Scope, shape tf.Output, begin tf.Output, end tf.Output, strides tf.Output, dy tf.Output, optional ...StridedSliceGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "StridedSliceGrad",
		Input: []tf.Input{
			shape, begin, end, strides, dy,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Return a slice from 'input'.
//
// The output tensor is a tensor with dimensions described by 'size'
// whose values are extracted from 'input' starting at the offsets in
// 'begin'.
//
// *Requirements*:
//   0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n)
//
// Arguments:
//
//	begin: begin[i] specifies the offset into the 'i'th dimension of
// 'input' to slice from.
//	size: size[i] specifies the number of elements of the 'i'th dimension
// of 'input' to slice. If size[i] is -1, all remaining elements in dimension
// i are included in the slice (i.e. this is equivalent to setting
// size[i] = input.dim_size(i) - begin[i]).
func Slice(scope *Scope, input tf.Output, begin tf.Output, size tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Slice",
		Input: []tf.Input{
			input, begin, size,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ShapeNAttr is an optional argument to ShapeN.
type ShapeNAttr func(optionalAttr)

// ShapeNOutType sets the optional out_type attribute to value.
// If not specified, defaults to DT_INT32
func ShapeNOutType(value tf.DataType) ShapeNAttr {
	return func(m optionalAttr) {
		m["out_type"] = value
	}
}

// Returns shape of tensors.
//
// This operation returns N 1-D integer tensors representing shape of `input[i]s`.
func ShapeN(scope *Scope, input []tf.Output, optional ...ShapeNAttr) (output []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ShapeN",
		Input: []tf.Input{
			tf.OutputList(input),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
		scope.UpdateErr("ShapeN", err)
		return
	}
	return output
}

// UniqueAttr is an optional argument to Unique.
type UniqueAttr func(optionalAttr)

// UniqueOutIdx sets the optional out_idx attribute to value.
// If not specified, defaults to DT_INT32
func UniqueOutIdx(value tf.DataType) UniqueAttr {
	return func(m optionalAttr) {
		m["out_idx"] = value
	}
}

// Finds unique elements in a 1-D tensor.
//
// This operation returns a tensor `y` containing all of the unique elements of `x`
// sorted in the same order that they occur in `x`. This operation also returns a
// tensor `idx` the same size as `x` that contains the index of each value of `x`
// in the unique output `y`. In other words:
//
// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
//
// For example:
//
// ```prettyprint
// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
// y, idx = unique(x)
// y ==> [1, 2, 4, 7, 8]
// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
// ```
//
// Arguments:
//	x: 1-D.
//
// Returns 1-D.1-D.
func Unique(scope *Scope, x tf.Output, optional ...UniqueAttr) (y tf.Output, idx tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Unique",
		Input: []tf.Input{
			x,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Reshapes a tensor.
//
// Given `tensor`, this operation returns a tensor that has the same values
// as `tensor` with shape `shape`.
//
// If one component of `shape` is the special value -1, the size of that dimension
// is computed so that the total size remains constant.  In particular, a `shape`
// of `[-1]` flattens into 1-D.  At most one component of `shape` can be -1.
//
// If `shape` is 1-D or higher, then the operation returns a tensor with shape
// `shape` filled with the values of `tensor`. In this case, the number of elements
// implied by `shape` must be the same as the number of elements in `tensor`.
//
// For example:
//
// ```prettyprint
// # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
// # tensor 't' has shape [9]
// reshape(t, [3, 3]) ==> [[1, 2, 3],
//                         [4, 5, 6],
//                         [7, 8, 9]]
//
// # tensor 't' is [[[1, 1], [2, 2]],
// #                [[3, 3], [4, 4]]]
// # tensor 't' has shape [2, 2, 2]
// reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
//                         [3, 3, 4, 4]]
//
// # tensor 't' is [[[1, 1, 1],
// #                 [2, 2, 2]],
// #                [[3, 3, 3],
// #                 [4, 4, 4]],
// #                [[5, 5, 5],
// #                 [6, 6, 6]]]
// # tensor 't' has shape [3, 2, 3]
// # pass '[-1]' to flatten 't'
// reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
//
// # -1 can also be used to infer the shape
//
// # -1 is inferred to be 9:
// reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
//                          [4, 4, 4, 5, 5, 5, 6, 6, 6]]
// # -1 is inferred to be 2:
// reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
//                          [4, 4, 4, 5, 5, 5, 6, 6, 6]]
// # -1 is inferred to be 3:
// reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
//                               [2, 2, 2],
//                               [3, 3, 3]],
//                              [[4, 4, 4],
//                               [5, 5, 5],
//                               [6, 6, 6]]]
//
// # tensor 't' is [7]
// # shape `[]` reshapes to a scalar
// reshape(t, []) ==> 7
// ```
//
// Arguments:
//
//	shape: Defines the shape of the output tensor.
func Reshape(scope *Scope, tensor tf.Output, shape tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Reshape",
		Input: []tf.Input{
			tensor, shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Checks a tensor for NaN and Inf values.
//
// When run, reports an `InvalidArgument` error if `tensor` has any values
// that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is.
//
// Arguments:
//
//	message: Prefix of the error message.
func CheckNumerics(scope *Scope, tensor tf.Output, message string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"message": message}
	opspec := tf.OpSpec{
		Type: "CheckNumerics",
		Input: []tf.Input{
			tensor,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// PreventGradientAttr is an optional argument to PreventGradient.
type PreventGradientAttr func(optionalAttr)

// PreventGradientMessage sets the optional message attribute to value.
//
// value: Will be printed in the error when anyone tries to differentiate
// this operation.
// If not specified, defaults to ""
func PreventGradientMessage(value string) PreventGradientAttr {
	return func(m optionalAttr) {
		m["message"] = value
	}
}

// An identity op that triggers an error if a gradient is requested.
//
// When executed in a graph, this op outputs its input tensor as-is.
//
// When building ops to compute gradients, the TensorFlow gradient system
// will return an error when trying to lookup the gradient of this op,
// because no gradient must ever be registered for this function.  This
// op exists to prevent subtle bugs from silently returning unimplemented
// gradients in some corner cases.
//
// Arguments:
//	input: any tensor.
//
// Returns the same input tensor.
func PreventGradient(scope *Scope, input tf.Output, optional ...PreventGradientAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "PreventGradient",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Stops gradient computation.
//
// When executed in a graph, this op outputs its input tensor as-is.
//
// When building ops to compute gradients, this op prevents the contribution of
// its inputs to be taken into account.  Normally, the gradient generator adds ops
// to a graph to compute the derivatives of a specified 'loss' by recursively
// finding out inputs that contributed to its computation.  If you insert this op
// in the graph it inputs are masked from the gradient generator.  They are not
// taken into account for computing gradients.
//
// This is useful any time you want to compute a value with TensorFlow but need
// to pretend that the value was a constant. Some examples include:
//
// *  The *EM* algorithm where the *M-step* should not involve backpropagation
//    through the output of the *E-step*.
// *  Contrastive divergence training of Boltzmann machines where, when
//    differentiating the energy function, the training must not backpropagate
//    through the graph that generated the samples from the model.
// *  Adversarial training, where no backprop should happen through the adversarial
//    example generation process.
func StopGradient(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "StopGradient",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Return a tensor with the same shape and contents as the input tensor or value.
func Identity(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Identity",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Gather values or slices from `params` according to `indices`.
//
// `params` is a Tensor of rank `P` and `indices` is a Tensor of rank `Q`.
//
// `indices` must be integer tensor, containing indices into `params`.
// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
//
// The innermost dimension of `indices` (with length `K`) corresponds to
// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
// dimension of `params`.
//
// Produces an output tensor with shape
//
// ```
// [d_0, ..., d_{Q-2}, params.shape[K], ..., params.shape[P-1]].
// ```
//
// Some examples below.
//
// Simple indexing into a matrix:
//
// ```python
//     indices = [[0, 0], [1, 1]]
//     params = [['a', 'b'], ['c', 'd']]
//     output = ['a', 'd']
// ```
//
// Slice indexing into a matrix:
//
// ```python
//     indices = [[1], [0]]
//     params = [['a', 'b'], ['c', 'd']]
//     output = [['c', 'd'], ['a', 'b']]
// ```
//
// Indexing into a 3-tensor:
//
// ```python
//     indices = [[1]]
//     params = [[['a0', 'b0'], ['c0', 'd0']],
//               [['a1', 'b1'], ['c1', 'd1']]]
//     output = [[['a1', 'b1'], ['c1', 'd1']]]
//
//
//     indices = [[0, 1], [1, 0]]
//     params = [[['a0', 'b0'], ['c0', 'd0']],
//               [['a1', 'b1'], ['c1', 'd1']]]
//     output = [['c0', 'd0'], ['a1', 'b1']]
//
//
//     indices = [[0, 0, 1], [1, 0, 1]]
//     params = [[['a0', 'b0'], ['c0', 'd0']],
//               [['a1', 'b1'], ['c1', 'd1']]]
//     output = ['b0', 'b1']
// ```
//
// Batched indexing into a matrix:
//
// ```python
//     indices = [[[0, 0]], [[0, 1]]]
//     params = [['a', 'b'], ['c', 'd']]
//     output = [['a'], ['b']]
// ```
//
// Batched slice indexing into a matrix:
//
// ```python
//     indices = [[[1]], [[0]]]
//     params = [['a', 'b'], ['c', 'd']]
//     output = [[['c', 'd']], [['a', 'b']]]
// ```
//
// Batched indexing into a 3-tensor:
//
// ```python
//     indices = [[[1]], [[0]]]
//     params = [[['a0', 'b0'], ['c0', 'd0']],
//               [['a1', 'b1'], ['c1', 'd1']]]
//     output = [[[['a1', 'b1'], ['c1', 'd1']]],
//               [[['a0', 'b0'], ['c0', 'd0']]]]
//
//     indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
//     params = [[['a0', 'b0'], ['c0', 'd0']],
//               [['a1', 'b1'], ['c1', 'd1']]]
//     output = [[['c0', 'd0'], ['a1', 'b1']],
//               [['a0', 'b0'], ['c1', 'd1']]]
//
//
//     indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
//     params = [[['a0', 'b0'], ['c0', 'd0']],
//               [['a1', 'b1'], ['c1', 'd1']]]
//     output = [['b0', 'b1'], ['d0', 'c1']]
// ```
//
// Arguments:
//	params: `P-D`.  The tensor from which to gather values.
//	indices: `Q-D`.  Index tensor having shape `[d_0, ..., d_{Q-2}, K]`.
//
// Returns `(P+Q-K-1)-D`.  Values from `params` gathered from indices given by
// `indices`.
func GatherNd(scope *Scope, params tf.Output, indices tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "GatherNd",
		Input: []tf.Input{
			params, indices,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// EditDistanceAttr is an optional argument to EditDistance.
type EditDistanceAttr func(optionalAttr)

// EditDistanceNormalize sets the optional normalize attribute to value.
//
// value: boolean (if true, edit distances are normalized by length of truth).
//
// The output is:
// If not specified, defaults to true
func EditDistanceNormalize(value bool) EditDistanceAttr {
	return func(m optionalAttr) {
		m["normalize"] = value
	}
}

// Computes the (possibly normalized) Levenshtein Edit Distance.
//
// The inputs are variable-length sequences provided by SparseTensors
//   (hypothesis_indices, hypothesis_values, hypothesis_shape)
// and
//   (truth_indices, truth_values, truth_shape).
//
// The inputs are:
//
// Arguments:
//	hypothesis_indices: The indices of the hypothesis list SparseTensor.
// This is an N x R int64 matrix.
//	hypothesis_values: The values of the hypothesis list SparseTensor.
// This is an N-length vector.
//	hypothesis_shape: The shape of the hypothesis list SparseTensor.
// This is an R-length vector.
//	truth_indices: The indices of the truth list SparseTensor.
// This is an M x R int64 matrix.
//	truth_values: The values of the truth list SparseTensor.
// This is an M-length vector.
//	truth_shape: truth indices, vector.
//
// Returns A dense float tensor with rank R - 1.
//
// For the example input:
//
//     // hypothesis represents a 2x1 matrix with variable-length values:
//     //   (0,0) = ["a"]
//     //   (1,0) = ["b"]
//     hypothesis_indices = [[0, 0, 0],
//                           [1, 0, 0]]
//     hypothesis_values = ["a", "b"]
//     hypothesis_shape = [2, 1, 1]
//
//     // truth represents a 2x2 matrix with variable-length values:
//     //   (0,0) = []
//     //   (0,1) = ["a"]
//     //   (1,0) = ["b", "c"]
//     //   (1,1) = ["a"]
//     truth_indices = [[0, 1, 0],
//                      [1, 0, 0],
//                      [1, 0, 1],
//                      [1, 1, 0]]
//     truth_values = ["a", "b", "c", "a"]
//     truth_shape = [2, 2, 2]
//     normalize = true
//
// The output will be:
//
//     // output is a 2x2 matrix with edit distances normalized by truth lengths.
//     output = [[inf, 1.0],  // (0,0): no truth, (0,1): no hypothesis
//               [0.5, 1.0]]  // (1,0): addition, (1,1): no hypothesis
func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values tf.Output, hypothesis_shape tf.Output, truth_indices tf.Output, truth_values tf.Output, truth_shape tf.Output, optional ...EditDistanceAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "EditDistance",
		Input: []tf.Input{
			hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns a batched matrix tensor with new batched diagonal values.
//
// Given `input` and `diagonal`, this operation returns a tensor with the
// same shape and values as `input`, except for the main diagonal of the
// innermost matrices.  These will be overwritten by the values in `diagonal`.
//
// The output is computed as follows:
//
// Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has
// `k` dimensions `[I, J, K, ..., min(M, N)]`.  Then the output is a
// tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where:
//
//   * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`.
//   * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`.
//
// Arguments:
//	input: Rank `k+1`, where `k >= 1`.
//	diagonal: Rank `k`, where `k >= 1`.
//
// Returns Rank `k+1`, with `output.shape = input.shape`.
func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "MatrixSetDiag",
		Input: []tf.Input{
			input, diagonal,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the diagonal part of the tensor.
//
// This operation returns a tensor with the `diagonal` part
// of the `input`. The `diagonal` part is computed as follows:
//
// Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
// tensor of rank `k` with dimensions `[D1,..., Dk]` where:
//
// `diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
//
// For example:
//
// ```prettyprint
// # 'input' is [[1, 0, 0, 0]
//               [0, 2, 0, 0]
//               [0, 0, 3, 0]
//               [0, 0, 0, 4]]
//
// tf.diag_part(input) ==> [1, 2, 3, 4]
// ```
//
// Arguments:
//	input: Rank k tensor where k is 2, 4, or 6.
//
// Returns The extracted diagonal.
func DiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "DiagPart",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// DequantizeAttr is an optional argument to Dequantize.
type DequantizeAttr func(optionalAttr)

// DequantizeMode sets the optional mode attribute to value.
// If not specified, defaults to "MIN_COMBINED"
func DequantizeMode(value string) DequantizeAttr {
	return func(m optionalAttr) {
		m["mode"] = value
	}
}

// Dequantize the 'input' tensor into a float Tensor.
//
// [min_range, max_range] are scalar floats that specify the range for
// the 'input' data. The 'mode' attribute controls exactly which calculations are
// used to convert the float values to their quantized equivalents.
//
// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
//
// ```
// if T == qint8, in[i] += (range(T) + 1)/ 2.0
// out[i] = min_range + (in[i]* (max_range - min_range) / range(T))
// ```
// here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
//
// *MIN_COMBINED Mode Example*
//
// If the input comes from a QuantizedRelu6, the output type is
// quint8 (range of 0-255) but the possible range of QuantizedRelu6 is
// 0-6.  The min_range and max_range values are therefore 0.0 and 6.0.
// Dequantize on quint8 will take each value, cast to float, and multiply
// by 6 / 255.
// Note that if quantizedtype is qint8, the operation will additionally add
// each value by 128 prior to casting.
//
// If the mode is 'MIN_FIRST', then this approach is used:
//
// ```
// number_of_steps = 1 << (# of bits in T)
// range_adjust = number_of_steps / (number_of_steps - 1)
// range = (range_max - range_min) * range_adjust
// range_scale = range / number_of_steps
// const double offset_input = static_cast<double>(input) - lowest_quantized;
// result = range_min + ((input - numeric_limits<T>::min()) * range_scale)
// ```
//
// Arguments:
//
//	min_range: The minimum scalar value possibly produced for the input.
//	max_range: The maximum scalar value possibly produced for the input.
func Dequantize(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, optional ...DequantizeAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Dequantize",
		Input: []tf.Input{
			input, min_range, max_range,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns a tensor of zeros with the same shape and type as x.
//
// Arguments:
//	x: a tensor of type T.
//
// Returns a tensor of the same shape and type as x but filled with zeros.
func ZerosLike(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ZerosLike",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Splits a tensor into `num_split` tensors along one dimension.
//
// Arguments:
//	value: The tensor to split.
//	size_splits: list containing the sizes of each output tensor along the split
// dimension. Must sum to the dimension of value along split_dim.
// Can contain one -1 indicating that dimension is to be inferred.
//	split_dim: 0-D.  The dimension along which to split.  Must be in the range
// `[0, rank(value))`.
//
//
// Returns Tensors whose shape matches that of `value`
// except along `split_dim`, where their sizes are
// `size_splits[i]`.
func SplitV(scope *Scope, value tf.Output, size_splits tf.Output, split_dim tf.Output, num_split int64) (output []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_split": num_split}
	opspec := tf.OpSpec{
		Type: "SplitV",
		Input: []tf.Input{
			value, size_splits, split_dim,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
		scope.UpdateErr("SplitV", err)
		return
	}
	return output
}

// Splits a tensor into `num_split` tensors along one dimension.
//
// Arguments:
//	split_dim: 0-D.  The dimension along which to split.  Must be in the range
// `[0, rank(value))`.
//	value: The tensor to split.
//	num_split: The number of ways to split.  Must evenly divide
// `value.shape[split_dim]`.
//
// Returns They are identically shaped tensors, whose shape matches that of `value`
// except along `split_dim`, where their sizes are
// `values.shape[split_dim] / num_split`.
func Split(scope *Scope, split_dim tf.Output, value tf.Output, num_split int64) (output []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_split": num_split}
	opspec := tf.OpSpec{
		Type: "Split",
		Input: []tf.Input{
			split_dim, value,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
		scope.UpdateErr("Split", err)
		return
	}
	return output
}

// Computes offsets of concat inputs within its output.
//
// For example:
//
// ```prettyprint
// # 'x' is [2, 2, 7]
// # 'y' is [2, 3, 7]
// # 'z' is [2, 5, 7]
// concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0]
// ```
//
// Arguments:
//	concat_dim: The dimension along which to concatenate.
//	shape: The `N` int32 vectors representing shape of tensors being concatenated.
//
// Returns The `N` int32 vectors representing the starting offset
//         of input tensors within the concatenated output.
//
// This is typically used by gradient computations for a concat operation.
func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset []tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ConcatOffset",
		Input: []tf.Input{
			concat_dim, tf.OutputList(shape),
		},
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if offset, idx, err = makeOutputList(op, idx, "offset"); err != nil {
		scope.UpdateErr("ConcatOffset", err)
		return
	}
	return offset
}

// Concatenates tensors along one dimension.
//
// Arguments:
//	concat_dim: 0-D.  The dimension along which to concatenate.  Must be in the
// range [0, rank(values)).
//	values: The `N` Tensors to concatenate. Their ranks and types must match,
// and their sizes must match in all dimensions except `concat_dim`.
//
// Returns A `Tensor` with the concatenation of values stacked along the
// `concat_dim` dimension.  This tensor's shape matches that of `values` except
// in `concat_dim` where it has the sum of the sizes.
func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Concat",
		Input: []tf.Input{
			concat_dim, tf.OutputList(values),
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Concatenates a list of `N` tensors along the first dimension.
//
// The input tensors are all required to have size 1 in the first dimension.
//
// For example:
//
// ```prettyprint
// # 'x' is [[1, 4]]
// # 'y' is [[2, 5]]
// # 'z' is [[3, 6]]
// parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
// ```
//
// The difference between concat and parallel_concat is that concat requires all
// of the inputs be computed before the operation will begin but doesn't require
// that the input shapes be known during graph construction.  Parallel concat
// will copy pieces of the input into the output as they become available, in
// some situations this can provide a performance benefit.
//
// Arguments:
//	values: Tensors to be concatenated. All must have size 1 in the first dimension
// and same shape.
//	shape: the final shape of the result; should be equal to the shapes of any input
// but with the number of input values in the first dimension.
//
// Returns The concatenated tensor.
func ParallelConcat(scope *Scope, values []tf.Output, shape tf.Shape) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"shape": shape}
	opspec := tf.OpSpec{
		Type: "ParallelConcat",
		Input: []tf.Input{
			tf.OutputList(values),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler.
type AllCandidateSamplerAttr func(optionalAttr)

// AllCandidateSamplerSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Generates labels for candidate sampling with a learned unigram distribution.
//
// See explanations of candidate sampling and the data formats at
// go/candidate-sampling.
//
// For each batch, this op picks a single set of sampled candidate labels.
//
// The advantages of sampling candidates per-batch are simplicity and the
// possibility of efficient dense matrix multiplication. The disadvantage is that
// the sampled candidates must be chosen independently of the context and of the
// true labels.
//
// Arguments:
//	true_classes: A batch_size * num_true matrix, in which each row contains the
// IDs of the num_true target_classes in the corresponding original label.
//	num_true: Number of true labels per context.
//	num_sampled: Number of candidates to produce per batch.
//	unique: If unique is true, we sample with rejection, so that all sampled
// candidates in a batch are unique. This requires some approximation to
// estimate the post-rejection sampling probabilities.
//
// Returns A vector of length num_sampled, in which each element is
// the ID of a sampled candidate.A batch_size * num_true matrix, representing
// the number of times each candidate is expected to occur in a batch
// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
// candidate representing the number of times the candidate is expected
// to occur in a batch of sampled candidates.  If unique=true, then this is a
// probability.
func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "AllCandidateSampler",
		Input: []tf.Input{
			true_classes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// FixedUnigramCandidateSamplerAttr is an optional argument to FixedUnigramCandidateSampler.
type FixedUnigramCandidateSamplerAttr func(optionalAttr)

// FixedUnigramCandidateSamplerVocabFile sets the optional vocab_file attribute to value.
//
// value: Each valid line in this file (which should have a CSV-like format)
// corresponds to a valid word ID. IDs are in sequential order, starting from
// num_reserved_ids. The last entry in each line is expected to be a value
// corresponding to the count or relative probability. Exactly one of vocab_file
// and unigrams needs to be passed to this op.
// If not specified, defaults to ""
func FixedUnigramCandidateSamplerVocabFile(value string) FixedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["vocab_file"] = value
	}
}

// FixedUnigramCandidateSamplerDistortion sets the optional distortion attribute to value.
//
// value: The distortion is used to skew the unigram probability distribution.
// Each weight is first raised to the distortion's power before adding to the
// internal unigram distribution. As a result, distortion = 1.0 gives regular
// unigram sampling (as defined by the vocab file), and distortion = 0.0 gives
// a uniform distribution.
// If not specified, defaults to 1
func FixedUnigramCandidateSamplerDistortion(value float32) FixedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["distortion"] = value
	}
}

// FixedUnigramCandidateSamplerNumReservedIds sets the optional num_reserved_ids attribute to value.
//
// value: Optionally some reserved IDs can be added in the range [0,
// ..., num_reserved_ids) by the users. One use case is that a special unknown
// word token is used as ID 0. These IDs will have a sampling probability of 0.
// If not specified, defaults to 0
func FixedUnigramCandidateSamplerNumReservedIds(value int64) FixedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["num_reserved_ids"] = value
	}
}

// FixedUnigramCandidateSamplerNumShards sets the optional num_shards attribute to value.
//
// value: A sampler can be used to sample from a subset of the original range
// in order to speed up the whole computation through parallelism. This parameter
// (together with 'shard') indicates the number of partitions that are being
// used in the overall computation.
// If not specified, defaults to 1
//
// REQUIRES: value >= 1
func FixedUnigramCandidateSamplerNumShards(value int64) FixedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["num_shards"] = value
	}
}

// FixedUnigramCandidateSamplerShard sets the optional shard attribute to value.
//
// value: A sampler can be used to sample from a subset of the original range
// in order to speed up the whole computation through parallelism. This parameter
// (together with 'num_shards') indicates the particular partition number of a
// sampler op, when partitioning is being used.
// If not specified, defaults to 0
//
// REQUIRES: value >= 0
func FixedUnigramCandidateSamplerShard(value int64) FixedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["shard"] = value
	}
}

// FixedUnigramCandidateSamplerUnigrams sets the optional unigrams attribute to value.
//
// value: A list of unigram counts or probabilities, one per ID in sequential
// order. Exactly one of vocab_file and unigrams should be passed to this op.
// If not specified, defaults to <>
func FixedUnigramCandidateSamplerUnigrams(value []float32) FixedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["unigrams"] = value
	}
}

// FixedUnigramCandidateSamplerSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func FixedUnigramCandidateSamplerSeed(value int64) FixedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// FixedUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func FixedUnigramCandidateSamplerSeed2(value int64) FixedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Generates labels for candidate sampling with a learned unigram distribution.
//
// A unigram sampler could use a fixed unigram distribution read from a
// file or passed in as an in-memory array instead of building up the distribution
// from data on the fly. There is also an option to skew the distribution by
// applying a distortion power to the weights.
//
// The vocabulary file should be in CSV-like format, with the last field
// being the weight associated with the word.
//
// For each batch, this op picks a single set of sampled candidate labels.
//
// The advantages of sampling candidates per-batch are simplicity and the
// possibility of efficient dense matrix multiplication. The disadvantage is that
// the sampled candidates must be chosen independently of the context and of the
// true labels.
//
// Arguments:
//	true_classes: A batch_size * num_true matrix, in which each row contains the
// IDs of the num_true target_classes in the corresponding original label.
//	num_true: Number of true labels per context.
//	num_sampled: Number of candidates to randomly sample per batch.
//	unique: If unique is true, we sample with rejection, so that all sampled
// candidates in a batch are unique. This requires some approximation to
// estimate the post-rejection sampling probabilities.
//	range_max: The sampler will sample integers from the interval [0, range_max).
//
// Returns A vector of length num_sampled, in which each element is
// the ID of a sampled candidate.A batch_size * num_true matrix, representing
// the number of times each candidate is expected to occur in a batch
// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
// candidate representing the number of times the candidate is expected
// to occur in a batch of sampled candidates.  If unique=true, then this is a
// probability.
func FixedUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...FixedUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FixedUnigramCandidateSampler",
		Input: []tf.Input{
			true_classes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// UniformCandidateSamplerAttr is an optional argument to UniformCandidateSampler.
type UniformCandidateSamplerAttr func(optionalAttr)

// UniformCandidateSamplerSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func UniformCandidateSamplerSeed(value int64) UniformCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// UniformCandidateSamplerSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func UniformCandidateSamplerSeed2(value int64) UniformCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Generates labels for candidate sampling with a uniform distribution.
//
// See explanations of candidate sampling and the data formats at
// go/candidate-sampling.
//
// For each batch, this op picks a single set of sampled candidate labels.
//
// The advantages of sampling candidates per-batch are simplicity and the
// possibility of efficient dense matrix multiplication. The disadvantage is that
// the sampled candidates must be chosen independently of the context and of the
// true labels.
//
// Arguments:
//	true_classes: A batch_size * num_true matrix, in which each row contains the
// IDs of the num_true target_classes in the corresponding original label.
//	num_true: Number of true labels per context.
//	num_sampled: Number of candidates to randomly sample per batch.
//	unique: If unique is true, we sample with rejection, so that all sampled
// candidates in a batch are unique. This requires some approximation to
// estimate the post-rejection sampling probabilities.
//	range_max: The sampler will sample integers from the interval [0, range_max).
//
// Returns A vector of length num_sampled, in which each element is
// the ID of a sampled candidate.A batch_size * num_true matrix, representing
// the number of times each candidate is expected to occur in a batch
// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
// candidate representing the number of times the candidate is expected
// to occur in a batch of sampled candidates.  If unique=true, then this is a
// probability.
func UniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...UniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "UniformCandidateSampler",
		Input: []tf.Input{
			true_classes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// AbortAttr is an optional argument to Abort.
type AbortAttr func(optionalAttr)

// AbortErrorMsg sets the optional error_msg attribute to value.
//
// value: A string which is the message associated with the exception.
// If not specified, defaults to ""
func AbortErrorMsg(value string) AbortAttr {
	return func(m optionalAttr) {
		m["error_msg"] = value
	}
}

// AbortExitWithoutError sets the optional exit_without_error attribute to value.
// If not specified, defaults to false
func AbortExitWithoutError(value bool) AbortAttr {
	return func(m optionalAttr) {
		m["exit_without_error"] = value
	}
}

// Raise a exception to abort the process when called. If exit_without_error is true, the process will exit normally, otherwise it will exit with a SIGABORT signal.
//
// Returns nothing but an exception.
//
// Returns the created operation.
func Abort(scope *Scope, optional ...AbortAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Abort",

		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Does nothing. Serves as a control trigger for scheduling.
//
// Only useful as a placeholder for control edges.
//
// Returns the created operation.
func ControlTrigger(scope *Scope) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ControlTrigger",
	}
	return scope.AddOperation(opspec)
}

// SpaceToDepth for tensors of type T.
//
// Rearranges blocks of spatial data, into depth. More specifically,
// this op outputs a copy of the input tensor where values from the `height`
// and `width` dimensions are moved to the `depth` dimension.
// The attr `block_size` indicates the input block size and how the data is moved.
//
//   * Non-overlapping blocks of size `block_size x block size` are rearranged
//     into depth at each location.
//   * The depth of the output tensor is `input_depth * block_size * block_size`.
//   * The input tensor's height and width must be divisible by block_size.
//
// That is, assuming the input is in the shape:
// `[batch, height, width, depth]`,
// the shape of the output will be:
// `[batch, height/block_size, width/block_size, depth*block_size*block_size]`
//
// This operation requires that the input tensor be of rank 4, and that
// `block_size` be >=1 and a divisor of both the input `height` and `width`.
//
// This operation is useful for resizing the activations between convolutions
// (but keeping all data), e.g. instead of pooling. It is also useful for training
// purely convolutional models.
//
// For example, given this input of shape `[1, 2, 2, 1]`, and block_size of 2:
//
// ```prettyprint
// x = [[[[1], [2]],
//       [[3], [4]]]]
// ```
//
// This operation will output a tensor of shape `[1, 1, 1, 4]`:
//
// ```prettyprint
// [[[[1, 2, 3, 4]]]]
// ```
//
// Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`,
// the corresponding output will have a single element (i.e. width and height are
// both 1) and will have a depth of 4 channels (1 * block_size * block_size).
// The output element shape is `[1, 1, 4]`.
//
// For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g.
//
// ```prettyprint
// x = [[[[1, 2, 3], [4, 5, 6]],
//       [[7, 8, 9], [10, 11, 12]]]]
// ```
//
// This operation, for block_size of 2, will return the following tensor of shape
// `[1, 1, 1, 12]`
//
// ```prettyprint
// [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
// ```
//
// Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2:
//
// ```prettyprint
// x = [[[[1],   [2],  [5],  [6]],
//       [[3],   [4],  [7],  [8]],
//       [[9],  [10], [13],  [14]],
//       [[11], [12], [15],  [16]]]]
// ```
//
// the operator will return the following tensor of shape `[1 2 2 4]`:
//
// ```prettyprint
// x = [[[[1, 2, 3, 4],
//        [5, 6, 7, 8]],
//       [[9, 10, 11, 12],
//        [13, 14, 15, 16]]]]
// ```
//
// Arguments:
//
//	block_size: The size of the spatial block.
func SpaceToDepth(scope *Scope, input tf.Output, block_size int64) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"block_size": block_size}
	opspec := tf.OpSpec{
		Type: "SpaceToDepth",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Creates a new tensor by applying sparse `updates` to individual
//
// values or slices within a zero tensor of the given `shape` tensor according to
// indices.  This operator is the inverse of the [tf.gather_nd](#gather_nd)
// operator which extracts values or slices from a given tensor.
//
// TODO(simister): Add a link to Variable.__getitem__ documentation on slice
// syntax.
//
// `shape` is a `TensorShape` with rank `P` and `indices` is a `Tensor` of rank
// `Q`.
//
// `indices` must be integer tensor, containing indices into `shape`.
// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
//
// The innermost dimension of `indices` (with length `K`) corresponds to
// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
// dimension of `shape`.
//
// `updates` is Tensor of rank `Q-1+P-K` with shape:
//
// ```
// [d_0, ..., d_{Q-2}, shape[K], ..., shape[P-1]].
// ```
//
// The simplest form of scatter is to insert individual elements in a tensor by
// index. For example, say we want to insert 4 scattered elements in a rank-1
// tensor with 8 elements.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/ScatterNd1.png" alt>
// </div>
//
// In Python, this scatter operation would look like this:
//
//     indices = tf.constant([[4], [3], [1], [7]])
//     updates = tf.constant([9, 10, 11, 12])
//     shape = tf.constant([8])
//     scatter = tf.scatter_nd(indices, updates, shape)
//     with tf.Session() as sess:
//       print sess.run(scatter)
//
// The resulting tensor would look like this:
//
//     [0, 11, 0, 10, 9, 0, 0, 12]
//
// We can also, insert entire slices of a higher rank tensor all at once. For
// example, if we wanted to insert two slices in the first dimension of a
// rank-3 tensor with two matrices of new values.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/ScatterNd2.png" alt>
// </div>
//
// In Python, this scatter operation would look like this:
//
//     indices = tf.constant([[0], [2]])
//     updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
//                             [7, 7, 7, 7], [8, 8, 8, 8]],
//                            [[5, 5, 5, 5], [6, 6, 6, 6],
//                             [7, 7, 7, 7], [8, 8, 8, 8]]])
//     shape = tf.constant([4, 4, 4])
//     scatter = tf.scatter_nd(indices, updates, shape)
//     with tf.Session() as sess:
//       print sess.run(scatter)
//
// The resulting tensor would look like this:
//
//     [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
//      [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
//      [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
//      [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]
//
// Arguments:
//	indices: A Tensor. Must be one of the following types: int32, int64.
// A tensor of indices into ref.
//	updates: A Tensor. Must have the same type as tensor. A tensor of updated values
// to store in ref.
//	shape: A vector. The shape of the resulting tensor.
//
// Returns A new tensor with the given shape and updates applied according
// to the indices.
func ScatterNd(scope *Scope, indices tf.Output, updates tf.Output, shape tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ScatterNd",
		Input: []tf.Input{
			indices, updates, shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Exits the current frame to its parent frame.
//
// Exit makes its input `data` available to the parent frame.
//
// Arguments:
//	data: The tensor to be made available to the parent frame.
//
// Returns The same tensor as `data`.
func Exit(scope *Scope, data tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Exit",
		Input: []tf.Input{
			data,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// EnterAttr is an optional argument to Enter.
type EnterAttr func(optionalAttr)

// EnterIsConstant sets the optional is_constant attribute to value.
//
// value: If true, the output is constant within the child frame.
// If not specified, defaults to false
func EnterIsConstant(value bool) EnterAttr {
	return func(m optionalAttr) {
		m["is_constant"] = value
	}
}

// EnterParallelIterations sets the optional parallel_iterations attribute to value.
//
// value: The number of iterations allowed to run in parallel.
// If not specified, defaults to 10
func EnterParallelIterations(value int64) EnterAttr {
	return func(m optionalAttr) {
		m["parallel_iterations"] = value
	}
}

// Creates or finds a child frame, and makes `data` available to the child frame.
//
// This op is used together with `Exit` to create loops in the graph.
// The unique `frame_name` is used by the `Executor` to identify frames. If
// `is_constant` is true, `output` is a constant in the child frame; otherwise
// it may be changed in the child frame. At most `parallel_iterations` iterations
// are run in parallel in the child frame.
//
// Arguments:
//	data: The tensor to be made available to the child frame.
//	frame_name: The name of the child frame.
//
// Returns The same tensor as `data`.
func Enter(scope *Scope, data tf.Output, frame_name string, optional ...EnterAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"frame_name": frame_name}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Enter",
		Input: []tf.Input{
			data,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Forwards the value of an available tensor from `inputs` to `output`.
//
// `Merge` waits for at least one of the tensors in `inputs` to become available.
// It is usually combined with `Switch` to implement branching.
//
// `Merge` forwards the first tensor for become available to `output`, and sets
// `value_index` to its index in `inputs`.
//
// Arguments:
//	inputs: The input tensors, exactly one of which will become available.
//
// Returns Will be set to the available input tensor.The index of the chosen input tensor in `inputs`.
func Merge(scope *Scope, inputs []tf.Output) (output tf.Output, value_index tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Merge",
		Input: []tf.Input{
			tf.OutputList(inputs),
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Forwards `data` to the output port determined by `pred`.
//
// If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise,
// the data goes to `output_false`.
//
// See also `RefSwitch` and `Merge`.
//
// Arguments:
//	data: The tensor to be forwarded to the appropriate output.
//	pred: A scalar that specifies which output port will receive data.
//
// Returns If `pred` is false, data will be forwarded to this output.If `pred` is true, data will be forwarded to this output.
func Switch(scope *Scope, data tf.Output, pred tf.Output) (output_false tf.Output, output_true tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Switch",
		Input: []tf.Input{
			data, pred,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// CTCGreedyDecoderAttr is an optional argument to CTCGreedyDecoder.
type CTCGreedyDecoderAttr func(optionalAttr)

// CTCGreedyDecoderMergeRepeated sets the optional merge_repeated attribute to value.
//
// value: If True, merge repeated classes in output.
// If not specified, defaults to false
func CTCGreedyDecoderMergeRepeated(value bool) CTCGreedyDecoderAttr {
	return func(m optionalAttr) {
		m["merge_repeated"] = value
	}
}

// Performs greedy decoding on the logits given in inputs.
//
// A note about the attribute merge_repeated: if enabled, when
// consecutive logits' maximum indices are the same, only the first of
// these is emitted.  Labeling the blank '*', the sequence "A B B * B B"
// becomes "A B B" if merge_repeated = True and "A B B B B" if
// merge_repeated = False.
//
// Regardless of the value of merge_repeated, if the maximum index of a given
// time and batch corresponds to the blank, index `(num_classes - 1)`, no new
// element is emitted.
//
// Arguments:
//	inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
//	sequence_length: A vector containing sequence lengths, size `(batch_size)`.
//
// Returns Indices matrix, size `(total_decoded_outputs x 2)`,
// of a `SparseTensor<int64, 2>`.  The rows store: [batch, time].Values vector, size: `(total_decoded_outputs)`,
// of a `SparseTensor<int64, 2>`.  The vector stores the decoded classes.Shape vector, size `(2)`, of the decoded SparseTensor.
// Values are: `[batch_size, max_decoded_length]`.Matrix, size `(batch_size x 1)`, containing sequence
// log-probabilities.
func CTCGreedyDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, optional ...CTCGreedyDecoderAttr) (decoded_indices tf.Output, decoded_values tf.Output, decoded_shape tf.Output, log_probability tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "CTCGreedyDecoder",
		Input: []tf.Input{
			inputs, sequence_length,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
}

// CTCLossAttr is an optional argument to CTCLoss.
type CTCLossAttr func(optionalAttr)

// CTCLossPreprocessCollapseRepeated sets the optional preprocess_collapse_repeated attribute to value.
//
// value: Scalar, if true then repeated labels are
// collapsed prior to the CTC calculation.
// If not specified, defaults to false
func CTCLossPreprocessCollapseRepeated(value bool) CTCLossAttr {
	return func(m optionalAttr) {
		m["preprocess_collapse_repeated"] = value
	}
}

// CTCLossCtcMergeRepeated sets the optional ctc_merge_repeated attribute to value.
//
// value: Scalar.  If set to false, *during* CTC calculation
// repeated non-blank labels will not be merged and are interpreted as
// individual labels.  This is a simplified version of CTC.
// If not specified, defaults to true
func CTCLossCtcMergeRepeated(value bool) CTCLossAttr {
	return func(m optionalAttr) {
		m["ctc_merge_repeated"] = value
	}
}

// Calculates the CTC Loss (log probability) for each batch entry.  Also calculates
//
// the gradient.  This class performs the softmax operation for you, so inputs
// should be e.g. linear projections of outputs by an LSTM.
//
// Arguments:
//	inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
//	labels_indices: The indices of a `SparseTensor<int32, 2>`.
// `labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for
// `(batch b, time t)`.
//	labels_values: The values (labels) associated with the given batch and time.
//	sequence_length: A vector containing sequence lengths (batch).
//
// Returns A vector (batch) containing log-probabilities.The gradient of `loss`.  3-D, shape:
// `(max_time x batch_size x num_classes)`.
func CTCLoss(scope *Scope, inputs tf.Output, labels_indices tf.Output, labels_values tf.Output, sequence_length tf.Output, optional ...CTCLossAttr) (loss tf.Output, gradient tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "CTCLoss",
		Input: []tf.Input{
			inputs, labels_indices, labels_values, sequence_length,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// StageAttr is an optional argument to Stage.
type StageAttr func(optionalAttr)

// StageContainer sets the optional container attribute to value.
//
// value: If non-empty, this queue is placed in the given container. Otherwise,
// a default container is used.
// If not specified, defaults to ""
func StageContainer(value string) StageAttr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// StageSharedName sets the optional shared_name attribute to value.
//
// value: It is necessary to match this name to the matching Unstage Op.
// If not specified, defaults to ""
func StageSharedName(value string) StageAttr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// Stage values similar to a lightweight Enqueue.  The basic functionality of this
//
// Op is similar to a queue with many fewer capabilities and options.  This Op is
// optimized for performance.
//
// Arguments:
//	values: a list of tensors
//
// Returns the created operation.
func Stage(scope *Scope, values []tf.Output, optional ...StageAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Stage",
		Input: []tf.Input{
			tf.OutputList(values),
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// FakeQuantWithMinMaxArgsAttr is an optional argument to FakeQuantWithMinMaxArgs.
type FakeQuantWithMinMaxArgsAttr func(optionalAttr)

// FakeQuantWithMinMaxArgsMin sets the optional min attribute to value.
// If not specified, defaults to -6
func FakeQuantWithMinMaxArgsMin(value float32) FakeQuantWithMinMaxArgsAttr {
	return func(m optionalAttr) {
		m["min"] = value
	}
}

// FakeQuantWithMinMaxArgsMax sets the optional max attribute to value.
// If not specified, defaults to 6
func FakeQuantWithMinMaxArgsMax(value float32) FakeQuantWithMinMaxArgsAttr {
	return func(m optionalAttr) {
		m["max"] = value
	}
}

// Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
//
// Attributes [min; max] define the clamping range for the 'inputs' data.  Op
// divides this range into 255 steps (total of 256 values), then replaces each
// 'inputs' value with the closest of the quantized step values.
//
// Quantization is called fake since the output is still in floating point.
func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsAttr) (outputs tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FakeQuantWithMinMaxArgs",
		Input: []tf.Input{
			inputs,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Deprecated. Use TensorArraySizeV3
func TensorArraySizeV2(scope *Scope, handle tf.Output, flow_in tf.Output) (size tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArraySizeV2",
		Input: []tf.Input{
			handle, flow_in,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Deprecated. Use TensorArrayScatterV3
func TensorArrayScatterV2(scope *Scope, handle tf.Output, indices tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayScatterV2",
		Input: []tf.Input{
			handle, indices, value, flow_in,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Deprecated. Use TensorArrayGradV3
func TensorArrayWriteV2(scope *Scope, handle tf.Output, index tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayWriteV2",
		Input: []tf.Input{
			handle, index, value, flow_in,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Deprecated. Use TensorArrayGradV3
func TensorArrayGradV2(scope *Scope, handle tf.Output, flow_in tf.Output, source string) (grad_handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"source": source}
	opspec := tf.OpSpec{
		Type: "TensorArrayGradV2",
		Input: []tf.Input{
			handle, flow_in,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceGatherAttr is an optional argument to ResourceGather.
type ResourceGatherAttr func(optionalAttr)

// ResourceGatherValidateIndices sets the optional validate_indices attribute to value.
// If not specified, defaults to true
func ResourceGatherValidateIndices(value bool) ResourceGatherAttr {
	return func(m optionalAttr) {
		m["validate_indices"] = value
	}
}

// Gather slices from the variable pointed to by `resource` according to `indices`.
//
// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
// Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
//
// ```python
//     # Scalar indices
//     output[:, ..., :] = params[indices, :, ... :]
//
//     # Vector indices
//     output[i, :, ..., :] = params[indices[i], :, ... :]
//
//     # Higher rank indices
//     output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
// ```
func ResourceGather(scope *Scope, resource tf.Output, indices tf.Output, dtype tf.DataType, optional ...ResourceGatherAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceGather",
		Input: []tf.Input{
			resource, indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Delete the TensorArray from its resource container.  This enables
//
// the user to close and release the resource in the middle of a step/run.
//
// Arguments:
//	handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
//
// Returns the created operation.
func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayCloseV3",
		Input: []tf.Input{
			handle,
		},
	}
	return scope.AddOperation(opspec)
}

// Get the current size of the TensorArray.
//
// Arguments:
//	handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
//	flow_in: A float scalar that enforces proper chaining of operations.
//
// Returns The current size of the TensorArray.
func TensorArraySizeV3(scope *Scope, handle tf.Output, flow_in tf.Output) (size tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArraySizeV3",
		Input: []tf.Input{
			handle, flow_in,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// LearnedUnigramCandidateSamplerAttr is an optional argument to LearnedUnigramCandidateSampler.
type LearnedUnigramCandidateSamplerAttr func(optionalAttr)

// LearnedUnigramCandidateSamplerSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func LearnedUnigramCandidateSamplerSeed(value int64) LearnedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// LearnedUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func LearnedUnigramCandidateSamplerSeed2(value int64) LearnedUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Generates labels for candidate sampling with a learned unigram distribution.
//
// See explanations of candidate sampling and the data formats at
// go/candidate-sampling.
//
// For each batch, this op picks a single set of sampled candidate labels.
//
// The advantages of sampling candidates per-batch are simplicity and the
// possibility of efficient dense matrix multiplication. The disadvantage is that
// the sampled candidates must be chosen independently of the context and of the
// true labels.
//
// Arguments:
//	true_classes: A batch_size * num_true matrix, in which each row contains the
// IDs of the num_true target_classes in the corresponding original label.
//	num_true: Number of true labels per context.
//	num_sampled: Number of candidates to randomly sample per batch.
//	unique: If unique is true, we sample with rejection, so that all sampled
// candidates in a batch are unique. This requires some approximation to
// estimate the post-rejection sampling probabilities.
//	range_max: The sampler will sample integers from the interval [0, range_max).
//
// Returns A vector of length num_sampled, in which each element is
// the ID of a sampled candidate.A batch_size * num_true matrix, representing
// the number of times each candidate is expected to occur in a batch
// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
// candidate representing the number of times the candidate is expected
// to occur in a batch of sampled candidates.  If unique=true, then this is a
// probability.
func LearnedUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LearnedUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "LearnedUnigramCandidateSampler",
		Input: []tf.Input{
			true_classes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Split the data from the input value into TensorArray elements.
//
// Assuming that `lengths` takes on values
//
//   ```(n0, n1, ..., n(T-1))```
//
// and that `value` has shape
//
//   ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```,
//
// this splits values into a TensorArray with T tensors.
//
// TensorArray index t will be the subtensor of values with starting position
//
//   ```(n0 + n1 + ... + n(t-1), 0, 0, ...)```
//
// and having size
//
//   ```nt x d0 x d1 x ...```
//
// Arguments:
//	handle: The handle to a TensorArray.
//	value: The concatenated tensor to write to the TensorArray.
//	lengths: The vector of lengths, how to split the rows of value into the
// TensorArray.
//	flow_in: A float scalar that enforces proper chaining of operations.
//
// Returns A float scalar that enforces proper chaining of operations.
func TensorArraySplitV3(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArraySplitV3",
		Input: []tf.Input{
			handle, value, lengths, flow_in,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns a diagonal tensor with a given diagonal values.
//
// Given a `diagonal`, this operation returns a tensor with the `diagonal` and
// everything else padded with zeros. The diagonal is computed as follows:
//
// Assume `diagonal` has dimensions [D1,..., Dk], then the output is a tensor of
// rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:
//
// `output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else.
//
// For example:
//
// ```prettyprint
// # 'diagonal' is [1, 2, 3, 4]
// tf.diag(diagonal) ==> [[1, 0, 0, 0]
//                        [0, 2, 0, 0]
//                        [0, 0, 3, 0]
//                        [0, 0, 0, 4]]
// ```
//
// Arguments:
//	diagonal: Rank k tensor where k is at most 3.
func Diag(scope *Scope, diagonal tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Diag",
		Input: []tf.Input{
			diagonal,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TensorArrayConcatV3Attr is an optional argument to TensorArrayConcatV3.
type TensorArrayConcatV3Attr func(optionalAttr)

// TensorArrayConcatV3ElementShapeExcept0 sets the optional element_shape_except0 attribute to value.
//
// value: The expected shape of an element, if known,
// excluding the first dimension. Used to validate the shapes of
// TensorArray elements. If this shape is not fully specified, concatenating
// zero-size TensorArrays is an error.
// If not specified, defaults to <unknown_rank:true >
func TensorArrayConcatV3ElementShapeExcept0(value tf.Shape) TensorArrayConcatV3Attr {
	return func(m optionalAttr) {
		m["element_shape_except0"] = value
	}
}

// Concat the elements from the TensorArray into value `value`.
//
// Takes `T` elements of shapes
//
//   ```
//   (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...)
//   ```
//
// and concatenates them into a Tensor of shape:
//
//   ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```
//
// All elements must have the same shape (excepting the first dimension).
//
// Arguments:
//	handle: The handle to a TensorArray.
//	flow_in: A float scalar that enforces proper chaining of operations.
//	dtype: The type of the elem that is returned.
//
// Returns All of the elements in the TensorArray, concatenated along the first
// axis.A vector of the row sizes of the original T elements in the
// value output.  In the example above, this would be the values:
// `(n1, n2, ..., n(T-1))`.
func TensorArrayConcatV3(scope *Scope, handle tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayConcatV3Attr) (value tf.Output, lengths tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayConcatV3",
		Input: []tf.Input{
			handle, flow_in,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Scatter the data from the input value into specific TensorArray elements.
//
// `indices` must be a vector, its length must match the first dim of `value`.
//
// Arguments:
//	handle: The handle to a TensorArray.
//	indices: The locations at which to write the tensor elements.
//	value: The concatenated tensor to write to the TensorArray.
//	flow_in: A float scalar that enforces proper chaining of operations.
//
// Returns A float scalar that enforces proper chaining of operations.
func TensorArrayScatterV3(scope *Scope, handle tf.Output, indices tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayScatterV3",
		Input: []tf.Input{
			handle, indices, value, flow_in,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Push an element onto the tensor_array.
//
// Arguments:
//	handle: The handle to a TensorArray.
//	index: The position to write to inside the TensorArray.
//	value: The tensor to write to the TensorArray.
//	flow_in: A float scalar that enforces proper chaining of operations.
//
// Returns A float scalar that enforces proper chaining of operations.
func TensorArrayWriteV3(scope *Scope, handle tf.Output, index tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayWriteV3",
		Input: []tf.Input{
			handle, index, value, flow_in,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Creates a TensorArray for storing the gradients of values in the given handle.
//
// If the given TensorArray gradient already exists, returns a reference to it.
//
// Locks the size of the original TensorArray by disabling its dynamic size flag.
//
// **A note about the input flow_in:**
//
// The handle flow_in forces the execution of the gradient lookup to occur
// only after certain other operations have occurred.  For example, when
// the forward TensorArray is dynamically sized, writes to this TensorArray
// may resize the object.  The gradient TensorArray is statically sized based
// on the size of the forward TensorArray when this operation executes.
// Furthermore, the size of the forward TensorArray is frozen by this call.
// As a result, the flow is used to ensure that the call to generate the gradient
// TensorArray only happens after all writes are executed.
//
// In the case of dynamically sized TensorArrays, gradient computation should
// only be performed on read operations that have themselves been chained via
// flow to occur only after all writes have executed. That way the final size
// of the forward TensorArray is known when this operation is called.
//
// **A note about the source attribute:**
//
// TensorArray gradient calls use an accumulator TensorArray object.  If
// multiple gradients are calculated and run in the same session, the multiple
// gradient nodes may accidentally flow throuth the same accumulator TensorArray.
// This double counts and generally breaks the TensorArray gradient flow.
//
// The solution is to identify which gradient call this particular
// TensorArray gradient is being called in.  This is performed by identifying
// a unique string (e.g. "gradients", "gradients_1", ...) from the input
// gradient Tensor's name.  This string is used as a suffix when creating
// the TensorArray gradient object here (the attribute `source`).
//
// The attribute `source` is added as a suffix to the forward TensorArray's
// name when performing the creation / lookup, so that each separate gradient
// calculation gets its own TensorArray accumulator.
//
// Arguments:
//	handle: The handle to the forward TensorArray.
//	flow_in: A float scalar that enforces proper chaining of operations.
//	source: The gradient source string, used to decide which gradient TensorArray
// to return.
func TensorArrayGradV3(scope *Scope, handle tf.Output, flow_in tf.Output, source string) (grad_handle tf.Output, flow_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"source": source}
	opspec := tf.OpSpec{
		Type: "TensorArrayGradV3",
		Input: []tf.Input{
			handle, flow_in,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// TensorArrayConcatV2Attr is an optional argument to TensorArrayConcatV2.
type TensorArrayConcatV2Attr func(optionalAttr)

// TensorArrayConcatV2ElementShapeExcept0 sets the optional element_shape_except0 attribute to value.
// If not specified, defaults to <unknown_rank:true >
func TensorArrayConcatV2ElementShapeExcept0(value tf.Shape) TensorArrayConcatV2Attr {
	return func(m optionalAttr) {
		m["element_shape_except0"] = value
	}
}

// Deprecated. Use TensorArrayConcatV3
func TensorArrayConcatV2(scope *Scope, handle tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayConcatV2Attr) (value tf.Output, lengths tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayConcatV2",
		Input: []tf.Input{
			handle, flow_in,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Returns the batched diagonal part of a batched tensor.
//
// This operation returns a tensor with the `diagonal` part
// of the batched `input`. The `diagonal` part is computed as follows:
//
// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
// tensor of rank `k - 1` with dimensions `[I, J, K, ..., min(M, N)]` where:
//
// `diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.
//
// The input must be at least a matrix.
//
// For example:
//
// ```prettyprint
// # 'input' is [[[1, 0, 0, 0]
//                [0, 2, 0, 0]
//                [0, 0, 3, 0]
//                [0, 0, 0, 4]],
//               [[5, 0, 0, 0]
//                [0, 6, 0, 0]
//                [0, 0, 7, 0]
//                [0, 0, 0, 8]]]
//
// and input.shape = (2, 4, 4)
//
// tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
//
// which has shape (2, 4)
// ```
//
// Arguments:
//	input: Rank `k` tensor where `k >= 2`.
//
// Returns The extracted diagonal(s) having shape
// `diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])]`.
func MatrixDiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "MatrixDiagPart",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QueueCloseV2Attr is an optional argument to QueueCloseV2.
type QueueCloseV2Attr func(optionalAttr)

// QueueCloseV2CancelPendingEnqueues sets the optional cancel_pending_enqueues attribute to value.
//
// value: If true, all pending enqueue requests that are
// blocked on the given queue will be cancelled.
// If not specified, defaults to false
func QueueCloseV2CancelPendingEnqueues(value bool) QueueCloseV2Attr {
	return func(m optionalAttr) {
		m["cancel_pending_enqueues"] = value
	}
}

// Closes the given queue.
//
// This operation signals that no more elements will be enqueued in the
// given queue. Subsequent Enqueue(Many) operations will fail.
// Subsequent Dequeue(Many) operations will continue to succeed if
// sufficient elements remain in the queue. Subsequent Dequeue(Many)
// operations that would block will fail immediately.
//
// Arguments:
//	handle: The handle to a queue.
//
// Returns the created operation.
func QueueCloseV2(scope *Scope, handle tf.Output, optional ...QueueCloseV2Attr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QueueCloseV2",
		Input: []tf.Input{
			handle,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Concatenates tensors along one dimension.
//
// Arguments:
//	values: List of `N` Tensors to concatenate. Their ranks and types must match,
// and their sizes must match in all dimensions except `concat_dim`.
//	axis: 0-D.  The dimension along which to concatenate.  Must be in the
// range [-rank(values), rank(values)).
//
// Returns A `Tensor` with the concatenation of values stacked along the
// `concat_dim` dimension.  This tensor's shape matches that of `values` except
// in `concat_dim` where it has the sum of the sizes.
func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ConcatV2",
		Input: []tf.Input{
			tf.OutputList(values), axis,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2.
type QueueDequeueUpToV2Attr func(optionalAttr)

// QueueDequeueUpToV2TimeoutMs sets the optional timeout_ms attribute to value.
//
// value: If the queue has fewer than n elements, this operation
// will block for up to timeout_ms milliseconds.
// Note: This option is not supported yet.
// If not specified, defaults to -1
func QueueDequeueUpToV2TimeoutMs(value int64) QueueDequeueUpToV2Attr {
	return func(m optionalAttr) {
		m["timeout_ms"] = value
	}
}

// Dequeues n tuples of one or more tensors from the given queue.
//
// This operation is not supported by all queues.  If a queue does not support
// DequeueUpTo, then an Unimplemented error is returned.
//
// If the queue is closed and there are more than 0 but less than n elements
// remaining, then instead of returning an OutOfRange error like
// QueueDequeueMany, less than `n` elements are returned immediately.  If the queue
// is closed and there are 0 elements left in the queue, then an OutOfRange
// error is returned just like in QueueDequeueMany.  Otherwise the behavior
// is identical to QueueDequeueMany:
//
// This operation concatenates queue-element component tensors along the
// 0th dimension to make a single component tensor.  All of the components
// in the dequeued tuple will have size n in the 0th dimension.
//
// This operation has k outputs, where k is the number of components in
// the tuples stored in the given queue, and output i is the ith
// component of the dequeued tuple.
//
// Arguments:
//	handle: The handle to a queue.
//	n: The number of tuples to dequeue.
//	component_types: The type of each component in a tuple.
//
// Returns One or more tensors that were dequeued as a tuple.
func QueueDequeueUpToV2(scope *Scope, handle tf.Output, n tf.Output, component_types []tf.DataType, optional ...QueueDequeueUpToV2Attr) (components []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"component_types": component_types}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QueueDequeueUpToV2",
		Input: []tf.Input{
			handle, n,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
		scope.UpdateErr("QueueDequeueUpToV2", err)
		return
	}
	return components
}

// Deprecated. Use TensorArrayCloseV3
//
// Returns the created operation.
func TensorArrayCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayCloseV2",
		Input: []tf.Input{
			handle,
		},
	}
	return scope.AddOperation(opspec)
}

// QueueDequeueManyV2Attr is an optional argument to QueueDequeueManyV2.
type QueueDequeueManyV2Attr func(optionalAttr)

// QueueDequeueManyV2TimeoutMs sets the optional timeout_ms attribute to value.
//
// value: If the queue has fewer than n elements, this operation
// will block for up to timeout_ms milliseconds.
// Note: This option is not supported yet.
// If not specified, defaults to -1
func QueueDequeueManyV2TimeoutMs(value int64) QueueDequeueManyV2Attr {
	return func(m optionalAttr) {
		m["timeout_ms"] = value
	}
}

// Dequeues n tuples of one or more tensors from the given queue.
//
// If the queue is closed and there are fewer than n elements, then an
// OutOfRange error is returned.
//
// This operation concatenates queue-element component tensors along the
// 0th dimension to make a single component tensor.  All of the components
// in the dequeued tuple will have size n in the 0th dimension.
//
// This operation has k outputs, where k is the number of components in
// the tuples stored in the given queue, and output i is the ith
// component of the dequeued tuple.
//
// N.B. If the queue is empty, this operation will block until n elements
// have been dequeued (or 'timeout_ms' elapses, if specified).
//
// Arguments:
//	handle: The handle to a queue.
//	n: The number of tuples to dequeue.
//	component_types: The type of each component in a tuple.
//
// Returns One or more tensors that were dequeued as a tuple.
func QueueDequeueManyV2(scope *Scope, handle tf.Output, n tf.Output, component_types []tf.DataType, optional ...QueueDequeueManyV2Attr) (components []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"component_types": component_types}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QueueDequeueManyV2",
		Input: []tf.Input{
			handle, n,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
		scope.UpdateErr("QueueDequeueManyV2", err)
		return
	}
	return components
}

// QueueEnqueueV2Attr is an optional argument to QueueEnqueueV2.
type QueueEnqueueV2Attr func(optionalAttr)

// QueueEnqueueV2TimeoutMs sets the optional timeout_ms attribute to value.
//
// value: If the queue is full, this operation will block for up to
// timeout_ms milliseconds.
// Note: This option is not supported yet.
// If not specified, defaults to -1
func QueueEnqueueV2TimeoutMs(value int64) QueueEnqueueV2Attr {
	return func(m optionalAttr) {
		m["timeout_ms"] = value
	}
}

// Enqueues a tuple of one or more tensors in the given queue.
//
// The components input has k elements, which correspond to the components of
// tuples stored in the given queue.
//
// N.B. If the queue is full, this operation will block until the given
// element has been enqueued (or 'timeout_ms' elapses, if specified).
//
// Arguments:
//	handle: The handle to a queue.
//	components: One or more tensors from which the enqueued tensors should be taken.
//
// Returns the created operation.
func QueueEnqueueV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueV2Attr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QueueEnqueueV2",
		Input: []tf.Input{
			handle, tf.OutputList(components),
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// UnstageAttr is an optional argument to Unstage.
type UnstageAttr func(optionalAttr)

// UnstageContainer sets the optional container attribute to value.
// If not specified, defaults to ""
func UnstageContainer(value string) UnstageAttr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// UnstageSharedName sets the optional shared_name attribute to value.
// If not specified, defaults to ""
func UnstageSharedName(value string) UnstageAttr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// Op is similar to a lightweight Dequeue.  The basic funtionality is similar to
//
// dequeue with many fewer capabilities and options.  This Op is optimized for
// performance.
func Unstage(scope *Scope, dtypes []tf.DataType, optional ...UnstageAttr) (values []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtypes": dtypes}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Unstage",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
		scope.UpdateErr("Unstage", err)
		return
	}
	return values
}

// PriorityQueueV2Attr is an optional argument to PriorityQueueV2.
type PriorityQueueV2Attr func(optionalAttr)

// PriorityQueueV2ComponentTypes sets the optional component_types attribute to value.
//
// value: The type of each component in a value.
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func PriorityQueueV2ComponentTypes(value []tf.DataType) PriorityQueueV2Attr {
	return func(m optionalAttr) {
		m["component_types"] = value
	}
}

// PriorityQueueV2Capacity sets the optional capacity attribute to value.
//
// value: The upper bound on the number of elements in this queue.
// Negative numbers mean no limit.
// If not specified, defaults to -1
func PriorityQueueV2Capacity(value int64) PriorityQueueV2Attr {
	return func(m optionalAttr) {
		m["capacity"] = value
	}
}

// PriorityQueueV2Container sets the optional container attribute to value.
//
// value: If non-empty, this queue is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func PriorityQueueV2Container(value string) PriorityQueueV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// PriorityQueueV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this queue will be shared under the given name
// across multiple sessions.
// If not specified, defaults to ""
func PriorityQueueV2SharedName(value string) PriorityQueueV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// A queue that produces elements sorted by the first component value.
//
// Note that the PriorityQueue requires the first component of any element
// to be a scalar int64, in addition to the other elements declared by
// component_types.  Therefore calls to Enqueue and EnqueueMany (resp. Dequeue
// and DequeueMany) on a PriorityQueue will all require (resp. output) one extra
// entry in their input (resp. output) lists.
//
// Arguments:
//	shapes: The shape of each component in a value. The length of this attr must
// be either 0 or the same as the length of component_types. If the length of
// this attr is 0, the shapes of queue elements are not constrained, and
// only one element may be dequeued at a time.
//
// Returns The handle to the queue.
func PriorityQueueV2(scope *Scope, shapes []tf.Shape, optional ...PriorityQueueV2Attr) (handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"shapes": shapes}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "PriorityQueueV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// PaddingFIFOQueueV2Attr is an optional argument to PaddingFIFOQueueV2.
type PaddingFIFOQueueV2Attr func(optionalAttr)

// PaddingFIFOQueueV2Shapes sets the optional shapes attribute to value.
//
// value: The shape of each component in a value. The length of this attr must
// be either 0 or the same as the length of component_types.
// Shapes of fixed rank but variable size are allowed by setting
// any shape dimension to -1.  In this case, the inputs' shape may vary along
// the given dimension, and DequeueMany will pad the given dimension with
// zeros up to the maximum shape of all elements in the given batch.
// If the length of this attr is 0, different queue elements may have
// different ranks and shapes, but only one element may be dequeued at a time.
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func PaddingFIFOQueueV2Shapes(value []tf.Shape) PaddingFIFOQueueV2Attr {
	return func(m optionalAttr) {
		m["shapes"] = value
	}
}

// PaddingFIFOQueueV2Capacity sets the optional capacity attribute to value.
//
// value: The upper bound on the number of elements in this queue.
// Negative numbers mean no limit.
// If not specified, defaults to -1
func PaddingFIFOQueueV2Capacity(value int64) PaddingFIFOQueueV2Attr {
	return func(m optionalAttr) {
		m["capacity"] = value
	}
}

// PaddingFIFOQueueV2Container sets the optional container attribute to value.
//
// value: If non-empty, this queue is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func PaddingFIFOQueueV2Container(value string) PaddingFIFOQueueV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// PaddingFIFOQueueV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this queue will be shared under the given name
// across multiple sessions.
// If not specified, defaults to ""
func PaddingFIFOQueueV2SharedName(value string) PaddingFIFOQueueV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// A queue that produces elements in first-in first-out order.
//
// Variable-size shapes are allowed by setting the corresponding shape dimensions
// to 0 in the shape attr.  In this case DequeueMany will pad up to the maximum
// size of any given element in the minibatch.  See below for details.
//
// Arguments:
//	component_types: The type of each component in a value.
//
// Returns The handle to the queue.
func PaddingFIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...PaddingFIFOQueueV2Attr) (handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"component_types": component_types}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "PaddingFIFOQueueV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// FIFOQueueV2Attr is an optional argument to FIFOQueueV2.
type FIFOQueueV2Attr func(optionalAttr)

// FIFOQueueV2Shapes sets the optional shapes attribute to value.
//
// value: The shape of each component in a value. The length of this attr must
// be either 0 or the same as the length of component_types. If the length of
// this attr is 0, the shapes of queue elements are not constrained, and
// only one element may be dequeued at a time.
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func FIFOQueueV2Shapes(value []tf.Shape) FIFOQueueV2Attr {
	return func(m optionalAttr) {
		m["shapes"] = value
	}
}

// FIFOQueueV2Capacity sets the optional capacity attribute to value.
//
// value: The upper bound on the number of elements in this queue.
// Negative numbers mean no limit.
// If not specified, defaults to -1
func FIFOQueueV2Capacity(value int64) FIFOQueueV2Attr {
	return func(m optionalAttr) {
		m["capacity"] = value
	}
}

// FIFOQueueV2Container sets the optional container attribute to value.
//
// value: If non-empty, this queue is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func FIFOQueueV2Container(value string) FIFOQueueV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// FIFOQueueV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this queue will be shared under the given name
// across multiple sessions.
// If not specified, defaults to ""
func FIFOQueueV2SharedName(value string) FIFOQueueV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// A queue that produces elements in first-in first-out order.
//
// Arguments:
//	component_types: The type of each component in a value.
//
// Returns The handle to the queue.
func FIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...FIFOQueueV2Attr) (handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"component_types": component_types}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FIFOQueueV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// StridedSliceAttr is an optional argument to StridedSlice.
type StridedSliceAttr func(optionalAttr)

// StridedSliceBeginMask sets the optional begin_mask attribute to value.
//
// value: a bitmask where a bit i being 1 means to ignore the begin
// value and instead use the largest interval possible. At runtime
// begin[i] will be replaced with `[0, n-1) if `stride[i] > 0` or
// `[-1, n-1]` if `stride[i] < 0`
// If not specified, defaults to 0
func StridedSliceBeginMask(value int64) StridedSliceAttr {
	return func(m optionalAttr) {
		m["begin_mask"] = value
	}
}

// StridedSliceEndMask sets the optional end_mask attribute to value.
//
// value: analogous to `begin_mask`
// If not specified, defaults to 0
func StridedSliceEndMask(value int64) StridedSliceAttr {
	return func(m optionalAttr) {
		m["end_mask"] = value
	}
}

// StridedSliceEllipsisMask sets the optional ellipsis_mask attribute to value.
//
// value: a bitmask where bit `i` being 1 means the `i`th
// position is actually an ellipsis. One bit at most can be 1.
// If `ellipsis_mask == 0`, then an implicit ellipsis mask of `1 << (m+1)`
// is provided. This means that `foo[3:5] == foo[3:5, ...]`. An ellipsis
// implicitly creates as many range specifications as necessary to fully
// specify the sliced range for every dimension. For example for a 4-dimensional
// tensor `foo` the slice `foo[2, ..., 5:8]` implies `foo[2, :, :, 5:8]`.
// If not specified, defaults to 0
func StridedSliceEllipsisMask(value int64) StridedSliceAttr {
	return func(m optionalAttr) {
		m["ellipsis_mask"] = value
	}
}

// StridedSliceNewAxisMask sets the optional new_axis_mask attribute to value.
//
// value: a bitmask where bit `i` being 1 means the `i`th
// specification creates a new shape 1 dimension. For example
// `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor.
// If not specified, defaults to 0
func StridedSliceNewAxisMask(value int64) StridedSliceAttr {
	return func(m optionalAttr) {
		m["new_axis_mask"] = value
	}
}

// StridedSliceShrinkAxisMask sets the optional shrink_axis_mask attribute to value.
//
// value: a bitmask where bit `i` implies that the `i`th
// specification should shrink the dimensionality. begin and end
// must imply a slice of size 1 in the dimension. For example in
// python one might do `foo[:, 3, :]` which would result in
// `shrink_axis_mask` being 2.
// If not specified, defaults to 0
func StridedSliceShrinkAxisMask(value int64) StridedSliceAttr {
	return func(m optionalAttr) {
		m["shrink_axis_mask"] = value
	}
}

// Return a strided slice from `input`.
//
// Note, most python users will want to use the Python `Tensor.__getitem__`
// or `Variable.__getitem__` rather than this op directly.
//
// The goal of this op is to produce a new tensor with a subset of
// the elements from the `n` dimensional `input` tensor. The subset is chosen using
// a sequence of `m` sparse range specifications encoded into the arguments
// of this function. Note, in some cases
// `m` could be equal to `n`, but this need not be the case. Each
// range specification entry can be one of the following:
//
// - An ellipsis (...). Ellipses are used to imply zero or more
//   dimensions of full-dimension selection and are produced using
//   `ellipsis_mask`. For example, `foo[...]` is the identity slice.
//
// - A new axis. This is used to insert a new shape=1 dimension and is
//   produced using `new_axis_mask`. For example, `foo[:, ...]` where
//   `foo` is shape `(3, 4)` produces a `(1, 3, 4)` tensor.
//
//
// - A range `begin:end:stride`. This is used to specify how much to choose from
//   a given dimension. `stride` can be any integer but 0.  `begin` is an integer
//   which represents the index of the first value to select while `end` represents
//   the index of the last value to select. The number of values selected in each
//   dimension is `end - begin` if `stride > 0` and `begin - end` if `stride < 0`.
//   `begin` and `end` can be negative where `-1` is the last element, `-2` is
//   the second to last. `begin_mask` controls whether to replace the explicitly
//   given `begin` with an implicit effective value of `0` if `stride > 0` and
//   `-1` if `stride < 0`. `end_mask` is analogous but produces the number
//   required to create the largest open interval. For example, given a shape
//   `(3,)` tensor `foo[:]`, the effective `begin` and `end` are `0` and `3`. Do
//   not assume this is equivalent to `foo[0:-1]` which has an effective `begin`
//   and `end` of `0` and `2`. Another example is `foo[-2::-1]` which reverses the
//   first dimension of a tensor while dropping the last two (in the original
//   order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is `[4,3]`.
//
// - A single index. This is used to keep only elements that have a given
//   index. For example (`foo[2, :]` on a shape `(5,6)` tensor produces a
//   shape `(6,)` tensor. This is encoded in `begin` and `end` and
//   `shrink_axis_mask`.
//
// Each conceptual range specification is encoded in the op's argument. This
// encoding is best understand by considering a non-trivial example. In
// particular,
// `foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as
//
// ```prettyprint
// begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0)
// end = [2, 4, x, x, -3, x]
// strides = [1, 1, x, x, -1, 1]
// begin_mask = 1<<4 | 1 << 5 = 48
// end_mask = 1<<5 = 32
// ellipsis_mask = 1<<3 = 8
// new_axis_mask = 1<<2 4
// shrink_axis_mask = 1<<0
// ```
//
// In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of
// the slice becomes (2, 1, 5, 5, 2, 5).
// Let us walk step by step through each argument specification.
//
// 1.  The first argument in the example slice is turned into `begin = 1` and
// `end = begin + 1 = 2`. To disambiguate from the original spec `2:4` we
// also set the appropriate bit in `shrink_axis_mask`.
//
// 2. `2:4` is contributes 2, 4, 1 to begin, end, and stride. All masks have
// zero bits contributed.
//
// 3. None is a synonym for `tf.newaxis`. This means insert a dimension of size 1
// dimension in the final shape. Dummy values are contributed to begin,
// end and stride, while the new_axis_mask bit is set.
//
// 4. `...` grab the full ranges from as many dimensions as needed to
// fully specify a slice for every dimension of the input shape.
//
// 5. `:-3:-1` shows the use of negative indices. A negative index `i` associated
// with a dimension that has shape `s` is converted to a positive index
// `s + i`. So `-1` becomes `s-1` (i.e. the last element). This conversion
// is done internally so begin, end and strides receive x, -3, and -1.
// The appropriate begin_mask bit is set to indicate the start range is the
// full range (ignoring the x).
//
// 6. `:` indicates that the entire contents of the corresponding dimension
// is selected. This is equivalent to `::` or `0::1`. begin, end, and strides
// receive 0, 0, and 1, respectively. The appropriate bits in `begin_mask` and
// `end_mask` are also set.
//
// *Requirements*:
//   `0 != strides[i] for i in [0, m)`
//   `ellipsis_mask must be a power of two (only one ellipsis)`
//
// Arguments:
//
//	begin: `begin[k]` specifies the offset into the `k`th range specification.
// The exact dimension this corresponds to will be determined by context.
// Out-of-bounds values will be silently clamped. If the `k`th bit of
// `begin_mask` then `begin[k]` is ignored and the full range of the
// appropriate dimension is used instead. Negative values causes indexing
// to start from the highest element e.g. If `foo==[1,2,3]` then `foo[-1]==3`.
//	end: `end[i]` is like `begin` with the exception that `end_mask` is
// used to determine full ranges.
//	strides: `strides[i]` specifies the increment in the `i`th specification
// after extracting a given element. Negative indices will reverse
// the original order. Out or range values are
// clamped to `[0,dim[i]) if slice[i]>0` or `[-1,dim[i]-1] if slice[i] < 0`
func StridedSlice(scope *Scope, input tf.Output, begin tf.Output, end tf.Output, strides tf.Output, optional ...StridedSliceAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "StridedSlice",
		Input: []tf.Input{
			input, begin, end, strides,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TensorArrayGatherV2Attr is an optional argument to TensorArrayGatherV2.
type TensorArrayGatherV2Attr func(optionalAttr)

// TensorArrayGatherV2ElementShape sets the optional element_shape attribute to value.
// If not specified, defaults to <unknown_rank:true >
func TensorArrayGatherV2ElementShape(value tf.Shape) TensorArrayGatherV2Attr {
	return func(m optionalAttr) {
		m["element_shape"] = value
	}
}

// Deprecated. Use TensorArrayGatherV3
func TensorArrayGatherV2(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV2Attr) (value tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayGatherV2",
		Input: []tf.Input{
			handle, indices, flow_in,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Interleave the values from the `data` tensors into a single tensor.
//
// Builds a merged tensor such that
//
// ```python
//     merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
// ```
//
// For example, if each `indices[m]` is scalar or vector, we have
//
// ```python
//     # Scalar indices:
//     merged[indices[m], ...] = data[m][...]
//
//     # Vector indices:
//     merged[indices[m][i], ...] = data[m][i, ...]
// ```
//
// Each `data[i].shape` must start with the corresponding `indices[i].shape`,
// and the rest of `data[i].shape` must be constant w.r.t. `i`.  That is, we
// must have `data[i].shape = indices[i].shape + constant`.  In terms of this
// `constant`, the output shape is
//
//     merged.shape = [max(indices)] + constant
//
// Values are merged in order, so if an index appears in both `indices[m][i]` and
// `indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the
// merged result.
//
// For example:
//
// ```python
//     indices[0] = 6
//     indices[1] = [4, 1]
//     indices[2] = [[5, 2], [0, 3]]
//     data[0] = [61, 62]
//     data[1] = [[41, 42], [11, 12]]
//     data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
//     merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
//               [51, 52], [61, 62]]
// ```
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/DynamicStitch.png" alt>
// </div>
func DynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "DynamicStitch",
		Input: []tf.Input{
			tf.OutputList(indices), tf.OutputList(data),
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes softmax cross entropy cost and gradients to backpropagate.
//
// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept
// a matrix of label probabilities, but rather a single label per row
// of features.  This label is considered to have probability 1.0 for the
// given row.
//
// Inputs are the logits, not probabilities.
//
// Arguments:
//	features: batch_size x num_classes matrix
//	labels: batch_size vector with values in [0, num_classes).
// This is the label for the given minibatch entry.
//
// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix).
func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSoftmaxCrossEntropyWithLogits",
		Input: []tf.Input{
			features, labels,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// TensorSummaryAttr is an optional argument to TensorSummary.
type TensorSummaryAttr func(optionalAttr)

// TensorSummaryDescription sets the optional description attribute to value.
//
// value: A json-encoded SummaryDescription proto.
// If not specified, defaults to ""
func TensorSummaryDescription(value string) TensorSummaryAttr {
	return func(m optionalAttr) {
		m["description"] = value
	}
}

// TensorSummaryLabels sets the optional labels attribute to value.
//
// value: An unused list of strings.
// If not specified, defaults to <>
func TensorSummaryLabels(value []string) TensorSummaryAttr {
	return func(m optionalAttr) {
		m["labels"] = value
	}
}

// TensorSummaryDisplayName sets the optional display_name attribute to value.
//
// value: An unused string.
// If not specified, defaults to ""
func TensorSummaryDisplayName(value string) TensorSummaryAttr {
	return func(m optionalAttr) {
		m["display_name"] = value
	}
}

// Outputs a `Summary` protocol buffer with a tensor.
//
// Arguments:
//	tensor: A tensor to serialize.
func TensorSummary(scope *Scope, tensor tf.Output, optional ...TensorSummaryAttr) (summary tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TensorSummary",
		Input: []tf.Input{
			tensor,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes softplus gradients for a softplus operation.
//
// Arguments:
//	gradients: The backpropagated gradients to the corresponding softplus operation.
//	features: The features passed as input to the corresponding softplus operation.
//
// Returns The gradients: `gradients / (1 + exp(-features))`.
func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SoftplusGrad",
		Input: []tf.Input{
			gradients, features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes softplus: `log(exp(features) + 1)`.
func Softplus(scope *Scope, features tf.Output) (activations tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Softplus",
		Input: []tf.Input{
			features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// BatchMatMulAttr is an optional argument to BatchMatMul.
type BatchMatMulAttr func(optionalAttr)

// BatchMatMulAdjX sets the optional adj_x attribute to value.
//
// value: If `True`, adjoint the slices of `x`. Defaults to `False`.
// If not specified, defaults to false
func BatchMatMulAdjX(value bool) BatchMatMulAttr {
	return func(m optionalAttr) {
		m["adj_x"] = value
	}
}

// BatchMatMulAdjY sets the optional adj_y attribute to value.
//
// value: If `True`, adjoint the slices of `y`. Defaults to `False`.
// If not specified, defaults to false
func BatchMatMulAdjY(value bool) BatchMatMulAttr {
	return func(m optionalAttr) {
		m["adj_y"] = value
	}
}

// Multiplies slices of two tensors in batches.
//
// Multiplies all slices of `Tensor` `x` and `y` (each slice can be
// viewed as an element of a batch), and arranges the individual results
// in a single output tensor of the same batch size. Each of the
// individual slices can optionally be adjointed (to adjoint a matrix
// means to transpose and conjugate it) before multiplication by setting
// the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
//
// The input tensors `x` and `y` are 3-D or higher with shape `[..., r_x, c_x]`
// and `[..., r_y, c_y]`.
//
// The output tensor is 3-D or higher with shape `[..., r_o, c_o]`, where:
//
//     r_o = c_x if adj_x else r_x
//     c_o = r_y if adj_y else c_y
//
// It is computed as:
//
//     output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
//
// Arguments:
//	x: 3-D or higher with shape `[..., r_x, c_x]`.
//	y: 3-D or higher with shape `[..., r_y, c_y]`.
//
// Returns 3-D or higher with shape `[..., r_o, c_o]`
func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "BatchMatMul",
		Input: []tf.Input{
			x, y,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul.
type SparseTensorDenseMatMulAttr func(optionalAttr)

// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value.
//
// value: Use the adjoint of A in the matrix multiply.  If A is complex, this
// is transpose(conj(A)).  Otherwise it's transpose(A).
// If not specified, defaults to false
func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr {
	return func(m optionalAttr) {
		m["adjoint_a"] = value
	}
}

// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value.
//
// value: Use the adjoint of B in the matrix multiply.  If B is complex, this
// is transpose(conj(B)).  Otherwise it's transpose(B).
// If not specified, defaults to false
func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr {
	return func(m optionalAttr) {
		m["adjoint_b"] = value
	}
}

// Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
//
// No validity checking is performed on the indices of A.  However, the following
// input format is recommended for optimal behavior:
//
// if adjoint_a == false:
//   A should be sorted in lexicographically increasing order.  Use SparseReorder
//   if you're not sure.
// if adjoint_a == true:
//   A should be sorted in order of increasing dimension 1 (i.e., "column major"
//   order instead of "row major" order).
//
// Arguments:
//	a_indices: 2-D.  The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.
//	a_values: 1-D.  The `values` of the `SparseTensor`, size `[nnz]` Vector.
//	a_shape: 1-D.  The `shape` of the `SparseTensor`, size `[2]` Vector.
//	b: 2-D.  A dense Matrix.
func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SparseTensorDenseMatMul",
		Input: []tf.Input{
			a_indices, a_values, a_shape, b,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes rectified linear 6: `min(max(features, 0), 6)`.
func Relu6(scope *Scope, features tf.Output) (activations tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Relu6",
		Input: []tf.Input{
			features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradient of morphological 2-D dilation with respect to the input.
//
// Arguments:
//	input: 4-D with shape `[batch, in_height, in_width, depth]`.
//	filter: 3-D with shape `[filter_height, filter_width, depth]`.
//	out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`.
//	strides: 1-D of length 4. The stride of the sliding window for each dimension of
// the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
//	rates: 1-D of length 4. The input stride for atrous morphological dilation.
// Must be: `[1, rate_height, rate_width, 1]`.
//	padding: The type of padding algorithm to use.
//
// Returns 4-D with shape `[batch, in_height, in_width, depth]`.
func Dilation2DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (in_backprop tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
	opspec := tf.OpSpec{
		Type: "Dilation2DBackpropInput",
		Input: []tf.Input{
			input, filter, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// FusedBatchNormGradAttr is an optional argument to FusedBatchNormGrad.
type FusedBatchNormGradAttr func(optionalAttr)

// FusedBatchNormGradEpsilon sets the optional epsilon attribute to value.
//
// value: A small float number added to the variance of x.
// If not specified, defaults to 0.0001
func FusedBatchNormGradEpsilon(value float32) FusedBatchNormGradAttr {
	return func(m optionalAttr) {
		m["epsilon"] = value
	}
}

// FusedBatchNormGradDataFormat sets the optional data_format attribute to value.
//
// value: The data format for y_backprop, x, x_backprop.
// Either "NHWC" (default) or "NCHW".
// If not specified, defaults to "NHWC"
func FusedBatchNormGradDataFormat(value string) FusedBatchNormGradAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// FusedBatchNormGradIsTraining sets the optional is_training attribute to value.
//
// value: A bool value to indicate the operation is for training (default)
// or inference.
// If not specified, defaults to true
func FusedBatchNormGradIsTraining(value bool) FusedBatchNormGradAttr {
	return func(m optionalAttr) {
		m["is_training"] = value
	}
}

// Gradient for batch normalization.
//
// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
// The size of 1D Tensors matches the dimension C of the 4D Tensors.
//
// Arguments:
//	y_backprop: A 4D Tensor for the gradient with respect to y.
//	x: A 4D Tensor for input data.
//	scale: A 1D Tensor for scaling factor, to scale the normalized x.
//	reserve_space_1: A 1D Tensor for the computed batch mean, to be reused
// in the gradient computation.
//	reserve_space_2: A 1D Tensor for the computed batch variance (inverted variance
// in the cuDNN case), to be used in the gradient computation.
//
// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input
// in FusedBatchNorm.
func FusedBatchNormGrad(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradAttr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FusedBatchNormGrad",
		Input: []tf.Input{
			y_backprop, x, scale, reserve_space_1, reserve_space_2,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
}

// Performs 3D average pooling on the input.
//
// Arguments:
//	input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
//
// Returns The average pooled output tensor.
func AvgPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "AvgPool3D",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Produces the max pool of the input tensor for quantized types.
//
// Arguments:
//	input: The 4D (batch x rows x cols x depth) Tensor to MaxReduce over.
//	min_input: The float value that the lowest quantized input value represents.
//	max_input: The float value that the highest quantized input value represents.
//	ksize: The size of the window for each dimension of the input tensor.
// The length must be 4 to match the number of dimensions of the input.
//	strides: The stride of the sliding window for each dimension of the input
// tensor. The length must be 4 to match the number of dimensions of the input.
//	padding: The type of padding algorithm to use.
//
// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
func QuantizedMaxPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "QuantizedMaxPool",
		Input: []tf.Input{
			input, min_input, max_input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient.
type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr)

// FakeQuantWithMinMaxArgsGradientMin sets the optional min attribute to value.
// If not specified, defaults to -6
func FakeQuantWithMinMaxArgsGradientMin(value float32) FakeQuantWithMinMaxArgsGradientAttr {
	return func(m optionalAttr) {
		m["min"] = value
	}
}

// FakeQuantWithMinMaxArgsGradientMax sets the optional max attribute to value.
// If not specified, defaults to 6
func FakeQuantWithMinMaxArgsGradientMax(value float32) FakeQuantWithMinMaxArgsGradientAttr {
	return func(m optionalAttr) {
		m["max"] = value
	}
}

// Compute gradients for a FakeQuantWithMinMaxArgs operation.
//
// Arguments:
//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation.
//	inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation.
//
// Returns Backpropagated gradients below the FakeQuantWithMinMaxArgs operation:
// `gradients * (inputs >= min && inputs <= max)`.
func FakeQuantWithMinMaxArgsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsGradientAttr) (backprops tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FakeQuantWithMinMaxArgsGradient",
		Input: []tf.Input{
			gradients, inputs,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes gradients of the maxpooling function.
//
// Arguments:
//	input: The original input.
//	grad: 4-D with shape `[batch, height, width, channels]`.  Gradients w.r.t. the
// output of `max_pool`.
//	argmax: The indices of the maximum values chosen for each output of `max_pool`.
//	ksize: The size of the window for each dimension of the input tensor.
//	strides: The stride of the sliding window for each dimension of the
// input tensor.
//	padding: The type of padding algorithm to use.
//
// Returns Gradients w.r.t. the input of `max_pool`.
func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "MaxPoolGradWithArgmax",
		Input: []tf.Input{
			input, grad, argmax,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradient of morphological 2-D dilation with respect to the filter.
//
// Arguments:
//	input: 4-D with shape `[batch, in_height, in_width, depth]`.
//	filter: 3-D with shape `[filter_height, filter_width, depth]`.
//	out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`.
//	strides: 1-D of length 4. The stride of the sliding window for each dimension of
// the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
//	rates: 1-D of length 4. The input stride for atrous morphological dilation.
// Must be: `[1, rate_height, rate_width, 1]`.
//	padding: The type of padding algorithm to use.
//
// Returns 3-D with shape `[filter_height, filter_width, depth]`.
func Dilation2DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (filter_backprop tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
	opspec := tf.OpSpec{
		Type: "Dilation2DBackpropFilter",
		Input: []tf.Input{
			input, filter, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the truth value of (x == y) element-wise.
//
// *NOTE*: `Equal` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Equal(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Equal",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradients of 3-D convolution with respect to the input.
//
// Arguments:
//	input_sizes: An integer vector representing the tensor shape of `input`,
// where `input` is a 5-D
// `[batch, depth, rows, cols, in_channels]` tensor.
//	filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
// `in_channels` must match between `input` and `filter`.
//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
// out_channels]`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
func Conv3DBackpropInputV2(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "Conv3DBackpropInputV2",
		Input: []tf.Input{
			input_sizes, filter, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns element-wise remainder of division.
//
// *NOTE*: `Mod` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Mod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Mod",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradients of 3-D convolution with respect to the filter.
//
// DEPRECATED at GraphDef version 10: Use Conv3DBackpropFilterV2
//
// Arguments:
//	input: Shape `[batch, depth, rows, cols, in_channels]`.
//	filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
// `in_channels` must match between `input` and `filter`.
//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
// out_channels]`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
func Conv3DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "Conv3DBackpropFilter",
		Input: []tf.Input{
			input, filter, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradients of 3-D convolution with respect to the input.
//
// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2
//
// Arguments:
//	input: Shape `[batch, depth, rows, cols, in_channels]`.
//	filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
// `in_channels` must match between `input` and `filter`.
//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
// out_channels]`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "Conv3DBackpropInput",
		Input: []tf.Input{
			input, filter, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ReverseSequenceAttr is an optional argument to ReverseSequence.
type ReverseSequenceAttr func(optionalAttr)

// ReverseSequenceBatchDim sets the optional batch_dim attribute to value.
//
// value: The dimension along which reversal is performed.
// If not specified, defaults to 0
func ReverseSequenceBatchDim(value int64) ReverseSequenceAttr {
	return func(m optionalAttr) {
		m["batch_dim"] = value
	}
}

// Reverses variable length slices.
//
// This op first slices `input` along the dimension `batch_dim`, and for each
// slice `i`, reverses the first `seq_lengths[i]` elements along
// the dimension `seq_dim`.
//
// The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`,
// and `seq_lengths` must be a vector of length `input.dims[batch_dim]`.
//
// The output slice `i` along dimension `batch_dim` is then given by input
// slice `i`, with the first `seq_lengths[i]` slices along dimension
// `seq_dim` reversed.
//
// For example:
//
// ```prettyprint
// # Given this:
// batch_dim = 0
// seq_dim = 1
// input.dims = (4, 8, ...)
// seq_lengths = [7, 2, 3, 5]
//
// # then slices of input are reversed on seq_dim, but only up to seq_lengths:
// output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
// output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
// output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
// output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
//
// # while entries past seq_lens are copied through:
// output[0, 7:, :, ...] = input[0, 7:, :, ...]
// output[1, 2:, :, ...] = input[1, 2:, :, ...]
// output[2, 3:, :, ...] = input[2, 3:, :, ...]
// output[3, 2:, :, ...] = input[3, 2:, :, ...]
// ```
//
// In contrast, if:
//
// ```prettyprint
// # Given this:
// batch_dim = 2
// seq_dim = 0
// input.dims = (8, ?, 4, ...)
// seq_lengths = [7, 2, 3, 5]
//
// # then slices of input are reversed on seq_dim, but only up to seq_lengths:
// output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
// output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
// output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
// output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
//
// # while entries past seq_lens are copied through:
// output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
// output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
// output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
// output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
// ```
//
// Arguments:
//	input: The input to reverse.
//	seq_lengths: 1-D with length `input.dims(batch_dim)` and
// `max(seq_lengths) <= input.dims(seq_dim)`
//	seq_dim: The dimension which is partially reversed.
//
// Returns The partially reversed input. It has the same shape as `input`.
func ReverseSequence(scope *Scope, input tf.Output, seq_lengths tf.Output, seq_dim int64, optional ...ReverseSequenceAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"seq_dim": seq_dim}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ReverseSequence",
		Input: []tf.Input{
			input, seq_lengths,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradient for the rsqrt of `x` wrt its input.
//
// Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy`
// is the corresponding input gradient.
func RsqrtGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "RsqrtGrad",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Execute a sub graph on a remote processor transferred by GraphTransferer.
//
// The graph specifications are serialized by protobuf as graph_transfer_info.
// The implementation / limitations may differ for each platform
// and each available peripheral.
func RemoteFusedGraphExecute(scope *Scope, values []tf.Output, N int64, U tf.DataType, serialized_graph_transfer_info string) (output []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"N": N, "U": U, "serialized_graph_transfer_info": serialized_graph_transfer_info}
	opspec := tf.OpSpec{
		Type: "RemoteFusedGraphExecute",
		Input: []tf.Input{
			tf.OutputList(values),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
		scope.UpdateErr("RemoteFusedGraphExecute", err)
		return
	}
	return output
}

// Computes the gradients of 3-D convolution with respect to the filter.
//
// Arguments:
//	input: Shape `[batch, depth, rows, cols, in_channels]`.
//	filter_sizes: An integer vector representing the tensor shape of `filter`,
// where `filter` is a 5-D
// `[filter_depth, filter_height, filter_width, in_channels, out_channels]`
// tensor.
//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
// out_channels]`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "Conv3DBackpropFilterV2",
		Input: []tf.Input{
			input, filter_sizes, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TensorArrayV2Attr is an optional argument to TensorArrayV2.
type TensorArrayV2Attr func(optionalAttr)

// TensorArrayV2ElementShape sets the optional element_shape attribute to value.
// If not specified, defaults to <unknown_rank:true >
func TensorArrayV2ElementShape(value tf.Shape) TensorArrayV2Attr {
	return func(m optionalAttr) {
		m["element_shape"] = value
	}
}

// TensorArrayV2DynamicSize sets the optional dynamic_size attribute to value.
// If not specified, defaults to false
func TensorArrayV2DynamicSize(value bool) TensorArrayV2Attr {
	return func(m optionalAttr) {
		m["dynamic_size"] = value
	}
}

// TensorArrayV2ClearAfterRead sets the optional clear_after_read attribute to value.
// If not specified, defaults to true
func TensorArrayV2ClearAfterRead(value bool) TensorArrayV2Attr {
	return func(m optionalAttr) {
		m["clear_after_read"] = value
	}
}

// TensorArrayV2TensorArrayName sets the optional tensor_array_name attribute to value.
// If not specified, defaults to ""
func TensorArrayV2TensorArrayName(value string) TensorArrayV2Attr {
	return func(m optionalAttr) {
		m["tensor_array_name"] = value
	}
}

// Deprecated. Use TensorArrayV3
func TensorArrayV2(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV2Attr) (handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayV2",
		Input: []tf.Input{
			size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Tensor`.
//
// The `SparseTensor` must have rank `R` greater than 1, and the first dimension
// is treated as the minibatch dimension.  Elements of the `SparseTensor`
// must be sorted in increasing order of this first dimension.  The serialized
// `SparseTensor` objects going into each row of `serialized_sparse` will have
// rank `R-1`.
//
// The minibatch size `N` is extracted from `sparse_shape[0]`.
//
// Arguments:
//	sparse_indices: 2-D.  The `indices` of the minibatch `SparseTensor`.
//	sparse_values: 1-D.  The `values` of the minibatch `SparseTensor`.
//	sparse_shape: 1-D.  The `shape` of the minibatch `SparseTensor`.
func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) (serialized_sparse tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SerializeManySparse",
		Input: []tf.Input{
			sparse_indices, sparse_values, sparse_shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Makes its input available to the next iteration.
//
// Arguments:
//	data: The tensor to be made available to the next iteration.
//
// Returns The same tensor as `data`.
func NextIteration(scope *Scope, data tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "NextIteration",
		Input: []tf.Input{
			data,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Does nothing. Only useful as a placeholder for control edges.
//
// Returns the created operation.
func NoOp(scope *Scope) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "NoOp",
	}
	return scope.AddOperation(opspec)
}

// Computes softsign: `features / (abs(features) + 1)`.
func Softsign(scope *Scope, features tf.Output) (activations tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Softsign",
		Input: []tf.Input{
			features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative.
type DepthwiseConv2dNativeAttr func(optionalAttr)

// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, height, width, channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, channels, height, width].
// If not specified, defaults to "NHWC"
func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
//
// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
// and a filter / kernel tensor of shape
// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
// a different filter to each input channel (expanding from 1 channel to
// `channel_multiplier` channels for each), then concatenates the results
// together. Thus, the output has `in_channels * channel_multiplier` channels.
//
// for k in 0..in_channels-1
//   for q in 0..channel_multiplier-1
//     output[b, i, j, k * channel_multiplier + q] =
//       sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
//                         filter[di, dj, k, q]
//
// Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
//
// Arguments:
//
//
//	strides: 1-D of length 4.  The stride of the sliding window for each dimension
// of `input`.
//	padding: The type of padding algorithm to use.
func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DepthwiseConv2dNative",
		Input: []tf.Input{
			input, filter,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Adds `bias` to `value`.
//
// This is a deprecated version of BiasAdd and will be soon removed.
//
// This is a special case of `tf.add` where `bias` is restricted to be 1-D.
// Broadcasting is supported, so `value` may have any number of dimensions.
//
// Arguments:
//	value: Any number of dimensions.
//	bias: 1-D with size the last dimension of `value`.
//
// Returns Broadcasted sum of `value` and `bias`.
func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "BiasAddV1",
		Input: []tf.Input{
			value, bias,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// FractionalAvgPoolGradAttr is an optional argument to FractionalAvgPoolGrad.
type FractionalAvgPoolGradAttr func(optionalAttr)

// FractionalAvgPoolGradOverlapping sets the optional overlapping attribute to value.
//
// value: When set to True, it means when pooling, the values at the boundary
// of adjacent pooling cells are used by both cells. For example:
//
// `index  0  1  2  3  4`
//
// `value  20 5  16 3  7`
//
// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
// The result would be [41/3, 26/3] for fractional avg pooling.
// If not specified, defaults to false
func FractionalAvgPoolGradOverlapping(value bool) FractionalAvgPoolGradAttr {
	return func(m optionalAttr) {
		m["overlapping"] = value
	}
}

// Computes gradient of the FractionalAvgPool function.
//
// Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
// FractionalAvgPoolGrad, we just need to evenly back-propagate each element of
// out_backprop to those indices that form the same pooling cell. Therefore, we
// just need to know the shape of original input tensor, instead of the whole
// tensor.
//
// Arguments:
//	orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool`
//	out_backprop: 4-D with shape `[batch, height, width, channels]`.  Gradients
// w.r.t. the output of `fractional_avg_pool`.
//	row_pooling_sequence: row pooling sequence, form pooling region with
// col_pooling_sequence.
//	col_pooling_sequence: column pooling sequence, form pooling region with
// row_pooling sequence.
//
// Returns 4-D.  Gradients w.r.t. the input of `fractional_avg_pool`.
func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalAvgPoolGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FractionalAvgPoolGrad",
		Input: []tf.Input{
			orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Conv2DBackpropInputAttr is an optional argument to Conv2DBackpropInput.
type Conv2DBackpropInputAttr func(optionalAttr)

// Conv2DBackpropInputUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value.
// If not specified, defaults to true
func Conv2DBackpropInputUseCudnnOnGpu(value bool) Conv2DBackpropInputAttr {
	return func(m optionalAttr) {
		m["use_cudnn_on_gpu"] = value
	}
}

// Conv2DBackpropInputDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, in_height, in_width, in_channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, in_channels, in_height, in_width].
// If not specified, defaults to "NHWC"
func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Computes the gradients of convolution with respect to the input.
//
// Arguments:
//	input_sizes: An integer vector representing the shape of `input`,
// where `input` is a 4-D `[batch, height, width, channels]` tensor.
//	filter: 4-D with shape
// `[filter_height, filter_width, in_channels, out_channels]`.
//	out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`.
// Gradients w.r.t. the output of the convolution.
//	strides: The stride of the sliding window for each dimension of the input
// of the convolution. Must be in the same order as the dimension specified with
// format.
//	padding: The type of padding algorithm to use.
//
// Returns 4-D with shape `[batch, in_height, in_width, in_channels]`.  Gradient
// w.r.t. the input of the convolution.
func Conv2DBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropInputAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Conv2DBackpropInput",
		Input: []tf.Input{
			input_sizes, filter, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// FusedBatchNormAttr is an optional argument to FusedBatchNorm.
type FusedBatchNormAttr func(optionalAttr)

// FusedBatchNormEpsilon sets the optional epsilon attribute to value.
//
// value: A small float number added to the variance of x.
// If not specified, defaults to 0.0001
func FusedBatchNormEpsilon(value float32) FusedBatchNormAttr {
	return func(m optionalAttr) {
		m["epsilon"] = value
	}
}

// FusedBatchNormDataFormat sets the optional data_format attribute to value.
//
// value: The data format for x and y. Either "NHWC" (default) or "NCHW".
// If not specified, defaults to "NHWC"
func FusedBatchNormDataFormat(value string) FusedBatchNormAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// FusedBatchNormIsTraining sets the optional is_training attribute to value.
//
// value: A bool value to indicate the operation is for training (default)
// or inference.
// If not specified, defaults to true
func FusedBatchNormIsTraining(value bool) FusedBatchNormAttr {
	return func(m optionalAttr) {
		m["is_training"] = value
	}
}

// Batch normalization.
//
// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
// The size of 1D Tensors matches the dimension C of the 4D Tensors.
//
// Arguments:
//	x: A 4D Tensor for input data.
//	scale: A 1D Tensor for scaling factor, to scale the normalized x.
//	offset: A 1D Tensor for offset, to shift to the normalized x.
//	mean: A 1D Tensor for population mean. Used for inference only;
// must be empty for training.
//	variance: A 1D Tensor for population variance. Used for inference only;
// must be empty for training.
//
// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow
// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by
// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused
// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance
// in the cuDNN case), to be used in the gradient computation.
func FusedBatchNorm(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormAttr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FusedBatchNorm",
		Input: []tf.Input{
			x, scale, offset, mean, variance,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
}

// RandomStandardNormalAttr is an optional argument to RandomStandardNormal.
type RandomStandardNormalAttr func(optionalAttr)

// RandomStandardNormalSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func RandomStandardNormalSeed(value int64) RandomStandardNormalAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// RandomStandardNormalSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func RandomStandardNormalSeed2(value int64) RandomStandardNormalAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Outputs random values from a normal distribution.
//
// The generated values will have mean 0 and standard deviation 1.
//
// Arguments:
//	shape: The shape of the output tensor.
//	dtype: The type of the output.
//
// Returns A tensor of the specified shape filled with random normal values.
func RandomStandardNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomStandardNormalAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RandomStandardNormal",
		Input: []tf.Input{
			shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes sigmoid of `x` element-wise.
//
// Specifically, `y = 1 / (1 + exp(-x))`.
func Sigmoid(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Sigmoid",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad.
type FractionalMaxPoolGradAttr func(optionalAttr)

// FractionalMaxPoolGradOverlapping sets the optional overlapping attribute to value.
//
// value: When set to True, it means when pooling, the values at the boundary
// of adjacent pooling cells are used by both cells. For example:
//
// `index  0  1  2  3  4`
//
// `value  20 5  16 3  7`
//
// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
// The result would be [20, 16] for fractional max pooling.
// If not specified, defaults to false
func FractionalMaxPoolGradOverlapping(value bool) FractionalMaxPoolGradAttr {
	return func(m optionalAttr) {
		m["overlapping"] = value
	}
}

// Computes gradient of the FractionalMaxPool function.
//
// Arguments:
//	orig_input: Original input for `fractional_max_pool`
//	orig_output: Original output for `fractional_max_pool`
//	out_backprop: 4-D with shape `[batch, height, width, channels]`.  Gradients
// w.r.t. the output of `fractional_max_pool`.
//	row_pooling_sequence: row pooling sequence, form pooling region with
// col_pooling_sequence.
//	col_pooling_sequence: column pooling sequence, form pooling region with
// row_pooling sequence.
//
// Returns 4-D.  Gradients w.r.t. the input of `fractional_max_pool`.
func FractionalMaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalMaxPoolGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FractionalMaxPoolGrad",
		Input: []tf.Input{
			orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyAdagradDAAttr is an optional argument to ResourceApplyAdagradDA.
type ResourceApplyAdagradDAAttr func(optionalAttr)

// ResourceApplyAdagradDAUseLocking sets the optional use_locking attribute to value.
//
// value: If True, updating of the var and accum tensors will be protected by
// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceApplyAdagradDAUseLocking(value bool) ResourceApplyAdagradDAAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the proximal adagrad scheme.
//
// Arguments:
//	var_: Should be from a Variable().
//	gradient_accumulator: Should be from a Variable().
//	gradient_squared_accumulator: Should be from a Variable().
//	grad: The gradient.
//	lr: Scaling factor. Must be a scalar.
//	l1: L1 regularization. Must be a scalar.
//	l2: L2 regularization. Must be a scalar.
//	global_step: Training step number. Must be a scalar.
//
// Returns the created operation.
func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceApplyAdagradDAAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyAdagradDA",
		Input: []tf.Input{
			var_, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits.
type ComputeAccidentalHitsAttr func(optionalAttr)

// ComputeAccidentalHitsSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Computes the ids of the positions in sampled_candidates that match true_labels.
//
// When doing log-odds NCE, the result of this op should be passed through a
// SparseToDense op, then added to the logits of the sampled candidates. This has
// the effect of 'removing' the sampled labels that match the true labels by
// making the classifier sure that they are sampled labels.
//
// Arguments:
//	true_classes: The true_classes output of UnpackSparseLabels.
//	sampled_candidates: The sampled_candidates output of CandidateSampler.
//	num_true: Number of true labels per context.
//
// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label
// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element
// is -FLOAT_MAX.
func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_true": num_true}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ComputeAccidentalHits",
		Input: []tf.Input{
			true_classes, sampled_candidates,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// AvgPoolGradAttr is an optional argument to AvgPoolGrad.
type AvgPoolGradAttr func(optionalAttr)

// AvgPoolGradDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, in_height, in_width, in_channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, in_channels, in_height, in_width].
// If not specified, defaults to "NHWC"
func AvgPoolGradDataFormat(value string) AvgPoolGradAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Computes gradients of the average pooling function.
//
// Arguments:
//	orig_input_shape: 1-D.  Shape of the original input to `avg_pool`.
//	grad: 4-D with shape `[batch, height, width, channels]`.  Gradients w.r.t.
// the output of `avg_pool`.
//	ksize: The size of the sliding window for each dimension of the input.
//	strides: The stride of the sliding window for each dimension of the input.
//	padding: The type of padding algorithm to use.
//
// Returns 4-D.  Gradients w.r.t. the input of `avg_pool`.
func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "AvgPoolGrad",
		Input: []tf.Input{
			orig_input_shape, grad,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the maximum along segments of a tensor.
//
// Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation)
// for an explanation of segments.
//
// Computes a tensor such that
// \\(output_i = \max_j(data_j)\\) where `max` is over `j` such
// that `segment_ids[j] == i`.
//
// If the max is empty for a given segment ID `i`, `output[i] = 0`.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/SegmentMax.png" alt>
// </div>
//
// Arguments:
//
//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
// first dimension.  Values should be sorted and can be repeated.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SegmentMax",
		Input: []tf.Input{
			data, segment_ids,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Saves input tensors slices to disk.
//
// This is like `Save` except that tensors can be listed in the saved file as being
// a slice of a larger tensor.  `shapes_and_slices` specifies the shape of the
// larger tensor and the slice that this tensor covers. `shapes_and_slices` must
// have as many elements as `tensor_names`.
//
// Elements of the `shapes_and_slices` input must either be:
//
// *  The empty string, in which case the corresponding tensor is
//    saved normally.
// *  A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the
//    `dimI` are the dimensions of the larger tensor and `slice-spec`
//    specifies what part is covered by the tensor to save.
//
// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1`
// where each `sliceI` is either:
//
// *  The string `-` meaning that the slice covers all indices of this dimension
// *  `start,length` where `start` and `length` are integers.  In that
//    case the slice covers `length` indices starting at `start`.
//
// See also `Save`.
//
// Arguments:
//	filename: Must have a single element. The name of the file to which we write the
// tensor.
//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
//	shapes_and_slices: Shape `[N]`.  The shapes and slice specifications to use when
// saving the tensors.
//	data: `N` tensors to save.
//
// Returns the created operation.
func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SaveSlices",
		Input: []tf.Input{
			filename, tensor_names, shapes_and_slices, tf.OutputList(data),
		},
	}
	return scope.AddOperation(opspec)
}

// Returns the rank of a tensor.
//
// This operation returns an integer representing the rank of `input`.
//
// For example:
//
// ```prettyprint
// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
// # shape of tensor 't' is [2, 2, 3]
// rank(t) ==> 3
// ```
//
// **Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
// of a tensor is the number of indices required to uniquely select each element
// of the tensor. Rank is also known as "order", "degree", or "ndims."
func Rank(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Rank",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// DecodeCSVAttr is an optional argument to DecodeCSV.
type DecodeCSVAttr func(optionalAttr)

// DecodeCSVFieldDelim sets the optional field_delim attribute to value.
//
// value: delimiter to separate fields in a record.
// If not specified, defaults to ","
func DecodeCSVFieldDelim(value string) DecodeCSVAttr {
	return func(m optionalAttr) {
		m["field_delim"] = value
	}
}

// Convert CSV records to tensors. Each column maps to one tensor.
//
// RFC 4180 format is expected for the CSV records.
// (https://tools.ietf.org/html/rfc4180)
// Note that we allow leading and trailing spaces with int or float field.
//
// Arguments:
//	records: Each string is a record/row in the csv and all records should have
// the same format.
//	record_defaults: One tensor per column of the input record, with either a
// scalar default value for that column or empty if the column is required.
//
// Returns Each tensor will have the same shape as records.
func DecodeCSV(scope *Scope, records tf.Output, record_defaults []tf.Output, optional ...DecodeCSVAttr) (output []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DecodeCSV",
		Input: []tf.Input{
			records, tf.OutputList(record_defaults),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
		scope.UpdateErr("DecodeCSV", err)
		return
	}
	return output
}

// BiasAddGradAttr is an optional argument to BiasAddGrad.
type BiasAddGradAttr func(optionalAttr)

// BiasAddGradDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the bias tensor will be added to the last dimension
// of the value tensor.
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, in_channels, in_height, in_width].
// The tensor will be added to "in_channels", the third-to-the-last
//     dimension.
// If not specified, defaults to "NHWC"
func BiasAddGradDataFormat(value string) BiasAddGradAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// The backward operation for "BiasAdd" on the "bias" tensor.
//
// It accumulates all the values from out_backprop into the feature dimension.
// For NHWC data format, the feature dimension is the last. For NCHW data format,
// the feature dimension is the third-to-last.
//
// Arguments:
//	out_backprop: Any number of dimensions.
//
// Returns 1-D with size the feature dimension of `out_backprop`.
func BiasAddGrad(scope *Scope, out_backprop tf.Output, optional ...BiasAddGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "BiasAddGrad",
		Input: []tf.Input{
			out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Convert JSON-encoded Example records to binary protocol buffer strings.
//
// This op translates a tensor containing Example records, encoded using
// the [standard JSON
// mapping](https://developers.google.com/protocol-buffers/docs/proto3#json),
// into a tensor containing the same records encoded as binary protocol
// buffers. The resulting tensor can then be fed to any of the other
// Example-parsing ops.
//
// Arguments:
//	json_examples: Each string is a JSON object serialized according to the JSON
// mapping of the Example proto.
//
// Returns Each string is a binary Example protocol buffer corresponding
// to the respective element of `json_examples`.
func DecodeJSONExample(scope *Scope, json_examples tf.Output) (binary_examples tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "DecodeJSONExample",
		Input: []tf.Input{
			json_examples,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Transforms a serialized tensorflow.TensorProto proto into a Tensor.
//
// Arguments:
//	serialized: A scalar string containing a serialized TensorProto proto.
//	out_type: The type of the serialized tensor.  The provided type must match the
// type of the serialized tensor and no implicit conversion will take place.
//
// Returns A Tensor of type `out_type`.
func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"out_type": out_type}
	opspec := tf.OpSpec{
		Type: "ParseTensor",
		Input: []tf.Input{
			serialized,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes acos of x element-wise.
func Acos(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Acos",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Copy a tensor setting everything outside a central band in each innermost matrix
//
// to zero.
//
// The `band` part is computed as follows:
// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
// tensor with the same shape where
//
// `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
//
// The indicator function
//
// `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) &&
//                  (num_upper < 0 || (n-m) <= num_upper)`.
//
// For example:
//
// ```prettyprint
// # if 'input' is [[ 0,  1,  2, 3]
//                  [-1,  0,  1, 2]
//                  [-2, -1,  0, 1]
//                  [-3, -2, -1, 0]],
//
// tf.matrix_band_part(input, 1, -1) ==> [[ 0,  1,  2, 3]
//                                        [-1,  0,  1, 2]
//                                        [ 0, -1,  0, 1]
//                                        [ 0,  0, -1, 0]],
//
// tf.matrix_band_part(input, 2, 1) ==> [[ 0,  1,  0, 0]
//                                       [-1,  0,  1, 0]
//                                       [-2, -1,  0, 1]
//                                       [ 0, -2, -1, 0]]
// ```
//
// Useful special cases:
//
// ```prettyprint
//  tf.matrix_band_part(input, 0, -1) ==> Upper triangular part.
//  tf.matrix_band_part(input, -1, 0) ==> Lower triangular part.
//  tf.matrix_band_part(input, 0, 0) ==> Diagonal.
// ```
//
// Arguments:
//	input: Rank `k` tensor.
//	num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire
// lower triangle.
//	num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep
// entire upper triangle.
//
// Returns Rank `k` tensor of the same shape as input. The extracted banded tensor.
func MatrixBandPart(scope *Scope, input tf.Output, num_lower tf.Output, num_upper tf.Output) (band tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "MatrixBandPart",
		Input: []tf.Input{
			input, num_lower, num_upper,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// DecodeRawAttr is an optional argument to DecodeRaw.
type DecodeRawAttr func(optionalAttr)

// DecodeRawLittleEndian sets the optional little_endian attribute to value.
//
// value: Whether the input `bytes` are in little-endian order.
// Ignored for `out_type` values that are stored in a single byte like
// `uint8`.
// If not specified, defaults to true
func DecodeRawLittleEndian(value bool) DecodeRawAttr {
	return func(m optionalAttr) {
		m["little_endian"] = value
	}
}

// Reinterpret the bytes of a string as a vector of numbers.
//
// Arguments:
//	bytes: All the elements must have the same length.
//
//
// Returns A Tensor with one more dimension than the input `bytes`.  The
// added dimension will have size equal to the length of the elements
// of `bytes` divided by the number of bytes to represent `out_type`.
func DecodeRaw(scope *Scope, bytes tf.Output, out_type tf.DataType, optional ...DecodeRawAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"out_type": out_type}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DecodeRaw",
		Input: []tf.Input{
			bytes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QueueDequeueV2Attr is an optional argument to QueueDequeueV2.
type QueueDequeueV2Attr func(optionalAttr)

// QueueDequeueV2TimeoutMs sets the optional timeout_ms attribute to value.
//
// value: If the queue is empty, this operation will block for up to
// timeout_ms milliseconds.
// Note: This option is not supported yet.
// If not specified, defaults to -1
func QueueDequeueV2TimeoutMs(value int64) QueueDequeueV2Attr {
	return func(m optionalAttr) {
		m["timeout_ms"] = value
	}
}

// Dequeues a tuple of one or more tensors from the given queue.
//
// This operation has k outputs, where k is the number of components
// in the tuples stored in the given queue, and output i is the ith
// component of the dequeued tuple.
//
// N.B. If the queue is empty, this operation will block until an element
// has been dequeued (or 'timeout_ms' elapses, if specified).
//
// Arguments:
//	handle: The handle to a queue.
//	component_types: The type of each component in a tuple.
//
// Returns One or more tensors that were dequeued as a tuple.
func QueueDequeueV2(scope *Scope, handle tf.Output, component_types []tf.DataType, optional ...QueueDequeueV2Attr) (components []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"component_types": component_types}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QueueDequeueV2",
		Input: []tf.Input{
			handle,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
		scope.UpdateErr("QueueDequeueV2", err)
		return
	}
	return components
}

// ParseSingleSequenceExampleAttr is an optional argument to ParseSingleSequenceExample.
type ParseSingleSequenceExampleAttr func(optionalAttr)

// ParseSingleSequenceExampleContextSparseTypes sets the optional context_sparse_types attribute to value.
//
// value: A list of Ncontext_sparse types; the data types of data in
// each context Feature given in context_sparse_keys.
// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
// DT_INT64 (Int64List), and DT_STRING (BytesList).
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func ParseSingleSequenceExampleContextSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
	return func(m optionalAttr) {
		m["context_sparse_types"] = value
	}
}

// ParseSingleSequenceExampleFeatureListDenseTypes sets the optional feature_list_dense_types attribute to value.
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func ParseSingleSequenceExampleFeatureListDenseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
	return func(m optionalAttr) {
		m["feature_list_dense_types"] = value
	}
}

// ParseSingleSequenceExampleContextDenseShapes sets the optional context_dense_shapes attribute to value.
//
// value: A list of Ncontext_dense shapes; the shapes of data in
// each context Feature given in context_dense_keys.
// The number of elements in the Feature corresponding to context_dense_key[j]
// must always equal context_dense_shapes[j].NumEntries().
// The shape of context_dense_values[j] will match context_dense_shapes[j].
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func ParseSingleSequenceExampleContextDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr {
	return func(m optionalAttr) {
		m["context_dense_shapes"] = value
	}
}

// ParseSingleSequenceExampleFeatureListSparseTypes sets the optional feature_list_sparse_types attribute to value.
//
// value: A list of Nfeature_list_sparse types; the data types
// of data in each FeatureList given in feature_list_sparse_keys.
// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
// DT_INT64 (Int64List), and DT_STRING (BytesList).
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func ParseSingleSequenceExampleFeatureListSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr {
	return func(m optionalAttr) {
		m["feature_list_sparse_types"] = value
	}
}

// ParseSingleSequenceExampleFeatureListDenseShapes sets the optional feature_list_dense_shapes attribute to value.
//
// value: A list of Nfeature_list_dense shapes; the shapes of
// data in each FeatureList given in feature_list_dense_keys.
// The shape of each Feature in the FeatureList corresponding to
// feature_list_dense_key[j] must always equal
// feature_list_dense_shapes[j].NumEntries().
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func ParseSingleSequenceExampleFeatureListDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr {
	return func(m optionalAttr) {
		m["feature_list_dense_shapes"] = value
	}
}

// Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
//
// Arguments:
//	serialized: A scalar containing a binary serialized SequenceExample proto.
//	feature_list_dense_missing_assumed_empty: A vector listing the
// FeatureList keys which may be missing from the SequenceExample.  If the
// associated FeatureList is missing, it is treated as empty.  By default,
// any FeatureList not listed in this vector must exist in the SequenceExample.
//	context_sparse_keys: A list of Ncontext_sparse string Tensors (scalars).
// The keys expected in the Examples' features associated with context_sparse
// values.
//	context_dense_keys: A list of Ncontext_dense string Tensors (scalars).
// The keys expected in the SequenceExamples' context features associated with
// dense values.
//	feature_list_sparse_keys: A list of Nfeature_list_sparse string Tensors
// (scalars).  The keys expected in the FeatureLists associated with sparse
// values.
//	feature_list_dense_keys: A list of Nfeature_list_dense string Tensors (scalars).
// The keys expected in the SequenceExamples' feature_lists associated
// with lists of dense values.
//	context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty).
// context_dense_defaults[j] provides default values
// when the SequenceExample's context map lacks context_dense_key[j].
// If an empty Tensor is provided for context_dense_defaults[j],
// then the Feature context_dense_keys[j] is required.
// The input type is inferred from context_dense_defaults[j], even when it's
// empty.  If context_dense_defaults[j] is not empty, its shape must match
// context_dense_shapes[j].
//	debug_name: A scalar containing the name of the serialized proto.
// May contain, for example, table key (descriptive) name for the
// corresponding serialized proto.  This is purely useful for debugging
// purposes, and the presence of values here has no effect on the output.
// May also be an empty scalar if no name is available.
func ParseSingleSequenceExample(scope *Scope, serialized tf.Output, feature_list_dense_missing_assumed_empty tf.Output, context_sparse_keys []tf.Output, context_dense_keys []tf.Output, feature_list_sparse_keys []tf.Output, feature_list_dense_keys []tf.Output, context_dense_defaults []tf.Output, debug_name tf.Output, optional ...ParseSingleSequenceExampleAttr) (context_sparse_indices []tf.Output, context_sparse_values []tf.Output, context_sparse_shapes []tf.Output, context_dense_values []tf.Output, feature_list_sparse_indices []tf.Output, feature_list_sparse_values []tf.Output, feature_list_sparse_shapes []tf.Output, feature_list_dense_values []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ParseSingleSequenceExample",
		Input: []tf.Input{
			serialized, feature_list_dense_missing_assumed_empty, tf.OutputList(context_sparse_keys), tf.OutputList(context_dense_keys), tf.OutputList(feature_list_sparse_keys), tf.OutputList(feature_list_dense_keys), tf.OutputList(context_dense_defaults), debug_name,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if context_sparse_indices, idx, err = makeOutputList(op, idx, "context_sparse_indices"); err != nil {
		scope.UpdateErr("ParseSingleSequenceExample", err)
		return
	}
	if context_sparse_values, idx, err = makeOutputList(op, idx, "context_sparse_values"); err != nil {
		scope.UpdateErr("ParseSingleSequenceExample", err)
		return
	}
	if context_sparse_shapes, idx, err = makeOutputList(op, idx, "context_sparse_shapes"); err != nil {
		scope.UpdateErr("ParseSingleSequenceExample", err)
		return
	}
	if context_dense_values, idx, err = makeOutputList(op, idx, "context_dense_values"); err != nil {
		scope.UpdateErr("ParseSingleSequenceExample", err)
		return
	}
	if feature_list_sparse_indices, idx, err = makeOutputList(op, idx, "feature_list_sparse_indices"); err != nil {
		scope.UpdateErr("ParseSingleSequenceExample", err)
		return
	}
	if feature_list_sparse_values, idx, err = makeOutputList(op, idx, "feature_list_sparse_values"); err != nil {
		scope.UpdateErr("ParseSingleSequenceExample", err)
		return
	}
	if feature_list_sparse_shapes, idx, err = makeOutputList(op, idx, "feature_list_sparse_shapes"); err != nil {
		scope.UpdateErr("ParseSingleSequenceExample", err)
		return
	}
	if feature_list_dense_values, idx, err = makeOutputList(op, idx, "feature_list_dense_values"); err != nil {
		scope.UpdateErr("ParseSingleSequenceExample", err)
		return
	}
	return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values
}

// RandomGammaAttr is an optional argument to RandomGamma.
type RandomGammaAttr func(optionalAttr)

// RandomGammaSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func RandomGammaSeed(value int64) RandomGammaAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// RandomGammaSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func RandomGammaSeed2(value int64) RandomGammaAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Outputs random values from the Gamma distribution(s) described by alpha.
//
// This op uses the algorithm by Marsaglia et al. to acquire samples via
// transformation-rejection from pairs of uniform and normal random variables.
// See http://dl.acm.org/citation.cfm?id=358414
//
// Arguments:
//	shape: 1-D integer tensor. Shape of independent samples to draw from each
// distribution described by the shape parameters given in alpha.
//	alpha: A tensor in which each scalar is a "shape" parameter describing the
// associated gamma distribution.
//
// Returns A tensor with shape `shape + shape(alpha)`. Each slice
// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
// `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha.
func RandomGamma(scope *Scope, shape tf.Output, alpha tf.Output, optional ...RandomGammaAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RandomGamma",
		Input: []tf.Input{
			shape, alpha,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// RandomShuffleAttr is an optional argument to RandomShuffle.
type RandomShuffleAttr func(optionalAttr)

// RandomShuffleSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func RandomShuffleSeed(value int64) RandomShuffleAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// RandomShuffleSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func RandomShuffleSeed2(value int64) RandomShuffleAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Randomly shuffles a tensor along its first dimension.
//
//   The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
//   to one and only one `output[i]`. For example, a mapping that might occur for a
//   3x2 tensor is:
//
// ```prettyprint
// [[1, 2],       [[5, 6],
//  [3, 4],  ==>   [1, 2],
//  [5, 6]]        [3, 4]]
// ```
//
// Arguments:
//	value: The tensor to be shuffled.
//
// Returns A tensor of same shape and type as `value`, shuffled along its first
// dimension.
func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RandomShuffle",
		Input: []tf.Input{
			value,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
//
// `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]`
// to 'outputs' tensor of same shape as `inputs`.
//
// [min; max] is the clamping range for the 'inputs' data in the corresponding
// depth channel.  Op divides this range into 255 steps (total of 256 values), then
// replaces each 'inputs' value with the closest of the quantized step values.
//
// This operation has a gradient and thus allows for training `min` and `max` values.
func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output) (outputs tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FakeQuantWithMinMaxVarsPerChannel",
		Input: []tf.Input{
			inputs, min, max,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TruncatedNormalAttr is an optional argument to TruncatedNormal.
type TruncatedNormalAttr func(optionalAttr)

// TruncatedNormalSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func TruncatedNormalSeed(value int64) TruncatedNormalAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// TruncatedNormalSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func TruncatedNormalSeed2(value int64) TruncatedNormalAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Outputs random values from a truncated normal distribution.
//
// The generated values follow a normal distribution with mean 0 and standard
// deviation 1, except that values whose magnitude is more than 2 standard
// deviations from the mean are dropped and re-picked.
//
// Arguments:
//	shape: The shape of the output tensor.
//	dtype: The type of the output.
//
// Returns A tensor of the specified shape filled with random truncated normal
// values.
func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...TruncatedNormalAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TruncatedNormal",
		Input: []tf.Input{
			shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SkipgramAttr is an optional argument to Skipgram.
type SkipgramAttr func(optionalAttr)

// SkipgramWindowSize sets the optional window_size attribute to value.
//
// value: The number of words to predict to the left and right of the target.
// If not specified, defaults to 5
func SkipgramWindowSize(value int64) SkipgramAttr {
	return func(m optionalAttr) {
		m["window_size"] = value
	}
}

// SkipgramMinCount sets the optional min_count attribute to value.
//
// value: The minimum number of word occurrences for it to be included in the
// vocabulary.
// If not specified, defaults to 5
func SkipgramMinCount(value int64) SkipgramAttr {
	return func(m optionalAttr) {
		m["min_count"] = value
	}
}

// SkipgramSubsample sets the optional subsample attribute to value.
//
// value: Threshold for word occurrence. Words that appear with higher
// frequency will be randomly down-sampled. Set to 0 to disable.
// If not specified, defaults to 0.001
func SkipgramSubsample(value float32) SkipgramAttr {
	return func(m optionalAttr) {
		m["subsample"] = value
	}
}

// Parses a text file and creates a batch of examples.
//
// DEPRECATED at GraphDef version 19: Moving word2vec into tensorflow_models/tutorials and deprecating its ops here as a result
//
// Arguments:
//	filename: The corpus's text file name.
//	batch_size: The size of produced batch.
//
// Returns A vector of words in the corpus.Frequencies of words. Sorted in the non-ascending order.Number of words per epoch in the data file.The current epoch number.The total number of words processed so far.A vector of word ids.A vector of word ids.
func Skipgram(scope *Scope, filename string, batch_size int64, optional ...SkipgramAttr) (vocab_word tf.Output, vocab_freq tf.Output, words_per_epoch tf.Output, current_epoch tf.Output, total_words_processed tf.Output, examples tf.Output, labels tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"filename": filename, "batch_size": batch_size}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Skipgram",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6)
}

// ParameterizedTruncatedNormalAttr is an optional argument to ParameterizedTruncatedNormal.
type ParameterizedTruncatedNormalAttr func(optionalAttr)

// ParameterizedTruncatedNormalSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func ParameterizedTruncatedNormalSeed(value int64) ParameterizedTruncatedNormalAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// ParameterizedTruncatedNormalSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func ParameterizedTruncatedNormalSeed2(value int64) ParameterizedTruncatedNormalAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Outputs random values from a normal distribution. The parameters may each be a
//
// scalar which applies to the entire output, or a vector of length shape[0] which
// stores the parameters for each batch.
//
// Arguments:
//	shape: The shape of the output tensor. Batches are indexed by the 0th dimension.
//	means: The mean parameter of each batch.
//	stdevs: The standard deviation parameter of each batch. Must be greater than 0.
//	minvals: The minimum cutoff. May be -infinity.
//	maxvals: The maximum cutoff. May be +infinity, and must be more than the minval
// for each batch.
//
// Returns A matrix of shape num_batches x samples_per_batch, filled with random
// truncated normal values using the parameters for each row.
func ParameterizedTruncatedNormal(scope *Scope, shape tf.Output, means tf.Output, stdevs tf.Output, minvals tf.Output, maxvals tf.Output, optional ...ParameterizedTruncatedNormalAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ParameterizedTruncatedNormal",
		Input: []tf.Input{
			shape, means, stdevs, minvals, maxvals,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Convert one or more images from HSV to RGB.
//
// Outputs a tensor of the same shape as the `images` tensor, containing the RGB
// value of the pixels. The output is only well defined if the value in `images`
// are in `[0,1]`.
//
// See `rgb_to_hsv` for a description of the HSV encoding.
//
// Arguments:
//	images: 1-D or higher rank. HSV data to convert. Last dimension must be size 3.
//
// Returns `images` converted to RGB.
func HSVToRGB(scope *Scope, images tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "HSVToRGB",
		Input: []tf.Input{
			images,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// RandomUniformIntAttr is an optional argument to RandomUniformInt.
type RandomUniformIntAttr func(optionalAttr)

// RandomUniformIntSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func RandomUniformIntSeed(value int64) RandomUniformIntAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// RandomUniformIntSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func RandomUniformIntSeed2(value int64) RandomUniformIntAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Outputs random integers from a uniform distribution.
//
// The generated values are uniform integers in the range `[minval, maxval)`.
// The lower bound `minval` is included in the range, while the upper bound
// `maxval` is excluded.
//
// The random integers are slightly biased unless `maxval - minval` is an exact
// power of two.  The bias is small for values of `maxval - minval` significantly
// smaller than the range of the output (either `2^32` or `2^64`).
//
// Arguments:
//	shape: The shape of the output tensor.
//	minval: 0-D.  Inclusive lower bound on the generated integers.
//	maxval: 0-D.  Exclusive upper bound on the generated integers.
//
// Returns A tensor of the specified shape filled with uniform random integers.
func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf.Output, optional ...RandomUniformIntAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RandomUniformInt",
		Input: []tf.Input{
			shape, minval, maxval,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SparseMatMulAttr is an optional argument to SparseMatMul.
type SparseMatMulAttr func(optionalAttr)

// SparseMatMulTransposeA sets the optional transpose_a attribute to value.
// If not specified, defaults to false
func SparseMatMulTransposeA(value bool) SparseMatMulAttr {
	return func(m optionalAttr) {
		m["transpose_a"] = value
	}
}

// SparseMatMulTransposeB sets the optional transpose_b attribute to value.
// If not specified, defaults to false
func SparseMatMulTransposeB(value bool) SparseMatMulAttr {
	return func(m optionalAttr) {
		m["transpose_b"] = value
	}
}

// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value.
// If not specified, defaults to false
func SparseMatMulAIsSparse(value bool) SparseMatMulAttr {
	return func(m optionalAttr) {
		m["a_is_sparse"] = value
	}
}

// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value.
// If not specified, defaults to false
func SparseMatMulBIsSparse(value bool) SparseMatMulAttr {
	return func(m optionalAttr) {
		m["b_is_sparse"] = value
	}
}

// Multiply matrix "a" by matrix "b".
//
// The inputs must be two-dimensional matrices and the inner dimension of "a" must
// match the outer dimension of "b". This op is optimized for the case where at
// least one of "a" or "b" is sparse. The breakeven for using this versus a dense
// matrix multiply on one platform was 30% zero values in the sparse matrix.
func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SparseMatMul",
		Input: []tf.Input{
			a, b,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SdcaOptimizerAttr is an optional argument to SdcaOptimizer.
type SdcaOptimizerAttr func(optionalAttr)

// SdcaOptimizerAdaptative sets the optional adaptative attribute to value.
//
// value: Whether to use Adapative SDCA for the inner loop.
// If not specified, defaults to false
func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr {
	return func(m optionalAttr) {
		m["adaptative"] = value
	}
}

// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
//
// linear models with L1 + L2 regularization. As global optimization objective is
// strongly-convex, the optimizer optimizes the dual objective at each step. The
// optimizer applies each update one example at a time. Examples are sampled
// uniformly, and the optimizer is learning rate free and enjoys linear convergence
// rate.
//
// Proximal Stochastic Dual Coordinate Ascent, Shalev-Shwartz, Shai; Zhang, Tong.
// 2012 arXiv1211.2717S: http://arxiv.org/pdf/1211.2717v1.pdf
//
//   Loss objective = \sum f_{i}(wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|
//
// Adding vs. Averaging in Distributed Primal-Dual Optimization.
// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik,
// Martin Takac http://arxiv.org/abs/1502.03508
//
// Stochastic Dual Coordinate Ascent with Adaptive Probabilities
// Dominik Csiba, Zheng Qu, Peter Richtarik https://arxiv.org/abs/1502.08053
//
// Arguments:
//	sparse_example_indices: a list of vectors which contain example indices.
//	sparse_feature_indices: a list of vectors which contain feature indices.
//	sparse_feature_values: a list of vectors which contains feature value
// associated with each feature group.
//	dense_features: a list of matrices which contains the dense feature values.
//	example_weights: a vector which contains the weight associated with each
// example.
//	example_labels: a vector which contains the label/target associated with each
// example.
//	sparse_indices: a list of vectors where each value is the indices which has
// corresponding weights in sparse_weights. This field maybe ommitted for the
// dense approach.
//	sparse_weights: a list of vectors where each value is the weight associated with
// a sparse feature group.
//	dense_weights: a list of vectors where the values are the weights associated
// with a dense feature group.
//	example_state_data: a list of vectors containing the example state data.
//	loss_type: Type of the primal loss. Currently SdcaSolver supports logistic,
// squared and hinge losses.
//	l1: Symmetric l1 regularization strength.
//	l2: Symmetric l2 regularization strength.
//	num_loss_partitions: Number of partitions of the global loss function.
//	num_inner_iterations: Number of iterations per mini-batch.
//
// Returns a list of vectors containing the updated example state
// data.a list of vectors where each value is the delta
// weights associated with a sparse feature group.a list of vectors where the values are the delta
// weights associated with a dense feature group.
func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerAttr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SdcaOptimizer",
		Input: []tf.Input{
			tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	out_example_state_data = op.Output(idx)
	if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil {
		scope.UpdateErr("SdcaOptimizer", err)
		return
	}
	if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil {
		scope.UpdateErr("SdcaOptimizer", err)
		return
	}
	return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights
}

// Computes the minimum along segments of a tensor.
//
// Read [the section on
// Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
// of segments.
//
// Computes a tensor such that
// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such
// that `segment_ids[j] == i`.
//
// If the min is empty for a given segment ID `i`, `output[i] = 0`.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/SegmentMin.png" alt>
// </div>
//
// Arguments:
//
//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
// first dimension.  Values should be sorted and can be repeated.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SegmentMin",
		Input: []tf.Input{
			data, segment_ids,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// RestoreAttr is an optional argument to Restore.
type RestoreAttr func(optionalAttr)

// RestorePreferredShard sets the optional preferred_shard attribute to value.
//
// value: Index of file to open first if multiple files match
// `file_pattern`.
// If not specified, defaults to -1
func RestorePreferredShard(value int64) RestoreAttr {
	return func(m optionalAttr) {
		m["preferred_shard"] = value
	}
}

// Restores a tensor from checkpoint files.
//
// Reads a tensor stored in one or several files. If there are several files (for
// instance because a tensor was saved as slices), `file_pattern` may contain
// wildcard symbols (`*` and `?`) in the filename portion only, not in the
// directory portion.
//
// If a `file_pattern` matches several files, `preferred_shard` can be used to hint
// in which file the requested tensor is likely to be found. This op will first
// open the file at index `preferred_shard` in the list of matching files and try
// to restore tensors from that file.  Only if some tensors or tensor slices are
// not found in that first file, then the Op opens all the files. Setting
// `preferred_shard` to match the value passed as the `shard` input
// of a matching `Save` Op may speed up Restore.  This attribute only affects
// performance, not correctness.  The default value -1 means files are processed in
// order.
//
// See also `RestoreSlice`.
//
// Arguments:
//	file_pattern: Must have a single element. The pattern of the files from
// which we read the tensor.
//	tensor_name: Must have a single element. The name of the tensor to be
// restored.
//	dt: The type of the tensor to be restored.
//
// Returns The restored tensor.
func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.DataType, optional ...RestoreAttr) (tensor tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dt": dt}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Restore",
		Input: []tf.Input{
			file_pattern, tensor_name,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// FusedResizeAndPadConv2DAttr is an optional argument to FusedResizeAndPadConv2D.
type FusedResizeAndPadConv2DAttr func(optionalAttr)

// FusedResizeAndPadConv2DResizeAlignCorners sets the optional resize_align_corners attribute to value.
//
// value: If true, rescale input by (new_height - 1) / (height - 1),
// which exactly aligns the 4 corners of images and resized images. If false, rescale
// by new_height / height. Treat similarly the width dimension.
// If not specified, defaults to false
func FusedResizeAndPadConv2DResizeAlignCorners(value bool) FusedResizeAndPadConv2DAttr {
	return func(m optionalAttr) {
		m["resize_align_corners"] = value
	}
}

// Performs a resize and padding as a preprocess during a convolution.
//
// It's often possible to do spatial transformations more efficiently as part of
// the packing stage of a convolution, so this op allows for an optimized
// implementation where these stages are fused together. This prevents the need to
// write out the intermediate results as whole tensors, reducing memory pressure,
// and we can get some latency gains by merging the transformation calculations.
// The data_format attribute for Conv2D isn't supported by this op, and defaults to
// 'NHWC' order.
// Internally this op uses a single per-graph scratch buffer, which means that it
// will block if multiple versions are being run in parallel. This is because this
// operator is primarily an optimization to minimize memory usage.
//
// Arguments:
//	input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
//	size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
// new size for the images.
//	paddings: A two-column matrix specifying the padding sizes. The number of
// rows must be the same as the rank of `input`.
//	filter: 4-D with shape
// `[filter_height, filter_width, in_channels, out_channels]`.
//
//	strides: 1-D of length 4.  The stride of the sliding window for each dimension
// of `input`. Must be in the same order as the dimension specified with format.
//	padding: The type of padding algorithm to use.
func FusedResizeAndPadConv2D(scope *Scope, input tf.Output, size tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string, optional ...FusedResizeAndPadConv2DAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FusedResizeAndPadConv2D",
		Input: []tf.Input{
			input, size, paddings, filter,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// DenseToSparseSetOperationAttr is an optional argument to DenseToSparseSetOperation.
type DenseToSparseSetOperationAttr func(optionalAttr)

// DenseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value.
// If not specified, defaults to true
func DenseToSparseSetOperationValidateIndices(value bool) DenseToSparseSetOperationAttr {
	return func(m optionalAttr) {
		m["validate_indices"] = value
	}
}

// Applies set operation along last dimension of `Tensor` and `SparseTensor`.
//
// See SetOperationOp::SetOperationFromContext for values of `set_operation`.
//
// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`,
// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same
// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but
// ignored.
//
// If `validate_indices` is `True`, this op validates the order and range of `set2`
// indices.
//
// Output `result` is a `SparseTensor` represented by `result_indices`,
// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
// dimension contains the result of `set_operation` applied to the corresponding
// `[0...n-1]` dimension of `set`.
//
// Arguments:
//	set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
// Dimension `n` contains values in a set, duplicates are allowed but ignored.
//	set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
// order.
//	set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
// order.
//	set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
// be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the
// max set size across `n-1` dimensions.
//
//
// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is
// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]`
// is the max result set size across all `0...n-1` dimensions.
func DenseToSparseSetOperation(scope *Scope, set1 tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...DenseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"set_operation": set_operation}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DenseToSparseSetOperation",
		Input: []tf.Input{
			set1, set2_indices, set2_values, set2_shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// TopKV2Attr is an optional argument to TopKV2.
type TopKV2Attr func(optionalAttr)

// TopKV2Sorted sets the optional sorted attribute to value.
//
// value: If true the resulting `k` elements will be sorted by the values in
// descending order.
// If not specified, defaults to true
func TopKV2Sorted(value bool) TopKV2Attr {
	return func(m optionalAttr) {
		m["sorted"] = value
	}
}

// Finds values and indices of the `k` largest elements for the last dimension.
//
// If the input is a vector (rank-1), finds the `k` largest entries in the vector
// and outputs their values and indices as vectors.  Thus `values[j]` is the
// `j`-th largest entry in `input`, and its index is `indices[j]`.
//
// For matrices (resp. higher rank input), computes the top `k` entries in each
// row (resp. vector along the last dimension).  Thus,
//
//     values.shape = indices.shape = input.shape[:-1] + [k]
//
// If two elements are equal, the lower-index element appears first.
//
// Arguments:
//	input: 1-D or higher with last dimension at least `k`.
//	k: 0-D.  Number of top elements to look for along the last dimension (along each
// row for matrices).
//
// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`.
func TopKV2(scope *Scope, input tf.Output, k tf.Output, optional ...TopKV2Attr) (values tf.Output, indices tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TopKV2",
		Input: []tf.Input{
			input, k,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Delete the tensor specified by its handle in the session.
//
// Arguments:
//	handle: The handle for a tensor stored in the session state.
//
// Returns the created operation.
func DeleteSessionTensor(scope *Scope, handle tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "DeleteSessionTensor",
		Input: []tf.Input{
			handle,
		},
	}
	return scope.AddOperation(opspec)
}

// DenseToDenseSetOperationAttr is an optional argument to DenseToDenseSetOperation.
type DenseToDenseSetOperationAttr func(optionalAttr)

// DenseToDenseSetOperationValidateIndices sets the optional validate_indices attribute to value.
// If not specified, defaults to true
func DenseToDenseSetOperationValidateIndices(value bool) DenseToDenseSetOperationAttr {
	return func(m optionalAttr) {
		m["validate_indices"] = value
	}
}

// Applies set operation along last dimension of 2 `Tensor` inputs.
//
// See SetOperationOp::SetOperationFromContext for values of `set_operation`.
//
// Output `result` is a `SparseTensor` represented by `result_indices`,
// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
// dimension contains the result of `set_operation` applied to the corresponding
// `[0...n-1]` dimension of `set`.
//
// Arguments:
//	set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
// Dimension `n` contains values in a set, duplicates are allowed but ignored.
//	set2: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`.
// Dimension `n` contains values in a set, duplicates are allowed but ignored.
//
//
// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is
// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]`
// is the max result set size across all `0...n-1` dimensions.
func DenseToDenseSetOperation(scope *Scope, set1 tf.Output, set2 tf.Output, set_operation string, optional ...DenseToDenseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"set_operation": set_operation}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DenseToDenseSetOperation",
		Input: []tf.Input{
			set1, set2,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// SetSizeAttr is an optional argument to SetSize.
type SetSizeAttr func(optionalAttr)

// SetSizeValidateIndices sets the optional validate_indices attribute to value.
// If not specified, defaults to true
func SetSizeValidateIndices(value bool) SetSizeAttr {
	return func(m optionalAttr) {
		m["validate_indices"] = value
	}
}

// Number of unique elements along last dimension of input `set`.
//
// Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`,
// and `set_shape`. The last dimension contains values in a set, duplicates are
// allowed but ignored.
//
// If `validate_indices` is `True`, this op validates the order and range of `set`
// indices.
//
// Arguments:
//	set_indices: 2D `Tensor`, indices of a `SparseTensor`.
//	set_values: 1D `Tensor`, values of a `SparseTensor`.
//	set_shape: 1D `Tensor`, shape of a `SparseTensor`.
//
// Returns For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st
// `n-1` dimensions as `set`. Each value is the number of unique elements in
// the corresponding `[0...n-1]` dimension of `set`.
func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shape tf.Output, optional ...SetSizeAttr) (size tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SetSize",
		Input: []tf.Input{
			set_indices, set_values, set_shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TakeManySparseFromTensorsMapAttr is an optional argument to TakeManySparseFromTensorsMap.
type TakeManySparseFromTensorsMapAttr func(optionalAttr)

// TakeManySparseFromTensorsMapContainer sets the optional container attribute to value.
//
// value: The container name for the `SparseTensorsMap` read by this op.
// If not specified, defaults to ""
func TakeManySparseFromTensorsMapContainer(value string) TakeManySparseFromTensorsMapAttr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// TakeManySparseFromTensorsMapSharedName sets the optional shared_name attribute to value.
//
// value: The shared name for the `SparseTensorsMap` read by this op.
// It should not be blank; rather the `shared_name` or unique Operation name
// of the Op that created the original `SparseTensorsMap` should be used.
// If not specified, defaults to ""
func TakeManySparseFromTensorsMapSharedName(value string) TakeManySparseFromTensorsMapAttr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
//
// The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where
// `N` is the minibatch size and the rows correspond to the output handles of
// `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`.  The ranks of the
// original `SparseTensor` objects that went into the given input ops must all
// match.  When the final `SparseTensor` is created, it has rank one
// higher than the ranks of the incoming `SparseTensor` objects
// (they have been concatenated along a new row dimension on the left).
//
// The output `SparseTensor` object's shape values for all dimensions but the
// first are the max across the input `SparseTensor` objects' shape values
// for the corresponding dimensions.  Its first shape value is `N`, the minibatch
// size.
//
// The input `SparseTensor` objects' indices are assumed ordered in
// standard lexicographic order.  If this is not the case, after this
// step run `SparseReorder` to restore index ordering.
//
// For example, if the handles represent an input, which is a `[2, 3]` matrix
// representing two original `SparseTensor` objects:
//
// ```
//     index = [ 0]
//             [10]
//             [20]
//     values = [1, 2, 3]
//     shape = [50]
// ```
//
// and
//
// ```
//     index = [ 2]
//             [10]
//     values = [4, 5]
//     shape = [30]
// ```
//
// then the final `SparseTensor` will be:
//
// ```
//     index = [0  0]
//             [0 10]
//             [0 20]
//             [1  2]
//             [1 10]
//     values = [1, 2, 3, 4, 5]
//     shape = [2 50]
// ```
//
// Arguments:
//	sparse_handles: 1-D, The `N` serialized `SparseTensor` objects.
// Shape: `[N]`.
//	dtype: The `dtype` of the `SparseTensor` objects stored in the
// `SparseTensorsMap`.
//
// Returns 2-D.  The `indices` of the minibatch `SparseTensor`.1-D.  The `values` of the minibatch `SparseTensor`.1-D.  The `shape` of the minibatch `SparseTensor`.
func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype tf.DataType, optional ...TakeManySparseFromTensorsMapAttr) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TakeManySparseFromTensorsMap",
		Input: []tf.Input{
			sparse_handles,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// AddSparseToTensorsMapAttr is an optional argument to AddSparseToTensorsMap.
type AddSparseToTensorsMapAttr func(optionalAttr)

// AddSparseToTensorsMapContainer sets the optional container attribute to value.
//
// value: The container name for the `SparseTensorsMap` created by this op.
// If not specified, defaults to ""
func AddSparseToTensorsMapContainer(value string) AddSparseToTensorsMapAttr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// AddSparseToTensorsMapSharedName sets the optional shared_name attribute to value.
//
// value: The shared name for the `SparseTensorsMap` created by this op.
// If blank, the new Operation's unique name is used.
// If not specified, defaults to ""
func AddSparseToTensorsMapSharedName(value string) AddSparseToTensorsMapAttr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
//
// A `SparseTensor` is represented by three tensors: `sparse_indices`,
// `sparse_values`, and `sparse_shape`.
//
// This operator takes the given `SparseTensor` and adds it to a container
// object (a `SparseTensorsMap`).  A unique key within this container is generated
// in the form of an `int64`, and this is the value that is returned.
//
// The `SparseTensor` can then be read out as part of a minibatch by passing
// the key as a vector element to `TakeManySparseFromTensorsMap`.  To ensure
// the correct `SparseTensorsMap` is accessed, ensure that the same
// `container` and `shared_name` are passed to that Op.  If no `shared_name`
// is provided here, instead use the *name* of the Operation created by calling
// `AddSparseToTensorsMap` as the `shared_name` passed to
// `TakeManySparseFromTensorsMap`.  Ensure the Operations are colocated.
//
// Arguments:
//	sparse_indices: 2-D.  The `indices` of the `SparseTensor`.
//	sparse_values: 1-D.  The `values` of the `SparseTensor`.
//	sparse_shape: 1-D.  The `shape` of the `SparseTensor`.
//
// Returns 0-D.  The handle of the `SparseTensor` now stored in the
// `SparseTensorsMap`.
func AddSparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddSparseToTensorsMapAttr) (sparse_handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "AddSparseToTensorsMap",
		Input: []tf.Input{
			sparse_indices, sparse_values, sparse_shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes softmax activations.
//
// For each batch `i` and class `j` we have
//
//     softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))
//
// Arguments:
//	logits: 2-D with shape `[batch_size, num_classes]`.
//
// Returns Same shape as `logits`.
func Softmax(scope *Scope, logits tf.Output) (softmax tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Softmax",
		Input: []tf.Input{
			logits,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// RandomShuffleQueueV2Attr is an optional argument to RandomShuffleQueueV2.
type RandomShuffleQueueV2Attr func(optionalAttr)

// RandomShuffleQueueV2Shapes sets the optional shapes attribute to value.
//
// value: The shape of each component in a value. The length of this attr must
// be either 0 or the same as the length of component_types. If the length of
// this attr is 0, the shapes of queue elements are not constrained, and
// only one element may be dequeued at a time.
// If not specified, defaults to <>
//
// REQUIRES: len(value) >= 0
func RandomShuffleQueueV2Shapes(value []tf.Shape) RandomShuffleQueueV2Attr {
	return func(m optionalAttr) {
		m["shapes"] = value
	}
}

// RandomShuffleQueueV2Capacity sets the optional capacity attribute to value.
//
// value: The upper bound on the number of elements in this queue.
// Negative numbers mean no limit.
// If not specified, defaults to -1
func RandomShuffleQueueV2Capacity(value int64) RandomShuffleQueueV2Attr {
	return func(m optionalAttr) {
		m["capacity"] = value
	}
}

// RandomShuffleQueueV2MinAfterDequeue sets the optional min_after_dequeue attribute to value.
//
// value: Dequeue will block unless there would be this
// many elements after the dequeue or the queue is closed. This
// ensures a minimum level of mixing of elements.
// If not specified, defaults to 0
func RandomShuffleQueueV2MinAfterDequeue(value int64) RandomShuffleQueueV2Attr {
	return func(m optionalAttr) {
		m["min_after_dequeue"] = value
	}
}

// RandomShuffleQueueV2Seed sets the optional seed attribute to value.
//
// value: If either seed or seed2 is set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, a random seed is used.
// If not specified, defaults to 0
func RandomShuffleQueueV2Seed(value int64) RandomShuffleQueueV2Attr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// RandomShuffleQueueV2Seed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func RandomShuffleQueueV2Seed2(value int64) RandomShuffleQueueV2Attr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// RandomShuffleQueueV2Container sets the optional container attribute to value.
//
// value: If non-empty, this queue is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func RandomShuffleQueueV2Container(value string) RandomShuffleQueueV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// RandomShuffleQueueV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this queue will be shared under the given name
// across multiple sessions.
// If not specified, defaults to ""
func RandomShuffleQueueV2SharedName(value string) RandomShuffleQueueV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// A queue that randomizes the order of elements.
//
// Arguments:
//	component_types: The type of each component in a value.
//
// Returns The handle to the queue.
func RandomShuffleQueueV2(scope *Scope, component_types []tf.DataType, optional ...RandomShuffleQueueV2Attr) (handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"component_types": component_types}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RandomShuffleQueueV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Outputs a `Summary` protocol buffer with scalar values.
//
// The input `tags` and `values` must have the same shape.  The generated summary
// has a summary value for each tag-value pair in `tags` and `values`.
//
// Arguments:
//	tags: Tags for the summary.
//	values: Same shape as `tags.  Values for the summary.
//
// Returns Scalar.  Serialized `Summary` protocol buffer.
func ScalarSummary(scope *Scope, tags tf.Output, values tf.Output) (summary tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ScalarSummary",
		Input: []tf.Input{
			tags, values,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Constructs a tensor by tiling a given tensor.
//
// This operation creates a new tensor by replicating `input` `multiples` times.
// The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
// and the values of `input` are replicated `multiples[i]` times along the 'i'th
// dimension. For example, tiling `[a b c d]` by `[2]` produces
// `[a b c d a b c d]`.
//
// Arguments:
//	input: 1-D or higher.
//	multiples: 1-D. Length must be the same as the number of dimensions in `input`
func Tile(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Tile",
		Input: []tf.Input{
			input, multiples,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the element-wise min of two SparseTensors.
//
// Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
//
// Arguments:
//	a_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
// SparseTensor, in the canonical lexicographic ordering.
//	a_values: 1-D.  `N` non-empty values corresponding to `a_indices`.
//	a_shape: 1-D.  Shape of the input SparseTensor.
//	b_indices: counterpart to `a_indices` for the other operand.
//	b_values: counterpart to `a_values` for the other operand; must be of the same dtype.
//	b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal.
//
// Returns 2-D.  The indices of the output SparseTensor.1-D.  The values of the output SparseTensor.
func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSparseMinimum",
		Input: []tf.Input{
			a_indices, a_values, a_shape, b_indices, b_values, b_shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Returns the set of files matching one or more glob patterns.
//
// Note that this routine only supports wildcard characters in the
// basename portion of the pattern, not in the directory portion.
//
// Arguments:
//	pattern: Shell wildcard pattern(s). Scalar or vector of type string.
//
// Returns A vector of matching filenames.
func MatchingFiles(scope *Scope, pattern tf.Output) (filenames tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "MatchingFiles",
		Input: []tf.Input{
			pattern,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradient of the sigmoid of `x` wrt its input.
//
// Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and
// `dy` is the corresponding input gradient.
func SigmoidGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SigmoidGrad",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Generate a sharded filename. The filename is printf formatted as
//
//    %s-%05d-of-%05d, basename, shard, num_shards.
func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ShardedFilename",
		Input: []tf.Input{
			basename, shard, num_shards,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation.
type SparseToSparseSetOperationAttr func(optionalAttr)

// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value.
// If not specified, defaults to true
func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr {
	return func(m optionalAttr) {
		m["validate_indices"] = value
	}
}

// Applies set operation along last dimension of 2 `SparseTensor` inputs.
//
// See SetOperationOp::SetOperationFromContext for values of `set_operation`.
//
// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the
// order and range of `set1` and `set2` indices.
//
// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`,
// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same
// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but
// ignored.
//
// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`,
// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same
// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but
// ignored.
//
// If `validate_indices` is `True`, this op validates the order and range of `set1`
// and `set2` indices.
//
// Output `result` is a `SparseTensor` represented by `result_indices`,
// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
// dimension contains the result of `set_operation` applied to the corresponding
// `[0...n-1]` dimension of `set`.
//
// Arguments:
//	set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
// order.
//	set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
// order.
//	set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must
// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the
// max set size across `0...n-1` dimensions.
//	set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
// order.
//	set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
// order.
//	set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the
// max set size across `0...n-1` dimensions.
//
//
// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is
// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]`
// is the max result set size across all `0...n-1` dimensions.
func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"set_operation": set_operation}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SparseToSparseSetOperation",
		Input: []tf.Input{
			set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
//
// This Op does not require `a_indices` be sorted in standard lexicographic order.
//
// Arguments:
//	a_indices: 2-D.  The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`.
//	a_values: 1-D.  The `values` of the `SparseTensor`, with shape `[nnz]`.
//	a_shape: 1-D.  The `shape` of the `SparseTensor`, with shape `[ndims]`.
//	b: `ndims`-D Tensor.  With shape `a_shape`.
func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseTensorDenseAdd",
		Input: []tf.Input{
			a_indices, a_values, a_shape, b,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Batch normalization.
//
// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization()
//
// This op is deprecated. Prefer `tf.nn.batch_normalization`.
//
// Arguments:
//	t: A 4D input Tensor.
//	m: A 1D mean Tensor with size matching the last dimension of t.
// This is the first output from tf.nn.moments,
// or a saved moving average thereof.
//	v: A 1D variance Tensor with size matching the last dimension of t.
// This is the second output from tf.nn.moments,
// or a saved moving average thereof.
//	beta: A 1D beta Tensor with size matching the last dimension of t.
// An offset to be added to the normalized tensor.
//	gamma: A 1D gamma Tensor with size matching the last dimension of t.
// If "scale_after_normalization" is true, this tensor will be multiplied
// with the normalized tensor.
//	variance_epsilon: A small float number to avoid dividing by 0.
//	scale_after_normalization: A bool indicating whether the resulted tensor
// needs to be multiplied with gamma.
func BatchNormWithGlobalNormalization(scope *Scope, t tf.Output, m tf.Output, v tf.Output, beta tf.Output, gamma tf.Output, variance_epsilon float32, scale_after_normalization bool) (result tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
	opspec := tf.OpSpec{
		Type: "BatchNormWithGlobalNormalization",
		Input: []tf.Input{
			t, m, v, beta, gamma,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Generate a glob pattern matching all sharded file names.
func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ShardedFilespec",
		Input: []tf.Input{
			basename, num_shards,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Saves the input tensors to disk.
//
// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
// is written to `filename` with name `tensor_names[i]`.
//
// See also `SaveSlices`.
//
// Arguments:
//	filename: Must have a single element. The name of the file to which we write
// the tensor.
//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
//	data: `N` tensors to save.
//
// Returns the created operation.
func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Save",
		Input: []tf.Input{
			filename, tensor_names, tf.OutputList(data),
		},
	}
	return scope.AddOperation(opspec)
}

// MaxPoolAttr is an optional argument to MaxPool.
type MaxPoolAttr func(optionalAttr)

// MaxPoolDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, in_height, in_width, in_channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, in_channels, in_height, in_width].
// If not specified, defaults to "NHWC"
func MaxPoolDataFormat(value string) MaxPoolAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Performs max pooling on the input.
//
// Arguments:
//	input: 4-D input to pool over.
//	ksize: The size of the window for each dimension of the input tensor.
//	strides: The stride of the sliding window for each dimension of the
// input tensor.
//	padding: The type of padding algorithm to use.
//
// Returns The max pooled output tensor.
func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MaxPool",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Merges summaries.
//
// This op creates a
// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
// protocol buffer that contains the union of all the values in the input
// summaries.
//
// When the Op is run, it reports an `InvalidArgument` error if multiple values
// in the summaries to merge use the same tag.
//
// Arguments:
//	inputs: Can be of any shape.  Each must contain serialized `Summary` protocol
// buffers.
//
// Returns Scalar. Serialized `Summary` protocol buffer.
func MergeSummary(scope *Scope, inputs []tf.Output) (summary tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "MergeSummary",
		Input: []tf.Input{
			tf.OutputList(inputs),
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// The gradient operator for the SparseAdd op.
//
// The SparseAdd op calculates A + B, where A, B, and the sum are all represented
// as `SparseTensor` objects.  This op takes in the upstream gradient w.r.t.
// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty
// values of A and B.
//
// Arguments:
//	backprop_val_grad: 1-D with shape `[nnz(sum)]`.  The gradient with respect to
// the non-empty values of the sum.
//	a_indices: 2-D.  The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.
//	b_indices: 2-D.  The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.
//	sum_indices: 2-D.  The `indices` of the sum `SparseTensor`, size
// `[nnz(sum), ndims]`.
//
// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the
// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the
// non-empty values of B.
func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseAddGrad",
		Input: []tf.Input{
			backprop_val_grad, a_indices, b_indices, sum_indices,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Read an element from the TensorArray into output `value`.
//
// Arguments:
//	handle: The handle to a TensorArray.
//
//	flow_in: A float scalar that enforces proper chaining of operations.
//	dtype: The type of the elem that is returned.
//
// Returns The tensor that is read from the TensorArray.
func TensorArrayReadV3(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	opspec := tf.OpSpec{
		Type: "TensorArrayReadV3",
		Input: []tf.Input{
			handle, index, flow_in,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Adds up a SparseTensor and a dense Tensor, using these special rules:
//
// (1) Broadcasts the dense side to have the same shape as the sparse side, if
//     eligible;
// (2) Then, only the dense values pointed to by the indices of the SparseTensor
//     participate in the cwise addition.
//
// By these rules, the result is a logical SparseTensor with exactly the same
// indices and shape, but possibly with different non-zero values.  The output of
// this Op is the resultant non-zero values.
//
// Arguments:
//	sp_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
// SparseTensor, possibly not in canonical ordering.
//	sp_values: 1-D.  `N` non-empty values corresponding to `sp_indices`.
//	sp_shape: 1-D.  Shape of the input SparseTensor.
//	dense: `R`-D.  The dense Tensor operand.
//
// Returns 1-D.  The `N` values that are operated on.
func SparseDenseCwiseAdd(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseDenseCwiseAdd",
		Input: []tf.Input{
			sp_indices, sp_values, sp_shape, dense,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes a 3-D convolution given 5-D `input` and `filter` tensors.
//
// In signal processing, cross-correlation is a measure of similarity of
// two waveforms as a function of a time-lag applied to one of them. This
// is also known as a sliding dot product or sliding inner-product.
//
// Our Conv3D implements a form of cross-correlation.
//
// Arguments:
//	input: Shape `[batch, in_depth, in_height, in_width, in_channels]`.
//	filter: Shape `[filter_depth, filter_height, filter_width, in_channels,
// out_channels]`. `in_channels` must match between `input` and `filter`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
func Conv3D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "Conv3D",
		Input: []tf.Input{
			input, filter,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// L2 Loss.
//
// Computes half the L2 norm of a tensor without the `sqrt`:
//
//     output = sum(t ** 2) / 2
//
// Arguments:
//	t: Typically 2-D, but may have any dimensions.
//
// Returns 0-D.
func L2Loss(scope *Scope, t tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "L2Loss",
		Input: []tf.Input{
			t,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes rectified linear: `max(features, 0)`.
func Relu(scope *Scope, features tf.Output) (activations tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Relu",
		Input: []tf.Input{
			features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the truth value of (x >= y) element-wise.
//
// *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func GreaterEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "GreaterEqual",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Converts each string in the input Tensor to its hash mod by a number of buckets.
//
// The hash function is deterministic on the content of the string within the
// process. The hash function is a keyed hash function, where attribute `key`
// defines the key of the hash function. `key` is an array of 2 elements.
//
// A strong hash is important when inputs may be malicious, e.g. URLs with
// additional components. Adversaries could try to make their inputs hash to the
// same bucket for a denial-of-service attack or to skew the results. A strong
// hash prevents this by making it difficult, if not infeasible, to compute inputs
// that hash to the same bucket. This comes at a cost of roughly 4x higher compute
// time than `tf.string_to_hash_bucket_fast`.
//
// Arguments:
//	input: The strings to assign a hash bucket.
//	num_buckets: The number of buckets.
//	key: The key for the keyed hash function passed as a list of two uint64
// elements.
//
// Returns A Tensor of the same shape as the input `string_tensor`.
func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key}
	opspec := tf.OpSpec{
		Type: "StringToHashBucketStrong",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes numerical negative value element-wise.
//
// I.e., \\(y = -x\\).
func Neg(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Neg",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// DestroyResourceOpAttr is an optional argument to DestroyResourceOp.
type DestroyResourceOpAttr func(optionalAttr)

// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value.
//
// value: whether to ignore the error when the resource
// doesn't exist.
// If not specified, defaults to true
func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr {
	return func(m optionalAttr) {
		m["ignore_lookup_error"] = value
	}
}

// Deletes the resource specified by the handle.
//
// All subsequent operations using the resource will result in a NotFound
// error status.
//
// Arguments:
//	resource: handle to the resource to delete.
//
// Returns the created operation.
func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DestroyResourceOp",
		Input: []tf.Input{
			resource,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum.
type ResourceApplyMomentumAttr func(optionalAttr)

// ResourceApplyMomentumUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var and accum tensors will be protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceApplyMomentumUseLocking(value bool) ResourceApplyMomentumAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// ResourceApplyMomentumUseNesterov sets the optional use_nesterov attribute to value.
//
// value: If `True`, the tensor passed to compute grad will be
// var - lr * momentum * accum, so in the end, the var you get is actually
// var - lr * momentum * accum.
// If not specified, defaults to false
func ResourceApplyMomentumUseNesterov(value bool) ResourceApplyMomentumAttr {
	return func(m optionalAttr) {
		m["use_nesterov"] = value
	}
}

// Update '*var' according to the momentum scheme. Set use_nesterov = True if you
//
// want to use Nesterov momentum.
//
// accum = accum * momentum + grad
// var -= lr * accum
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	lr: Scaling factor. Must be a scalar.
//	grad: The gradient.
//	momentum: Momentum. Must be a scalar.
//
// Returns the created operation.
func ResourceApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyMomentumAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyMomentum",
		Input: []tf.Input{
			var_, accum, lr, grad, momentum,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Returns element-wise integer closest to x.
//
// If the result is midway between two representable values,
// the even representable is chosen.
// For example:
//
// ```
// rint(-1.5) ==> -2.0
// rint(0.5000001) ==> 1.0
// rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.]
// ```
func Rint(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Rint",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the 1-dimensional discrete Fourier Transform over the inner-most
//
// dimension of `input`.
//
// Arguments:
//	input: A complex64 tensor.
//
// Returns A complex64 tensor of the same shape as `input`. The inner-most
//   dimension of `input` is replaced with its 1D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.fft
// @end_compatibility
func FFT(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FFT",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax.
type MaxPoolWithArgmaxAttr func(optionalAttr)

// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value.
// If not specified, defaults to DT_INT64
func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr {
	return func(m optionalAttr) {
		m["Targmax"] = value
	}
}

// Performs max pooling on the input and outputs both max values and indices.
//
// The indices in `argmax` are flattened, so that a maximum value at position
// `[b, y, x, c]` becomes flattened index
// `((b * height + y) * width + x) * channels + c`.
//
// Arguments:
//	input: 4-D with shape `[batch, height, width, channels]`.  Input to pool over.
//	ksize: The size of the window for each dimension of the input tensor.
//	strides: The stride of the sliding window for each dimension of the
// input tensor.
//	padding: The type of padding algorithm to use.
//
// Returns The max pooled output tensor.4-D.  The flattened indices of the max values chosen for each output.
func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MaxPoolWithArgmax",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Computes the Eigen Decomposition of a batch of square self-adjoint matrices.
//
// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead.
//
// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
// form square matrices, with the same constraints as the single matrix
// SelfAdjointEig.
//
// The result is a [..., M+1, M] matrix with [..., 0,:] containing the
// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
//
// Arguments:
//	input: Shape is `[..., M, M]`.
//
// Returns Shape is `[..., M+1, M]`.
func SelfAdjointEig(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SelfAdjointEig",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// FractionalMaxPoolAttr is an optional argument to FractionalMaxPool.
type FractionalMaxPoolAttr func(optionalAttr)

// FractionalMaxPoolPseudoRandom sets the optional pseudo_random attribute to value.
//
// value: When set to True, generates the pooling sequence in a
// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin
// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
// difference between pseudorandom and random.
// If not specified, defaults to false
func FractionalMaxPoolPseudoRandom(value bool) FractionalMaxPoolAttr {
	return func(m optionalAttr) {
		m["pseudo_random"] = value
	}
}

// FractionalMaxPoolOverlapping sets the optional overlapping attribute to value.
//
// value: When set to True, it means when pooling, the values at the boundary
// of adjacent pooling cells are used by both cells. For example:
//
// `index  0  1  2  3  4`
//
// `value  20 5  16 3  7`
//
// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
// The result would be [20, 16] for fractional max pooling.
// If not specified, defaults to false
func FractionalMaxPoolOverlapping(value bool) FractionalMaxPoolAttr {
	return func(m optionalAttr) {
		m["overlapping"] = value
	}
}

// FractionalMaxPoolDeterministic sets the optional deterministic attribute to value.
//
// value: When set to True, a fixed pooling region will be used when
// iterating over a FractionalMaxPool node in the computation graph. Mainly used
// in unit test to make FractionalMaxPool deterministic.
// If not specified, defaults to false
func FractionalMaxPoolDeterministic(value bool) FractionalMaxPoolAttr {
	return func(m optionalAttr) {
		m["deterministic"] = value
	}
}

// FractionalMaxPoolSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func FractionalMaxPoolSeed(value int64) FractionalMaxPoolAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// FractionalMaxPoolSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func FractionalMaxPoolSeed2(value int64) FractionalMaxPoolAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Performs fractional max pooling on the input.
//
// Fractional max pooling is slightly different than regular max pooling.  In
// regular max pooling, you downsize an input set by taking the maximum value of
// smaller N x N subsections of the set (often 2x2), and try to reduce the set by
// a factor of N, where N is an integer.  Fractional max pooling, as you might
// expect from the word "fractional", means that the overall reduction ratio N
// does not have to be an integer.
//
// The sizes of the pooling regions are generated randomly but are fairly uniform.
// For example, let's look at the height dimension, and the constraints on the
// list of rows that will be pool boundaries.
//
// First we define the following:
//
// 1.  input_row_length : the number of rows from the input set
// 2.  output_row_length : which will be smaller than the input
// 3.  alpha = input_row_length / output_row_length : our reduction ratio
// 4.  K = floor(alpha)
// 5.  row_pooling_sequence : this is the result list of pool boundary rows
//
// Then, row_pooling_sequence should satisfy:
//
// 1.  a[0] = 0 : the first value of the sequence is 0
// 2.  a[end] = input_row_length : the last value of the sequence is the size
// 3.  K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size
// 4.  length(row_pooling_sequence) = output_row_length+1
//
// For more details on fractional max pooling, see this paper:
// [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071)
//
// Arguments:
//	value: 4-D with shape `[batch, height, width, channels]`.
//	pooling_ratio: Pooling ratio for each dimension of `value`, currently only
// supports row and col dimension and should be >= 1.0. For example, a valid
// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
// must be 1.0 because we don't allow pooling on batch and channels
// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
// respectively.
//
// Returns output tensor after fractional max pooling.row pooling sequence, needed to calculate gradient.column pooling sequence, needed to calculate gradient.
func FractionalMaxPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalMaxPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"pooling_ratio": pooling_ratio}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FractionalMaxPool",
		Input: []tf.Input{
			value,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Reorders a SparseTensor into the canonical, row-major ordering.
//
// Note that by convention, all sparse ops preserve the canonical ordering along
// increasing dimension number. The only time ordering can be violated is during
// manual manipulation of the indices and values vectors to add entries.
//
// Reordering does not affect the shape of the SparseTensor.
//
// If the tensor has rank `R` and `N` non-empty values, `input_indices` has
// shape `[N, R]`, input_values has length `N`, and input_shape has length `R`.
//
// Arguments:
//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
// SparseTensor, possibly not in canonical ordering.
//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
//	input_shape: 1-D.  Shape of the input SparseTensor.
//
// Returns 2-D.  `N x R` matrix with the same indices as input_indices, but
// in canonical row-major ordering.1-D.  `N` non-empty values corresponding to `output_indices`.
func SparseReorder(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseReorder",
		Input: []tf.Input{
			input_indices, input_values, input_shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// PackAttr is an optional argument to Pack.
type PackAttr func(optionalAttr)

// PackAxis sets the optional axis attribute to value.
//
// value: Dimension along which to pack.  Negative values wrap around, so the
// valid range is `[-(R+1), R+1)`.
// If not specified, defaults to 0
func PackAxis(value int64) PackAttr {
	return func(m optionalAttr) {
		m["axis"] = value
	}
}

// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
//
// Packs the `N` tensors in `values` into a tensor with rank one higher than each
// tensor in `values`, by packing them along the `axis` dimension.
// Given a list of tensors of shape `(A, B, C)`;
//
// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
// Etc.
//
// For example:
//
// ```prettyprint
// # 'x' is [1, 4]
// # 'y' is [2, 5]
// # 'z' is [3, 6]
// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
// ```
//
// This is the opposite of `unpack`.
//
// Arguments:
//	values: Must be of same shape and type.
//
// Returns The packed tensor.
func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Pack",
		Input: []tf.Input{
			tf.OutputList(values),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Deprecated. Use TensorArraySplitV3
func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TensorArraySplitV2",
		Input: []tf.Input{
			handle, value, lengths, flow_in,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QuantizedReluAttr is an optional argument to QuantizedRelu.
type QuantizedReluAttr func(optionalAttr)

// QuantizedReluOutType sets the optional out_type attribute to value.
// If not specified, defaults to DT_QUINT8
func QuantizedReluOutType(value tf.DataType) QuantizedReluAttr {
	return func(m optionalAttr) {
		m["out_type"] = value
	}
}

// Computes Quantized Rectified Linear: `max(features, 0)`
//
// Arguments:
//
//	min_features: The float value that the lowest quantized value represents.
//	max_features: The float value that the highest quantized value represents.
//
// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizedRelu",
		Input: []tf.Input{
			features, min_features, max_features,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// ResourceSparseApplyProximalGradientDescentAttr is an optional argument to ResourceSparseApplyProximalGradientDescent.
type ResourceSparseApplyProximalGradientDescentAttr func(optionalAttr)

// ResourceSparseApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value.
//
// value: If True, the subtraction will be protected by a lock;
// otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceSparseApplyProximalGradientDescentUseLocking(value bool) ResourceSparseApplyProximalGradientDescentAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Sparse update '*var' as FOBOS algorithm with fixed learning rate.
//
// That is for rows we have grad for, we update var as follows:
// prox_v = var - alpha * grad
// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
//
// Arguments:
//	var_: Should be from a Variable().
//	alpha: Scaling factor. Must be a scalar.
//	l1: L1 regularization. Must be a scalar.
//	l2: L2 regularization. Must be a scalar.
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var and accum.
//
// Returns the created operation.
func ResourceSparseApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalGradientDescentAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyProximalGradientDescent",
		Input: []tf.Input{
			var_, alpha, l1, l2, grad, indices,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Computes rectified linear gradients for a Relu operation.
//
// Arguments:
//	gradients: The backpropagated gradients to the corresponding Relu operation.
//	features: The features passed as input to the corresponding Relu operation, OR
// the outputs of that operation (both work equivalently).
//
// Returns `gradients * (features > 0)`.
func ReluGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReluGrad",
		Input: []tf.Input{
			gradients, features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradient for the inverse of `x` wrt its input.
//
// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
// is the corresponding input gradient.
func ReciprocalGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReciprocalGrad",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the Cholesky decomposition of one or more square matrices.
//
// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
// form square matrices, with the same constraints as the single matrix Cholesky
// decomposition above. The output is a tensor of the same shape as the input
// containing the Cholesky decompositions for all input submatrices `[..., :, :]`.
//
// Arguments:
//	input: Shape is `[..., M, M]`.
//
// Returns Shape is `[..., M, M]`.
func Cholesky(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Cholesky",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Writes contents to the file at input filename. Creates file if not existing.
//
// Arguments:
//	filename: scalar. The name of the file to which we write the contents.
//	contents: scalar. The content to be written to the output file.
//
// Returns the created operation.
func WriteFile(scope *Scope, filename tf.Output, contents tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "WriteFile",
		Input: []tf.Input{
			filename, contents,
		},
	}
	return scope.AddOperation(opspec)
}

// Reverses specific dimensions of a tensor.
//
// NOTE `tf.reverse` has now changed behavior in preparation for 1.0.
// `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0.
//
// Given a `tensor`, and a `int32` tensor `axis` representing the set of
// dimensions of `tensor` to reverse. This operation reverses each dimension
// `i` for which there exists `j` s.t. `axis[j] == i`.
//
// `tensor` can have up to 8 dimensions. The number of dimensions specified
// in `axis` may be 0 or more entries. If an index is specified more than
// once, a InvalidArgument error is raised.
//
// For example:
//
// ```prettyprint
// # tensor 't' is [[[[ 0,  1,  2,  3],
// #                  [ 4,  5,  6,  7],
// #                  [ 8,  9, 10, 11]],
// #                 [[12, 13, 14, 15],
// #                  [16, 17, 18, 19],
// #                  [20, 21, 22, 23]]]]
// # tensor 't' shape is [1, 2, 3, 4]
//
// # 'dims' is [3] or 'dims' is -1
// reverse(t, dims) ==> [[[[ 3,  2,  1,  0],
//                         [ 7,  6,  5,  4],
//                         [ 11, 10, 9, 8]],
//                        [[15, 14, 13, 12],
//                         [19, 18, 17, 16],
//                         [23, 22, 21, 20]]]]
//
// # 'dims' is '[1]' (or 'dims' is '[-3]')
// reverse(t, dims) ==> [[[[12, 13, 14, 15],
//                         [16, 17, 18, 19],
//                         [20, 21, 22, 23]
//                        [[ 0,  1,  2,  3],
//                         [ 4,  5,  6,  7],
//                         [ 8,  9, 10, 11]]]]
//
// # 'dims' is '[2]' (or 'dims' is '[-2]')
// reverse(t, dims) ==> [[[[8, 9, 10, 11],
//                         [4, 5, 6, 7],
//                         [0, 1, 2, 3]]
//                        [[20, 21, 22, 23],
//                         [16, 17, 18, 19],
//                         [12, 13, 14, 15]]]]
// ```
//
// Arguments:
//	tensor: Up to 8-D.
//	axis: 1-D. The indices of the dimensions to reverse.
//
// Returns The same shape as `tensor`.
func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReverseV2",
		Input: []tf.Input{
			tensor, axis,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyCenteredRMSPropAttr is an optional argument to ResourceApplyCenteredRMSProp.
type ResourceApplyCenteredRMSPropAttr func(optionalAttr)

// ResourceApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var, mg, ms, and mom tensors is
// protected by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceApplyCenteredRMSPropUseLocking(value bool) ResourceApplyCenteredRMSPropAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the centered RMSProp algorithm.
//
// The centered RMSProp algorithm uses an estimate of the centered second moment
// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
// uses the (uncentered) second moment. This often helps with training, but is
// slightly more expensive in terms of computation and memory.
//
// Note that in dense implementation of this algorithm, mg, ms, and mom will
// update even if the grad is zero, but in this sparse implementation, mg, ms,
// and mom will not update in iterations during which the grad is zero.
//
// mean_square = decay * mean_square + (1-decay) * gradient ** 2
// mean_grad = decay * mean_grad + (1-decay) * gradient
//
// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
//
// mg <- rho * mg_{t-1} + (1-rho) * grad
// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon)
// var <- var - mom
//
// Arguments:
//	var_: Should be from a Variable().
//	mg: Should be from a Variable().
//	ms: Should be from a Variable().
//	mom: Should be from a Variable().
//	lr: Scaling factor. Must be a scalar.
//	rho: Decay rate. Must be a scalar.
//
//	epsilon: Ridge term. Must be a scalar.
//	grad: The gradient.
//
// Returns the created operation.
func ResourceApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyCenteredRMSPropAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyCenteredRMSProp",
		Input: []tf.Input{
			var_, mg, ms, mom, lr, rho, momentum, epsilon, grad,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Compute the inverse 3-dimensional discrete Fourier Transform over the inner-most
//
// 3 dimensions of `input`.
//
// Arguments:
//	input: A complex64 tensor.
//
// Returns A complex64 tensor of the same shape as `input`. The inner-most 3
//   dimensions of `input` are replaced with their inverse 3D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.ifftn with 3 dimensions.
// @end_compatibility
func IFFT3D(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IFFT3D",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Given a quantized tensor described by (input, input_min, input_max), outputs a
//
// range that covers the actual values present in that tensor.  This op is
// typically used to produce the requested_output_min and requested_output_max for
// Requantize.
//
// Arguments:
//
//	input_min: The float value that the minimum quantized input value represents.
//	input_max: The float value that the maximum quantized input value represents.
//
// Returns The computed min output.the computed max output.
func RequantizationRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output) (output_min tf.Output, output_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "RequantizationRange",
		Input: []tf.Input{
			input, input_min, input_max,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// DepthwiseConv2dNativeBackpropInputAttr is an optional argument to DepthwiseConv2dNativeBackpropInput.
type DepthwiseConv2dNativeBackpropInputAttr func(optionalAttr)

// DepthwiseConv2dNativeBackpropInputDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, height, width, channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, channels, height, width].
// If not specified, defaults to "NHWC"
func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dNativeBackpropInputAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Computes the gradients of depthwise convolution with respect to the input.
//
// Arguments:
//	input_sizes: An integer vector representing the shape of `input`, based
// on `data_format`.  For example, if `data_format` is 'NHWC' then
//  `input` is a 4-D `[batch, height, width, channels]` tensor.
//	filter: 4-D with shape
// `[filter_height, filter_width, in_channels, depthwise_multiplier]`.
//	out_backprop: 4-D with shape  based on `data_format`.
// For example, if `data_format` is 'NHWC' then
// out_backprop shape is `[batch, out_height, out_width, out_channels]`.
// Gradients w.r.t. the output of the convolution.
//	strides: The stride of the sliding window for each dimension of the input
// of the convolution.
//	padding: The type of padding algorithm to use.
//
// Returns 4-D with shape according to `data_format`.  For example, if
// `data_format` is 'NHWC', output shape is `[batch, in_height,
// in_width, in_channels]`.  Gradient w.r.t. the input of the
// convolution.
func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropInputAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DepthwiseConv2dNativeBackpropInput",
		Input: []tf.Input{
			input_sizes, filter, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes gradients for the exponential linear (Elu) operation.
//
// Arguments:
//	gradients: The backpropagated gradients to the corresponding Elu operation.
//	outputs: The outputs of the corresponding Elu operation.
//
// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0,
// `gradients` otherwise.
func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "EluGrad",
		Input: []tf.Input{
			gradients, outputs,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Converts each string in the input Tensor to its hash mod by a number of buckets.
//
// The hash function is deterministic on the content of the string within the
// process.
//
// Note that the hash function may change from time to time.
// This functionality will be deprecated and it's recommended to use
// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`.
//
// Arguments:
//
//	num_buckets: The number of buckets.
//
// Returns A Tensor of the same shape as the input `string_tensor`.
func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_buckets": num_buckets}
	opspec := tf.OpSpec{
		Type: "StringToHashBucket",
		Input: []tf.Input{
			string_tensor,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// CumsumAttr is an optional argument to Cumsum.
type CumsumAttr func(optionalAttr)

// CumsumExclusive sets the optional exclusive attribute to value.
// If not specified, defaults to false
func CumsumExclusive(value bool) CumsumAttr {
	return func(m optionalAttr) {
		m["exclusive"] = value
	}
}

// CumsumReverse sets the optional reverse attribute to value.
// If not specified, defaults to false
func CumsumReverse(value bool) CumsumAttr {
	return func(m optionalAttr) {
		m["reverse"] = value
	}
}

// Compute the cumulative sum of the tensor `x` along `axis`.
//
// By default, this op performs an inclusive cumsum, which means that the first
// element of the input is identical to the first element of the output:
// ```prettyprint
// tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c]
// ```
//
// By setting the `exclusive` kwarg to `True`, an exclusive cumsum is
// performed instead:
// ```prettyprint
// tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b]
// ```
//
// By setting the `reverse` kwarg to `True`, the cumsum is performed in the
// opposite direction:
// ```prettyprint
// tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c]
// ```
// This is more efficient than using separate `tf.reverse` ops.
//
// The `reverse` and `exclusive` kwargs can also be combined:
// ```prettyprint
// tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0]
// ```
func Cumsum(scope *Scope, x tf.Output, axis tf.Output, optional ...CumsumAttr) (out tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Cumsum",
		Input: []tf.Input{
			x, axis,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam.
type ResourceApplyAdamAttr func(optionalAttr)

// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var, m, and v tensors will be protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceApplyAdamUseLocking(value bool) ResourceApplyAdamAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the Adam algorithm.
//
// lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
// m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t
// v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t
// variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
//
// Arguments:
//	var_: Should be from a Variable().
//	m: Should be from a Variable().
//	v: Should be from a Variable().
//	beta1_power: Must be a scalar.
//	beta2_power: Must be a scalar.
//	lr: Scaling factor. Must be a scalar.
//	beta1: Momentum factor. Must be a scalar.
//	beta2: Momentum factor. Must be a scalar.
//	epsilon: Ridge term. Must be a scalar.
//	grad: The gradient.
//
// Returns the created operation.
func ResourceApplyAdam(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyAdam",
		Input: []tf.Input{
			var_, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Compute the 3-dimensional discrete Fourier Transform over the inner-most 3
//
// dimensions of `input`.
//
// Arguments:
//	input: A complex64 tensor.
//
// Returns A complex64 tensor of the same shape as `input`. The inner-most 3
//   dimensions of `input` are replaced with their 3D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.fftn with 3 dimensions.
// @end_compatibility
func FFT3D(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FFT3D",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SizeAttr is an optional argument to Size.
type SizeAttr func(optionalAttr)

// SizeOutType sets the optional out_type attribute to value.
// If not specified, defaults to DT_INT32
func SizeOutType(value tf.DataType) SizeAttr {
	return func(m optionalAttr) {
		m["out_type"] = value
	}
}

// Returns the size of a tensor.
//
// This operation returns an integer representing the number of elements in
// `input`.
//
// For example:
//
// ```prettyprint
// # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
// size(t) ==> 12
// ```
func Size(scope *Scope, input tf.Output, optional ...SizeAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Size",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp.
type ResourceApplyRMSPropAttr func(optionalAttr)

// ResourceApplyRMSPropUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var, ms, and mom tensors is protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the RMSProp algorithm.
//
// Note that in dense implementation of this algorithm, ms and mom will
// update even if the grad is zero, but in this sparse implementation, ms
// and mom will not update in iterations during which the grad is zero.
//
// mean_square = decay * mean_square + (1-decay) * gradient ** 2
// Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
//
// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
// var <- var - mom
//
// Arguments:
//	var_: Should be from a Variable().
//	ms: Should be from a Variable().
//	mom: Should be from a Variable().
//	lr: Scaling factor. Must be a scalar.
//	rho: Decay rate. Must be a scalar.
//
//	epsilon: Ridge term. Must be a scalar.
//	grad: The gradient.
//
// Returns the created operation.
func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyRMSPropAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyRMSProp",
		Input: []tf.Input{
			var_, ms, mom, lr, rho, momentum, epsilon, grad,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Get the value of the tensor specified by its handle.
//
// Arguments:
//	handle: The handle for a tensor stored in the session state.
//	dtype: The type of the output value.
//
// Returns The tensor for the given handle.
func GetSessionTensor(scope *Scope, handle tf.Output, dtype tf.DataType) (value tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	opspec := tf.OpSpec{
		Type: "GetSessionTensor",
		Input: []tf.Input{
			handle,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent.
type ResourceApplyProximalGradientDescentAttr func(optionalAttr)

// ResourceApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value.
//
// value: If True, the subtraction will be protected by a lock;
// otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceApplyProximalGradientDescentUseLocking(value bool) ResourceApplyProximalGradientDescentAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' as FOBOS algorithm with fixed learning rate.
//
// prox_v = var - alpha * delta
// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
//
// Arguments:
//	var_: Should be from a Variable().
//	alpha: Scaling factor. Must be a scalar.
//	l1: L1 regularization. Must be a scalar.
//	l2: L2 regularization. Must be a scalar.
//	delta: The change.
//
// Returns the created operation.
func ResourceApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, delta tf.Output, optional ...ResourceApplyProximalGradientDescentAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyProximalGradientDescent",
		Input: []tf.Input{
			var_, alpha, l1, l2, delta,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Compute the 2-dimensional discrete Fourier Transform over the inner-most
//
// 2 dimensions of `input`.
//
// Arguments:
//	input: A complex64 tensor.
//
// Returns A complex64 tensor of the same shape as `input`. The inner-most 2
//   dimensions of `input` are replaced with their 2D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.fft2
// @end_compatibility
func FFT2D(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FFT2D",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Creates a tensor filled with a scalar value.
//
// This operation creates a tensor of shape `dims` and fills it with `value`.
//
// For example:
//
// ```prettyprint
// # Output tensor has shape [2, 3].
// fill([2, 3], 9) ==> [[9, 9, 9]
//                      [9, 9, 9]]
// ```
//
// Arguments:
//	dims: 1-D. Represents the shape of the output tensor.
//	value: 0-D (scalar). Value to fill the returned tensor.
//
// @compatibility(numpy)
// Equivalent to np.full
// @end_compatibility
func Fill(scope *Scope, dims tf.Output, value tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Fill",
		Input: []tf.Input{
			dims, value,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the inverse 2-dimensional discrete Fourier Transform over the inner-most
//
// 2 dimensions of `input`.
//
// Arguments:
//	input: A complex64 tensor.
//
// Returns A complex64 tensor of the same shape as `input`. The inner-most 2
//   dimensions of `input` are replaced with their inverse 2D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.ifft2
// @end_compatibility
func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IFFT2D",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad.
type ResourceApplyProximalAdagradAttr func(optionalAttr)

// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
//
// value: If True, updating of the var and accum tensors will be protected by
// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
//
// accum += grad * grad
// prox_v = var - lr * grad * (1 / sqrt(accum))
// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	lr: Scaling factor. Must be a scalar.
//	l1: L1 regularization. Must be a scalar.
//	l2: L2 regularization. Must be a scalar.
//	grad: The gradient.
//
// Returns the created operation.
func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyProximalAdagrad",
		Input: []tf.Input{
			var_, accum, lr, l1, l2, grad,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// TensorArrayV3Attr is an optional argument to TensorArrayV3.
type TensorArrayV3Attr func(optionalAttr)

// TensorArrayV3ElementShape sets the optional element_shape attribute to value.
//
// value: The expected shape of an element, if known. Used to
// validate the shapes of TensorArray elements. If this shape is not
// fully specified, gathering zero-size TensorArrays is an error.
// If not specified, defaults to <unknown_rank:true >
func TensorArrayV3ElementShape(value tf.Shape) TensorArrayV3Attr {
	return func(m optionalAttr) {
		m["element_shape"] = value
	}
}

// TensorArrayV3DynamicSize sets the optional dynamic_size attribute to value.
//
// value: A boolean that determines whether writes to the TensorArray
// are allowed to grow the size.  By default, this is not allowed.
// If not specified, defaults to false
func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr {
	return func(m optionalAttr) {
		m["dynamic_size"] = value
	}
}

// TensorArrayV3ClearAfterRead sets the optional clear_after_read attribute to value.
//
// value: If true (default), Tensors in the TensorArray are cleared
// after being read.  This disables multiple read semantics but allows early
// release of memory.
// If not specified, defaults to true
func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr {
	return func(m optionalAttr) {
		m["clear_after_read"] = value
	}
}

// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value.
//
// value: Overrides the name used for the temporary tensor_array
// resource. Default value is the name of the 'TensorArray' op (which
// is guaranteed unique).
// If not specified, defaults to ""
func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr {
	return func(m optionalAttr) {
		m["tensor_array_name"] = value
	}
}

// An array of Tensors of given size, with data written via Write and read
//
// via Read or Pack.
//
// Arguments:
//	size: The size of the array.
//	dtype: The type of the elements on the tensor_array.
//
// Returns The handle to the TensorArray.A scalar used to control gradient flow.
func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayV3",
		Input: []tf.Input{
			size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// ResourceApplyGradientDescentAttr is an optional argument to ResourceApplyGradientDescent.
type ResourceApplyGradientDescentAttr func(optionalAttr)

// ResourceApplyGradientDescentUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, the subtraction will be protected by a lock;
// otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceApplyGradientDescentUseLocking(value bool) ResourceApplyGradientDescentAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' by subtracting 'alpha' * 'delta' from it.
//
// Arguments:
//	var_: Should be from a Variable().
//	alpha: Scaling factor. Must be a scalar.
//	delta: The change.
//
// Returns the created operation.
func ResourceApplyGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, delta tf.Output, optional ...ResourceApplyGradientDescentAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyGradientDescent",
		Input: []tf.Input{
			var_, alpha, delta,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// MultinomialAttr is an optional argument to Multinomial.
type MultinomialAttr func(optionalAttr)

// MultinomialSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 is set to be non-zero, the internal random number
// generator is seeded by the given seed.  Otherwise, a random seed is used.
// If not specified, defaults to 0
func MultinomialSeed(value int64) MultinomialAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// MultinomialSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func MultinomialSeed2(value int64) MultinomialAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Draws samples from a multinomial distribution.
//
// Arguments:
//	logits: 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice `[i, :]`
// represents the unnormalized log probabilities for all classes.
//	num_samples: 0-D.  Number of independent samples to draw for each row slice.
//
// Returns 2-D Tensor with shape `[batch_size, num_samples]`.  Each slice `[i, :]`
// contains the drawn class labels with range `[0, num_classes)`.
func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Multinomial",
		Input: []tf.Input{
			logits, num_samples,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA.
type ResourceSparseApplyAdagradDAAttr func(optionalAttr)

// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value.
//
// value: If True, updating of the var and accum tensors will be protected by
// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
//
// Arguments:
//	var_: Should be from a Variable().
//	gradient_accumulator: Should be from a Variable().
//	gradient_squared_accumulator: Should be from a Variable().
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var and accum.
//	lr: Learning rate. Must be a scalar.
//	l1: L1 regularization. Must be a scalar.
//	l2: L2 regularization. Must be a scalar.
//	global_step: Training step number. Must be a scalar.
//
// Returns the created operation.
func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyAdagradDA",
		Input: []tf.Input{
			var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Computes gradients of average pooling function.
//
// Arguments:
//	orig_input_shape: The original input dimensions.
//	grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
//
// Returns The backprop for input.
func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "AvgPool3DGrad",
		Input: []tf.Input{
			orig_input_shape, grad,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QuantizedRelu6Attr is an optional argument to QuantizedRelu6.
type QuantizedRelu6Attr func(optionalAttr)

// QuantizedRelu6OutType sets the optional out_type attribute to value.
// If not specified, defaults to DT_QUINT8
func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr {
	return func(m optionalAttr) {
		m["out_type"] = value
	}
}

// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
//
// Arguments:
//
//	min_features: The float value that the lowest quantized value represents.
//	max_features: The float value that the highest quantized value represents.
//
// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizedRelu6",
		Input: []tf.Input{
			features, min_features, max_features,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2.
type FixedLengthRecordReaderV2Attr func(optionalAttr)

// FixedLengthRecordReaderV2HeaderBytes sets the optional header_bytes attribute to value.
// If not specified, defaults to 0
func FixedLengthRecordReaderV2HeaderBytes(value int64) FixedLengthRecordReaderV2Attr {
	return func(m optionalAttr) {
		m["header_bytes"] = value
	}
}

// FixedLengthRecordReaderV2FooterBytes sets the optional footer_bytes attribute to value.
// If not specified, defaults to 0
func FixedLengthRecordReaderV2FooterBytes(value int64) FixedLengthRecordReaderV2Attr {
	return func(m optionalAttr) {
		m["footer_bytes"] = value
	}
}

// FixedLengthRecordReaderV2Container sets the optional container attribute to value.
//
// value: If non-empty, this reader is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this reader is named in the given bucket
// with this shared_name. Otherwise, the node name is used instead.
// If not specified, defaults to ""
func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// A Reader that outputs fixed-length records from a file.
//
// Returns The handle to reference the Reader.
func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...FixedLengthRecordReaderV2Attr) (reader_handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"record_bytes": record_bytes}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FixedLengthRecordReaderV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp.
type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr)

// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var, mg, ms, and mom tensors is
// protected by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the centered RMSProp algorithm.
//
// The centered RMSProp algorithm uses an estimate of the centered second moment
// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
// uses the (uncentered) second moment. This often helps with training, but is
// slightly more expensive in terms of computation and memory.
//
// Note that in dense implementation of this algorithm, mg, ms, and mom will
// update even if the grad is zero, but in this sparse implementation, mg, ms,
// and mom will not update in iterations during which the grad is zero.
//
// mean_square = decay * mean_square + (1-decay) * gradient ** 2
// mean_grad = decay * mean_grad + (1-decay) * gradient
// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
//
// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
// var <- var - mom
//
// Arguments:
//	var_: Should be from a Variable().
//	mg: Should be from a Variable().
//	ms: Should be from a Variable().
//	mom: Should be from a Variable().
//	lr: Scaling factor. Must be a scalar.
//	rho: Decay rate. Must be a scalar.
//
//	epsilon: Ridge term. Must be a scalar.
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var, ms and mom.
//
// Returns the created operation.
func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyCenteredRMSProp",
		Input: []tf.Input{
			var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Computes the mean along segments of a tensor.
//
// Read [the section on
// Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
// of segments.
//
// Computes a tensor such that
// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
// over `j` such that `segment_ids[j] == i` and `N` is the total number of
// values summed.
//
// If the mean is empty for a given segment ID `i`, `output[i] = 0`.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/SegmentMean.png" alt>
// </div>
//
// Arguments:
//
//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
// first dimension.  Values should be sorted and can be repeated.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SegmentMean",
		Input: []tf.Input{
			data, segment_ids,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// CumprodAttr is an optional argument to Cumprod.
type CumprodAttr func(optionalAttr)

// CumprodExclusive sets the optional exclusive attribute to value.
// If not specified, defaults to false
func CumprodExclusive(value bool) CumprodAttr {
	return func(m optionalAttr) {
		m["exclusive"] = value
	}
}

// CumprodReverse sets the optional reverse attribute to value.
// If not specified, defaults to false
func CumprodReverse(value bool) CumprodAttr {
	return func(m optionalAttr) {
		m["reverse"] = value
	}
}

// Compute the cumulative product of the tensor `x` along `axis`.
//
// By default, this op performs an inclusive cumprod, which means that the first
// element of the input is identical to the first element of the output:
// ```prettyprint
// tf.cumprod([a, b, c]) ==> [a, a * b, a * b * c]
// ```
//
// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
// performed instead:
// ```prettyprint
// tf.cumprod([a, b, c], exclusive=True) ==> [1, a, a * b]
// ```
//
// By setting the `reverse` kwarg to `True`, the cumprod is performed in the
// opposite direction:
// ```prettyprint
// tf.cumprod([a, b, c], reverse=True) ==> [a * b * c, b * c, c]
// ```
// This is more efficient than using separate `tf.reverse` ops.
//
// The `reverse` and `exclusive` kwargs can also be combined:
// ```prettyprint
// tf.cumprod([a, b, c], exclusive=True, reverse=True) ==> [b * c, c, 1]
// ```
func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Cumprod",
		Input: []tf.Input{
			x, axis,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Adds two `SparseTensor` objects to produce another `SparseTensor`.
//
// The input `SparseTensor` objects' indices are assumed ordered in standard
// lexicographic order.  If this is not the case, before this step run
// `SparseReorder` to restore index ordering.
//
// By default, if two values sum to zero at some index, the output `SparseTensor`
// would still include that particular location in its index, storing a zero in the
// corresponding value slot.  To override this, callers can specify `thresh`,
// indicating that if the sum has a magnitude strictly smaller than `thresh`, its
// corresponding value and index would then not be included.  In particular,
// `thresh == 0` (default) means everything is kept and actual thresholding happens
// only for a positive value.
//
// In the following shapes, `nnz` is the count after taking `thresh` into account.
//
// Arguments:
//	a_indices: 2-D.  The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.
//	a_values: 1-D.  The `values` of the first `SparseTensor`, size `[nnz]` Vector.
//	a_shape: 1-D.  The `shape` of the first `SparseTensor`, size `[ndims]` Vector.
//	b_indices: 2-D.  The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.
//	b_values: 1-D.  The `values` of the second `SparseTensor`, size `[nnz]` Vector.
//	b_shape: 1-D.  The `shape` of the second `SparseTensor`, size `[ndims]` Vector.
//	thresh: 0-D.  The magnitude threshold that determines if an output value/index
// pair takes space.
func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseAdd",
		Input: []tf.Input{
			a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor.
//
// This operation folds the padded areas of `input` by `MirrorPad` according to the
// `paddings` you specify. `paddings` must be the same as `paddings` argument
// given to the corresponding `MirrorPad` op.
//
// The folded size of each dimension D of the output is:
//
// `input.dim_size(D) - paddings(D, 0) - paddings(D, 1)`
//
// For example:
//
// ```prettyprint
// # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
// # 'paddings' is [[0, 1]], [0, 1]].
// # 'mode' is SYMMETRIC.
// # rank of 't' is 2.
// pad(t, paddings) ==> [[ 1,  5]
//                       [11, 28]]
// ```
//
// Arguments:
//	input: The input tensor to be folded.
//	paddings: A two-column matrix specifying the padding sizes. The number of
// rows must be the same as the rank of `input`.
//	mode: The mode used in the `MirrorPad` op.
//
// Returns The folded tensor.
func MirrorPadGrad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"mode": mode}
	opspec := tf.OpSpec{
		Type: "MirrorPadGrad",
		Input: []tf.Input{
			input, paddings,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the inverse permutation of a tensor.
//
// This operation computes the inverse of an index permutation. It takes a 1-D
// integer tensor `x`, which represents the indices of a zero-based array, and
// swaps each value with its index position. In other words, for an output tensor
// `y` and an input tensor `x`, this operation computes the following:
//
// `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
//
// The values must include 0. There can be no duplicate values or negative values.
//
// For example:
//
// ```prettyprint
// # tensor `x` is [3, 4, 0, 2, 1]
// invert_permutation(x) ==> [2, 4, 3, 0, 1]
// ```
//
// Arguments:
//	x: 1-D.
//
// Returns 1-D.
func InvertPermutation(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "InvertPermutation",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Reverses specific dimensions of a tensor.
//
// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
// of `tensor`, this operation reverses each dimension i of `tensor` where
// `dims[i]` is `True`.
//
// `tensor` can have up to 8 dimensions. The number of dimensions
// of `tensor` must equal the number of elements in `dims`. In other words:
//
// `rank(tensor) = size(dims)`
//
// For example:
//
// ```prettyprint
// # tensor 't' is [[[[ 0,  1,  2,  3],
// #                  [ 4,  5,  6,  7],
// #                  [ 8,  9, 10, 11]],
// #                 [[12, 13, 14, 15],
// #                  [16, 17, 18, 19],
// #                  [20, 21, 22, 23]]]]
// # tensor 't' shape is [1, 2, 3, 4]
//
// # 'dims' is [False, False, False, True]
// reverse(t, dims) ==> [[[[ 3,  2,  1,  0],
//                         [ 7,  6,  5,  4],
//                         [ 11, 10, 9, 8]],
//                        [[15, 14, 13, 12],
//                         [19, 18, 17, 16],
//                         [23, 22, 21, 20]]]]
//
// # 'dims' is [False, True, False, False]
// reverse(t, dims) ==> [[[[12, 13, 14, 15],
//                         [16, 17, 18, 19],
//                         [20, 21, 22, 23]
//                        [[ 0,  1,  2,  3],
//                         [ 4,  5,  6,  7],
//                         [ 8,  9, 10, 11]]]]
//
// # 'dims' is [False, False, True, False]
// reverse(t, dims) ==> [[[[8, 9, 10, 11],
//                         [4, 5, 6, 7],
//                         [0, 1, 2, 3]]
//                        [[20, 21, 22, 23],
//                         [16, 17, 18, 19],
//                         [12, 13, 14, 15]]]]
// ```
//
// Arguments:
//	tensor: Up to 8-D.
//	dims: 1-D. The dimensions to reverse.
//
// Returns The same shape as `tensor`.
func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Reverse",
		Input: []tf.Input{
			tensor, dims,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Conv2DAttr is an optional argument to Conv2D.
type Conv2DAttr func(optionalAttr)

// Conv2DUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value.
// If not specified, defaults to true
func Conv2DUseCudnnOnGpu(value bool) Conv2DAttr {
	return func(m optionalAttr) {
		m["use_cudnn_on_gpu"] = value
	}
}

// Conv2DDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, height, width, channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, channels, height, width].
// If not specified, defaults to "NHWC"
func Conv2DDataFormat(value string) Conv2DAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Computes a 2-D convolution given 4-D `input` and `filter` tensors.
//
// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
// and a filter / kernel tensor of shape
// `[filter_height, filter_width, in_channels, out_channels]`, this op
// performs the following:
//
// 1. Flattens the filter to a 2-D matrix with shape
//    `[filter_height * filter_width * in_channels, output_channels]`.
// 2. Extracts image patches from the input tensor to form a *virtual*
//    tensor of shape `[batch, out_height, out_width,
//    filter_height * filter_width * in_channels]`.
// 3. For each patch, right-multiplies the filter matrix and the image patch
//    vector.
//
// In detail, with the default NHWC format,
//
//     output[b, i, j, k] =
//         sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
//                         filter[di, dj, q, k]
//
// Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
//
// Arguments:
//	input: A 4-D tensor. The dimension order is interpreted according to the value
// of `data_format`, see below for details.
//	filter: A 4-D tensor of shape
// `[filter_height, filter_width, in_channels, out_channels]`
//	strides: 1-D tensor of length 4.  The stride of the sliding window for each
// dimension of `input`. The dimension order is determined by the value of
//   `data_format`, see below for details.
//	padding: The type of padding algorithm to use.
//
// Returns A 4-D tensor. The dimension order is determined by the value of
// `data_format`, see below for details.
func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv2DAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Conv2D",
		Input: []tf.Input{
			input, filter,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// StringJoinAttr is an optional argument to StringJoin.
type StringJoinAttr func(optionalAttr)

// StringJoinSeparator sets the optional separator attribute to value.
//
// value: string, an optional join separator.
// If not specified, defaults to ""
func StringJoinSeparator(value string) StringJoinAttr {
	return func(m optionalAttr) {
		m["separator"] = value
	}
}

// Joins the strings in the given list of string tensors into one tensor;
//
// with the given separator (default is an empty separator).
//
// Arguments:
//	inputs: A list of string tensors.  The tensors must all have the same shape,
// or be scalars.  Scalars may be mixed in; these will be broadcast to the shape
// of non-scalar inputs.
func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "StringJoin",
		Input: []tf.Input{
			tf.OutputList(inputs),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Transforms a vector of brain.Example protos (as strings) into typed tensors.
//
// Arguments:
//	serialized: A vector containing a batch of binary serialized Example protos.
//	names: A vector containing the names of the serialized protos.
// May contain, for example, table key (descriptive) names for the
// corresponding serialized protos.  These are purely useful for debugging
// purposes, and the presence of values here has no effect on the output.
// May also be an empty vector if no names are available.
// If non-empty, this vector must be the same length as "serialized".
//	sparse_keys: A list of Nsparse string Tensors (scalars).
// The keys expected in the Examples' features associated with sparse values.
//	dense_keys: A list of Ndense string Tensors (scalars).
// The keys expected in the Examples' features associated with dense values.
//	dense_defaults: A list of Ndense Tensors (some may be empty).
// dense_defaults[j] provides default values
// when the example's feature_map lacks dense_key[j].  If an empty Tensor is
// provided for dense_defaults[j], then the Feature dense_keys[j] is required.
// The input type is inferred from dense_defaults[j], even when it's empty.
// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
// then the shape of dense_defaults[j] must match that of dense_shapes[j].
// If dense_shapes[j] has an undefined major dimension (variable strides dense
// feature), dense_defaults[j] must contain a single element:
// the padding element.
//	sparse_types: A list of Nsparse types; the data types of data in each Feature
// given in sparse_keys.
// Currently the ParseExample supports DT_FLOAT (FloatList),
// DT_INT64 (Int64List), and DT_STRING (BytesList).
//	dense_shapes: A list of Ndense shapes; the shapes of data in each Feature
// given in dense_keys.
// The number of elements in the Feature corresponding to dense_key[j]
// must always equal dense_shapes[j].NumEntries().
// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output
// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN):
// The dense outputs are just the inputs row-stacked by batch.
// This works for dense_shapes[j] = (-1, D1, ..., DN).  In this case
// the shape of the output Tensor dense_values[j] will be
// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks
// of elements of length D1 * .... * DN, across all minibatch entries
// in the input.  Any minibatch entry with less than M blocks of elements of
// length D1 * ... * DN will be padded with the corresponding default_value
// scalar element along the second dimension.
func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys []tf.Output, dense_keys []tf.Output, dense_defaults []tf.Output, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"sparse_types": sparse_types, "dense_shapes": dense_shapes}
	opspec := tf.OpSpec{
		Type: "ParseExample",
		Input: []tf.Input{
			serialized, names, tf.OutputList(sparse_keys), tf.OutputList(dense_keys), tf.OutputList(dense_defaults),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil {
		scope.UpdateErr("ParseExample", err)
		return
	}
	if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
		scope.UpdateErr("ParseExample", err)
		return
	}
	if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
		scope.UpdateErr("ParseExample", err)
		return
	}
	if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
		scope.UpdateErr("ParseExample", err)
		return
	}
	return sparse_indices, sparse_values, sparse_shapes, dense_values
}

// Compute the pairwise cross product.
//
// `a` and `b` must be the same shape; they can either be simple 3-element vectors,
// or any shape where the innermost dimension is 3. In the latter case, each pair
// of corresponding 3-element vectors is cross-multiplied independently.
//
// Arguments:
//	a: A tensor containing 3-element vectors.
//	b: Another tensor, of same type and shape as `a`.
//
// Returns Pairwise cross product of the vectors in `a` and `b`.
func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Cross",
		Input: []tf.Input{
			a, b,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the inverse 2-dimensional discrete Fourier Transform of a real-valued
//
// signal over the inner-most 2 dimensions of `input`.
//
// The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`:
// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
// from the size of the inner-most 2 dimensions of `input`. If the FFT length used
// to compute `input` is odd, it should be provided since it cannot be inferred
// properly.
//
// Arguments:
//	input: A complex64 tensor.
//	fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
//
// Returns A float32 tensor of the same rank as `input`. The inner-most 2
//   dimensions of `input` are replaced with the `fft_length` samples of their
//   inverse 2D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.irfft2
// @end_compatibility
func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IRFFT2D",
		Input: []tf.Input{
			input, fft_length,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns element-wise remainder of division. This emulates C semantics where
//
// true, this follows C semantics in that the result here is consistent
// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`.
//
// *NOTE*: `Mod` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func TruncateMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TruncateMod",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad.
type ResourceApplyAdagradAttr func(optionalAttr)

// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var and accum tensors will be protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the adagrad scheme.
//
// accum += grad * grad
// var -= lr * grad * (1 / sqrt(accum))
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	lr: Scaling factor. Must be a scalar.
//	grad: The gradient.
//
// Returns the created operation.
func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyAdagrad",
		Input: []tf.Input{
			var_, accum, lr, grad,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// SparseReduceSumAttr is an optional argument to SparseReduceSum.
type SparseReduceSumAttr func(optionalAttr)

// SparseReduceSumKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the sum of elements across dimensions of a SparseTensor.
//
// This Op takes a SparseTensor and is the sparse counterpart to
// `tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`
// instead of a sparse one.
//
// Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
// with length 1.
//
// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
// with a single element is returned.  Additionally, the axes can be negative,
// which are interpreted according to the indexing rules in Python.
//
// Arguments:
//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
// SparseTensor, possibly not in canonical ordering.
//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
//	input_shape: 1-D.  Shape of the input SparseTensor.
//	reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
//
// Returns `R-K`-D.  The reduced Tensor.
func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SparseReduceSum",
		Input: []tf.Input{
			input_indices, input_values, input_shape, reduction_axes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes gradients of max pooling function.
//
// Arguments:
//	orig_input: The original input tensor.
//	orig_output: The original output tensor.
//	grad: Output backprop of shape `[batch, depth, rows, cols, channels]`.
//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "MaxPool3DGrad",
		Input: []tf.Input{
			orig_input, orig_output, grad,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ThreadUnsafeUnigramCandidateSamplerAttr is an optional argument to ThreadUnsafeUnigramCandidateSampler.
type ThreadUnsafeUnigramCandidateSamplerAttr func(optionalAttr)

// ThreadUnsafeUnigramCandidateSamplerSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func ThreadUnsafeUnigramCandidateSamplerSeed(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// ThreadUnsafeUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func ThreadUnsafeUnigramCandidateSamplerSeed2(value int64) ThreadUnsafeUnigramCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Generates labels for candidate sampling with a learned unigram distribution.
//
// See explanations of candidate sampling and the data formats at
// go/candidate-sampling.
//
// For each batch, this op picks a single set of sampled candidate labels.
//
// The advantages of sampling candidates per-batch are simplicity and the
// possibility of efficient dense matrix multiplication. The disadvantage is that
// the sampled candidates must be chosen independently of the context and of the
// true labels.
//
// Arguments:
//	true_classes: A batch_size * num_true matrix, in which each row contains the
// IDs of the num_true target_classes in the corresponding original label.
//	num_true: Number of true labels per context.
//	num_sampled: Number of candidates to randomly sample per batch.
//	unique: If unique is true, we sample with rejection, so that all sampled
// candidates in a batch are unique. This requires some approximation to
// estimate the post-rejection sampling probabilities.
//	range_max: The sampler will sample integers from the interval [0, range_max).
//
// Returns A vector of length num_sampled, in which each element is
// the ID of a sampled candidate.A batch_size * num_true matrix, representing
// the number of times each candidate is expected to occur in a batch
// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
// candidate representing the number of times the candidate is expected
// to occur in a batch of sampled candidates.  If unique=true, then this is a
// probability.
func ThreadUnsafeUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...ThreadUnsafeUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ThreadUnsafeUnigramCandidateSampler",
		Input: []tf.Input{
			true_classes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad.
type ResourceSparseApplyProximalAdagradAttr func(optionalAttr)

// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
//
// value: If True, updating of the var and accum tensors will be protected by
// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
//
// That is for rows we have grad for, we update var and accum as follows:
// accum += grad * grad
// prox_v = var
// prox_v -= lr * grad * (1 / sqrt(accum))
// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	lr: Learning rate. Must be a scalar.
//	l1: L1 regularization. Must be a scalar.
//	l2: L2 regularization. Must be a scalar.
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var and accum.
//
// Returns the created operation.
func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyProximalAdagrad",
		Input: []tf.Input{
			var_, accum, lr, l1, l2, grad, indices,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Decode web-safe base64-encoded strings.
//
// Input may or may not have padding at the end. See EncodeBase64 for padding.
// Web-safe means that input must use - and _ instead of + and /.
//
// Arguments:
//	input: Base64 strings to decode.
//
// Returns Decoded strings.
func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "DecodeBase64",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes hyperbolic tangent of `x` element-wise.
func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Tanh",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TextLineReaderV2Attr is an optional argument to TextLineReaderV2.
type TextLineReaderV2Attr func(optionalAttr)

// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value.
//
// value: Number of lines to skip from the beginning of every file.
// If not specified, defaults to 0
func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr {
	return func(m optionalAttr) {
		m["skip_header_lines"] = value
	}
}

// TextLineReaderV2Container sets the optional container attribute to value.
//
// value: If non-empty, this reader is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func TextLineReaderV2Container(value string) TextLineReaderV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// TextLineReaderV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this reader is named in the given bucket
// with this shared_name. Otherwise, the node name is used instead.
// If not specified, defaults to ""
func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// A Reader that outputs the lines of a file delimited by '\n'.
//
// Returns The handle to reference the Reader.
func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TextLineReaderV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox.
type SampleDistortedBoundingBoxAttr func(optionalAttr)

// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to non-zero, the random number
// generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
// seed.
// If not specified, defaults to 0
func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value.
//
// value: The cropped area of the image must contain at least this
// fraction of any bounding box supplied. The value of this parameter should be
// non-negative. In the case of 0, the cropped area does not need to overlap
// any of the bounding boxes supplied.
// If not specified, defaults to 0.1
func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr {
	return func(m optionalAttr) {
		m["min_object_covered"] = value
	}
}

// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value.
//
// value: The cropped area of the image must have an aspect ratio =
// width / height within this range.
// If not specified, defaults to <f:0.75 f:1.33 >
func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
	return func(m optionalAttr) {
		m["aspect_ratio_range"] = value
	}
}

// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value.
//
// value: The cropped area of the image must contain a fraction of the
// supplied image within in this range.
// If not specified, defaults to <f:0.05 f:1 >
func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
	return func(m optionalAttr) {
		m["area_range"] = value
	}
}

// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value.
//
// value: Number of attempts at generating a cropped region of the image
// of the specified constraints. After `max_attempts` failures, return the entire
// image.
// If not specified, defaults to 100
func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr {
	return func(m optionalAttr) {
		m["max_attempts"] = value
	}
}

// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value.
//
// value: Controls behavior if no bounding boxes supplied.
// If true, assume an implicit bounding box covering the whole input. If false,
// raise an error.
// If not specified, defaults to false
func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr {
	return func(m optionalAttr) {
		m["use_image_if_no_bounding_boxes"] = value
	}
}

// Generate a single randomly distorted bounding box for an image.
//
// Bounding box annotations are often supplied in addition to ground-truth labels
// in image recognition or object localization tasks. A common technique for
// training such a system is to randomly distort an image while preserving
// its content, i.e. *data augmentation*. This Op outputs a randomly distorted
// localization of an object, i.e. bounding box, given an `image_size`,
// `bounding_boxes` and a series of constraints.
//
// The output of this Op is a single bounding box that may be used to crop the
// original image. The output is returned as 3 tensors: `begin`, `size` and
// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
// what the bounding box looks like.
//
// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
// height of the underlying image.
//
// For example,
//
// ```python
//     # Generate a single distorted bounding box.
//     begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
//         tf.shape(image),
//         bounding_boxes=bounding_boxes)
//
//     # Draw the bounding box in an image summary.
//     image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
//                                                   bbox_for_draw)
//     tf.image_summary('images_with_box', image_with_box)
//
//     # Employ the bounding box to distort the image.
//     distorted_image = tf.slice(image, begin, size)
// ```
//
// Note that if no bounding box information is available, setting
// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
// false and no bounding boxes are supplied, an error is raised.
//
// Arguments:
//	image_size: 1-D, containing `[height, width, channels]`.
//	bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
// associated with the image.
//
// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to
// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box.
// Provide as input to `tf.image.draw_bounding_boxes`.
func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SampleDistortedBoundingBox",
		Input: []tf.Input{
			image_size, bounding_boxes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Returns the truth value of (x > y) element-wise.
//
// *NOTE*: `Greater` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Greater",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp.
type ResourceSparseApplyRMSPropAttr func(optionalAttr)

// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var, ms, and mom tensors is protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the RMSProp algorithm.
//
// Note that in dense implementation of this algorithm, ms and mom will
// update even if the grad is zero, but in this sparse implementation, ms
// and mom will not update in iterations during which the grad is zero.
//
// mean_square = decay * mean_square + (1-decay) * gradient ** 2
// Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
//
// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
// var <- var - mom
//
// Arguments:
//	var_: Should be from a Variable().
//	ms: Should be from a Variable().
//	mom: Should be from a Variable().
//	lr: Scaling factor. Must be a scalar.
//	rho: Decay rate. Must be a scalar.
//
//	epsilon: Ridge term. Must be a scalar.
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var, ms and mom.
//
// Returns the created operation.
func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyRMSProp",
		Input: []tf.Input{
			var_, ms, mom, lr, rho, momentum, epsilon, grad, indices,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// QuantizeV2Attr is an optional argument to QuantizeV2.
type QuantizeV2Attr func(optionalAttr)

// QuantizeV2Mode sets the optional mode attribute to value.
// If not specified, defaults to "MIN_COMBINED"
func QuantizeV2Mode(value string) QuantizeV2Attr {
	return func(m optionalAttr) {
		m["mode"] = value
	}
}

// Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
//
// [min_range, max_range] are scalar floats that specify the range for
// the 'input' data. The 'mode' attribute controls exactly which calculations are
// used to convert the float values to their quantized equivalents.
//
// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
//
// ```
// out[i] = (in[i] - min_range) * range(T) / (max_range - min_range)
// if T == qint8, out[i] -= (range(T) + 1) / 2.0
// ```
// here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
//
// *MIN_COMBINED Mode Example*
//
// Assume the input is type float and has a possible range of [0.0, 6.0] and the
// output type is quint8 ([0, 255]). The min_range and max_range values should be
// specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each
// value of the input by 255/6 and cast to quint8.
//
// If the output type was qint8 ([-128, 127]), the operation will additionally
// subtract each value by 128 prior to casting, so that the range of values aligns
// with the range of qint8.
//
// If the mode is 'MIN_FIRST', then this approach is used:
//
// ```
// number_of_steps = 1 << (# of bits in T)
// range_adjust = number_of_steps / (number_of_steps - 1)
// range = (range_max - range_min) * range_adjust
// range_scale = number_of_steps / range
// quantized = round(input * range_scale) - round(range_min * range_scale) +
//   numeric_limits<T>::min()
// quantized = max(quantized, numeric_limits<T>::min())
// quantized = min(quantized, numeric_limits<T>::max())
// ```
//
// The biggest difference between this and MIN_COMBINED is that the minimum range
// is rounded first, before it's subtracted from the rounded value. With
// MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
// and dequantizing will introduce a larger and larger error.
//
// One thing to watch out for is that the operator may choose to adjust the
// requested minimum and maximum values slightly during the quantization process,
// so you should always use the output ports as the range for further calculations.
// For example, if the requested minimum and maximum values are close to equal,
// they will be separated by a small epsilon value to prevent ill-formed quantized
// buffers from being created. Otherwise, you can end up with buffers where all the
// quantized values map to the same float value, which causes problems for
// operations that have to perform further calculations on them.
//
// Arguments:
//
//	min_range: The minimum scalar value possibly produced for the input.
//	max_range: The maximum scalar value possibly produced for the input.
//
//
// Returns The quantized data produced from the float input.The actual minimum scalar value used for the output.The actual maximum scalar value used for the output.
func QuantizeV2(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, T tf.DataType, optional ...QuantizeV2Attr) (output tf.Output, output_min tf.Output, output_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"T": T}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizeV2",
		Input: []tf.Input{
			input, min_range, max_range,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// DepthwiseConv2dNativeBackpropFilterAttr is an optional argument to DepthwiseConv2dNativeBackpropFilter.
type DepthwiseConv2dNativeBackpropFilterAttr func(optionalAttr)

// DepthwiseConv2dNativeBackpropFilterDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, height, width, channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, channels, height, width].
// If not specified, defaults to "NHWC"
func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2dNativeBackpropFilterAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Computes the gradients of depthwise convolution with respect to the filter.
//
// Arguments:
//	input: 4-D with shape based on `data_format`.  For example, if
// `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height,
// in_width, in_channels]` tensor.
//	filter_sizes: An integer vector representing the tensor shape of `filter`,
// where `filter` is a 4-D
// `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor.
//	out_backprop: 4-D with shape  based on `data_format`.
// For example, if `data_format` is 'NHWC' then
// out_backprop shape is `[batch, out_height, out_width, out_channels]`.
// Gradients w.r.t. the output of the convolution.
//	strides: The stride of the sliding window for each dimension of the input
// of the convolution.
//	padding: The type of padding algorithm to use.
//
// Returns 4-D with shape
// `[filter_height, filter_width, in_channels, out_channels]`.  Gradient w.r.t.
// the `filter` input of the convolution.
func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropFilterAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DepthwiseConv2dNativeBackpropFilter",
		Input: []tf.Input{
			input, filter_sizes, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns which elements of x are Inf.
//
// @compatibility(numpy)
// Equivalent to np.isinf
// @end_compatibility
func IsInf(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IsInf",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl.
type ResourceSparseApplyFtrlAttr func(optionalAttr)

// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var and accum tensors will be protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update relevant entries in '*var' according to the Ftrl-proximal scheme.
//
// That is for rows we have grad for, we update var, accum and linear as follows:
// accum_new = accum + grad * grad
// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
// accum = accum_new
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	linear: Should be from a Variable().
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var and accum.
//	lr: Scaling factor. Must be a scalar.
//	l1: L1 regularization. Must be a scalar.
//	l2: L2 regularization. Must be a scalar.
//	lr_power: Scaling factor. Must be a scalar.
//
// Returns the created operation.
func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyFtrl",
		Input: []tf.Input{
			var_, accum, linear, grad, indices, lr, l1, l2, lr_power,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Component-wise multiplies a SparseTensor by a dense Tensor.
//
// The output locations corresponding to the implicitly zero elements in the sparse
// tensor will be zero (i.e., will not take up storage space), regardless of the
// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).
//
// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
// the other direction.
//
// Arguments:
//	sp_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
// SparseTensor, possibly not in canonical ordering.
//	sp_values: 1-D.  `N` non-empty values corresponding to `sp_indices`.
//	sp_shape: 1-D.  Shape of the input SparseTensor.
//	dense: `R`-D.  The dense Tensor operand.
//
// Returns 1-D.  The `N` values that are operated on.
func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseDenseCwiseMul",
		Input: []tf.Input{
			sp_indices, sp_values, sp_shape, dense,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Shuffle dimensions of x according to a permutation.
//
// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
//   `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Transpose",
		Input: []tf.Input{
			x, perm,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Reads and outputs the entire contents of the input filename.
func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReadFile",
		Input: []tf.Input{
			filename,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Output a fact about factorials.
func Fact(scope *Scope) (fact tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Fact",
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// PrintAttr is an optional argument to Print.
type PrintAttr func(optionalAttr)

// PrintMessage sets the optional message attribute to value.
//
// value: A string, prefix of the error message.
// If not specified, defaults to ""
func PrintMessage(value string) PrintAttr {
	return func(m optionalAttr) {
		m["message"] = value
	}
}

// PrintFirstN sets the optional first_n attribute to value.
//
// value: Only log `first_n` number of times. -1 disables logging.
// If not specified, defaults to -1
func PrintFirstN(value int64) PrintAttr {
	return func(m optionalAttr) {
		m["first_n"] = value
	}
}

// PrintSummarize sets the optional summarize attribute to value.
//
// value: Only print this many entries of each tensor.
// If not specified, defaults to 3
func PrintSummarize(value int64) PrintAttr {
	return func(m optionalAttr) {
		m["summarize"] = value
	}
}

// Prints a list of tensors.
//
// Passes `input` through to `output` and prints `data` when evaluating.
//
// Arguments:
//	input: The tensor passed to `output`
//	data: A list of tensors to print out when op is evaluated.
//
// Returns = The unmodified `input` tensor
func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Print",
		Input: []tf.Input{
			input, tf.OutputList(data),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResizeAreaAttr is an optional argument to ResizeArea.
type ResizeAreaAttr func(optionalAttr)

// ResizeAreaAlignCorners sets the optional align_corners attribute to value.
//
// value: If true, rescale input by (new_height - 1) / (height - 1), which
// exactly aligns the 4 corners of images and resized images. If false, rescale
// by new_height / height. Treat similarly the width dimension.
// If not specified, defaults to false
func ResizeAreaAlignCorners(value bool) ResizeAreaAttr {
	return func(m optionalAttr) {
		m["align_corners"] = value
	}
}

// Resize `images` to `size` using area interpolation.
//
// Input images can be of different types but output images are always float.
//
// Arguments:
//	images: 4-D with shape `[batch, height, width, channels]`.
//	size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
// new size for the images.
//
// Returns 4-D with shape
// `[batch, new_height, new_width, channels]`.
func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResizeArea",
		Input: []tf.Input{
			images, size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// RealAttr is an optional argument to Real.
type RealAttr func(optionalAttr)

// RealTout sets the optional Tout attribute to value.
// If not specified, defaults to DT_FLOAT
func RealTout(value tf.DataType) RealAttr {
	return func(m optionalAttr) {
		m["Tout"] = value
	}
}

// Returns the real part of a complex number.
//
// Given a tensor `input` of complex numbers, this operation returns a tensor of
// type `float` that is the real part of each element in `input`. All elements in
// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real
//  part returned by this operation and *b* is the imaginary part.
//
// For example:
//
// ```
// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
// tf.real(input) ==> [-2.25, 3.25]
// ```
func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Real",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the next record (key, value pair) produced by a Reader.
//
// Will dequeue from the input queue if necessary (e.g. when the
// Reader needs to start reading from a new file since it has finished
// with the previous file).
//
// Arguments:
//	reader_handle: Handle to a Reader.
//	queue_handle: Handle to a Queue, with string work items.
//
// Returns A scalar.A scalar.
func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output) (key tf.Output, value tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReaderReadV2",
		Input: []tf.Input{
			reader_handle, queue_handle,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// LRNAttr is an optional argument to LRN.
type LRNAttr func(optionalAttr)

// LRNDepthRadius sets the optional depth_radius attribute to value.
//
// value: 0-D.  Half-width of the 1-D normalization window.
// If not specified, defaults to 5
func LRNDepthRadius(value int64) LRNAttr {
	return func(m optionalAttr) {
		m["depth_radius"] = value
	}
}

// LRNBias sets the optional bias attribute to value.
//
// value: An offset (usually positive to avoid dividing by 0).
// If not specified, defaults to 1
func LRNBias(value float32) LRNAttr {
	return func(m optionalAttr) {
		m["bias"] = value
	}
}

// LRNAlpha sets the optional alpha attribute to value.
//
// value: A scale factor, usually positive.
// If not specified, defaults to 1
func LRNAlpha(value float32) LRNAttr {
	return func(m optionalAttr) {
		m["alpha"] = value
	}
}

// LRNBeta sets the optional beta attribute to value.
//
// value: An exponent.
// If not specified, defaults to 0.5
func LRNBeta(value float32) LRNAttr {
	return func(m optionalAttr) {
		m["beta"] = value
	}
}

// Local Response Normalization.
//
// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
// dimension), and each vector is normalized independently.  Within a given vector,
// each component is divided by the weighted, squared sum of inputs within
// `depth_radius`.  In detail,
//
//     sqr_sum[a, b, c, d] =
//         sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
//     output = input / (bias + alpha * sqr_sum) ** beta
//
// For details, see [Krizhevsky et al., ImageNet classification with deep
// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
//
// Arguments:
//	input: 4-D.
func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "LRN",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad.
type ResourceSparseApplyAdagradAttr func(optionalAttr)

// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var and accum tensors will be protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
//
// That is for rows we have grad for, we update var and accum as follows:
// accum += grad * grad
// var -= lr * grad * (1 / sqrt(accum))
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	lr: Learning rate. Must be a scalar.
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var and accum.
//
// Returns the created operation.
func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyAdagrad",
		Input: []tf.Input{
			var_, accum, lr, grad, indices,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// LRNGradAttr is an optional argument to LRNGrad.
type LRNGradAttr func(optionalAttr)

// LRNGradDepthRadius sets the optional depth_radius attribute to value.
//
// value: A depth radius.
// If not specified, defaults to 5
func LRNGradDepthRadius(value int64) LRNGradAttr {
	return func(m optionalAttr) {
		m["depth_radius"] = value
	}
}

// LRNGradBias sets the optional bias attribute to value.
//
// value: An offset (usually > 0 to avoid dividing by 0).
// If not specified, defaults to 1
func LRNGradBias(value float32) LRNGradAttr {
	return func(m optionalAttr) {
		m["bias"] = value
	}
}

// LRNGradAlpha sets the optional alpha attribute to value.
//
// value: A scale factor, usually positive.
// If not specified, defaults to 1
func LRNGradAlpha(value float32) LRNGradAttr {
	return func(m optionalAttr) {
		m["alpha"] = value
	}
}

// LRNGradBeta sets the optional beta attribute to value.
//
// value: An exponent.
// If not specified, defaults to 0.5
func LRNGradBeta(value float32) LRNGradAttr {
	return func(m optionalAttr) {
		m["beta"] = value
	}
}

// Gradients for Local Response Normalization.
//
// Arguments:
//	input_grads: 4-D with shape `[batch, height, width, channels]`.
//	input_image: 4-D with shape `[batch, height, width, channels]`.
//	output_image: 4-D with shape `[batch, height, width, channels]`.
//
// Returns The gradients for LRN.
func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "LRNGrad",
		Input: []tf.Input{
			input_grads, input_image, output_image,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// StringToNumberAttr is an optional argument to StringToNumber.
type StringToNumberAttr func(optionalAttr)

// StringToNumberOutType sets the optional out_type attribute to value.
//
// value: The numeric type to interpret each string in `string_tensor` as.
// If not specified, defaults to DT_FLOAT
func StringToNumberOutType(value tf.DataType) StringToNumberAttr {
	return func(m optionalAttr) {
		m["out_type"] = value
	}
}

// Converts each string in the input Tensor to the specified numeric type.
//
// (Note that int32 overflow results in an error while float overflow
// results in a rounded value.)
//
// Returns A Tensor of the same shape as the input `string_tensor`.
func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToNumberAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "StringToNumber",
		Input: []tf.Input{
			string_tensor,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the truth value of NOT x element-wise.
func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "LogicalNot",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Pads a tensor with zeros.
//
// This operation pads a `input` with zeros according to the `paddings` you
// specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the
// rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
// how many zeros to add before the contents of `input` in that dimension, and
// `paddings[D, 1]` indicates how many zeros to add after the contents of `input`
// in that dimension.
//
// The padded size of each dimension D of the output is:
//
// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
//
// For example:
//
// ```prettyprint
// # 't' is [[1, 1], [2, 2]]
// # 'paddings' is [[1, 1], [2, 2]]
// # rank of 't' is 2
// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
//                       [0, 0, 1, 1, 0, 0]
//                       [0, 0, 2, 2, 0, 0]
//                       [0, 0, 0, 0, 0, 0]]
// ```
func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Pad",
		Input: []tf.Input{
			input, paddings,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the number of elements in the given queue.
//
// Arguments:
//	handle: The handle to a queue.
//
// Returns The number of elements in the given queue.
func QueueSizeV2(scope *Scope, handle tf.Output) (size tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "QueueSizeV2",
		Input: []tf.Input{
			handle,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Outputs a `Summary` protocol buffer with a histogram.
//
// The generated
// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
// has one summary value containing a histogram for `values`.
//
// This op reports an `InvalidArgument` error if any value is not finite.
//
// Arguments:
//	tag: Scalar.  Tag to use for the `Summary.Value`.
//	values: Any shape. Values to use to build the histogram.
//
// Returns Scalar. Serialized `Summary` protocol buffer.
func HistogramSummary(scope *Scope, tag tf.Output, values tf.Output) (summary tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "HistogramSummary",
		Input: []tf.Input{
			tag, values,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// AsStringAttr is an optional argument to AsString.
type AsStringAttr func(optionalAttr)

// AsStringPrecision sets the optional precision attribute to value.
//
// value: The post-decimal precision to use for floating point numbers.
// Only used if precision > -1.
// If not specified, defaults to -1
func AsStringPrecision(value int64) AsStringAttr {
	return func(m optionalAttr) {
		m["precision"] = value
	}
}

// AsStringScientific sets the optional scientific attribute to value.
//
// value: Use scientific notation for floating point numbers.
// If not specified, defaults to false
func AsStringScientific(value bool) AsStringAttr {
	return func(m optionalAttr) {
		m["scientific"] = value
	}
}

// AsStringShortest sets the optional shortest attribute to value.
//
// value: Use shortest representation (either scientific or standard) for
// floating point numbers.
// If not specified, defaults to false
func AsStringShortest(value bool) AsStringAttr {
	return func(m optionalAttr) {
		m["shortest"] = value
	}
}

// AsStringWidth sets the optional width attribute to value.
//
// value: Pad pre-decimal numbers to this width.
// Applies to both floating point and integer numbers.
// Only used if width > -1.
// If not specified, defaults to -1
func AsStringWidth(value int64) AsStringAttr {
	return func(m optionalAttr) {
		m["width"] = value
	}
}

// AsStringFill sets the optional fill attribute to value.
//
// value: The value to pad if width > -1.  If empty, pads with spaces.
// Another typical value is '0'.  String cannot be longer than 1 character.
// If not specified, defaults to ""
func AsStringFill(value string) AsStringAttr {
	return func(m optionalAttr) {
		m["fill"] = value
	}
}

// Converts each entry in the given tensor to strings.  Supports many numeric
//
// types and boolean.
func AsString(scope *Scope, input tf.Output, optional ...AsStringAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "AsString",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Says whether the targets are in the top `K` predictions.
//
// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
// prediction for the target class is among the top `k` predictions among
// all predictions for example `i`. Note that the behavior of `InTopK` differs
// from the `TopK` op in its handling of ties; if multiple classes have the
// same prediction value and straddle the top-`k` boundary, all of those
// classes are considered to be in the top `k`.
//
// More formally, let
//
//   \\(predictions_i\\) be the predictions for all classes for example `i`,
//   \\(targets_i\\) be the target class for example `i`,
//   \\(out_i\\) be the output for example `i`,
//
// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
//
// Arguments:
//	predictions: A `batch_size` x `classes` tensor.
//	targets: A `batch_size` vector of class ids.
//	k: Number of top elements to look at for computing precision.
//
// Returns Computed Precision at `k` as a `bool Tensor`.
func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (precision tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"k": k}
	opspec := tf.OpSpec{
		Type: "InTopK",
		Input: []tf.Input{
			predictions, targets,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// GatherAttr is an optional argument to Gather.
type GatherAttr func(optionalAttr)

// GatherValidateIndices sets the optional validate_indices attribute to value.
// If not specified, defaults to true
func GatherValidateIndices(value bool) GatherAttr {
	return func(m optionalAttr) {
		m["validate_indices"] = value
	}
}

// Gather slices from `params` according to `indices`.
//
// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
// Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
//
// ```python
//     # Scalar indices
//     output[:, ..., :] = params[indices, :, ... :]
//
//     # Vector indices
//     output[i, :, ..., :] = params[indices[i], :, ... :]
//
//     # Higher rank indices
//     output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
// ```
//
// If `indices` is a permutation and `len(indices) == params.shape[0]` then
// this operation will permute `params` accordingly.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/Gather.png" alt>
// </div>
func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...GatherAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Gather",
		Input: []tf.Input{
			params, indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Adjust the contrast of one or more images.
//
// `images` is a tensor of at least 3 dimensions.  The last 3 dimensions are
// interpreted as `[height, width, channels]`.  The other dimensions only
// represent a collection of images, such as `[batch, height, width, channels].`
//
// Contrast is adjusted independently for each channel of each image.
//
// For each channel, the Op first computes the mean of the image pixels in the
// channel and then adjusts each component of each pixel to
// `(x - mean) * contrast_factor + mean`.
//
// Arguments:
//	images: Images to adjust.  At least 3-D.
//	contrast_factor: A float multiplier for adjusting contrast.
//
// Returns The contrast-adjusted image or images.
func AdjustContrastv2(scope *Scope, images tf.Output, contrast_factor tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "AdjustContrastv2",
		Input: []tf.Input{
			images, contrast_factor,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes softsign gradients for a softsign operation.
//
// Arguments:
//	gradients: The backpropagated gradients to the corresponding softsign operation.
//	features: The features passed as input to the corresponding softsign operation.
//
// Returns The gradients: `gradients / (1 + abs(-features)) ** 2`.
func SoftsignGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SoftsignGrad",
		Input: []tf.Input{
			gradients, features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the polygamma function \\(\psi^{(n)}(x)\\).
//
// The polygamma function is defined as:
//
// ```
// \psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x)
// ```
// where \\(\psi(x)\\) is the digamma function.
func Polygamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Polygamma",
		Input: []tf.Input{
			a, x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
//
// The `input` tensor has shape `[batch, in_height, in_width, depth]` and the
// `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each
// input channel is processed independently of the others with its own structuring
// function. The `output` tensor has shape
// `[batch, out_height, out_width, depth]`. The spatial dimensions of the output
// tensor depend on the `padding` algorithm. We currently only support the default
// "NHWC" `data_format`.
//
// In detail, the grayscale morphological 2-D dilation is the max-sum correlation
// (for consistency with `conv2d`, we use unmirrored filters):
//
//     output[b, y, x, c] =
//        max_{dy, dx} input[b,
//                           strides[1] * y + rates[1] * dy,
//                           strides[2] * x + rates[2] * dx,
//                           c] +
//                     filter[dy, dx, c]
//
// Max-pooling is a special case when the filter has size equal to the pooling
// kernel size and contains all zeros.
//
// Note on duality: The dilation of `input` by the `filter` is equal to the
// negation of the erosion of `-input` by the reflected `filter`.
//
// Arguments:
//	input: 4-D with shape `[batch, in_height, in_width, depth]`.
//	filter: 3-D with shape `[filter_height, filter_width, depth]`.
//	strides: The stride of the sliding window for each dimension of the input
// tensor. Must be: `[1, stride_height, stride_width, 1]`.
//	rates: The input stride for atrous morphological dilation. Must be:
// `[1, rate_height, rate_width, 1]`.
//	padding: The type of padding algorithm to use.
//
// Returns 4-D with shape `[batch, out_height, out_width, depth]`.
func Dilation2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, rates []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding}
	opspec := tf.OpSpec{
		Type: "Dilation2D",
		Input: []tf.Input{
			input, filter,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Decode the first frame of a GIF-encoded image to a uint8 tensor.
//
// GIF with frame or transparency compression are not supported
// convert animated GIF from compressed to uncompressed by:
//
// convert $src.gif -coalesce $dst.gif
//
// Arguments:
//	contents: 0-D.  The GIF-encoded image.
//
// Returns 4-D with shape `[num_frames, height, width, 3]`. RGB order
func DecodeGif(scope *Scope, contents tf.Output) (image tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "DecodeGif",
		Input: []tf.Input{
			contents,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// EncodeBase64Attr is an optional argument to EncodeBase64.
type EncodeBase64Attr func(optionalAttr)

// EncodeBase64Pad sets the optional pad attribute to value.
//
// value: Bool whether padding is applied at the ends.
// If not specified, defaults to false
func EncodeBase64Pad(value bool) EncodeBase64Attr {
	return func(m optionalAttr) {
		m["pad"] = value
	}
}

// Encode strings into web-safe base64 format.
//
// Refer to the following article for more information on base64 format:
// en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the
// end so that the encoded has length multiple of 4. See Padding section of the
// link above.
//
// Web-safe means that the encoder uses - and _ instead of + and /.
//
// Arguments:
//	input: Strings to be encoded.
//
// Returns Input strings encoded in base64.
func EncodeBase64(scope *Scope, input tf.Output, optional ...EncodeBase64Attr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "EncodeBase64",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Produce a string tensor that encodes the state of a Reader.
//
// Not all Readers support being serialized, so this can produce an
// Unimplemented error.
//
// Arguments:
//	reader_handle: Handle to a Reader.
func ReaderSerializeStateV2(scope *Scope, reader_handle tf.Output) (state tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReaderSerializeStateV2",
		Input: []tf.Input{
			reader_handle,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Return substrings from `Tensor` of strings.
//
// For each string in the input `Tensor`, creates a substring starting at index
// `pos` with a total length of `len`.
//
// If `len` defines a substring that would extend beyond the length of the input
// string, then as many characters as possible are used.
//
// If `pos` is negative or specifies a character index larger than any of the input
// strings, then an `InvalidArgumentError` is thrown.
//
// `pos` and `len` must have the same shape, otherwise a `ValueError` is thrown on
// Op creation.
//
// *NOTE*: `Substr` supports broadcasting up to two dimensions. More about
// broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
//
// ---
//
// Examples
//
// Using scalar `pos` and `len`:
//
// ```
// input = [b'Hello', b'World']
// position = 1
// length = 3
//
// output = [b'ell', b'orl']
// ```
//
// Using `pos` and `len` with same shape as `input`:
//
// ```
// input = [[b'ten', b'eleven', b'twelve'],
//          [b'thirteen', b'fourteen', b'fifteen'],
//          [b'sixteen', b'seventeen', b'eighteen']]
// position = [[1, 2, 3],
//             [1, 2, 3],
//             [1, 2, 3]]
// length =   [[2, 3, 4],
//             [4, 3, 2],
//             [5, 5, 5]]
//
// output = [[b'en', b'eve', b'lve'],
//           [b'hirt', b'urt', b'te'],
//           [b'ixtee', b'vente', b'hteen']]
// ```
//
// Broadcasting `pos` and `len` onto `input`:
//
// ```
// input = [[b'ten', b'eleven', b'twelve'],
//          [b'thirteen', b'fourteen', b'fifteen'],
//          [b'sixteen', b'seventeen', b'eighteen'],
//          [b'nineteen', b'twenty', b'twentyone']]
// position = [1, 2, 3]
// length =   [1, 2, 3]
//
// output = [[b'e', b'ev', b'lve'],
//           [b'h', b'ur', b'tee'],
//           [b'i', b've', b'hte'],
//           [b'i', b'en', b'nty']]
// ```
//
// Broadcasting `input` onto `pos` and `len`:
//
// ```
// input = b'thirteen'
// position = [1, 5, 7]
// length =   [3, 2, 1]
//
// output = [b'hir', b'ee', b'n"]
// ```
//
// Arguments:
//	input: Tensor of strings
//	pos: Scalar defining the position of first character in each substring
//	len: Scalar defining the number of characters to include in each substring
//
// Returns Tensor of substrings
func Substr(scope *Scope, input tf.Output, pos tf.Output, len tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Substr",
		Input: []tf.Input{
			input, pos, len,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the sum along sparse segments of a tensor divided by the sqrt of N.
//
// N is the size of the segment being reduced.
//
// Read [the section on
// Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
// of segments.
//
// Arguments:
//
//	indices: A 1-D tensor. Has same rank as `segment_ids`.
//	segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSegmentSqrtN",
		Input: []tf.Input{
			data, indices, segment_ids,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Component-wise divides a SparseTensor by a dense Tensor.
//
// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
// the other direction.
//
// Arguments:
//	sp_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
// SparseTensor, possibly not in canonical ordering.
//	sp_values: 1-D.  `N` non-empty values corresponding to `sp_indices`.
//	sp_shape: 1-D.  Shape of the input SparseTensor.
//	dense: `R`-D.  The dense Tensor operand.
//
// Returns 1-D.  The `N` values that are operated on.
func SparseDenseCwiseDiv(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseDenseCwiseDiv",
		Input: []tf.Input{
			sp_indices, sp_values, sp_shape, dense,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Reads the value of a variable.
//
// The tensor returned by this operation is immutable.
//
// The value returned by this operation is guaranteed to be influenced by all the
// writes on which this operation depends directly or indirectly, and to not be
// influenced by any of the writes which depend directly or indirectly on this
// operation.
//
// Arguments:
//	resource: handle to the resource in which to store the variable.
//	dtype: the dtype of the value.
func ReadVariableOp(scope *Scope, resource tf.Output, dtype tf.DataType) (value tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	opspec := tf.OpSpec{
		Type: "ReadVariableOp",
		Input: []tf.Input{
			resource,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ProdAttr is an optional argument to Prod.
type ProdAttr func(optionalAttr)

// ProdKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func ProdKeepDims(value bool) ProdAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the product of elements across dimensions of a tensor.
//
// Reduces `input` along the dimensions given in `reduction_indices`. Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
// retained with length 1.
//
// Arguments:
//	input: The tensor to reduce.
//	reduction_indices: The dimensions to reduce.
//
// Returns The reduced tensor.
func Prod(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...ProdAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Prod",
		Input: []tf.Input{
			input, reduction_indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResizeBilinearAttr is an optional argument to ResizeBilinear.
type ResizeBilinearAttr func(optionalAttr)

// ResizeBilinearAlignCorners sets the optional align_corners attribute to value.
//
// value: If true, rescale input by (new_height - 1) / (height - 1), which
// exactly aligns the 4 corners of images and resized images. If false, rescale
// by new_height / height. Treat similarly the width dimension.
// If not specified, defaults to false
func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr {
	return func(m optionalAttr) {
		m["align_corners"] = value
	}
}

// Resize `images` to `size` using bilinear interpolation.
//
// Input images can be of different types but output images are always float.
//
// Arguments:
//	images: 4-D with shape `[batch, height, width, channels]`.
//	size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
// new size for the images.
//
// Returns 4-D with shape
// `[batch, new_height, new_width, channels]`.
func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResizeBilinear",
		Input: []tf.Input{
			images, size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// RandomPoissonAttr is an optional argument to RandomPoisson.
type RandomPoissonAttr func(optionalAttr)

// RandomPoissonSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func RandomPoissonSeed(value int64) RandomPoissonAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// RandomPoissonSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func RandomPoissonSeed2(value int64) RandomPoissonAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Outputs random values from the Poisson distribution(s) described by rate.
//
// This op uses two algorithms, depending on rate. If rate >= 10, then
// the algorithm by Hormann is used to acquire samples via
// transformation-rejection.
// See http://www.sciencedirect.com/science/article/pii/0167668793909974.
//
// Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform
// random variables.
// See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer
// Programming, Volume 2. Addison Wesley
//
// Arguments:
//	shape: 1-D integer tensor. Shape of independent samples to draw from each
// distribution described by the shape parameters given in rate.
//	rate: A tensor in which each scalar is a "rate" parameter describing the
// associated poisson distribution.
//
// Returns A tensor with shape `shape + shape(rate)`. Each slice
// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
// `rate[i0, i1, ...iN]`. The dtype of the output matches the dtype of
// rate.
func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RandomPoisson",
		Input: []tf.Input{
			shape, rate,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta.
type ResourceApplyAdadeltaAttr func(optionalAttr)

// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value.
//
// value: If True, updating of the var, accum and update_accum tensors will be protected by
// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the adadelta scheme.
//
// accum = rho() * accum + (1 - rho()) * grad.square();
// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
// update_accum = rho() * update_accum + (1 - rho()) * update.square();
// var -= update;
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	accum_update: Should be from a Variable().
//	lr: Scaling factor. Must be a scalar.
//	rho: Decay factor. Must be a scalar.
//	epsilon: Constant factor. Must be a scalar.
//	grad: The gradient.
//
// Returns the created operation.
func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyAdadelta",
		Input: []tf.Input{
			var_, accum, accum_update, lr, rho, epsilon, grad,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression.
type NonMaxSuppressionAttr func(optionalAttr)

// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value.
//
// value: A float representing the threshold for deciding whether boxes
// overlap too much with respect to IOU.
// If not specified, defaults to 0.5
func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr {
	return func(m optionalAttr) {
		m["iou_threshold"] = value
	}
}

// Greedily selects a subset of bounding boxes in descending order of score,
//
// pruning away boxes that have high intersection-over-union (IOU) overlap
// with previously selected boxes.  Bounding boxes are supplied as
// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
// diagonal pair of box corners and the coordinates can be provided as normalized
// (i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
// is agnostic to where the origin is in the coordinate system.  Note that this
// algorithm is invariant to orthogonal transformations and translations
// of the coordinate system; thus translating or reflections of the coordinate
// system result in the same boxes being selected by the algorithm.
//
// The output of this operation is a set of integers indexing into the input
// collection of bounding boxes representing the selected boxes.  The bounding
// box coordinates corresponding to the selected indices can then be obtained
// using the `tf.gather operation`.  For example:
//
//   selected_indices = tf.image.non_max_suppression(
//       boxes, scores, max_output_size, iou_threshold)
//   selected_boxes = tf.gather(boxes, selected_indices)
//
// Arguments:
//	boxes: A 2-D float tensor of shape `[num_boxes, 4]`.
//	scores: A 1-D float tensor of shape `[num_boxes]` representing a single
// score corresponding to each box (each row of boxes).
//	max_output_size: A scalar integer tensor representing the maximum number of
// boxes to be selected by non max suppression.
//
// Returns A 1-D integer tensor of shape `[M]` representing the selected
// indices from the boxes tensor, where `M <= max_output_size`.
func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "NonMaxSuppression",
		Input: []tf.Input{
			boxes, scores, max_output_size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Applies softmax to a batched N-D `SparseTensor`.
//
// The inputs represent an N-D SparseTensor  with logical shape `[..., B, C]`
// (where `N >= 2`), and with indices sorted in the canonical lexicographic order.
//
// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost
// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly
// zero elements do not participate*.  Specifically, the algorithm is equivalent
// to the following:
//
//   (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix
//       with shape `[B, C]`, along the size-C dimension;
//   (2) Masks out the original implicitly-zero locations;
//   (3) Renormalizes the remaining elements.
//
// Hence, the `SparseTensor` result has exactly the same non-zero indices and
// shape.
//
// Arguments:
//	sp_indices: 2-D.  `NNZ x R` matrix with the indices of non-empty values in a
// SparseTensor, in canonical ordering.
//	sp_values: 1-D.  `NNZ` non-empty values corresponding to `sp_indices`.
//	sp_shape: 1-D.  Shape of the input SparseTensor.
//
// Returns 1-D.  The `NNZ` values for the result `SparseTensor`.
func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSoftmax",
		Input: []tf.Input{
			sp_indices, sp_values, sp_shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes gradients for SparseSegmentMean.
//
// Returns tensor "output" with same shape as grad, except for dimension 0 whose
// value is output_dim0.
//
// Arguments:
//	grad: gradient propagated to the SparseSegmentMean op.
//	indices: indices passed to the corresponding SparseSegmentMean op.
//	segment_ids: segment_ids passed to the corresponding SparseSegmentMean op.
//	output_dim0: dimension 0 of "data" passed to SparseSegmentMean op.
func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSegmentMeanGrad",
		Input: []tf.Input{
			grad, indices, segment_ids, output_dim0,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QuantizedReluXAttr is an optional argument to QuantizedReluX.
type QuantizedReluXAttr func(optionalAttr)

// QuantizedReluXOutType sets the optional out_type attribute to value.
// If not specified, defaults to DT_QUINT8
func QuantizedReluXOutType(value tf.DataType) QuantizedReluXAttr {
	return func(m optionalAttr) {
		m["out_type"] = value
	}
}

// Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
//
// Arguments:
//
//
//	min_features: The float value that the lowest quantized value represents.
//	max_features: The float value that the highest quantized value represents.
//
// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents.
func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluXAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizedReluX",
		Input: []tf.Input{
			features, max_value, min_features, max_features,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// MergeV2CheckpointsAttr is an optional argument to MergeV2Checkpoints.
type MergeV2CheckpointsAttr func(optionalAttr)

// MergeV2CheckpointsDeleteOldDirs sets the optional delete_old_dirs attribute to value.
//
// value: see above.
// If not specified, defaults to true
func MergeV2CheckpointsDeleteOldDirs(value bool) MergeV2CheckpointsAttr {
	return func(m optionalAttr) {
		m["delete_old_dirs"] = value
	}
}

// V2 format specific: merges the metadata files of sharded checkpoints.  The
//
// result is one logical checkpoint, with one physical metadata file and renamed
// data files.
//
// Intended for "grouping" multiple checkpoints in a sharded checkpoint setup.
//
// If delete_old_dirs is true, attempts to delete recursively the dirname of each
// path in the input checkpoint_prefixes.  This is useful when those paths are non
// user-facing temporary locations.
//
// Arguments:
//	checkpoint_prefixes: prefixes of V2 checkpoints to merge.
//	destination_prefix: scalar.  The desired final prefix.  Allowed to be the same
// as one of the checkpoint_prefixes.
//
// Returns the created operation.
func MergeV2Checkpoints(scope *Scope, checkpoint_prefixes tf.Output, destination_prefix tf.Output, optional ...MergeV2CheckpointsAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MergeV2Checkpoints",
		Input: []tf.Input{
			checkpoint_prefixes, destination_prefix,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// UnpackAttr is an optional argument to Unpack.
type UnpackAttr func(optionalAttr)

// UnpackAxis sets the optional axis attribute to value.
//
// value: Dimension along which to unpack.  Negative values wrap around, so the
// valid range is `[-R, R)`.
// If not specified, defaults to 0
func UnpackAxis(value int64) UnpackAttr {
	return func(m optionalAttr) {
		m["axis"] = value
	}
}

// Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
//
// Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
// For example, given a tensor of shape `(A, B, C, D)`;
//
// If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]`
//   and each tensor in `output` will have shape `(B, C, D)`. (Note that the
//   dimension unpacked along is gone, unlike `split`).
//
// If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]`
//   and each tensor in `output` will have shape `(A, C, D)`.
// Etc.
//
// This is the opposite of `pack`.
//
// Arguments:
//	value: 1-D or higher, with `axis` dimension size equal to `num`.
//
//
// Returns The list of tensors unpacked from `value`.
func Unpack(scope *Scope, value tf.Output, num int64, optional ...UnpackAttr) (output []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num": num}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Unpack",
		Input: []tf.Input{
			value,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if output, idx, err = makeOutputList(op, idx, "output"); err != nil {
		scope.UpdateErr("Unpack", err)
		return
	}
	return output
}

// Split a `SparseTensor` into `num_split` tensors along one dimension.
//
// If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices
// `[0 : shape[split_dim] % num_split]` gets one extra dimension.
// For example, if `split_dim = 1` and `num_split = 2` and the input is
//
//     input_tensor = shape = [2, 7]
//     [    a   d e  ]
//     [b c          ]
//
// Graphically the output tensors are:
//
//     output_tensor[0] = shape = [2, 4]
//     [    a  ]
//     [b c    ]
//
//     output_tensor[1] = shape = [2, 3]
//     [ d e  ]
//     [      ]
//
// Arguments:
//	split_dim: 0-D.  The dimension along which to split.  Must be in the range
// `[0, rank(shape))`.
//	indices: 2-D tensor represents the indices of the sparse tensor.
//	values: 1-D tensor represents the values of the sparse tensor.
//	shape: 1-D. tensor represents the shape of the sparse tensor.
// output indices: A list of 1-D tensors represents the indices of the output
// sparse tensors.
//	num_split: The number of ways to split.
//
// Returns A list of 1-D tensors represents the values of the output sparse
// tensors.A list of 1-D tensors represents the shape of the output sparse
// tensors.
func SparseSplit(scope *Scope, split_dim tf.Output, indices tf.Output, values tf.Output, shape tf.Output, num_split int64) (output_indices []tf.Output, output_values []tf.Output, output_shape []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_split": num_split}
	opspec := tf.OpSpec{
		Type: "SparseSplit",
		Input: []tf.Input{
			split_dim, indices, values, shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if output_indices, idx, err = makeOutputList(op, idx, "output_indices"); err != nil {
		scope.UpdateErr("SparseSplit", err)
		return
	}
	if output_values, idx, err = makeOutputList(op, idx, "output_values"); err != nil {
		scope.UpdateErr("SparseSplit", err)
		return
	}
	if output_shape, idx, err = makeOutputList(op, idx, "output_shape"); err != nil {
		scope.UpdateErr("SparseSplit", err)
		return
	}
	return output_indices, output_values, output_shape
}

// ReduceJoinAttr is an optional argument to ReduceJoin.
type ReduceJoinAttr func(optionalAttr)

// ReduceJoinKeepDims sets the optional keep_dims attribute to value.
//
// value: If `True`, retain reduced dimensions with length `1`.
// If not specified, defaults to false
func ReduceJoinKeepDims(value bool) ReduceJoinAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// ReduceJoinSeparator sets the optional separator attribute to value.
//
// value: The separator to use when joining.
// If not specified, defaults to ""
func ReduceJoinSeparator(value string) ReduceJoinAttr {
	return func(m optionalAttr) {
		m["separator"] = value
	}
}

// Joins a string Tensor across the given dimensions.
//
// Computes the string join across dimensions in the given string Tensor of shape
// `[d_0, d_1, ..., d_n-1]`.  Returns a new Tensor created by joining the input
// strings with the given separator (default: empty string).  Negative indices are
// counted backwards from the end, with `-1` being equivalent to `n - 1`.
//
// For example:
//
// ```
// # tensor `a` is [["a", "b"], ["c", "d"]]
// tf.reduce_join(a, 0) ==> ["ac", "bd"]
// tf.reduce_join(a, 1) ==> ["ab", "cd"]
// tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
// tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
// tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
// tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
// tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
// tf.reduce_join(a, [0, 1]) ==> ["acbd"]
// tf.reduce_join(a, [1, 0]) ==> ["abcd"]
// tf.reduce_join(a, []) ==> ["abcd"]
// ```
//
// Arguments:
//	inputs: The input to be joined.  All reduced indices must have non-zero size.
//	reduction_indices: The dimensions to reduce over.  Dimensions are reduced in the
// order specified.  Omitting `reduction_indices` is equivalent to passing
// `[n-1, n-2, ..., 0]`.  Negative indices from `-n` to `-1` are supported.
//
// Returns Has shape equal to that of the input with reduced dimensions removed or
// set to `1` depending on `keep_dims`.
func ReduceJoin(scope *Scope, inputs tf.Output, reduction_indices tf.Output, optional ...ReduceJoinAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ReduceJoin",
		Input: []tf.Input{
			inputs, reduction_indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SvdAttr is an optional argument to Svd.
type SvdAttr func(optionalAttr)

// SvdComputeUv sets the optional compute_uv attribute to value.
//
// value: If true, left and right singular vectors will be
// computed and returned in `u` and `v`, respectively.
// If false, `u` and `v` are not set and should never referenced.
// If not specified, defaults to true
func SvdComputeUv(value bool) SvdAttr {
	return func(m optionalAttr) {
		m["compute_uv"] = value
	}
}

// SvdFullMatrices sets the optional full_matrices attribute to value.
//
// value: If true, compute full-sized `u` and `v`. If false
// (the default), compute only the leading `P` singular vectors.
// Ignored if `compute_uv` is `False`.
// If not specified, defaults to false
func SvdFullMatrices(value bool) SvdAttr {
	return func(m optionalAttr) {
		m["full_matrices"] = value
	}
}

// Computes the singular value decompositions of one or more matrices.
//
// Computes the SVD of each inner matrix in `input` such that
// `input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])`
//
// ```prettyprint
// # a is a tensor containing a batch of matrices.
// # s is a tensor of singular values for each matrix.
// # u is the tensor containing of left singular vectors for each matrix.
// # v is the tensor containing of right singular vectors for each matrix.
// s, u, v = svd(a)
// s, _, _ = svd(a, compute_uv=False)
// ```
//
// Arguments:
//	input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
//
// Returns Singular values. Shape is `[..., P]`.Left singular vectors. If `full_matrices` is `False` then shape is
// `[..., M, P]`; if `full_matrices` is `True` then shape is
// `[..., M, M]`. Undefined if `compute_uv` is `False`.Left singular vectors. If `full_matrices` is `False` then shape is
// `[..., N, P]`. If `full_matrices` is `True` then shape is `[..., N, N]`.
// Undefined if `compute_uv` is false.
func Svd(scope *Scope, input tf.Output, optional ...SvdAttr) (s tf.Output, u tf.Output, v tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Svd",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// AssertAttr is an optional argument to Assert.
type AssertAttr func(optionalAttr)

// AssertSummarize sets the optional summarize attribute to value.
//
// value: Print this many entries of each tensor.
// If not specified, defaults to 3
func AssertSummarize(value int64) AssertAttr {
	return func(m optionalAttr) {
		m["summarize"] = value
	}
}

// Asserts that the given condition is true.
//
// If `condition` evaluates to false, print the list of tensors in `data`.
// `summarize` determines how many entries of the tensors to print.
//
// Arguments:
//	condition: The condition to evaluate.
//	data: The tensors to print out when condition is false.
//
// Returns the created operation.
func Assert(scope *Scope, condition tf.Output, data []tf.Output, optional ...AssertAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Assert",
		Input: []tf.Input{
			condition, tf.OutputList(data),
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// RandomUniformAttr is an optional argument to RandomUniform.
type RandomUniformAttr func(optionalAttr)

// RandomUniformSeed sets the optional seed attribute to value.
//
// value: If either `seed` or `seed2` are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func RandomUniformSeed(value int64) RandomUniformAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// RandomUniformSeed2 sets the optional seed2 attribute to value.
//
// value: A second seed to avoid seed collision.
// If not specified, defaults to 0
func RandomUniformSeed2(value int64) RandomUniformAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Outputs random values from a uniform distribution.
//
// The generated values follow a uniform distribution in the range `[0, 1)`. The
// lower bound 0 is included in the range, while the upper bound 1 is excluded.
//
// Arguments:
//	shape: The shape of the output tensor.
//	dtype: The type of the output.
//
// Returns A tensor of the specified shape filled with uniform random values.
func RandomUniform(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomUniformAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RandomUniform",
		Input: []tf.Input{
			shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl.
type ResourceApplyFtrlAttr func(optionalAttr)

// ResourceApplyFtrlUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var and accum tensors will be protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// Update '*var' according to the Ftrl-proximal scheme.
//
// accum_new = accum + grad * grad
// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
// accum = accum_new
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	linear: Should be from a Variable().
//	grad: The gradient.
//	lr: Scaling factor. Must be a scalar.
//	l1: L1 regulariation. Must be a scalar.
//	l2: L2 regulariation. Must be a scalar.
//	lr_power: Scaling factor. Must be a scalar.
//
// Returns the created operation.
func ResourceApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceApplyFtrl",
		Input: []tf.Input{
			var_, accum, linear, grad, lr, l1, l2, lr_power,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// AnyAttr is an optional argument to Any.
type AnyAttr func(optionalAttr)

// AnyKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func AnyKeepDims(value bool) AnyAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the "logical or" of elements across dimensions of a tensor.
//
// Reduces `input` along the dimensions given in `reduction_indices`. Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
// retained with length 1.
//
// Arguments:
//	input: The tensor to reduce.
//	reduction_indices: The dimensions to reduce.
//
// Returns The reduced tensor.
func Any(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...AnyAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Any",
		Input: []tf.Input{
			input, reduction_indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
//
// The Hurwitz zeta function is defined as:
//
// ```
// \zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}
// ```
func Zeta(scope *Scope, x tf.Output, q tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Zeta",
		Input: []tf.Input{
			x, q,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the inverse 1-dimensional discrete Fourier Transform over the inner-most
//
// dimension of `input`.
//
// Arguments:
//	input: A complex64 tensor.
//
// Returns A complex64 tensor of the same shape as `input`. The inner-most
//   dimension of `input` is replaced with its inverse 1D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.ifft
// @end_compatibility
func IFFT(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IFFT",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the inverse 1-dimensional discrete Fourier Transform of a real-valued
//
// signal over the inner-most dimension of `input`.
//
// The inner-most dimension of `input` is assumed to be the result of `RFFT`: the
// `fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If
// `fft_length` is not provided, it is computed from the size of the inner-most
// dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to
// compute `input` is odd, it should be provided since it cannot be inferred
// properly.
//
// Arguments:
//	input: A complex64 tensor.
//	fft_length: An int32 tensor of shape [1]. The FFT length.
//
// Returns A float32 tensor of the same rank as `input`. The inner-most
//   dimension of `input` is replaced with the `fft_length` samples of its inverse
//   1D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.irfft
// @end_compatibility
func IRFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IRFFT",
		Input: []tf.Input{
			input, fft_length,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Saves tensors in V2 checkpoint format.
//
// By default, saves the named tensors in full.  If the caller wishes to save
// specific slices of full tensors, "shape_and_slices" should be non-empty strings
// and correspondingly well-formed.
//
// Arguments:
//	prefix: Must have a single element. The prefix of the V2 checkpoint to which we
// write the tensors.
//	tensor_names: shape {N}. The names of the tensors to be saved.
//	shape_and_slices: shape {N}.  The slice specs of the tensors to be saved.
// Empty strings indicate that they are non-partitioned tensors.
//	tensors: `N` tensors to save.
//
// Returns the created operation.
func SaveV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, tensors []tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SaveV2",
		Input: []tf.Input{
			prefix, tensor_names, shape_and_slices, tf.OutputList(tensors),
		},
	}
	return scope.AddOperation(opspec)
}

// MatrixTriangularSolveAttr is an optional argument to MatrixTriangularSolve.
type MatrixTriangularSolveAttr func(optionalAttr)

// MatrixTriangularSolveLower sets the optional lower attribute to value.
//
// value: Boolean indicating whether the innermost matrices in `matrix` are
// lower or upper triangular.
// If not specified, defaults to true
func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr {
	return func(m optionalAttr) {
		m["lower"] = value
	}
}

// MatrixTriangularSolveAdjoint sets the optional adjoint attribute to value.
//
// value: Boolean indicating whether to solve with `matrix` or its (block-wise)
//          adjoint.
//
// @compatibility(numpy)
// Equivalent to np.linalg.triangular_solve
// @end_compatibility
// If not specified, defaults to false
func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr {
	return func(m optionalAttr) {
		m["adjoint"] = value
	}
}

// Solves systems of linear equations with upper or lower triangular matrices by
//
// backsubstitution.
//
// `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form
// square matrices. If `lower` is `True` then the strictly upper triangular part
// of each inner-most matrix is assumed to be zero and not accessed.
// If `lower` is False then the strictly lower triangular part of each inner-most
// matrix is assumed to be zero and not accessed.
// `rhs` is a tensor of shape `[..., M, K]`.
//
// The output is a tensor of shape `[..., M, K]`. If `adjoint` is
// `True` then the innermost matrices in output` satisfy matrix equations
// `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
// If `adjoint` is `False` then the strictly then the  innermost matrices in
// `output` satisfy matrix equations
// `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`.
//
// Arguments:
//	matrix: Shape is `[..., M, M]`.
//	rhs: Shape is `[..., M, K]`.
//
// Returns Shape is `[..., M, K]`.
func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixTriangularSolveAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MatrixTriangularSolve",
		Input: []tf.Input{
			matrix, rhs,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Adds a value to the current value of a variable.
//
// Any ReadVariableOp which depends directly or indirectly on this assign is
// guaranteed to see the incremented value or a subsequent newer one.
//
// Outputs the incremented value, which can be used to totally order the
// increments to this variable.
//
// Arguments:
//	resource: handle to the resource in which to store the variable.
//	value: the value by which the variable will be incremented.
//
// Returns the created operation.
func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "AssignAddVariableOp",
		Input: []tf.Input{
			resource, value,
		},
	}
	return scope.AddOperation(opspec)
}

// Compute the 1-dimensional discrete Fourier Transform of a real-valued signal
//
// over the inner-most dimension of `input`.
//
// Since the DFT of a real signal is Hermitian-symmetric, `RFFT` only returns the
// `fft_length / 2 + 1` unique components of the FFT: the zero-frequency term,
// followed by the `fft_length / 2` positive-frequency terms.
//
// Arguments:
//	input: A float32 tensor.
//	fft_length: An int32 tensor of shape [1]. The FFT length.
//
// Returns A complex64 tensor of the same rank as `input`. The inner-most
//   dimension of `input` is replaced with the `fft_length / 2 + 1` unique
//   frequency components of its 1D Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.fft.rfft
// @end_compatibility
func RFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "RFFT",
		Input: []tf.Input{
			input, fft_length,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradient for the tanh of `x` wrt its input.
//
// Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and `dy`
// is the corresponding input gradient.
func TanhGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TanhGrad",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// AddManySparseToTensorsMapAttr is an optional argument to AddManySparseToTensorsMap.
type AddManySparseToTensorsMapAttr func(optionalAttr)

// AddManySparseToTensorsMapContainer sets the optional container attribute to value.
//
// value: The container name for the `SparseTensorsMap` created by this op.
// If not specified, defaults to ""
func AddManySparseToTensorsMapContainer(value string) AddManySparseToTensorsMapAttr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// AddManySparseToTensorsMapSharedName sets the optional shared_name attribute to value.
//
// value: The shared name for the `SparseTensorsMap` created by this op.
// If blank, the new Operation's unique name is used.
// If not specified, defaults to ""
func AddManySparseToTensorsMapSharedName(value string) AddManySparseToTensorsMapAttr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
//
// A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`,
// `sparse_values`, and `sparse_shape`, where
//
// ```sparse_indices.shape[1] == sparse_shape.shape[0] == R```
//
// An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor`
// having a first `sparse_indices` column taking values between `[0, N)`, where
// the minibatch size `N == sparse_shape[0]`.
//
// The input `SparseTensor` must have rank `R` greater than 1, and the first
// dimension is treated as the minibatch dimension.  Elements of the `SparseTensor`
// must be sorted in increasing order of this first dimension.  The stored
// `SparseTensor` objects pointed to by each row of the output `sparse_handles`
// will have rank `R-1`.
//
// The `SparseTensor` values can then be read out as part of a minibatch by passing
// the given keys as vector elements to `TakeManySparseFromTensorsMap`.  To ensure
// the correct `SparseTensorsMap` is accessed, ensure that the same
// `container` and `shared_name` are passed to that Op.  If no `shared_name`
// is provided here, instead use the *name* of the Operation created by calling
// `AddManySparseToTensorsMap` as the `shared_name` passed to
// `TakeManySparseFromTensorsMap`.  Ensure the Operations are colocated.
//
// Arguments:
//	sparse_indices: 2-D.  The `indices` of the minibatch `SparseTensor`.
// `sparse_indices[:, 0]` must be ordered values in `[0, N)`.
//	sparse_values: 1-D.  The `values` of the minibatch `SparseTensor`.
//	sparse_shape: 1-D.  The `shape` of the minibatch `SparseTensor`.
// The minibatch size `N == sparse_shape[0]`.
//
// Returns 1-D.  The handles of the `SparseTensor` now stored in the
// `SparseTensorsMap`.  Shape: `[N]`.
func AddManySparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddManySparseToTensorsMapAttr) (sparse_handles tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "AddManySparseToTensorsMap",
		Input: []tf.Input{
			sparse_indices, sparse_values, sparse_shape,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Converts each string in the input Tensor to its hash mod by a number of buckets.
//
// The hash function is deterministic on the content of the string within the
// process and will never change. However, it is not suitable for cryptography.
// This function may be used when CPU time is scarce and inputs are trusted or
// unimportant. There is a risk of adversaries constructing inputs that all hash
// to the same bucket. To prevent this problem, use a strong hash function with
// `tf.string_to_hash_bucket_strong`.
//
// Arguments:
//	input: The strings to assign a hash bucket.
//	num_buckets: The number of buckets.
//
// Returns A Tensor of the same shape as the input `string_tensor`.
func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_buckets": num_buckets}
	opspec := tf.OpSpec{
		Type: "StringToHashBucketFast",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// UniqueWithCountsAttr is an optional argument to UniqueWithCounts.
type UniqueWithCountsAttr func(optionalAttr)

// UniqueWithCountsOutIdx sets the optional out_idx attribute to value.
// If not specified, defaults to DT_INT32
func UniqueWithCountsOutIdx(value tf.DataType) UniqueWithCountsAttr {
	return func(m optionalAttr) {
		m["out_idx"] = value
	}
}

// Finds unique elements in a 1-D tensor.
//
// This operation returns a tensor `y` containing all of the unique elements of `x`
// sorted in the same order that they occur in `x`. This operation also returns a
// tensor `idx` the same size as `x` that contains the index of each value of `x`
// in the unique output `y`. Finally, it returns a third tensor `count` that
// contains the count of each element of `y` in `x`. In other words:
//
// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
//
// For example:
//
// ```prettyprint
// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
// y, idx, count = unique_with_counts(x)
// y ==> [1, 2, 4, 7, 8]
// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
// count ==> [2, 1, 3, 1, 2]
// ```
//
// Arguments:
//	x: 1-D.
//
// Returns 1-D.1-D.1-D.
func UniqueWithCounts(scope *Scope, x tf.Output, optional ...UniqueWithCountsAttr) (y tf.Output, idx tf.Output, count tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "UniqueWithCounts",
		Input: []tf.Input{
			x,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// ComplexAttr is an optional argument to Complex.
type ComplexAttr func(optionalAttr)

// ComplexTout sets the optional Tout attribute to value.
// If not specified, defaults to DT_COMPLEX64
func ComplexTout(value tf.DataType) ComplexAttr {
	return func(m optionalAttr) {
		m["Tout"] = value
	}
}

// Converts two real numbers to a complex number.
//
// Given a tensor `real` representing the real part of a complex number, and a
// tensor `imag` representing the imaginary part of a complex number, this
// operation returns complex numbers elementwise of the form \\(a + bj\\), where
// *a* represents the `real` part and *b* represents the `imag` part.
//
// The input tensors `real` and `imag` must have the same shape.
//
// For example:
//
// ```
// # tensor 'real' is [2.25, 3.25]
// # tensor `imag` is [4.75, 5.75]
// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
// ```
func Complex(scope *Scope, real tf.Output, imag tf.Output, optional ...ComplexAttr) (out tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Complex",
		Input: []tf.Input{
			real, imag,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ImagAttr is an optional argument to Imag.
type ImagAttr func(optionalAttr)

// ImagTout sets the optional Tout attribute to value.
// If not specified, defaults to DT_FLOAT
func ImagTout(value tf.DataType) ImagAttr {
	return func(m optionalAttr) {
		m["Tout"] = value
	}
}

// Returns the imaginary part of a complex number.
//
// Given a tensor `input` of complex numbers, this operation returns a tensor of
// type `float` that is the imaginary part of each element in `input`. All
// elements in `input` must be complex numbers of the form \\(a + bj\\), where *a*
// is the real part and *b* is the imaginary part returned by this operation.
//
// For example:
//
// ```
// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
// tf.imag(input) ==> [4.75, 5.75]
// ```
func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Imag",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes fingerprints of the input strings.
//
// Arguments:
//	input: vector of strings to compute fingerprints on.
//
// Returns a (N,2) shaped matrix where N is the number of elements in the input
// vector. Each row contains the low and high parts of the fingerprint.
func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SdcaFprint",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns a batched diagonal tensor with a given batched diagonal values.
//
// Given a `diagonal`, this operation returns a tensor with the `diagonal` and
// everything else padded with zeros. The diagonal is computed as follows:
//
// Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a
// tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:
//
// `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.
//
// For example:
//
// ```prettyprint
// # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
//
// and diagonal.shape = (2, 4)
//
// tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
//                                      [0, 2, 0, 0]
//                                      [0, 0, 3, 0]
//                                      [0, 0, 0, 4]],
//                                     [[5, 0, 0, 0]
//                                      [0, 6, 0, 0]
//                                      [0, 0, 7, 0]
//                                      [0, 0, 0, 8]]]
//
// which has shape (2, 4, 4)
// ```
//
// Arguments:
//	diagonal: Rank `k`, where `k >= 1`.
//
// Returns Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.
func MatrixDiag(scope *Scope, diagonal tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "MatrixDiag",
		Input: []tf.Input{
			diagonal,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Performs 3D max pooling on the input.
//
// Arguments:
//	input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
//	strides: 1-D tensor of length 5. The stride of the sliding window for each
// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
//	padding: The type of padding algorithm to use.
//
// Returns The max pooled output tensor.
func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "MaxPool3D",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns x // y element-wise.
//
// *NOTE*: `FloorDiv` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func FloorDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FloorDiv",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TopKAttr is an optional argument to TopK.
type TopKAttr func(optionalAttr)

// TopKSorted sets the optional sorted attribute to value.
//
// value: If true the resulting `k` elements will be sorted by the values in
// descending order.
// If not specified, defaults to true
func TopKSorted(value bool) TopKAttr {
	return func(m optionalAttr) {
		m["sorted"] = value
	}
}

// Finds values and indices of the `k` largest elements for the last dimension.
//
// DEPRECATED at GraphDef version 7: Use TopKV2 instead
//
// If the input is a vector (rank-1), finds the `k` largest entries in the vector
// and outputs their values and indices as vectors.  Thus `values[j]` is the
// `j`-th largest entry in `input`, and its index is `indices[j]`.
//
// For matrices (resp. higher rank input), computes the top `k` entries in each
// row (resp. vector along the last dimension).  Thus,
//
//     values.shape = indices.shape = input.shape[:-1] + [k]
//
// If two elements are equal, the lower-index element appears first.
//
// If `k` varies dynamically, use `TopKV2` below.
//
// Arguments:
//	input: 1-D or higher with last dimension at least `k`.
//	k: Number of top elements to look for along the last dimension (along each
// row for matrices).
//
// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`.
func TopK(scope *Scope, input tf.Output, k int64, optional ...TopKAttr) (values tf.Output, indices tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"k": k}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TopK",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Returns the number of records this Reader has produced.
//
// This is the same as the number of ReaderRead executions that have
// succeeded.
//
// Arguments:
//	reader_handle: Handle to a Reader.
func ReaderNumRecordsProducedV2(scope *Scope, reader_handle tf.Output) (records_produced tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReaderNumRecordsProducedV2",
		Input: []tf.Input{
			reader_handle,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes exponential of x - 1 element-wise.
//
// I.e., \\(y = (\exp x) - 1\\).
func Expm1(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Expm1",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns x - y element-wise.
//
// *NOTE*: `Sub` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Sub(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Sub",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Split elements of `input` based on `delimiter` into a `SparseTensor`.
//
// Let N be the size of source (typically N will be the batch size). Split each
// element of `input` based on `delimiter` and return a `SparseTensor`
// containing the splitted tokens. Empty tokens are ignored.
//
// `delimiter` can be empty, or a string of split characters. If `delimiter` is an
//  empty string, each element of `input` is split into individual single-byte
//  character strings, including splitting of UTF-8 multibyte sequences. Otherwise
//  every character of `delimiter` is a potential split point.
//
// For example:
//   N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output
//   will be
//
//   indices = [0, 0;
//              0, 1;
//              1, 0;
//              1, 1;
//              1, 2]
//   shape = [2, 3]
//   values = ['hello', 'world', 'a', 'b', 'c']
//
// Arguments:
//	input: 1-D. Strings to split.
//	delimiter: 0-D. Delimiter characters (bytes), or empty string.
//
// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse
// tensor, where the first value is N and the second value is the maximum number
// of tokens in a single input entry.
func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output) (indices tf.Output, values tf.Output, shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "StringSplit",
		Input: []tf.Input{
			input, delimiter,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Compute the inverse 3-dimensional discrete Fourier Transform of a real-valued
//
// signal over the inner-most 3 dimensions of `input`.
//
// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`:
// The inner-most dimension contains the `fft_length / 2 + 1` unique components of
// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
// from the size of the inner-most 3 dimensions of `input`. If the FFT length used
// to compute `input` is odd, it should be provided since it cannot be inferred
// properly.
//
// Arguments:
//	input: A complex64 tensor.
//	fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
//
// Returns A float32 tensor of the same rank as `input`. The inner-most 3
//   dimensions of `input` are replaced with the `fft_length` samples of their
//   inverse 3D real Fourier Transform.
//
// @compatibility(numpy)
// Equivalent to np.irfftn with 3 dimensions.
// @end_compatibility
func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IRFFT3D",
		Input: []tf.Input{
			input, fft_length,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the truth value of (x != y) element-wise.
//
// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "NotEqual",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// FractionalAvgPoolAttr is an optional argument to FractionalAvgPool.
type FractionalAvgPoolAttr func(optionalAttr)

// FractionalAvgPoolPseudoRandom sets the optional pseudo_random attribute to value.
//
// value: When set to True, generates the pooling sequence in a
// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin
// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
// difference between pseudorandom and random.
// If not specified, defaults to false
func FractionalAvgPoolPseudoRandom(value bool) FractionalAvgPoolAttr {
	return func(m optionalAttr) {
		m["pseudo_random"] = value
	}
}

// FractionalAvgPoolOverlapping sets the optional overlapping attribute to value.
//
// value: When set to True, it means when pooling, the values at the boundary
// of adjacent pooling cells are used by both cells. For example:
//
// `index  0  1  2  3  4`
//
// `value  20 5  16 3  7`
//
// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
// The result would be [41/3, 26/3] for fractional avg pooling.
// If not specified, defaults to false
func FractionalAvgPoolOverlapping(value bool) FractionalAvgPoolAttr {
	return func(m optionalAttr) {
		m["overlapping"] = value
	}
}

// FractionalAvgPoolDeterministic sets the optional deterministic attribute to value.
//
// value: When set to True, a fixed pooling region will be used when
// iterating over a FractionalAvgPool node in the computation graph. Mainly used
// in unit test to make FractionalAvgPool deterministic.
// If not specified, defaults to false
func FractionalAvgPoolDeterministic(value bool) FractionalAvgPoolAttr {
	return func(m optionalAttr) {
		m["deterministic"] = value
	}
}

// FractionalAvgPoolSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func FractionalAvgPoolSeed(value int64) FractionalAvgPoolAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// FractionalAvgPoolSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func FractionalAvgPoolSeed2(value int64) FractionalAvgPoolAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Performs fractional average pooling on the input.
//
// Fractional average pooling is similar to Fractional max pooling in the pooling
// region generation step. The only difference is that after pooling regions are
// generated, a mean operation is performed instead of a max operation in each
// pooling region.
//
// Arguments:
//	value: 4-D with shape `[batch, height, width, channels]`.
//	pooling_ratio: Pooling ratio for each dimension of `value`, currently only
// supports row and col dimension and should be >= 1.0. For example, a valid
// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
// must be 1.0 because we don't allow pooling on batch and channels
// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
// respectively.
//
// Returns output tensor after fractional avg pooling.row pooling sequence, needed to calculate gradient.column pooling sequence, needed to calculate gradient.
func FractionalAvgPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalAvgPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"pooling_ratio": pooling_ratio}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "FractionalAvgPool",
		Input: []tf.Input{
			value,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// RandomCropAttr is an optional argument to RandomCrop.
type RandomCropAttr func(optionalAttr)

// RandomCropSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func RandomCropSeed(value int64) RandomCropAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// RandomCropSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func RandomCropSeed2(value int64) RandomCropAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Randomly crop `image`.
//
// DEPRECATED at GraphDef version 8: Random crop is now pure Python
//
// `size` is a 1-D int64 tensor with 2 elements representing the crop height and
// width.  The values must be non negative.
//
// This Op picks a random location in `image` and crops a `height` by `width`
// rectangle from that location.  The random location is picked so the cropped
// area will fit inside the original image.
//
// Arguments:
//	image: 3-D of shape `[height, width, channels]`.
//	size: 1-D of length 2 containing: `crop_height`, `crop_width`..
//
// Returns 3-D of shape `[crop_height, crop_width, channels].`
func RandomCrop(scope *Scope, image tf.Output, size tf.Output, optional ...RandomCropAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RandomCrop",
		Input: []tf.Input{
			image, size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns immutable tensor from memory region.
//
// The current implementation memmaps the tensor from a file.
//
// Arguments:
//	dtype: Type of the returned tensor.
//	shape: Shape of the returned tensor.
//	memory_region_name: Name of readonly memory region used by the tensor, see
// NewReadOnlyMemoryRegionFromFile in tensorflow::Env.
func ImmutableConst(scope *Scope, dtype tf.DataType, shape tf.Shape, memory_region_name string) (tensor tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype, "shape": shape, "memory_region_name": memory_region_name}
	opspec := tf.OpSpec{
		Type: "ImmutableConst",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Deserialize and concatenate `SparseTensors` from a serialized minibatch.
//
// The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where
// `N` is the minibatch size and the rows correspond to packed outputs of
// `SerializeSparse`.  The ranks of the original `SparseTensor` objects
// must all match.  When the final `SparseTensor` is created, it has rank one
// higher than the ranks of the incoming `SparseTensor` objects
// (they have been concatenated along a new row dimension).
//
// The output `SparseTensor` object's shape values for all dimensions but the
// first are the max across the input `SparseTensor` objects' shape values
// for the corresponding dimensions.  Its first shape value is `N`, the minibatch
// size.
//
// The input `SparseTensor` objects' indices are assumed ordered in
// standard lexicographic order.  If this is not the case, after this
// step run `SparseReorder` to restore index ordering.
//
// For example, if the serialized input is a `[2 x 3]` matrix representing two
// original `SparseTensor` objects:
//
//     index = [ 0]
//             [10]
//             [20]
//     values = [1, 2, 3]
//     shape = [50]
//
// and
//
//     index = [ 2]
//             [10]
//     values = [4, 5]
//     shape = [30]
//
// then the final deserialized `SparseTensor` will be:
//
//     index = [0  0]
//             [0 10]
//             [0 20]
//             [1  2]
//             [1 10]
//     values = [1, 2, 3, 4, 5]
//     shape = [2 50]
//
// Arguments:
//	serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects.
// Must have 3 columns.
//	dtype: The `dtype` of the serialized `SparseTensor` objects.
func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	opspec := tf.OpSpec{
		Type: "DeserializeManySparse",
		Input: []tf.Input{
			serialized_sparse,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Performs a padding as a preprocess during a convolution.
//
// Similar to FusedResizeAndPadConv2d, this op allows for an optimized
// implementation where the spatial padding transformation stage is fused with the
// im2col lookup, but in this case without the bilinear filtering required for
// resizing. Fusing the padding prevents the need to write out the intermediate
// results as whole tensors, reducing memory pressure, and we can get some latency
// gains by merging the transformation calculations.
// The data_format attribute for Conv2D isn't supported by this op, and 'NHWC'
// order is used instead.
// Internally this op uses a single per-graph scratch buffer, which means that it
// will block if multiple versions are being run in parallel. This is because this
// operator is primarily an optimization to minimize memory usage.
//
// Arguments:
//	input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
//	paddings: A two-column matrix specifying the padding sizes. The number of
// rows must be the same as the rank of `input`.
//	filter: 4-D with shape
// `[filter_height, filter_width, in_channels, out_channels]`.
//
//	strides: 1-D of length 4.  The stride of the sliding window for each dimension
// of `input`. Must be in the same order as the dimension specified with format.
//	padding: The type of padding algorithm to use.
func FusedPadConv2D(scope *Scope, input tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "FusedPadConv2D",
		Input: []tf.Input{
			input, paddings, filter,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Restore a reader to a previously saved state.
//
// Not all Readers support being restored, so this can produce an
// Unimplemented error.
//
// Arguments:
//	reader_handle: Handle to a Reader.
//	state: Result of a ReaderSerializeState of a Reader with type
// matching reader_handle.
//
// Returns the created operation.
func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReaderRestoreStateV2",
		Input: []tf.Input{
			reader_handle, state,
		},
	}
	return scope.AddOperation(opspec)
}

// Computes the absolute value of a tensor.
//
// Given a tensor `x`, this operation returns a tensor containing the absolute
// value of each element in `x`. For example, if x is an input element and y is
// an output element, this operation computes \\(y = |x|\\).
func Abs(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Abs",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
//
// For an explanation see "Differentiation of the Cholesky algorithm" by
// Iain Murray http://arxiv.org/abs/1602.07527.
//
// Arguments:
//	l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`.
// Algorithm depends only on lower triangular part of the innermost matrices of
// this tensor.
//	grad: df/dl where f is some scalar function. Shape is `[..., M, M]`.
// Algorithm depends only on lower triangular part of the innermost matrices of
// this tensor.
//
// Returns Symmetrized version of df/dA . Shape is `[..., M, M]`
func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "CholeskyGrad",
		Input: []tf.Input{
			l, grad,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Concatenates a list of `SparseTensor` along the specified dimension.
//
// Concatenation is with respect to the dense versions of these sparse tensors.
// It is assumed that each input is a `SparseTensor` whose elements are ordered
// along increasing dimension number.
//
// All inputs' shapes must match, except for the concat dimension.  The
// `indices`, `values`, and `shapes` lists must have the same length.
//
// The output shape is identical to the inputs', except along the concat
// dimension, where it is the sum of the inputs' sizes along that dimension.
//
// The output elements will be resorted to preserve the sort order along
// increasing dimension number.
//
// This op runs in `O(M log M)` time, where `M` is the total number of non-empty
// values across all inputs. This is due to the need for an internal sort in
// order to concatenate efficiently across an arbitrary dimension.
//
// For example, if `concat_dim = 1` and the inputs are
//
//     sp_inputs[0]: shape = [2, 3]
//     [0, 2]: "a"
//     [1, 0]: "b"
//     [1, 1]: "c"
//
//     sp_inputs[1]: shape = [2, 4]
//     [0, 1]: "d"
//     [0, 2]: "e"
//
// then the output will be
//
//     shape = [2, 7]
//     [0, 2]: "a"
//     [0, 4]: "d"
//     [0, 5]: "e"
//     [1, 0]: "b"
//     [1, 1]: "c"
//
// Graphically this is equivalent to doing
//
//     [    a] concat [  d e  ] = [    a   d e  ]
//     [b c  ]        [       ]   [b c          ]
//
// Arguments:
//	indices: 2-D.  Indices of each input `SparseTensor`.
//	values: 1-D.  Non-empty values of each `SparseTensor`.
//	shapes: 1-D.  Shapes of each `SparseTensor`.
//	concat_dim: Dimension to concatenate along. Must be in range [-rank, rank),
// where rank is the number of dimensions in each input `SparseTensor`.
//
// Returns 2-D.  Indices of the concatenated `SparseTensor`.1-D.  Non-empty values of the concatenated `SparseTensor`.1-D.  Shape of the concatenated `SparseTensor`.
func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, concat_dim int64) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"concat_dim": concat_dim}
	opspec := tf.OpSpec{
		Type: "SparseConcat",
		Input: []tf.Input{
			tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes),
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// IdentityReaderV2Attr is an optional argument to IdentityReaderV2.
type IdentityReaderV2Attr func(optionalAttr)

// IdentityReaderV2Container sets the optional container attribute to value.
//
// value: If non-empty, this reader is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func IdentityReaderV2Container(value string) IdentityReaderV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// IdentityReaderV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this reader is named in the given bucket
// with this shared_name. Otherwise, the node name is used instead.
// If not specified, defaults to ""
func IdentityReaderV2SharedName(value string) IdentityReaderV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// A Reader that outputs the queued work as both the key and value.
//
// To use, enqueue strings in a Queue.  ReaderRead will take the front
// work string and output (work, work).
//
// Returns The handle to reference the Reader.
func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "IdentityReaderV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Produces the average pool of the input tensor for quantized types.
//
// Arguments:
//	input: 4-D with shape `[batch, height, width, channels]`.
//	min_input: The float value that the lowest quantized input value represents.
//	max_input: The float value that the highest quantized input value represents.
//	ksize: The size of the window for each dimension of the input tensor.
// The length must be 4 to match the number of dimensions of the input.
//	strides: The stride of the sliding window for each dimension of the input
// tensor.  The length must be 4 to match the number of dimensions of the input.
//	padding: The type of padding algorithm to use.
//
// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
func QuantizedAvgPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	opspec := tf.OpSpec{
		Type: "QuantizedAvgPool",
		Input: []tf.Input{
			input, min_input, max_input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Adds Tensor 'bias' to Tensor 'input' for Quantized types.
//
// Broadcasts the values of bias on dimensions 0..N-2 of 'input'.
//
// Arguments:
//
//	bias: A 1D bias Tensor with size matching the last dimension of 'input'.
//	min_input: The float value that the lowest quantized input value represents.
//	max_input: The float value that the highest quantized input value represents.
//	min_bias: The float value that the lowest quantized bias value represents.
//	max_bias: The float value that the highest quantized bias value represents.
//
//
// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
func QuantizedBiasAdd(scope *Scope, input tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_bias tf.Output, max_bias tf.Output, out_type tf.DataType) (output tf.Output, min_out tf.Output, max_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"out_type": out_type}
	opspec := tf.OpSpec{
		Type: "QuantizedBiasAdd",
		Input: []tf.Input{
			input, bias, min_input, max_input, min_bias, max_bias,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// QuantizedConv2DAttr is an optional argument to QuantizedConv2D.
type QuantizedConv2DAttr func(optionalAttr)

// QuantizedConv2DOutType sets the optional out_type attribute to value.
// If not specified, defaults to DT_QINT32
func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
	return func(m optionalAttr) {
		m["out_type"] = value
	}
}

// Computes a 2D convolution given quantized 4D input and filter tensors.
//
// The inputs are quantized tensors where the lowest value represents the real
// number of the associated minimum, and the highest represents the maximum.
// This means that you can only interpret the quantized output in the same way, by
// taking the returned minimum and maximum values into account.
//
// Arguments:
//
//	filter: filter's input_depth dimension must match input's depth dimensions.
//	min_input: The float value that the lowest quantized input value represents.
//	max_input: The float value that the highest quantized input value represents.
//	min_filter: The float value that the lowest quantized filter value represents.
//	max_filter: The float value that the highest quantized filter value represents.
//	strides: The stride of the sliding window for each dimension of the input
// tensor.
//	padding: The type of padding algorithm to use.
//
// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizedConv2D",
		Input: []tf.Input{
			input, filter, min_input, max_input, min_filter, max_filter,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Quantized Batch normalization.
//
// This op is deprecated and will be removed in the future. Prefer
// `tf.nn.batch_normalization`.
//
// Arguments:
//	t: A 4D input Tensor.
//	t_min: The value represented by the lowest quantized input.
//	t_max: The value represented by the highest quantized input.
//	m: A 1D mean Tensor with size matching the last dimension of t.
// This is the first output from tf.nn.moments,
// or a saved moving average thereof.
//	m_min: The value represented by the lowest quantized mean.
//	m_max: The value represented by the highest quantized mean.
//	v: A 1D variance Tensor with size matching the last dimension of t.
// This is the second output from tf.nn.moments,
// or a saved moving average thereof.
//	v_min: The value represented by the lowest quantized variance.
//	v_max: The value represented by the highest quantized variance.
//	beta: A 1D beta Tensor with size matching the last dimension of t.
// An offset to be added to the normalized tensor.
//	beta_min: The value represented by the lowest quantized offset.
//	beta_max: The value represented by the highest quantized offset.
//	gamma: A 1D gamma Tensor with size matching the last dimension of t.
// If "scale_after_normalization" is true, this tensor will be multiplied
// with the normalized tensor.
//	gamma_min: The value represented by the lowest quantized gamma.
//	gamma_max: The value represented by the highest quantized gamma.
//
//	variance_epsilon: A small float number to avoid dividing by 0.
//	scale_after_normalization: A bool indicating whether the resulted tensor
// needs to be multiplied with gamma.
func QuantizedBatchNormWithGlobalNormalization(scope *Scope, t tf.Output, t_min tf.Output, t_max tf.Output, m tf.Output, m_min tf.Output, m_max tf.Output, v tf.Output, v_min tf.Output, v_max tf.Output, beta tf.Output, beta_min tf.Output, beta_max tf.Output, gamma tf.Output, gamma_min tf.Output, gamma_max tf.Output, out_type tf.DataType, variance_epsilon float32, scale_after_normalization bool) (result tf.Output, result_min tf.Output, result_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"out_type": out_type, "variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
	opspec := tf.OpSpec{
		Type: "QuantizedBatchNormWithGlobalNormalization",
		Input: []tf.Input{
			t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Add all input tensors element wise.
//
// Arguments:
//	inputs: Must all be the same size and shape.
func AddN(scope *Scope, inputs []tf.Output) (sum tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "AddN",
		Input: []tf.Input{
			tf.OutputList(inputs),
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MaxAttr is an optional argument to Max.
type MaxAttr func(optionalAttr)

// MaxKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func MaxKeepDims(value bool) MaxAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the maximum of elements across dimensions of a tensor.
//
// Reduces `input` along the dimensions given in `reduction_indices`. Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
// retained with length 1.
//
// Arguments:
//	input: The tensor to reduce.
//	reduction_indices: The dimensions to reduce.
//
// Returns The reduced tensor.
func Max(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MaxAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Max",
		Input: []tf.Input{
			input, reduction_indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Cast x of type SrcT to y of DstT.
func Cast(scope *Scope, x tf.Output, DstT tf.DataType) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"DstT": DstT}
	opspec := tf.OpSpec{
		Type: "Cast",
		Input: []tf.Input{
			x,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the truth value of x AND y element-wise.
//
// *NOTE*: `LogicalAnd` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func LogicalAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "LogicalAnd",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ComplexAbsAttr is an optional argument to ComplexAbs.
type ComplexAbsAttr func(optionalAttr)

// ComplexAbsTout sets the optional Tout attribute to value.
// If not specified, defaults to DT_FLOAT
func ComplexAbsTout(value tf.DataType) ComplexAbsAttr {
	return func(m optionalAttr) {
		m["Tout"] = value
	}
}

// Computes the complex absolute value of a tensor.
//
// Given a tensor `x` of complex numbers, this operation returns a tensor of type
// `float` or `double` that is the absolute value of each element in `x`. All
// elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute
// value is computed as \\( \sqrt{a^2 + b^2}\\).
func ComplexAbs(scope *Scope, x tf.Output, optional ...ComplexAbsAttr) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ComplexAbs",
		Input: []tf.Input{
			x,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Draw bounding boxes on a batch of images.
//
// Outputs a copy of `images` but draws on top of the pixels zero or more bounding
// boxes specified by the locations in `boxes`. The coordinates of the each
// bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The
// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
// height of the underlying image.
//
// For example, if an image is 100 x 200 pixels and the bounding box is
// `[0.1, 0.2, 0.5, 0.9]`, the bottom-left and upper-right coordinates of the
// bounding box will be `(10, 40)` to `(50, 180)`.
//
// Parts of the bounding box may fall outside the image.
//
// Arguments:
//	images: 4-D with shape `[batch, height, width, depth]`. A batch of images.
//	boxes: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding
// boxes.
//
// Returns 4-D with the same shape as `images`. The batch of input images with
// bounding boxes drawn on the images.
func DrawBoundingBoxes(scope *Scope, images tf.Output, boxes tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "DrawBoundingBoxes",
		Input: []tf.Input{
			images, boxes,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the element-wise max of two SparseTensors.
//
// Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
//
// Arguments:
//	a_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
// SparseTensor, in the canonical lexicographic ordering.
//	a_values: 1-D.  `N` non-empty values corresponding to `a_indices`.
//	a_shape: 1-D.  Shape of the input SparseTensor.
//	b_indices: counterpart to `a_indices` for the other operand.
//	b_values: counterpart to `a_values` for the other operand; must be of the same dtype.
//	b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal.
//
// Returns 2-D.  The indices of the output SparseTensor.1-D.  The values of the output SparseTensor.
func SparseSparseMaximum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSparseMaximum",
		Input: []tf.Input{
			a_indices, a_values, a_shape, b_indices, b_values, b_shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Computes the gradient for the inverse of `x` wrt its input.
//
// DEPRECATED at GraphDef version 17: Use ReciprocalGrad
//
// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
// is the corresponding input gradient.
func InvGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "InvGrad",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the reciprocal of x element-wise.
//
// I.e., \\(y = 1 / x\\).
func Reciprocal(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Reciprocal",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
//
// See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
// ](http://arxiv.org/abs/1511.07289)
func Elu(scope *Scope, features tf.Output) (activations tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Elu",
		Input: []tf.Input{
			features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes square of x element-wise.
//
// I.e., \\(y = x * x = x^2\\).
func Square(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Square",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns element-wise remainder of division. When `x < 0` xor `y < 0` is
//
// true, this follows Python semantics in that the result here is consistent
// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`.
//
// *NOTE*: `FloorMod` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func FloorMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FloorMod",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes square root of x element-wise.
//
// I.e., \\(y = \sqrt{x} = x^{1/2}\\).
func Sqrt(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Sqrt",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Inserts a dimension of 1 into a tensor's shape.
//
// Given a tensor `input`, this operation inserts a dimension of 1 at the
// dimension index `dim` of `input`'s shape. The dimension index `dim` starts at
// zero; if you specify a negative number for `dim` it is counted backward from
// the end.
//
// This operation is useful if you want to add a batch dimension to a single
// element. For example, if you have a single image of shape `[height, width,
// channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
// which will make the shape `[1, height, width, channels]`.
//
// Other examples:
//
// ```prettyprint
// # 't' is a tensor of shape [2]
// shape(expand_dims(t, 0)) ==> [1, 2]
// shape(expand_dims(t, 1)) ==> [2, 1]
// shape(expand_dims(t, -1)) ==> [2, 1]
//
// # 't2' is a tensor of shape [2, 3, 5]
// shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
// shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
// shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
// ```
//
// This operation requires that:
//
// `-1-input.dims() <= dim <= input.dims()`
//
// This operation is related to `squeeze()`, which removes dimensions of
// size 1.
//
// Arguments:
//
//	dim: 0-D (scalar). Specifies the dimension index at which to
// expand the shape of `input`.
//
// Returns Contains the same data as `input`, but its shape has an additional
// dimension of size 1 added.
func ExpandDims(scope *Scope, input tf.Output, dim tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ExpandDims",
		Input: []tf.Input{
			input, dim,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// AllAttr is an optional argument to All.
type AllAttr func(optionalAttr)

// AllKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func AllKeepDims(value bool) AllAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the "logical and" of elements across dimensions of a tensor.
//
// Reduces `input` along the dimensions given in `reduction_indices`. Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
// retained with length 1.
//
// Arguments:
//	input: The tensor to reduce.
//	reduction_indices: The dimensions to reduce.
//
// Returns The reduced tensor.
func All(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...AllAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "All",
		Input: []tf.Input{
			input, reduction_indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// CTCBeamSearchDecoderAttr is an optional argument to CTCBeamSearchDecoder.
type CTCBeamSearchDecoderAttr func(optionalAttr)

// CTCBeamSearchDecoderMergeRepeated sets the optional merge_repeated attribute to value.
//
// value: If true, merge repeated classes in output.
// If not specified, defaults to true
func CTCBeamSearchDecoderMergeRepeated(value bool) CTCBeamSearchDecoderAttr {
	return func(m optionalAttr) {
		m["merge_repeated"] = value
	}
}

// Performs beam search decoding on the logits given in input.
//
// A note about the attribute merge_repeated: For the beam search decoder,
// this means that if consecutive entries in a beam are the same, only
// the first of these is emitted.  That is, when the top path is "A B B B B",
// "A B" is returned if merge_repeated = True but "A B B B B" is
// returned if merge_repeated = False.
//
// Arguments:
//	inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
//	sequence_length: A vector containing sequence lengths, size `(batch)`.
//	beam_width: A scalar >= 0 (beam search beam width).
//	top_paths: A scalar >= 0, <= beam_width (controls output size).
//
// Returns A list (length: top_paths) of indices matrices.  Matrix j,
// size `(total_decoded_outputs[j] x 2)`, has indices of a
// `SparseTensor<int64, 2>`.  The rows store: [batch, time].A list (length: top_paths) of values vectors.  Vector j,
// size `(length total_decoded_outputs[j])`, has the values of a
// `SparseTensor<int64, 2>`.  The vector stores the decoded classes for beam j.A list (length: top_paths) of shape vector.  Vector j,
// size `(2)`, stores the shape of the decoded `SparseTensor[j]`.
// Its values are: `[batch_size, max_decoded_length[j]]`.A matrix, shaped: `(batch_size x top_paths)`.  The
// sequence log-probabilities.
func CTCBeamSearchDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, beam_width int64, top_paths int64, optional ...CTCBeamSearchDecoderAttr) (decoded_indices []tf.Output, decoded_values []tf.Output, decoded_shape []tf.Output, log_probability tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"beam_width": beam_width, "top_paths": top_paths}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "CTCBeamSearchDecoder",
		Input: []tf.Input{
			inputs, sequence_length,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if decoded_indices, idx, err = makeOutputList(op, idx, "decoded_indices"); err != nil {
		scope.UpdateErr("CTCBeamSearchDecoder", err)
		return
	}
	if decoded_values, idx, err = makeOutputList(op, idx, "decoded_values"); err != nil {
		scope.UpdateErr("CTCBeamSearchDecoder", err)
		return
	}
	if decoded_shape, idx, err = makeOutputList(op, idx, "decoded_shape"); err != nil {
		scope.UpdateErr("CTCBeamSearchDecoder", err)
		return
	}
	log_probability = op.Output(idx)
	return decoded_indices, decoded_values, decoded_shape, log_probability
}

// Computes reciprocal of square root of x element-wise.
//
// I.e., \\(y = 1 / \sqrt{x}\\).
func Rsqrt(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Rsqrt",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// RecordInputAttr is an optional argument to RecordInput.
type RecordInputAttr func(optionalAttr)

// RecordInputFileRandomSeed sets the optional file_random_seed attribute to value.
//
// value: Random seeds used to produce randomized records.
// If not specified, defaults to 301
func RecordInputFileRandomSeed(value int64) RecordInputAttr {
	return func(m optionalAttr) {
		m["file_random_seed"] = value
	}
}

// RecordInputFileShuffleShiftRatio sets the optional file_shuffle_shift_ratio attribute to value.
//
// value: Shifts the list of files after the list is randomly
// shuffled.
// If not specified, defaults to 0
func RecordInputFileShuffleShiftRatio(value float32) RecordInputAttr {
	return func(m optionalAttr) {
		m["file_shuffle_shift_ratio"] = value
	}
}

// RecordInputFileBufferSize sets the optional file_buffer_size attribute to value.
//
// value: The randomization shuffling buffer.
// If not specified, defaults to 10000
func RecordInputFileBufferSize(value int64) RecordInputAttr {
	return func(m optionalAttr) {
		m["file_buffer_size"] = value
	}
}

// RecordInputFileParallelism sets the optional file_parallelism attribute to value.
//
// value: How many sstables are opened and concurrently iterated over.
// If not specified, defaults to 16
func RecordInputFileParallelism(value int64) RecordInputAttr {
	return func(m optionalAttr) {
		m["file_parallelism"] = value
	}
}

// RecordInputBatchSize sets the optional batch_size attribute to value.
//
// value: The batch size.
// If not specified, defaults to 32
func RecordInputBatchSize(value int64) RecordInputAttr {
	return func(m optionalAttr) {
		m["batch_size"] = value
	}
}

// Emits randomized records.
//
// Arguments:
//	file_pattern: Glob pattern for the data files.
//
// Returns A tensor of shape [batch_size].
func RecordInput(scope *Scope, file_pattern string, optional ...RecordInputAttr) (records tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"file_pattern": file_pattern}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RecordInput",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Rounds the values of a tensor to the nearest integer, element-wise.
//
// Rounds half to even.  Also known as bankers rounding. If you want to round
// according to the current system rounding mode use std::cint.
func Round(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Round",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Generates values in an interval.
//
// A sequence of `num` evenly-spaced values are generated beginning at `start`.
// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`,
// so that the last one is exactly `stop`.
//
// For example:
//
// ```
// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0  11.0  12.0]
// ```
//
// Arguments:
//	start: First entry in the range.
//	stop: Last entry in the range.
//	num: Number of values to generate.
//
// Returns 1-D. The generated values.
func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "LinSpace",
		Input: []tf.Input{
			start, stop, num,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes natural logarithm of x element-wise.
//
// I.e., \\(y = \log_e x\\).
func Log(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Log",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes rectified linear 6 gradients for a Relu6 operation.
//
// Arguments:
//	gradients: The backpropagated gradients to the corresponding Relu6 operation.
//	features: The features passed as input to the corresponding Relu6 operation.
//
// Returns The gradients:
// `gradients * (features > 0) * (features < 6)`.
func Relu6Grad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Relu6Grad",
		Input: []tf.Input{
			gradients, features,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResizeBicubicAttr is an optional argument to ResizeBicubic.
type ResizeBicubicAttr func(optionalAttr)

// ResizeBicubicAlignCorners sets the optional align_corners attribute to value.
//
// value: If true, rescale input by (new_height - 1) / (height - 1), which
// exactly aligns the 4 corners of images and resized images. If false, rescale
// by new_height / height. Treat similarly the width dimension.
// If not specified, defaults to false
func ResizeBicubicAlignCorners(value bool) ResizeBicubicAttr {
	return func(m optionalAttr) {
		m["align_corners"] = value
	}
}

// Resize `images` to `size` using bicubic interpolation.
//
// Input images can be of different types but output images are always float.
//
// Arguments:
//	images: 4-D with shape `[batch, height, width, channels]`.
//	size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
// new size for the images.
//
// Returns 4-D with shape
// `[batch, new_height, new_width, channels]`.
func ResizeBicubic(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBicubicAttr) (resized_images tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResizeBicubic",
		Input: []tf.Input{
			images, size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes natural logarithm of (1 + x) element-wise.
//
// I.e., \\(y = \log_e (1 + x)\\).
func Log1p(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Log1p",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the log of the absolute value of `Gamma(x)` element-wise.
func Lgamma(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Lgamma",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns element-wise largest integer not greater than x.
func Floor(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Floor",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the Gauss error function of `x` element-wise.
func Erf(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Erf",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the complementary error function of `x` element-wise.
func Erfc(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Erfc",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the 2-dimensional discrete Fourier Transform of a real-valued signal
//
// over the inner-most 2 dimensions of `input`.
//
// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the
// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
// of `output`: the zero-frequency term, followed by the `fft_length / 2`
// positive-frequency terms.
//
// Arguments:
//	input: A float32 tensor.
//	fft_length: An int32 tensor of shape [2]. The FFT length for each dimension.
//
// Returns A complex64 tensor of the same rank as `input`. The inner-most 2
//   dimensions of `input` are replaced with their 2D Fourier Transform. The
//   inner-most dimension contains `fft_length / 2 + 1` unique frequency
//   components.
//
// @compatibility(numpy)
// Equivalent to np.fft.rfft2
// @end_compatibility
func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "RFFT2D",
		Input: []tf.Input{
			input, fft_length,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Gradients for batch normalization.
//
// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization()
//
// This op is deprecated. See `tf.nn.batch_normalization`.
//
// Arguments:
//	t: A 4D input Tensor.
//	m: A 1D mean Tensor with size matching the last dimension of t.
// This is the first output from tf.nn.moments,
// or a saved moving average thereof.
//	v: A 1D variance Tensor with size matching the last dimension of t.
// This is the second output from tf.nn.moments,
// or a saved moving average thereof.
//	gamma: A 1D gamma Tensor with size matching the last dimension of t.
// If "scale_after_normalization" is true, this Tensor will be multiplied
// with the normalized Tensor.
//	backprop: 4D backprop Tensor.
//	variance_epsilon: A small float number to avoid dividing by 0.
//	scale_after_normalization: A bool indicating whether the resulted tensor
// needs to be multiplied with gamma.
//
// Returns 4D backprop tensor for input.1D backprop tensor for mean.1D backprop tensor for variance.1D backprop tensor for beta.1D backprop tensor for gamma.
func BatchNormWithGlobalNormalizationGrad(scope *Scope, t tf.Output, m tf.Output, v tf.Output, gamma tf.Output, backprop tf.Output, variance_epsilon float32, scale_after_normalization bool) (dx tf.Output, dm tf.Output, dv tf.Output, db tf.Output, dg tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization}
	opspec := tf.OpSpec{
		Type: "BatchNormWithGlobalNormalizationGrad",
		Input: []tf.Input{
			t, m, v, gamma, backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4)
}

// EncodeJpegAttr is an optional argument to EncodeJpeg.
type EncodeJpegAttr func(optionalAttr)

// EncodeJpegFormat sets the optional format attribute to value.
//
// value: Per pixel image format.
// If not specified, defaults to ""
func EncodeJpegFormat(value string) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["format"] = value
	}
}

// EncodeJpegQuality sets the optional quality attribute to value.
//
// value: Quality of the compression from 0 to 100 (higher is better and slower).
// If not specified, defaults to 95
func EncodeJpegQuality(value int64) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["quality"] = value
	}
}

// EncodeJpegProgressive sets the optional progressive attribute to value.
//
// value: If True, create a JPEG that loads progressively (coarse to fine).
// If not specified, defaults to false
func EncodeJpegProgressive(value bool) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["progressive"] = value
	}
}

// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value.
//
// value: If True, spend CPU/RAM to reduce size with no quality change.
// If not specified, defaults to false
func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["optimize_size"] = value
	}
}

// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value.
//
// value: See http://en.wikipedia.org/wiki/Chroma_subsampling.
// If not specified, defaults to true
func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["chroma_downsampling"] = value
	}
}

// EncodeJpegDensityUnit sets the optional density_unit attribute to value.
//
// value: Unit used to specify `x_density` and `y_density`:
// pixels per inch (`'in'`) or centimeter (`'cm'`).
// If not specified, defaults to "in"
func EncodeJpegDensityUnit(value string) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["density_unit"] = value
	}
}

// EncodeJpegXDensity sets the optional x_density attribute to value.
//
// value: Horizontal pixels per density unit.
// If not specified, defaults to 300
func EncodeJpegXDensity(value int64) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["x_density"] = value
	}
}

// EncodeJpegYDensity sets the optional y_density attribute to value.
//
// value: Vertical pixels per density unit.
// If not specified, defaults to 300
func EncodeJpegYDensity(value int64) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["y_density"] = value
	}
}

// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value.
//
// value: If not empty, embed this XMP metadata in the image header.
// If not specified, defaults to ""
func EncodeJpegXmpMetadata(value string) EncodeJpegAttr {
	return func(m optionalAttr) {
		m["xmp_metadata"] = value
	}
}

// JPEG-encode an image.
//
// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`.
//
// The attr `format` can be used to override the color format of the encoded
// output.  Values can be:
//
// *   `''`: Use a default format based on the number of channels in the image.
// *   `grayscale`: Output a grayscale JPEG image.  The `channels` dimension
//     of `image` must be 1.
// *   `rgb`: Output an RGB JPEG image. The `channels` dimension
//     of `image` must be 3.
//
// If `format` is not specified or is the empty string, a default format is picked
// in function of the number of channels in `image`:
//
// *   1: Output a grayscale image.
// *   3: Output an RGB image.
//
// Arguments:
//	image: 3-D with shape `[height, width, channels]`.
//
// Returns 0-D. JPEG-encoded image.
func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "EncodeJpeg",
		Input: []tf.Input{
			image,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes sin of x element-wise.
func Sin(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Sin",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the determinant of one ore more square matrices.
//
// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
// form square matrices. The output is a tensor containing the determinants
// for all input submatrices `[..., :, :]`.
//
// Arguments:
//	input: Shape is `[..., M, M]`.
//
// Returns Shape is `[...]`.
func MatrixDeterminant(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "MatrixDeterminant",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes cos of x element-wise.
func Cos(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Cos",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes tan of x element-wise.
func Tan(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Tan",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// BatchToSpace for 4-D tensors of type T.
//
// This is a legacy version of the more general BatchToSpaceND.
//
// Rearranges (permutes) data from batch into blocks of spatial data, followed by
// cropping. This is the reverse transformation of SpaceToBatch. More specifically,
// this op outputs a copy of the input tensor where values from the `batch`
// dimension are moved in spatial blocks to the `height` and `width` dimensions,
// followed by cropping along the `height` and `width` dimensions.
//
// Arguments:
//	input: 4-D tensor with shape
// `[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
//   depth]`. Note that the batch size of the input tensor must be divisible by
// `block_size * block_size`.
//	crops: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
// how many elements to crop from the intermediate result across the spatial
// dimensions as follows:
//
//     crops = [[crop_top, crop_bottom], [crop_left, crop_right]]
//
//
// Returns 4-D with shape `[batch, height, width, depth]`, where:
//
//       height = height_pad - crop_top - crop_bottom
//       width = width_pad - crop_left - crop_right
//
// The attr `block_size` must be greater than one. It indicates the block size.
//
// Some examples:
//
// (1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2:
//
// ```prettyprint
// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
// ```
//
// The output tensor has shape `[1, 2, 2, 1]` and value:
//
// ```prettyprint
// x = [[[[1], [2]], [[3], [4]]]]
// ```
//
// (2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2:
//
// ```prettyprint
// [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
// ```
//
// The output tensor has shape `[1, 2, 2, 3]` and value:
//
// ```prettyprint
// x = [[[[1, 2, 3], [4, 5, 6]],
//       [[7, 8, 9], [10, 11, 12]]]]
// ```
//
// (3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2:
//
// ```prettyprint
// x = [[[[1], [3]], [[9], [11]]],
//      [[[2], [4]], [[10], [12]]],
//      [[[5], [7]], [[13], [15]]],
//      [[[6], [8]], [[14], [16]]]]
// ```
//
// The output tensor has shape `[1, 4, 4, 1]` and value:
//
// ```prettyprint
// x = [[[1],   [2],  [3],  [4]],
//      [[5],   [6],  [7],  [8]],
//      [[9],  [10], [11],  [12]],
//      [[13], [14], [15],  [16]]]
// ```
//
// (4) For the following input of shape `[8, 1, 2, 1]` and block_size of 2:
//
// ```prettyprint
// x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
//      [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
// ```
//
// The output tensor has shape `[2, 2, 4, 1]` and value:
//
// ```prettyprint
// x = [[[[1], [3]], [[5], [7]]],
//      [[[2], [4]], [[10], [12]]],
//      [[[5], [7]], [[13], [15]]],
//      [[[6], [8]], [[14], [16]]]]
// ```
func BatchToSpace(scope *Scope, input tf.Output, crops tf.Output, block_size int64) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"block_size": block_size}
	opspec := tf.OpSpec{
		Type: "BatchToSpace",
		Input: []tf.Input{
			input, crops,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SparseToDenseAttr is an optional argument to SparseToDense.
type SparseToDenseAttr func(optionalAttr)

// SparseToDenseValidateIndices sets the optional validate_indices attribute to value.
//
// value: If true, indices are checked to make sure they are sorted in
// lexicographic order and that there are no repeats.
// If not specified, defaults to true
func SparseToDenseValidateIndices(value bool) SparseToDenseAttr {
	return func(m optionalAttr) {
		m["validate_indices"] = value
	}
}

// Converts a sparse representation into a dense tensor.
//
// Builds an array `dense` with shape `output_shape` such that
//
// ```prettyprint
// # If sparse_indices is scalar
// dense[i] = (i == sparse_indices ? sparse_values : default_value)
//
// # If sparse_indices is a vector, then for each i
// dense[sparse_indices[i]] = sparse_values[i]
//
// # If sparse_indices is an n by d matrix, then for each i in [0, n)
// dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
// ```
//
// All other values in `dense` are set to `default_value`.  If `sparse_values` is a
// scalar, all sparse indices are set to this single value.
//
// Indices should be sorted in lexicographic order, and indices must not
// contain any repeats. If `validate_indices` is true, these properties
// are checked during execution.
//
// Arguments:
//	sparse_indices: 0-D, 1-D, or 2-D.  `sparse_indices[i]` contains the complete
// index where `sparse_values[i]` will be placed.
//	output_shape: 1-D.  Shape of the dense output tensor.
//	sparse_values: 1-D.  Values corresponding to each row of `sparse_indices`,
// or a scalar value to be used for all sparse indices.
//	default_value: Scalar value to set for indices not specified in
// `sparse_indices`.
//
// Returns Dense output tensor of shape `output_shape`.
func SparseToDense(scope *Scope, sparse_indices tf.Output, output_shape tf.Output, sparse_values tf.Output, default_value tf.Output, optional ...SparseToDenseAttr) (dense tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SparseToDense",
		Input: []tf.Input{
			sparse_indices, output_shape, sparse_values, default_value,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes asin of x element-wise.
func Asin(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Asin",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Convert the quantized 'input' tensor into a lower-precision 'output', using the
//
// output range specified with 'requested_output_min' and 'requested_output_max'.
//
// [input_min, input_max] are scalar floats that specify the range for the float
// interpretation of the 'input' data. For example, if input_min is -1.0f and
// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
//
// Arguments:
//
//	input_min: The float value that the minimum quantized input value represents.
//	input_max: The float value that the maximum quantized input value represents.
//	requested_output_min: The float value that the minimum quantized output value represents.
//	requested_output_max: The float value that the maximum quantized output value represents.
//	out_type: The type of the output. Should be a lower bit depth than Tinput.
//
// Returns The requested_output_min value is copied into this output.The requested_output_max value is copied into this output.
func Requantize(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, requested_output_min tf.Output, requested_output_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"out_type": out_type}
	opspec := tf.OpSpec{
		Type: "Requantize",
		Input: []tf.Input{
			input, input_min, input_max, requested_output_min, requested_output_max,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Returns the index with the smallest value across dimensions of a tensor.
//
// Arguments:
//
//	dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
// of the input Tensor to reduce across. For vectors, use dimension = 0.
func ArgMin(scope *Scope, input tf.Output, dimension tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ArgMin",
		Input: []tf.Input{
			input, dimension,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes atan of x element-wise.
func Atan(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Atan",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceSparseApplyAdadeltaAttr is an optional argument to ResourceSparseApplyAdadelta.
type ResourceSparseApplyAdadeltaAttr func(optionalAttr)

// ResourceSparseApplyAdadeltaUseLocking sets the optional use_locking attribute to value.
//
// value: If True, updating of the var and accum tensors will be protected by
// a lock; otherwise the behavior is undefined, but may exhibit less contention.
// If not specified, defaults to false
func ResourceSparseApplyAdadeltaUseLocking(value bool) ResourceSparseApplyAdadeltaAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// var: Should be from a Variable().
//
// Arguments:
//
//	accum: Should be from a Variable().
//	accum_update: : Should be from a Variable().
//	lr: Learning rate. Must be a scalar.
//	rho: Decay factor. Must be a scalar.
//	epsilon: Constant factor. Must be a scalar.
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var and accum.
//
// Returns the created operation.
func ResourceSparseApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdadeltaAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyAdadelta",
		Input: []tf.Input{
			var_, accum, accum_update, lr, rho, epsilon, grad, indices,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Returns which elements of x are NaN.
//
// @compatibility(numpy)
// Equivalent to np.isnan
// @end_compatibility
func IsNan(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IsNan",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns an element-wise indication of the sign of a number.
//
// `y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`.
//
// For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
func Sign(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Sign",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns element-wise smallest integer in not less than x.
func Ceil(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Ceil",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes exponential of x element-wise.  \\(y = e^x\\).
func Exp(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Exp",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the Max along segments of a tensor.
//
// Read [the section on
// Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
// of segments.
//
// This operator is similar to the [unsorted segment sum operator](../../api_docs/python/math_ops.md#UnsortedSegmentSum).
// Instead of computing the sum over segments, it computes the maximum
// such that:
//
// \\(output_i = \max_j data_j\\) where max is over `j` such
// that `segment_ids[j] == i`.
//
// If the maximum is empty for a given segment ID `i`, it outputs the smallest possible value for specific numeric type,
//  `output[i] = numeric_limits<T>::min()`.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/UnsortedSegmentSum.png" alt>
// </div>
//
// Arguments:
//
//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
// first dimension.
//
//
// Returns Has same shape as data, except for dimension 0 which
// has size `num_segments`.
func UnsortedSegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "UnsortedSegmentMax",
		Input: []tf.Input{
			data, segment_ids, num_segments,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns x + y element-wise.
//
// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Add",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// BiasAddAttr is an optional argument to BiasAdd.
type BiasAddAttr func(optionalAttr)

// BiasAddDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the bias tensor will be added to the last dimension
// of the value tensor.
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, in_channels, in_height, in_width].
// The tensor will be added to "in_channels", the third-to-the-last
//     dimension.
// If not specified, defaults to "NHWC"
func BiasAddDataFormat(value string) BiasAddAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Adds `bias` to `value`.
//
// This is a special case of `tf.add` where `bias` is restricted to be 1-D.
// Broadcasting is supported, so `value` may have any number of dimensions.
//
// Arguments:
//	value: Any number of dimensions.
//	bias: 1-D with size the last dimension of `value`.
//
// Returns Broadcasted sum of `value` and `bias`.
func BiasAdd(scope *Scope, value tf.Output, bias tf.Output, optional ...BiasAddAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "BiasAdd",
		Input: []tf.Input{
			value, bias,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SparseReduceSumSparseAttr is an optional argument to SparseReduceSumSparse.
type SparseReduceSumSparseAttr func(optionalAttr)

// SparseReduceSumSparseKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func SparseReduceSumSparseKeepDims(value bool) SparseReduceSumSparseAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the sum of elements across dimensions of a SparseTensor.
//
// This Op takes a SparseTensor and is the sparse counterpart to
// `tf.reduce_sum()`.  In contrast to SparseReduceSum, this Op returns a
// SparseTensor.
//
// Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
// with length 1.
//
// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
// with a single element is returned.  Additionally, the axes can be negative,
// which are interpreted according to the indexing rules in Python.
//
// Arguments:
//	input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
// SparseTensor, possibly not in canonical ordering.
//	input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
//	input_shape: 1-D.  Shape of the input SparseTensor.
//	reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
func SparseReduceSumSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SparseReduceSumSparse",
		Input: []tf.Input{
			input_indices, input_values, input_shape, reduction_axes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Returns x * y element-wise.
//
// *NOTE*: `Mul` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Mul(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Mul",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns x / y element-wise.
//
// *NOTE*: `Div` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Div(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Div",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ApproximateEqualAttr is an optional argument to ApproximateEqual.
type ApproximateEqualAttr func(optionalAttr)

// ApproximateEqualTolerance sets the optional tolerance attribute to value.
// If not specified, defaults to 1e-05
func ApproximateEqualTolerance(value float32) ApproximateEqualAttr {
	return func(m optionalAttr) {
		m["tolerance"] = value
	}
}

// Returns the truth value of abs(x-y) < tolerance element-wise.
func ApproximateEqual(scope *Scope, x tf.Output, y tf.Output, optional ...ApproximateEqualAttr) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ApproximateEqual",
		Input: []tf.Input{
			x, y,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the max of x and y (i.e. x > y ? x : y) element-wise.
//
// *NOTE*: `Maximum` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Maximum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Maximum",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler.
type LogUniformCandidateSamplerAttr func(optionalAttr)

// LogUniformCandidateSamplerSeed sets the optional seed attribute to value.
//
// value: If either seed or seed2 are set to be non-zero, the random number
// generator is seeded by the given seed.  Otherwise, it is seeded by a
// random seed.
// If not specified, defaults to 0
func LogUniformCandidateSamplerSeed(value int64) LogUniformCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed"] = value
	}
}

// LogUniformCandidateSamplerSeed2 sets the optional seed2 attribute to value.
//
// value: An second seed to avoid seed collision.
// If not specified, defaults to 0
func LogUniformCandidateSamplerSeed2(value int64) LogUniformCandidateSamplerAttr {
	return func(m optionalAttr) {
		m["seed2"] = value
	}
}

// Generates labels for candidate sampling with a log-uniform distribution.
//
// See explanations of candidate sampling and the data formats at
// go/candidate-sampling.
//
// For each batch, this op picks a single set of sampled candidate labels.
//
// The advantages of sampling candidates per-batch are simplicity and the
// possibility of efficient dense matrix multiplication. The disadvantage is that
// the sampled candidates must be chosen independently of the context and of the
// true labels.
//
// Arguments:
//	true_classes: A batch_size * num_true matrix, in which each row contains the
// IDs of the num_true target_classes in the corresponding original label.
//	num_true: Number of true labels per context.
//	num_sampled: Number of candidates to randomly sample per batch.
//	unique: If unique is true, we sample with rejection, so that all sampled
// candidates in a batch are unique. This requires some approximation to
// estimate the post-rejection sampling probabilities.
//	range_max: The sampler will sample integers from the interval [0, range_max).
//
// Returns A vector of length num_sampled, in which each element is
// the ID of a sampled candidate.A batch_size * num_true matrix, representing
// the number of times each candidate is expected to occur in a batch
// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
// candidate representing the number of times the candidate is expected
// to occur in a batch of sampled candidates.  If unique=true, then this is a
// probability.
func LogUniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LogUniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "LogUniformCandidateSampler",
		Input: []tf.Input{
			true_classes,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Returns the truth value of (x < y) element-wise.
//
// *NOTE*: `Less` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Less(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Less",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute gradients for a FakeQuantWithMinMaxVars operation.
//
// Arguments:
//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
//	inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation.
// min, max: Quantization interval, scalar floats.
//
//
//
// Returns Backpropagated gradients w.r.t. inputs:
// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter:
// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter:
// `sum(gradients * (inputs > max))`.
func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "FakeQuantWithMinMaxVarsGradient",
		Input: []tf.Input{
			gradients, inputs, min, max,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Returns the min of x and y (i.e. x < y ? x : y) element-wise.
//
// *NOTE*: `Minimum` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func Minimum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Minimum",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the power of one value to another.
//
// Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
// corresponding elements in `x` and `y`. For example:
//
// ```
// # tensor 'x' is [[2, 2]], [3, 3]]
// # tensor 'y' is [[8, 16], [2, 3]]
// tf.pow(x, y) ==> [[256, 65536], [9, 27]]
// ```
func Pow(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Pow",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the upper regularized incomplete Gamma function `Q(a, x)`.
//
// The upper regularized incomplete Gamma function is defined as:
//
// ```
// Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)
// ```
// where
// ```
// Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt
// ```
// is the upper incomplete Gama function.
//
// Note, above `P(a, x)` (`Igamma`) is the lower regularized complete
// Gamma function.
func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Igammac",
		Input: []tf.Input{
			a, x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the lower regularized incomplete Gamma function `Q(a, x)`.
//
// The lower regularized incomplete Gamma function is defined as:
//
// ```
// P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)
// ```
// where
// ```
// gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt
// ```
// is the lower incomplete Gamma function.
//
// Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete
// Gamma function.
func Igamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Igamma",
		Input: []tf.Input{
			a, x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
//
// The regularized incomplete beta integral is defined as:
//
// ```
// I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}
// ```
// where
//
// ```
// B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt
// ```
//
// is the incomplete beta function and \\(B(a, b)\\) is the *complete*
// beta function.
func Betainc(scope *Scope, a tf.Output, b tf.Output, x tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Betainc",
		Input: []tf.Input{
			a, b, x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes log softmax activations.
//
// For each batch `i` and class `j` we have
//
//     logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
//
// Arguments:
//	logits: 2-D with shape `[batch_size, num_classes]`.
//
// Returns Same shape as `logits`.
func LogSoftmax(scope *Scope, logits tf.Output) (logsoftmax tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "LogSoftmax",
		Input: []tf.Input{
			logits,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the truth value of (x <= y) element-wise.
//
// *NOTE*: `LessEqual` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "LessEqual",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the truth value of x OR y element-wise.
//
// *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func LogicalOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "LogicalOr",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Selects elements from `t` or `e`, depending on `condition`.
//
// The `t`, and `e` tensors must all have the same shape, and the
// output will also have that shape.
//
// The `condition` tensor must be a scalar if `t` and `e` are scalars.
// If `t` and `e` are vectors or higher rank, then `condition` must be either a
// scalar, a vector with size matching the first dimension of `t`, or must have
// the same shape as `t`.
//
// The `condition` tensor acts as a mask that chooses, based on the value at each
// element, whether the corresponding element / row in the output should be
// taken from `t` (if true) or `e` (if false).
//
// If `condition` is a vector and `t` and `e` are higher rank matrices, then
// it chooses which row (outer dimension) to copy from `t` and `e`.
// If `condition` has the same shape as `t` and `e`, then it chooses which
// element to copy from `t` and `e`.
//
// For example:
//
// ```prettyprint
// # 'condition' tensor is [[True,  False]
// #                        [False, True]]
// # 't' is [[1, 2],
// #         [3, 4]]
// # 'e' is [[5, 6],
// #         [7, 8]]
// select(condition, t, e) ==> [[1, 6],
//                              [7, 4]]
//
//
// # 'condition' tensor is [True, False]
// # 't' is [[1, 2],
// #         [3, 4]]
// # 'e' is [[5, 6],
// #         [7, 8]]
// select(condition, t, e) ==> [[1, 2],
//                              [7, 8]]
//
// ```
//
// Arguments:
//
//	t: = A `Tensor` which may have the same shape as `condition`.
// If `condition` is rank 1, `t` may have higher rank,
// but its first dimension must match the size of `condition`.
//	e: = A `Tensor` with the same type and shape as `t`.
//
// Returns = A `Tensor` with the same type and shape as `t` and `e`.
func Select(scope *Scope, condition tf.Output, t tf.Output, e tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Select",
		Input: []tf.Input{
			condition, t, e,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MatMulAttr is an optional argument to MatMul.
type MatMulAttr func(optionalAttr)

// MatMulTransposeA sets the optional transpose_a attribute to value.
//
// value: If true, "a" is transposed before multiplication.
// If not specified, defaults to false
func MatMulTransposeA(value bool) MatMulAttr {
	return func(m optionalAttr) {
		m["transpose_a"] = value
	}
}

// MatMulTransposeB sets the optional transpose_b attribute to value.
//
// value: If true, "b" is transposed before multiplication.
// If not specified, defaults to false
func MatMulTransposeB(value bool) MatMulAttr {
	return func(m optionalAttr) {
		m["transpose_b"] = value
	}
}

// Multiply the matrix "a" by the matrix "b".
//
// The inputs must be two-dimensional matrices and the inner dimension of
// "a" (after being transposed if transpose_a is true) must match the
// outer dimension of "b" (after being transposed if transposed_b is
// true).
//
// *Note*: The default kernel implementation for MatMul on GPUs uses
// cublas.
func MatMul(scope *Scope, a tf.Output, b tf.Output, optional ...MatMulAttr) (product tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MatMul",
		Input: []tf.Input{
			a, b,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MeanAttr is an optional argument to Mean.
type MeanAttr func(optionalAttr)

// MeanKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func MeanKeepDims(value bool) MeanAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the mean of elements across dimensions of a tensor.
//
// Reduces `input` along the dimensions given in `reduction_indices`. Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
// retained with length 1.
//
// Arguments:
//	input: The tensor to reduce.
//	reduction_indices: The dimensions to reduce.
//
// Returns The reduced tensor.
func Mean(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MeanAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Mean",
		Input: []tf.Input{
			input, reduction_indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns which elements of x are finite.
//
// @compatibility(numpy)
// Equivalent to np.isfinite
// @end_compatibility
func IsFinite(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "IsFinite",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the index with the largest value across dimensions of a tensor.
//
// Arguments:
//
//	dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
// of the input Tensor to reduce across. For vectors, use dimension = 0.
func ArgMax(scope *Scope, input tf.Output, dimension tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ArgMax",
		Input: []tf.Input{
			input, dimension,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the sum along segments of a tensor.
//
// Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation)
// for an explanation of segments.
//
// Computes a tensor such that
// \\(output_i = \sum_j data_j\\) where sum is over `j` such
// that `segment_ids[j] == i`.
//
// If the sum is empty for a given segment ID `i`, `output[i] = 0`.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/SegmentSum.png" alt>
// </div>
//
// Arguments:
//
//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
// first dimension.  Values should be sorted and can be repeated.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
func SegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SegmentSum",
		Input: []tf.Input{
			data, segment_ids,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ImageSummaryAttr is an optional argument to ImageSummary.
type ImageSummaryAttr func(optionalAttr)

// ImageSummaryMaxImages sets the optional max_images attribute to value.
//
// value: Max number of batch elements to generate images for.
// If not specified, defaults to 3
//
// REQUIRES: value >= 1
func ImageSummaryMaxImages(value int64) ImageSummaryAttr {
	return func(m optionalAttr) {
		m["max_images"] = value
	}
}

// ImageSummaryBadColor sets the optional bad_color attribute to value.
//
// value: Color to use for pixels with non-finite values.
// If not specified, defaults to <dtype:DT_UINT8 tensor_shape:<dim:<size:4 > > int_val:255 int_val:0 int_val:0 int_val:255 >
func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
	return func(m optionalAttr) {
		m["bad_color"] = value
	}
}

// Outputs a `Summary` protocol buffer with images.
//
// The summary has up to `max_images` summary values containing images. The
// images are built from `tensor` which must be 4-D with shape `[batch_size,
// height, width, channels]` and where `channels` can be:
//
// *  1: `tensor` is interpreted as Grayscale.
// *  3: `tensor` is interpreted as RGB.
// *  4: `tensor` is interpreted as RGBA.
//
// The images have the same number of channels as the input tensor. For float
// input, the values are normalized one image at a time to fit in the range
// `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
// normalization algorithms:
//
// *  If the input values are all positive, they are rescaled so the largest one
//    is 255.
//
// *  If any input value is negative, the values are shifted so input value 0.0
//    is at 127.  They are then rescaled so that either the smallest value is 0,
//    or the largest one is 255.
//
// The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
// build the `tag` of the summary values:
//
// *  If `max_images` is 1, the summary value tag is '*tag*/image'.
// *  If `max_images` is greater than 1, the summary value tags are
//    generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
//
// The `bad_color` argument is the color to use in the generated images for
// non-finite input values.  It is a `unit8` 1-D tensor of length `channels`.
// Each element must be in the range `[0, 255]` (It represents the value of a
// pixel in the output image).  Non-finite values in the input tensor are
// replaced by this tensor in the output image.  The default value is the color
// red.
//
// Arguments:
//	tag: Scalar. Used to build the `tag` attribute of the summary values.
//	tensor: 4-D of shape `[batch_size, height, width, channels]` where
// `channels` is 1, 3, or 4.
//
// Returns Scalar. Serialized `Summary` protocol buffer.
func ImageSummary(scope *Scope, tag tf.Output, tensor tf.Output, optional ...ImageSummaryAttr) (summary tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ImageSummary",
		Input: []tf.Input{
			tag, tensor,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Reshapes a SparseTensor to represent values in a new dense shape.
//
// This operation has the same semantics as reshape on the represented dense
// tensor.  The `input_indices` are recomputed based on the requested `new_shape`.
//
// If one component of `new_shape` is the special value -1, the size of that
// dimension is computed so that the total dense size remains constant.  At
// most one component of `new_shape` can be -1.  The number of dense elements
// implied by `new_shape` must be the same as the number of dense elements
// originally implied by `input_shape`.
//
// Reshaping does not affect the order of values in the SparseTensor.
//
// If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape`
// has length `R_out`, then `input_indices` has shape `[N, R_in]`,
// `input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and
// `output_shape` has length `R_out`.
//
// Arguments:
//	input_indices: 2-D.  `N x R_in` matrix with the indices of non-empty values in a
// SparseTensor.
//	input_shape: 1-D.  `R_in` vector with the input SparseTensor's dense shape.
//	new_shape: 1-D.  `R_out` vector with the requested new dense shape.
//
// Returns 2-D.  `N x R_out` matrix with the updated indices of non-empty
// values in the output SparseTensor.1-D.  `R_out` vector with the full dense shape of the output
// SparseTensor.  This is the same as `new_shape` but with any -1 dimensions
// filled in.
func SparseReshape(scope *Scope, input_indices tf.Output, input_shape tf.Output, new_shape tf.Output) (output_indices tf.Output, output_shape tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseReshape",
		Input: []tf.Input{
			input_indices, input_shape, new_shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Computes the product along segments of a tensor.
//
// Read [the section on
// Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
// of segments.
//
// Computes a tensor such that
// \\(output_i = \prod_j data_j\\) where the product is over `j` such
// that `segment_ids[j] == i`.
//
// If the product is empty for a given segment ID `i`, `output[i] = 1`.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/SegmentProd.png" alt>
// </div>
//
// Arguments:
//
//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
// first dimension.  Values should be sorted and can be repeated.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
func SegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SegmentProd",
		Input: []tf.Input{
			data, segment_ids,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the sum along segments of a tensor.
//
// Read [the section on
// Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
// of segments.
//
// Computes a tensor such that
// `(output[i] = sum_{j...} data[j...]` where the sum is over tuples `j...` such
// that `segment_ids[j...] == i`.  Unlike `SegmentSum`, `segment_ids`
// need not be sorted and need not cover all values in the full
// range of valid values.
//
// If the sum is empty for a given segment ID `i`, `output[i] = 0`.
//
// `num_segments` should equal the number of distinct segment IDs.
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/UnsortedSegmentSum.png" alt>
// </div>
//
// Arguments:
//
//	segment_ids: A tensor whose shape is a prefix of `data.shape`.
//
//
// Returns Has same shape as data, except for the first `segment_ids.rank`
// dimensions, which are replaced with a single dimension which has size
// `num_segments`.
func UnsortedSegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "UnsortedSegmentSum",
		Input: []tf.Input{
			data, segment_ids, num_segments,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the sum along sparse segments of a tensor.
//
// Read [the section on
// Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
// of segments.
//
// Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first
// dimension, selecting a subset of dimension 0, specified by `indices`.
//
// For example:
//
// ```prettyprint
// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
//
// # Select two rows, one segment.
// tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
//   ==> [[0 0 0 0]]
//
// # Select two rows, two segment.
// tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
//   ==> [[ 1  2  3  4]
//        [-1 -2 -3 -4]]
//
// # Select all rows, two segments.
// tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
//   ==> [[0 0 0 0]
//        [5 6 7 8]]
//
// # Which is equivalent to:
// tf.segment_sum(c, tf.constant([0, 0, 1]))
// ```
//
// Arguments:
//
//	indices: A 1-D tensor. Has same rank as `segment_ids`.
//	segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
func SparseSegmentSum(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSegmentSum",
		Input: []tf.Input{
			data, indices, segment_ids,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Counts the number of occurrences of each value in an integer array.
//
// Outputs a vector with length `size` and the same dtype as `weights`. If
// `weights` are empty, then index `i` stores the number of times the value `i` is
// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
// the value in `weights` at each index where the corresponding value in `arr` is
// `i`.
//
// Values in `arr` outside of the range [0, size) are ignored.
//
// Arguments:
//	arr: int32 `Tensor`.
//	size: non-negative int32 scalar `Tensor`.
//	weights: is an int32, int64, float32, or float64 `Tensor` with the same
// shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights
// equal to 1.
//
// Returns 1D `Tensor` with length equal to `size`. The counts or summed weights for
// each value in the range [0, size).
func Bincount(scope *Scope, arr tf.Output, size tf.Output, weights tf.Output) (bins tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Bincount",
		Input: []tf.Input{
			arr, size, weights,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// CropAndResizeGradBoxesAttr is an optional argument to CropAndResizeGradBoxes.
type CropAndResizeGradBoxesAttr func(optionalAttr)

// CropAndResizeGradBoxesMethod sets the optional method attribute to value.
//
// value: A string specifying the interpolation method. Only 'bilinear' is
// supported for now.
// If not specified, defaults to "bilinear"
func CropAndResizeGradBoxesMethod(value string) CropAndResizeGradBoxesAttr {
	return func(m optionalAttr) {
		m["method"] = value
	}
}

// Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
//
// Arguments:
//	grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
//	image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
// Both `image_height` and `image_width` need to be positive.
//	boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
// specifies the coordinates of a box in the `box_ind[i]` image and is specified
// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
// `[0, 1]` interval of normalized image height is mapped to
// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
// which case the sampled crop is an up-down flipped version of the original
// image. The width dimension is treated similarly. Normalized coordinates
// outside the `[0, 1]` range are allowed, in which case we use
// `extrapolation_value` to extrapolate the input image values.
//	box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
// The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
//
// Returns A 2-D tensor of shape `[num_boxes, 4]`.
func CropAndResizeGradBoxes(scope *Scope, grads tf.Output, image tf.Output, boxes tf.Output, box_ind tf.Output, optional ...CropAndResizeGradBoxesAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "CropAndResizeGradBoxes",
		Input: []tf.Input{
			grads, image, boxes, box_ind,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Reshapes a quantized tensor as per the Reshape op.
//
// ```
//
// Arguments:
//
//	shape: Defines the shape of the output tensor.
//	input_min: The minimum value of the input.
//	input_max: The maximum value of the input.
//
// Returns This value is copied from input_min.This value is copied from input_max.
func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "QuantizedReshape",
		Input: []tf.Input{
			tensor, shape, input_min, input_max,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// Computes gradients for SparseSegmentSqrtN.
//
// Returns tensor "output" with same shape as grad, except for dimension 0 whose
// value is output_dim0.
//
// Arguments:
//	grad: gradient propagated to the SparseSegmentSqrtN op.
//	indices: indices passed to the corresponding SparseSegmentSqrtN op.
//	segment_ids: segment_ids passed to the corresponding SparseSegmentSqrtN op.
//	output_dim0: dimension 0 of "data" passed to SparseSegmentSqrtN op.
func SparseSegmentSqrtNGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSegmentSqrtNGrad",
		Input: []tf.Input{
			grad, indices, segment_ids, output_dim0,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Creates a sequence of numbers.
//
// This operation creates a sequence of numbers that begins at `start` and
// extends by increments of `delta` up to but not including `limit`.
//
// For example:
//
// ```
// # 'start' is 3
// # 'limit' is 18
// # 'delta' is 3
// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
// ```
//
// Arguments:
//	start: 0-D (scalar). First entry in the sequence.
//	limit: 0-D (scalar). Upper limit of sequence, exclusive.
//	delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`.
//
// Returns 1-D.
func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Range",
		Input: []tf.Input{
			start, limit, delta,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum.
type ResourceSparseApplyMomentumAttr func(optionalAttr)

// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value.
//
// value: If `True`, updating of the var and accum tensors will be protected
// by a lock; otherwise the behavior is undefined, but may exhibit less
// contention.
// If not specified, defaults to false
func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr {
	return func(m optionalAttr) {
		m["use_locking"] = value
	}
}

// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value.
//
// value: If `True`, the tensor passed to compute grad will be
// var - lr * momentum * accum, so in the end, the var you get is actually
// var - lr * momentum * accum.
// If not specified, defaults to false
func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr {
	return func(m optionalAttr) {
		m["use_nesterov"] = value
	}
}

// Update relevant entries in '*var' and '*accum' according to the momentum scheme.
//
// Set use_nesterov = True if you want to use Nesterov momentum.
//
// That is for rows we have grad for, we update var and accum as follows:
//
// accum = accum * momentum + grad
// var -= lr * accum
//
// Arguments:
//	var_: Should be from a Variable().
//	accum: Should be from a Variable().
//	lr: Learning rate. Must be a scalar.
//	grad: The gradient.
//	indices: A vector of indices into the first dimension of var and accum.
//	momentum: Momentum. Must be a scalar.
//
// Returns the created operation.
func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResourceSparseApplyMomentum",
		Input: []tf.Input{
			var_, accum, lr, grad, indices, momentum,
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Returns the complex conjugate of a complex number.
//
// Given a tensor `input` of complex numbers, this operation returns a tensor of
// complex numbers that are the complex conjugate of each element in `input`. The
// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the
// real part and *b* is the imaginary part.
//
// The complex conjugate returned by this operation is of the form \\(a - bj\\).
//
// For example:
//
// ```
// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]
// ```
func Conj(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Conj",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// RestoreSliceAttr is an optional argument to RestoreSlice.
type RestoreSliceAttr func(optionalAttr)

// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value.
//
// value: Index of file to open first if multiple files match
// `file_pattern`. See the documentation for `Restore`.
// If not specified, defaults to -1
func RestoreSlicePreferredShard(value int64) RestoreSliceAttr {
	return func(m optionalAttr) {
		m["preferred_shard"] = value
	}
}

// Restores a tensor from checkpoint files.
//
// This is like `Restore` except that restored tensor can be listed as filling
// only a slice of a larger tensor.  `shape_and_slice` specifies the shape of the
// larger tensor and the slice that the restored tensor covers.
//
// The `shape_and_slice` input has the same format as the
// elements of the `shapes_and_slices` input of the `SaveSlices` op.
//
// Arguments:
//	file_pattern: Must have a single element. The pattern of the files from
// which we read the tensor.
//	tensor_name: Must have a single element. The name of the tensor to be
// restored.
//	shape_and_slice: Scalar. The shapes and slice specifications to use when
// restoring a tensors.
//	dt: The type of the tensor to be restored.
//
// Returns The restored tensor.
func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dt": dt}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "RestoreSlice",
		Input: []tf.Input{
			file_pattern, tensor_name, shape_and_slice,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// A placeholder op that passes through `input` when its output is not fed.
//
// Arguments:
//	input: The default value to produce when `output` is not fed.
//	shape: The (possibly partial) shape of the tensor.
//
// Returns A placeholder tensor that defaults to `input` if it is not fed.
func PlaceholderWithDefault(scope *Scope, input tf.Output, shape tf.Shape) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"shape": shape}
	opspec := tf.OpSpec{
		Type: "PlaceholderWithDefault",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Deprecated. Use TensorArrayReadV3
func TensorArrayReadV2(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	opspec := tf.OpSpec{
		Type: "TensorArrayReadV2",
		Input: []tf.Input{
			handle, index, flow_in,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QuantizedMatMulAttr is an optional argument to QuantizedMatMul.
type QuantizedMatMulAttr func(optionalAttr)

// QuantizedMatMulToutput sets the optional Toutput attribute to value.
// If not specified, defaults to DT_QINT32
func QuantizedMatMulToutput(value tf.DataType) QuantizedMatMulAttr {
	return func(m optionalAttr) {
		m["Toutput"] = value
	}
}

// QuantizedMatMulTransposeA sets the optional transpose_a attribute to value.
//
// value: If true, `a` is transposed before multiplication.
// If not specified, defaults to false
func QuantizedMatMulTransposeA(value bool) QuantizedMatMulAttr {
	return func(m optionalAttr) {
		m["transpose_a"] = value
	}
}

// QuantizedMatMulTransposeB sets the optional transpose_b attribute to value.
//
// value: If true, `b` is transposed before multiplication.
// If not specified, defaults to false
func QuantizedMatMulTransposeB(value bool) QuantizedMatMulAttr {
	return func(m optionalAttr) {
		m["transpose_b"] = value
	}
}

// QuantizedMatMulTactivation sets the optional Tactivation attribute to value.
//
// value: The type of output produced by activation function
// following this operation.
// If not specified, defaults to DT_QUINT8
func QuantizedMatMulTactivation(value tf.DataType) QuantizedMatMulAttr {
	return func(m optionalAttr) {
		m["Tactivation"] = value
	}
}

// Perform a quantized matrix multiplication of  `a` by the matrix `b`.
//
// The inputs must be two-dimensional matrices and the inner dimension of
// `a` (after being transposed if `transpose_a` is non-zero) must match the
// outer dimension of `b` (after being transposed if `transposed_b` is
// non-zero).
//
// Arguments:
//	a: Must be a two-dimensional tensor.
//	b: Must be a two-dimensional tensor.
//	min_a: The float value that the lowest quantized `a` value represents.
//	max_a: The float value that the highest quantized `a` value represents.
//	min_b: The float value that the lowest quantized `b` value represents.
//	max_b: The float value that the highest quantized `b` value represents.
//
// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
func QuantizedMatMul(scope *Scope, a tf.Output, b tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizedMatMul",
		Input: []tf.Input{
			a, b, min_a, max_a, min_b, max_b,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// QuantizedMulAttr is an optional argument to QuantizedMul.
type QuantizedMulAttr func(optionalAttr)

// QuantizedMulToutput sets the optional Toutput attribute to value.
// If not specified, defaults to DT_QINT32
func QuantizedMulToutput(value tf.DataType) QuantizedMulAttr {
	return func(m optionalAttr) {
		m["Toutput"] = value
	}
}

// Returns x * y element-wise, working on quantized buffers.
//
// Arguments:
//
//
//	min_x: The float value that the lowest quantized `x` value represents.
//	max_x: The float value that the highest quantized `x` value represents.
//	min_y: The float value that the lowest quantized `y` value represents.
//	max_y: The float value that the highest quantized `y` value represents.
//
// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents.
//
// *NOTE*: `QuantizedMul` supports limited forms of broadcasting. More about
// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func QuantizedMul(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedMulAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QuantizedMul",
		Input: []tf.Input{
			x, y, min_x, max_x, min_y, max_y,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// QueueEnqueueManyV2Attr is an optional argument to QueueEnqueueManyV2.
type QueueEnqueueManyV2Attr func(optionalAttr)

// QueueEnqueueManyV2TimeoutMs sets the optional timeout_ms attribute to value.
//
// value: If the queue is too full, this operation will block for up
// to timeout_ms milliseconds.
// Note: This option is not supported yet.
// If not specified, defaults to -1
func QueueEnqueueManyV2TimeoutMs(value int64) QueueEnqueueManyV2Attr {
	return func(m optionalAttr) {
		m["timeout_ms"] = value
	}
}

// Enqueues zero or more tuples of one or more tensors in the given queue.
//
// This operation slices each component tensor along the 0th dimension to
// make multiple queue elements. All of the tuple components must have the
// same size in the 0th dimension.
//
// The components input has k elements, which correspond to the components of
// tuples stored in the given queue.
//
// N.B. If the queue is full, this operation will block until the given
// elements have been enqueued (or 'timeout_ms' elapses, if specified).
//
// Arguments:
//	handle: The handle to a queue.
//	components: One or more tensors from which the enqueued tensors should
// be taken.
//
// Returns the created operation.
func QueueEnqueueManyV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueManyV2Attr) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "QueueEnqueueManyV2",
		Input: []tf.Input{
			handle, tf.OutputList(components),
		},
		Attrs: attrs,
	}
	return scope.AddOperation(opspec)
}

// Forwards the input to the output.
//
// This operator represents the loop termination condition used by the
// "pivot" switches of a loop.
//
// Arguments:
//	input: A boolean scalar, representing the branch predicate of the Switch op.
//
// Returns The same tensor as `input`.
func LoopCond(scope *Scope, input tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "LoopCond",
		Input: []tf.Input{
			input,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns (x - y)(x - y) element-wise.
//
// *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SquaredDifference",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Convert the quantized 'input' tensor into a lower-precision 'output', using the
//
// actual distribution of the values to maximize the usage of the lower bit depth
// and adjusting the output min and max ranges accordingly.
//
// [input_min, input_max] are scalar floats that specify the range for the float
// interpretation of the 'input' data. For example, if input_min is -1.0f and
// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
//
// This operator tries to squeeze as much precision as possible into an output with
// a lower bit depth by calculating the actual min and max values found in the
// data. For example, maybe that quint16 input has no values lower than 16,384 and
// none higher than 49,152. That means only half the range is actually needed, all
// the float interpretations are between -0.5f and 0.5f, so if we want to compress
// the data into a quint8 output, we can use that range rather than the theoretical
// -1.0f to 1.0f that is suggested by the input min and max.
//
// In practice, this is most useful for taking output from operations like
// QuantizedMatMul that can produce higher bit-depth outputs than their inputs and
// may have large potential output ranges, but in practice have a distribution of
// input values that only uses a small fraction of the possible range. By feeding
// that output into this operator, we can reduce it from 32 bits down to 8 with
// minimal loss of accuracy.
//
// Arguments:
//
//	input_min: The float value that the minimum quantized input value represents.
//	input_max: The float value that the maximum quantized input value represents.
//	out_type: The type of the output. Should be a lower bit depth than Tinput.
//
// Returns The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents.
func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"out_type": out_type}
	opspec := tf.OpSpec{
		Type: "QuantizeDownAndShrinkRange",
		Input: []tf.Input{
			input, input_min, input_max,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1), op.Output(2)
}

// DecodePngAttr is an optional argument to DecodePng.
type DecodePngAttr func(optionalAttr)

// DecodePngChannels sets the optional channels attribute to value.
//
// value: Number of color channels for the decoded image.
// If not specified, defaults to 0
func DecodePngChannels(value int64) DecodePngAttr {
	return func(m optionalAttr) {
		m["channels"] = value
	}
}

// DecodePngDtype sets the optional dtype attribute to value.
// If not specified, defaults to DT_UINT8
func DecodePngDtype(value tf.DataType) DecodePngAttr {
	return func(m optionalAttr) {
		m["dtype"] = value
	}
}

// Decode a PNG-encoded image to a uint8 or uint16 tensor.
//
// The attr `channels` indicates the desired number of color channels for the
// decoded image.
//
// Accepted values are:
//
// *   0: Use the number of channels in the PNG-encoded image.
// *   1: output a grayscale image.
// *   3: output an RGB image.
// *   4: output an RGBA image.
//
// If needed, the PNG-encoded image is transformed to match the requested number
// of color channels.
//
// Arguments:
//	contents: 0-D.  The PNG-encoded image.
//
// Returns 3-D with shape `[height, width, channels]`.
func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (image tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DecodePng",
		Input: []tf.Input{
			contents,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// AudioSummaryV2Attr is an optional argument to AudioSummaryV2.
type AudioSummaryV2Attr func(optionalAttr)

// AudioSummaryV2MaxOutputs sets the optional max_outputs attribute to value.
//
// value: Max number of batch elements to generate audio for.
// If not specified, defaults to 3
//
// REQUIRES: value >= 1
func AudioSummaryV2MaxOutputs(value int64) AudioSummaryV2Attr {
	return func(m optionalAttr) {
		m["max_outputs"] = value
	}
}

// Outputs a `Summary` protocol buffer with audio.
//
// The summary has up to `max_outputs` summary values containing audio. The
// audio is built from `tensor` which must be 3-D with shape `[batch_size,
// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
//
// The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
// build the `tag` of the summary values:
//
// *  If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
// *  If `max_outputs` is greater than 1, the summary value tags are
//    generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
//
// Arguments:
//	tag: Scalar. Used to build the `tag` attribute of the summary values.
//	tensor: 2-D of shape `[batch_size, frames]`.
//	sample_rate: The sample rate of the signal in hertz.
//
// Returns Scalar. Serialized `Summary` protocol buffer.
func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate tf.Output, optional ...AudioSummaryV2Attr) (summary tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "AudioSummaryV2",
		Input: []tf.Input{
			tag, tensor, sample_rate,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// QrAttr is an optional argument to Qr.
type QrAttr func(optionalAttr)

// QrFullMatrices sets the optional full_matrices attribute to value.
//
// value: If true, compute full-sized `q` and `r`. If false
// (the default), compute only the leading `P` columns of `q`.
// If not specified, defaults to false
func QrFullMatrices(value bool) QrAttr {
	return func(m optionalAttr) {
		m["full_matrices"] = value
	}
}

// Computes the QR decompositions of one or more matrices.
//
// Computes the QR decomposition of each inner matrix in `tensor` such that
// `tensor[..., :, :] = q[..., :, :] * r[..., :,:])`
//
// ```prettyprint
// # a is a tensor.
// # q is a tensor of orthonormal matrices.
// # r is a tensor of upper triangular matrices.
// q, r = qr(a)
// q_full, r_full = qr(a, full_matrices=True)
// ```
//
// Arguments:
//	input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
//
// Returns Orthonormal basis for range of `a`. If `full_matrices` is `False` then
// shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
// `[..., M, M]`.Triangular factor. If `full_matrices` is `False` then shape is
// `[..., P, N]`. If `full_matrices` is `True` then shape is `[..., M, N]`.
func Qr(scope *Scope, input tf.Output, optional ...QrAttr) (q tf.Output, r tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Qr",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// AudioSummaryAttr is an optional argument to AudioSummary.
type AudioSummaryAttr func(optionalAttr)

// AudioSummaryMaxOutputs sets the optional max_outputs attribute to value.
//
// value: Max number of batch elements to generate audio for.
// If not specified, defaults to 3
//
// REQUIRES: value >= 1
func AudioSummaryMaxOutputs(value int64) AudioSummaryAttr {
	return func(m optionalAttr) {
		m["max_outputs"] = value
	}
}

// Outputs a `Summary` protocol buffer with audio.
//
// DEPRECATED at GraphDef version 15: Use AudioSummaryV2.
//
// The summary has up to `max_outputs` summary values containing audio. The
// audio is built from `tensor` which must be 3-D with shape `[batch_size,
// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
//
// The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
// build the `tag` of the summary values:
//
// *  If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
// *  If `max_outputs` is greater than 1, the summary value tags are
//    generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
//
// Arguments:
//	tag: Scalar. Used to build the `tag` attribute of the summary values.
//	tensor: 2-D of shape `[batch_size, frames]`.
//	sample_rate: The sample rate of the signal in hertz.
//
// Returns Scalar. Serialized `Summary` protocol buffer.
func AudioSummary(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate float32, optional ...AudioSummaryAttr) (summary tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"sample_rate": sample_rate}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "AudioSummary",
		Input: []tf.Input{
			tag, tensor,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResizeNearestNeighborAttr is an optional argument to ResizeNearestNeighbor.
type ResizeNearestNeighborAttr func(optionalAttr)

// ResizeNearestNeighborAlignCorners sets the optional align_corners attribute to value.
//
// value: If true, rescale input by (new_height - 1) / (height - 1), which
// exactly aligns the 4 corners of images and resized images. If false, rescale
// by new_height / height. Treat similarly the width dimension.
// If not specified, defaults to false
func ResizeNearestNeighborAlignCorners(value bool) ResizeNearestNeighborAttr {
	return func(m optionalAttr) {
		m["align_corners"] = value
	}
}

// Resize `images` to `size` using nearest neighbor interpolation.
//
// Arguments:
//	images: 4-D with shape `[batch, height, width, channels]`.
//	size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
// new size for the images.
//
// Returns 4-D with shape
// `[batch, new_height, new_width, channels]`.
func ResizeNearestNeighbor(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeNearestNeighborAttr) (resized_images tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResizeNearestNeighbor",
		Input: []tf.Input{
			images, size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the gradient for the sqrt of `x` wrt its input.
//
// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy`
// is the corresponding input gradient.
func SqrtGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SqrtGrad",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MatrixInverseAttr is an optional argument to MatrixInverse.
type MatrixInverseAttr func(optionalAttr)

// MatrixInverseAdjoint sets the optional adjoint attribute to value.
// If not specified, defaults to false
func MatrixInverseAdjoint(value bool) MatrixInverseAttr {
	return func(m optionalAttr) {
		m["adjoint"] = value
	}
}

// Computes the inverse of one or more square invertible matrices or their
//
// adjoints (conjugate transposes).
//
// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
// form square matrices. The output is a tensor of the same shape as the input
// containing the inverse for all input submatrices `[..., :, :]`.
//
// The op uses LU decomposition with partial pivoting to compute the inverses.
//
// If a matrix is not invertible there is no guarantee what the op does. It
// may detect the condition and raise an exception or it may simply return a
// garbage result.
//
// Arguments:
//	input: Shape is `[..., M, M]`.
//
// Returns Shape is `[..., M, M]`.
//
// @compatibility(numpy)
// Equivalent to np.linalg.inv
// @end_compatibility
func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MatrixInverse",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TFRecordReaderV2Attr is an optional argument to TFRecordReaderV2.
type TFRecordReaderV2Attr func(optionalAttr)

// TFRecordReaderV2Container sets the optional container attribute to value.
//
// value: If non-empty, this reader is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func TFRecordReaderV2Container(value string) TFRecordReaderV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// TFRecordReaderV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this reader is named in the given bucket
// with this shared_name. Otherwise, the node name is used instead.
// If not specified, defaults to ""
func TFRecordReaderV2SharedName(value string) TFRecordReaderV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// TFRecordReaderV2CompressionType sets the optional compression_type attribute to value.
// If not specified, defaults to ""
func TFRecordReaderV2CompressionType(value string) TFRecordReaderV2Attr {
	return func(m optionalAttr) {
		m["compression_type"] = value
	}
}

// A Reader that outputs the records from a TensorFlow Records file.
//
// Returns The handle to reference the Reader.
func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TFRecordReaderV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MatrixSolveAttr is an optional argument to MatrixSolve.
type MatrixSolveAttr func(optionalAttr)

// MatrixSolveAdjoint sets the optional adjoint attribute to value.
//
// value: Boolean indicating whether to solve with `matrix` or its (block-wise)
// adjoint.
// If not specified, defaults to false
func MatrixSolveAdjoint(value bool) MatrixSolveAttr {
	return func(m optionalAttr) {
		m["adjoint"] = value
	}
}

// Solves systems of linear equations.
//
// `Matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
// form square matrices. `Rhs` is a tensor of shape `[..., M, K]`. The `output` is
// a tensor shape `[..., M, K]`.  If `adjoint` is `False` then each output matrix
// satisfies `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
// If `adjoint` is `True` then each output matrix satisfies
// `adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`.
//
// Arguments:
//	matrix: Shape is `[..., M, M]`.
//	rhs: Shape is `[..., M, K]`.
//
// Returns Shape is `[..., M, K]`.
func MatrixSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixSolveAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MatrixSolve",
		Input: []tf.Input{
			matrix, rhs,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SumAttr is an optional argument to Sum.
type SumAttr func(optionalAttr)

// SumKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func SumKeepDims(value bool) SumAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the sum of elements across dimensions of a tensor.
//
// Reduces `input` along the dimensions given in `reduction_indices`. Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
// retained with length 1.
//
// Arguments:
//	input: The tensor to reduce.
//	reduction_indices: The dimensions to reduce.
//
// Returns The reduced tensor.
func Sum(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...SumAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Sum",
		Input: []tf.Input{
			input, reduction_indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
//
// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
// becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
// are placed in `outputs[i]` in lexicographic order of `js`, and the first
// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
// In detail,
//
// ```python
//     outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
//
//     outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
// ```
//
// `data.shape` must start with `partitions.shape`.
//
// For example:
//
// ```python
//     # Scalar partitions.
//     partitions = 1
//     num_partitions = 2
//     data = [10, 20]
//     outputs[0] = []  # Empty with shape [0, 2]
//     outputs[1] = [[10, 20]]
//
//     # Vector partitions.
//     partitions = [0, 0, 1, 1, 0]
//     num_partitions = 2
//     data = [10, 20, 30, 40, 50]
//     outputs[0] = [10, 20, 50]
//     outputs[1] = [30, 40]
// ```
//
// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
// <img style="width:100%" src="../../images/DynamicPartition.png" alt>
// </div>
//
// Arguments:
//
//	partitions: Any shape.  Indices in the range `[0, num_partitions)`.
//	num_partitions: The number of partitions to output.
func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"num_partitions": num_partitions}
	opspec := tf.OpSpec{
		Type: "DynamicPartition",
		Input: []tf.Input{
			data, partitions,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
		scope.UpdateErr("DynamicPartition", err)
		return
	}
	return outputs
}

// Serialize a `SparseTensor` into a string 3-vector (1-D `Tensor`) object.
//
// Arguments:
//	sparse_indices: 2-D.  The `indices` of the `SparseTensor`.
//	sparse_values: 1-D.  The `values` of the `SparseTensor`.
//	sparse_shape: 1-D.  The `shape` of the `SparseTensor`.
func SerializeSparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) (serialized_sparse tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SerializeSparse",
		Input: []tf.Input{
			sparse_indices, sparse_values, sparse_shape,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the reciprocal of x element-wise.
//
// DEPRECATED at GraphDef version 17: Use Reciprocal
//
// I.e., \\(y = 1 / x\\).
func Inv(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Inv",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns x / y element-wise for integer types.
//
// Truncation designates that negative numbers will round fractional quantities
// toward zero. I.e. -7 / 5 = 1. This matches C semantics but it is different
// than Python semantics. See `FloorDiv` for a division function that matches
// Python Semantics.
//
// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "TruncateDiv",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Restores tensors from a V2 checkpoint.
//
// For backward compatibility with the V1 format, this Op currently allows
// restoring from a V1 checkpoint as well:
//   - This Op first attempts to find the V2 index file pointed to by "prefix", and
//     if found proceed to read it as a V2 checkpoint;
//   - Otherwise the V1 read path is invoked.
// Relying on this behavior is not recommended, as the ability to fall back to read
// V1 might be deprecated and eventually removed.
//
// By default, restores the named tensors in full.  If the caller wishes to restore
// specific slices of stored tensors, "shape_and_slices" should be non-empty
// strings and correspondingly well-formed.
//
// Callers must ensure all the named tensors are indeed stored in the checkpoint.
//
// Arguments:
//	prefix: Must have a single element.  The prefix of a V2 checkpoint.
//	tensor_names: shape {N}.  The names of the tensors to be restored.
//	shape_and_slices: shape {N}.  The slice specs of the tensors to be restored.
// Empty strings indicate that they are non-partitioned tensors.
//	dtypes: shape {N}.  The list of expected dtype for the tensors.  Must match
// those stored in the checkpoint.
//
// Returns shape {N}.  The restored tensors, whose shapes are read from the
// checkpoint directly.
func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtypes": dtypes}
	opspec := tf.OpSpec{
		Type: "RestoreV2",
		Input: []tf.Input{
			prefix, tensor_names, shape_and_slices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	if scope.Err() != nil {
		return
	}
	var idx int
	var err error
	if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil {
		scope.UpdateErr("RestoreV2", err)
		return
	}
	return tensors
}

// AvgPoolAttr is an optional argument to AvgPool.
type AvgPoolAttr func(optionalAttr)

// AvgPoolDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, in_height, in_width, in_channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, in_channels, in_height, in_width].
// If not specified, defaults to "NHWC"
func AvgPoolDataFormat(value string) AvgPoolAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Performs average pooling on the input.
//
// Each entry in `output` is the mean of the corresponding size `ksize`
// window in `value`.
//
// Arguments:
//	value: 4-D with shape `[batch, height, width, channels]`.
//	ksize: The size of the sliding window for each dimension of `value`.
//	strides: The stride of the sliding window for each dimension of `value`.
//	padding: The type of padding algorithm to use.
//
// Returns The average pooled output tensor.
func AvgPool(scope *Scope, value tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "AvgPool",
		Input: []tf.Input{
			value,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes the mean along sparse segments of a tensor.
//
// Read [the section on
// Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
// of segments.
//
// Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first
// dimension, selecting a subset of dimension 0, specified by `indices`.
//
// Arguments:
//
//	indices: A 1-D tensor. Has same rank as `segment_ids`.
//	segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
//
// Returns Has same shape as data, except for dimension 0 which
// has size `k`, the number of segments.
func SparseSegmentMean(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SparseSegmentMean",
		Input: []tf.Input{
			data, indices, segment_ids,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// WholeFileReaderV2Attr is an optional argument to WholeFileReaderV2.
type WholeFileReaderV2Attr func(optionalAttr)

// WholeFileReaderV2Container sets the optional container attribute to value.
//
// value: If non-empty, this reader is placed in the given container.
// Otherwise, a default container is used.
// If not specified, defaults to ""
func WholeFileReaderV2Container(value string) WholeFileReaderV2Attr {
	return func(m optionalAttr) {
		m["container"] = value
	}
}

// WholeFileReaderV2SharedName sets the optional shared_name attribute to value.
//
// value: If non-empty, this reader is named in the given bucket
// with this shared_name. Otherwise, the node name is used instead.
// If not specified, defaults to ""
func WholeFileReaderV2SharedName(value string) WholeFileReaderV2Attr {
	return func(m optionalAttr) {
		m["shared_name"] = value
	}
}

// A Reader that outputs the entire contents of a file as a value.
//
// To use, enqueue filenames in a Queue.  The output of ReaderRead will
// be a filename (key) and the contents of that file (value).
//
// Returns The handle to reference the Reader.
func WholeFileReaderV2(scope *Scope, optional ...WholeFileReaderV2Attr) (reader_handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "WholeFileReaderV2",

		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns up to `num_records` (key, value) pairs produced by a Reader.
//
// Will dequeue from the input queue if necessary (e.g. when the
// Reader needs to start reading from a new file since it has finished
// with the previous file).
// It may return less than `num_records` even before the last batch.
//
// Arguments:
//	reader_handle: Handle to a `Reader`.
//	queue_handle: Handle to a `Queue`, with string work items.
//	num_records: number of records to read from `Reader`.
//
// Returns A 1-D tensor.A 1-D tensor.
func ReaderReadUpToV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output, num_records tf.Output) (keys tf.Output, values tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReaderReadUpToV2",
		Input: []tf.Input{
			reader_handle, queue_handle, num_records,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Conv2DBackpropFilterAttr is an optional argument to Conv2DBackpropFilter.
type Conv2DBackpropFilterAttr func(optionalAttr)

// Conv2DBackpropFilterUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value.
// If not specified, defaults to true
func Conv2DBackpropFilterUseCudnnOnGpu(value bool) Conv2DBackpropFilterAttr {
	return func(m optionalAttr) {
		m["use_cudnn_on_gpu"] = value
	}
}

// Conv2DBackpropFilterDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, in_height, in_width, in_channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, in_channels, in_height, in_width].
// If not specified, defaults to "NHWC"
func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Computes the gradients of convolution with respect to the filter.
//
// Arguments:
//	input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
//	filter_sizes: An integer vector representing the tensor shape of `filter`,
// where `filter` is a 4-D
// `[filter_height, filter_width, in_channels, out_channels]` tensor.
//	out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`.
// Gradients w.r.t. the output of the convolution.
//	strides: The stride of the sliding window for each dimension of the input
// of the convolution. Must be in the same order as the dimension specified with
// format.
//	padding: The type of padding algorithm to use.
//
// Returns 4-D with shape
// `[filter_height, filter_width, in_channels, out_channels]`.  Gradient w.r.t.
// the `filter` input of the convolution.
func Conv2DBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropFilterAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Conv2DBackpropFilter",
		Input: []tf.Input{
			input, filter_sizes, out_backprop,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes Psi, the derivative of Lgamma (the log of the absolute value of
//
// `Gamma(x)`), element-wise.
func Digamma(scope *Scope, x tf.Output) (y tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "Digamma",
		Input: []tf.Input{
			x,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MinAttr is an optional argument to Min.
type MinAttr func(optionalAttr)

// MinKeepDims sets the optional keep_dims attribute to value.
//
// value: If true, retain reduced dimensions with length 1.
// If not specified, defaults to false
func MinKeepDims(value bool) MinAttr {
	return func(m optionalAttr) {
		m["keep_dims"] = value
	}
}

// Computes the minimum of elements across dimensions of a tensor.
//
// Reduces `input` along the dimensions given in `reduction_indices`. Unless
// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
// `reduction_indices`. If `keep_dims` is true, the reduced dimensions are
// retained with length 1.
//
// Arguments:
//	input: The tensor to reduce.
//	reduction_indices: The dimensions to reduce.
//
// Returns The reduced tensor.
func Min(scope *Scope, input tf.Output, reduction_indices tf.Output, optional ...MinAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Min",
		Input: []tf.Input{
			input, reduction_indices,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns the number of work units this Reader has finished processing.
//
// Arguments:
//	reader_handle: Handle to a Reader.
func ReaderNumWorkUnitsCompletedV2(scope *Scope, reader_handle tf.Output) (units_completed tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReaderNumWorkUnitsCompletedV2",
		Input: []tf.Input{
			reader_handle,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Returns x / y element-wise for real types.
//
// If `x` and `y` are reals, this will return the floating-point division.
//
// *NOTE*: `Div` supports broadcasting. More about broadcasting
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
func RealDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "RealDiv",
		Input: []tf.Input{
			x, y,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// CropAndResizeGradImageAttr is an optional argument to CropAndResizeGradImage.
type CropAndResizeGradImageAttr func(optionalAttr)

// CropAndResizeGradImageMethod sets the optional method attribute to value.
//
// value: A string specifying the interpolation method. Only 'bilinear' is
// supported for now.
// If not specified, defaults to "bilinear"
func CropAndResizeGradImageMethod(value string) CropAndResizeGradImageAttr {
	return func(m optionalAttr) {
		m["method"] = value
	}
}

// Computes the gradient of the crop_and_resize op wrt the input image tensor.
//
// Arguments:
//	grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
//	boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
// specifies the coordinates of a box in the `box_ind[i]` image and is specified
// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
// `[0, 1]` interval of normalized image height is mapped to
// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
// which case the sampled crop is an up-down flipped version of the original
// image. The width dimension is treated similarly. Normalized coordinates
// outside the `[0, 1]` range are allowed, in which case we use
// `extrapolation_value` to extrapolate the input image values.
//	box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
// The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
//	image_size: A 1-D tensor with value `[batch, image_height, image_width, depth]`
// containing the original image size. Both `image_height` and `image_width` need
// to be positive.
//
//
// Returns A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
func CropAndResizeGradImage(scope *Scope, grads tf.Output, boxes tf.Output, box_ind tf.Output, image_size tf.Output, T tf.DataType, optional ...CropAndResizeGradImageAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"T": T}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "CropAndResizeGradImage",
		Input: []tf.Input{
			grads, boxes, box_ind, image_size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ShapeAttr is an optional argument to Shape.
type ShapeAttr func(optionalAttr)

// ShapeOutType sets the optional out_type attribute to value.
// If not specified, defaults to DT_INT32
func ShapeOutType(value tf.DataType) ShapeAttr {
	return func(m optionalAttr) {
		m["out_type"] = value
	}
}

// Returns the shape of a tensor.
//
// This operation returns a 1-D integer tensor representing the shape of `input`.
//
// For example:
//
// ```prettyprint
// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
// shape(t) ==> [2, 2, 3]
// ```
func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "Shape",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Computes softmax cross entropy cost and gradients to backpropagate.
//
// Inputs are the logits, not probabilities.
//
// Arguments:
//	features: batch_size x num_classes matrix
//	labels: batch_size x num_classes matrix
// The caller must ensure that each batch of labels represents a valid
// probability distribution.
//
// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix).
func SoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SoftmaxCrossEntropyWithLogits",
		Input: []tf.Input{
			features, labels,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// ResizeBilinearGradAttr is an optional argument to ResizeBilinearGrad.
type ResizeBilinearGradAttr func(optionalAttr)

// ResizeBilinearGradAlignCorners sets the optional align_corners attribute to value.
//
// value: If true, rescale grads by (orig_height - 1) / (height - 1), which
// exactly aligns the 4 corners of grads and original_image. If false, rescale by
// orig_height / height. Treat similarly the width dimension.
// If not specified, defaults to false
func ResizeBilinearGradAlignCorners(value bool) ResizeBilinearGradAttr {
	return func(m optionalAttr) {
		m["align_corners"] = value
	}
}

// Computes the gradient of bilinear interpolation.
//
// Arguments:
//	grads: 4-D with shape `[batch, height, width, channels]`.
//	original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`,
// The image tensor that was resized.
//
// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`.
// Gradients with respect to the input image. Input image must have been
// float or double.
func ResizeBilinearGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBilinearGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResizeBilinearGrad",
		Input: []tf.Input{
			grads, original_image,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ResizeNearestNeighborGradAttr is an optional argument to ResizeNearestNeighborGrad.
type ResizeNearestNeighborGradAttr func(optionalAttr)

// ResizeNearestNeighborGradAlignCorners sets the optional align_corners attribute to value.
//
// value: If true, rescale grads by (orig_height - 1) / (height - 1), which
// exactly aligns the 4 corners of grads and original_image. If false, rescale by
// orig_height / height. Treat similarly the width dimension.
// If not specified, defaults to false
func ResizeNearestNeighborGradAlignCorners(value bool) ResizeNearestNeighborGradAttr {
	return func(m optionalAttr) {
		m["align_corners"] = value
	}
}

// Computes the gradient of nearest neighbor interpolation.
//
// Arguments:
//	grads: 4-D with shape `[batch, height, width, channels]`.
//	size: = A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The
// original input size.
//
// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients
// with respect to the input image.
func ResizeNearestNeighborGrad(scope *Scope, grads tf.Output, size tf.Output, optional ...ResizeNearestNeighborGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ResizeNearestNeighborGrad",
		Input: []tf.Input{
			grads, size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// DecodeJpegAttr is an optional argument to DecodeJpeg.
type DecodeJpegAttr func(optionalAttr)

// DecodeJpegChannels sets the optional channels attribute to value.
//
// value: Number of color channels for the decoded image.
// If not specified, defaults to 0
func DecodeJpegChannels(value int64) DecodeJpegAttr {
	return func(m optionalAttr) {
		m["channels"] = value
	}
}

// DecodeJpegRatio sets the optional ratio attribute to value.
//
// value: Downscaling ratio.
// If not specified, defaults to 1
func DecodeJpegRatio(value int64) DecodeJpegAttr {
	return func(m optionalAttr) {
		m["ratio"] = value
	}
}

// DecodeJpegFancyUpscaling sets the optional fancy_upscaling attribute to value.
//
// value: If true use a slower but nicer upscaling of the
// chroma planes (yuv420/422 only).
// If not specified, defaults to true
func DecodeJpegFancyUpscaling(value bool) DecodeJpegAttr {
	return func(m optionalAttr) {
		m["fancy_upscaling"] = value
	}
}

// DecodeJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value.
//
// value: If true try to recover an image from truncated input.
// If not specified, defaults to false
func DecodeJpegTryRecoverTruncated(value bool) DecodeJpegAttr {
	return func(m optionalAttr) {
		m["try_recover_truncated"] = value
	}
}

// DecodeJpegAcceptableFraction sets the optional acceptable_fraction attribute to value.
//
// value: The minimum required fraction of lines before a truncated
// input is accepted.
// If not specified, defaults to 1
func DecodeJpegAcceptableFraction(value float32) DecodeJpegAttr {
	return func(m optionalAttr) {
		m["acceptable_fraction"] = value
	}
}

// DecodeJpegDctMethod sets the optional dct_method attribute to value.
//
// value: string specifying a hint about the algorithm used for
// decompression.  Defaults to "" which maps to a system-specific
// default.  Currently valid values are ["INTEGER_FAST",
// "INTEGER_ACCURATE"].  The hint may be ignored (e.g., the internal
// jpeg library changes to a version that does not have that specific
// option.)
// If not specified, defaults to ""
func DecodeJpegDctMethod(value string) DecodeJpegAttr {
	return func(m optionalAttr) {
		m["dct_method"] = value
	}
}

// Decode a JPEG-encoded image to a uint8 tensor.
//
// The attr `channels` indicates the desired number of color channels for the
// decoded image.
//
// Accepted values are:
//
// *   0: Use the number of channels in the JPEG-encoded image.
// *   1: output a grayscale image.
// *   3: output an RGB image.
//
// If needed, the JPEG-encoded image is transformed to match the requested number
// of color channels.
//
// The attr `ratio` allows downscaling the image by an integer factor during
// decoding.  Allowed values are: 1, 2, 4, and 8.  This is much faster than
// downscaling the image later.
//
// Arguments:
//	contents: 0-D.  The JPEG-encoded image.
//
// Returns 3-D with shape `[height, width, channels]`..
func DecodeJpeg(scope *Scope, contents tf.Output, optional ...DecodeJpegAttr) (image tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "DecodeJpeg",
		Input: []tf.Input{
			contents,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3.
type TensorArrayGatherV3Attr func(optionalAttr)

// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value.
//
// value: The expected shape of an element, if known. Used to
// validate the shapes of TensorArray elements. If this shape is not
// fully specified, gathering zero-size TensorArrays is an error.
// If not specified, defaults to <unknown_rank:true >
func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr {
	return func(m optionalAttr) {
		m["element_shape"] = value
	}
}

// Gather specific elements from the TensorArray into output `value`.
//
// All elements selected by `indices` must have the same shape.
//
// Arguments:
//	handle: The handle to a TensorArray.
//	indices: The locations in the TensorArray from which to read tensor elements.
//	flow_in: A float scalar that enforces proper chaining of operations.
//	dtype: The type of the elem that is returned.
//
// Returns All of the elements in the TensorArray, concatenated along a new
// axis (the new dimension 0).
func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"dtype": dtype}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "TensorArrayGatherV3",
		Input: []tf.Input{
			handle, indices, flow_in,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Compute the 3-dimensional discrete Fourier Transform of a real-valued signal
//
// over the inner-most 3 dimensions of `input`.
//
// Since the DFT of a real signal is Hermitian-symmetric, `RFFT3D` only returns the
// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
// of `output`: the zero-frequency term, followed by the `fft_length / 2`
// positive-frequency terms.
//
// Arguments:
//	input: A float32 tensor.
//	fft_length: An int32 tensor of shape [3]. The FFT length for each dimension.
//
// Returns A complex64 tensor of the same rank as `input`. The inner-most 3
//   dimensions of `input` are replaced with the their 3D Fourier Transform. The
//   inner-most dimension contains `fft_length / 2 + 1` unique frequency
//   components.
//
// @compatibility(numpy)
// Equivalent to np.fft.rfftn with 3 dimensions.
// @end_compatibility
func RFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "RFFT3D",
		Input: []tf.Input{
			input, fft_length,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Deprecated. Disallowed in GraphDef version >= 2.
//
// DEPRECATED at GraphDef version 2: Use AdjustContrastv2 instead
func AdjustContrast(scope *Scope, images tf.Output, contrast_factor tf.Output, min_value tf.Output, max_value tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "AdjustContrast",
		Input: []tf.Input{
			images, contrast_factor, min_value, max_value,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Store the input tensor in the state of the current session.
//
// Arguments:
//	value: The tensor to be stored.
//
// Returns The handle for the tensor stored in the session state, represented
// as a ResourceHandle object.
func GetSessionHandleV2(scope *Scope, value tf.Output) (handle tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "GetSessionHandleV2",
		Input: []tf.Input{
			value,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Restore a Reader to its initial clean state.
//
// Arguments:
//	reader_handle: Handle to a Reader.
//
// Returns the created operation.
func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "ReaderResetV2",
		Input: []tf.Input{
			reader_handle,
		},
	}
	return scope.AddOperation(opspec)
}

// Adjust the hue of one or more images.
//
// `images` is a tensor of at least 3 dimensions.  The last dimension is
// interpretted as channels, and must be three.
//
// The input image is considered in the RGB colorspace. Conceptually, the RGB
// colors are first mapped into HSV. A delta is then applied all the hue values,
// and then remapped back to RGB colorspace.
//
// Arguments:
//	images: Images to adjust.  At least 3-D.
//	delta: A float delta to add to the hue.
//
// Returns The hue-adjusted image or images.
func AdjustHue(scope *Scope, images tf.Output, delta tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "AdjustHue",
		Input: []tf.Input{
			images, delta,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2.
type SelfAdjointEigV2Attr func(optionalAttr)

// SelfAdjointEigV2ComputeV sets the optional compute_v attribute to value.
//
// value: If `True` then eigenvectors will be computed and returned in `v`.
// Otherwise, only the eigenvalues will be computed.
// If not specified, defaults to true
func SelfAdjointEigV2ComputeV(value bool) SelfAdjointEigV2Attr {
	return func(m optionalAttr) {
		m["compute_v"] = value
	}
}

// Computes the eigen decomposition of one or more square self-adjoint matrices.
//
// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`.
//
// ```prettyprint
// # a is a tensor.
// # e is a tensor of eigenvalues.
// # v is a tensor of eigenvectors.
// e, v = self_adjoint_eig(a)
// e = self_adjoint_eig(a, compute_v=False)
// ```
//
// Arguments:
//	input: `Tensor` input of shape `[N, N]`.
//
// Returns Eigenvalues. Shape is `[N]`.Eigenvectors. Shape is `[N, N]`.
func SelfAdjointEigV2(scope *Scope, input tf.Output, optional ...SelfAdjointEigV2Attr) (e tf.Output, v tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "SelfAdjointEigV2",
		Input: []tf.Input{
			input,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Adjust the saturation of one or more images.
//
// `images` is a tensor of at least 3 dimensions.  The last dimension is
// interpretted as channels, and must be three.
//
// The input image is considered in the RGB colorspace. Conceptually, the RGB
// colors are first mapped into HSV. A scale is then applied all the saturation
// values, and then remapped back to RGB colorspace.
//
// Arguments:
//	images: Images to adjust.  At least 3-D.
//	scale: A float scale to add to the saturation.
//
// Returns The hue-adjusted image or images.
func AdjustSaturation(scope *Scope, images tf.Output, scale tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "AdjustSaturation",
		Input: []tf.Input{
			images, scale,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// EncodePngAttr is an optional argument to EncodePng.
type EncodePngAttr func(optionalAttr)

// EncodePngCompression sets the optional compression attribute to value.
//
// value: Compression level.
// If not specified, defaults to -1
func EncodePngCompression(value int64) EncodePngAttr {
	return func(m optionalAttr) {
		m["compression"] = value
	}
}

// PNG-encode an image.
//
// `image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]`
// where `channels` is:
//
// *   1: for grayscale.
// *   2: for grayscale + alpha.
// *   3: for RGB.
// *   4: for RGBA.
//
// The ZLIB compression level, `compression`, can be -1 for the PNG-encoder
// default or a value from 0 to 9.  9 is the highest compression level, generating
// the smallest output, but is slower.
//
// Arguments:
//	image: 3-D with shape `[height, width, channels]`.
//
// Returns 0-D. PNG-encoded image.
func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (contents tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "EncodePng",
		Input: []tf.Input{
			image,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MatrixSolveLsAttr is an optional argument to MatrixSolveLs.
type MatrixSolveLsAttr func(optionalAttr)

// MatrixSolveLsFast sets the optional fast attribute to value.
// If not specified, defaults to true
func MatrixSolveLsFast(value bool) MatrixSolveLsAttr {
	return func(m optionalAttr) {
		m["fast"] = value
	}
}

// Solves one or more linear least-squares problems.
//
// `matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions
// form matrices of size `[M, N]`. Rhs is a tensor of shape `[..., M, K]`.
// The output is a tensor shape `[..., N, K]` where each output matrix solves
// each of the equations matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]
// in the least squares sense.
//
// matrix and right-hand sides in the batch:
//
// `matrix`=\\(A \in \Re^{m \times n}\\),
// `rhs`=\\(B  \in \Re^{m \times k}\\),
// `output`=\\(X  \in \Re^{n \times k}\\),
// `l2_regularizer`=\\(\lambda\\).
//
// If `fast` is `True`, then the solution is computed by solving the normal
// equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then
// \\(X = (A^T A + \lambda I)^{-1} A^T B\\), which solves the least-squares
// problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 +
// \lambda ||Z||_F^2\\). If \\(m \lt n\\) then `output` is computed as
// \\(X = A^T (A A^T + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the
// minimum-norm solution to the under-determined linear system, i.e.
// \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||Z||_F^2 \\), subject to
// \\(A Z = B\\). Notice that the fast path is only numerically stable when
// \\(A\\) is numerically full rank and has a condition number
// \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or\\(\lambda\\) is
// sufficiently large.
//
// If `fast` is `False` an algorithm based on the numerically robust complete
// orthogonal decomposition is used. This computes the minimum-norm
// least-squares solution, even when \\(A\\) is rank deficient. This path is
// typically 6-7 times slower than the fast path. If `fast` is `False` then
// `l2_regularizer` is ignored.
//
// Arguments:
//	matrix: Shape is `[..., M, N]`.
//	rhs: Shape is `[..., M, K]`.
//	l2_regularizer: Scalar tensor.
//
// @compatibility(numpy)
// Equivalent to np.linalg.lstsq
// @end_compatibility
//
// Returns Shape is `[..., N, K]`.
func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer tf.Output, optional ...MatrixSolveLsAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MatrixSolveLs",
		Input: []tf.Input{
			matrix, rhs, l2_regularizer,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// Converts one or more images from RGB to HSV.
//
// Outputs a tensor of the same shape as the `images` tensor, containing the HSV
// value of the pixels. The output is only well defined if the value in `images`
// are in `[0,1]`.
//
// `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
// `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
// corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
//
// Arguments:
//	images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3.
//
// Returns `images` converted to HSV.
func RGBToHSV(scope *Scope, images tf.Output) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "RGBToHSV",
		Input: []tf.Input{
			images,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// ExtractGlimpseAttr is an optional argument to ExtractGlimpse.
type ExtractGlimpseAttr func(optionalAttr)

// ExtractGlimpseCentered sets the optional centered attribute to value.
//
// value: indicates if the offset coordinates are centered relative to
// the image, in which case the (0, 0) offset is relative to the center
// of the input images. If false, the (0,0) offset corresponds to the
// upper left corner of the input images.
// If not specified, defaults to true
func ExtractGlimpseCentered(value bool) ExtractGlimpseAttr {
	return func(m optionalAttr) {
		m["centered"] = value
	}
}

// ExtractGlimpseNormalized sets the optional normalized attribute to value.
//
// value: indicates if the offset coordinates are normalized.
// If not specified, defaults to true
func ExtractGlimpseNormalized(value bool) ExtractGlimpseAttr {
	return func(m optionalAttr) {
		m["normalized"] = value
	}
}

// ExtractGlimpseUniformNoise sets the optional uniform_noise attribute to value.
//
// value: indicates if the noise should be generated using a
// uniform distribution or a Gaussian distribution.
// If not specified, defaults to true
func ExtractGlimpseUniformNoise(value bool) ExtractGlimpseAttr {
	return func(m optionalAttr) {
		m["uniform_noise"] = value
	}
}

// Extracts a glimpse from the input tensor.
//
// Returns a set of windows called glimpses extracted at location
// `offsets` from the input tensor. If the windows only partially
// overlaps the inputs, the non overlapping areas will be filled with
// random noise.
//
// The result is a 4-D tensor of shape `[batch_size, glimpse_height,
// glimpse_width, channels]`. The channels and batch dimensions are the
// same as that of the input tensor. The height and width of the output
// windows are specified in the `size` parameter.
//
// The argument `normalized` and `centered` controls how the windows are built:
//
// * If the coordinates are normalized but not centered, 0.0 and 1.0
//   correspond to the minimum and maximum of each height and width
//   dimension.
// * If the coordinates are both normalized and centered, they range from
//   -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper
//   left corner, the lower right corner is located at (1.0, 1.0) and the
//   center is at (0, 0).
// * If the coordinates are not normalized they are interpreted as
//   numbers of pixels.
//
// Arguments:
//	input: A 4-D float tensor of shape `[batch_size, height, width, channels]`.
//	size: A 1-D tensor of 2 elements containing the size of the glimpses
// to extract.  The glimpse height must be specified first, following
// by the glimpse width.
//	offsets: A 2-D integer tensor of shape `[batch_size, 2]` containing
// the y, x locations of the center of each window.
//
// Returns A tensor representing the glimpses `[batch_size,
// glimpse_height, glimpse_width, channels]`.
func ExtractGlimpse(scope *Scope, input tf.Output, size tf.Output, offsets tf.Output, optional ...ExtractGlimpseAttr) (glimpse tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "ExtractGlimpse",
		Input: []tf.Input{
			input, size, offsets,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// MaxPoolGradAttr is an optional argument to MaxPoolGrad.
type MaxPoolGradAttr func(optionalAttr)

// MaxPoolGradDataFormat sets the optional data_format attribute to value.
//
// value: Specify the data format of the input and output data. With the
// default format "NHWC", the data is stored in the order of:
//     [batch, in_height, in_width, in_channels].
// Alternatively, the format could be "NCHW", the data storage order of:
//     [batch, in_channels, in_height, in_width].
// If not specified, defaults to "NHWC"
func MaxPoolGradDataFormat(value string) MaxPoolGradAttr {
	return func(m optionalAttr) {
		m["data_format"] = value
	}
}

// Computes gradients of the maxpooling function.
//
// Arguments:
//	orig_input: The original input tensor.
//	orig_output: The original output tensor.
//	grad: 4-D.  Gradients w.r.t. the output of `max_pool`.
//	ksize: The size of the window for each dimension of the input tensor.
//	strides: The stride of the sliding window for each dimension of the
// input tensor.
//	padding: The type of padding algorithm to use.
//
// Returns Gradients w.r.t. the input to `max_pool`.
func MaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradAttr) (output tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "MaxPoolGrad",
		Input: []tf.Input{
			orig_input, orig_output, grad,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}

// CropAndResizeAttr is an optional argument to CropAndResize.
type CropAndResizeAttr func(optionalAttr)

// CropAndResizeMethod sets the optional method attribute to value.
//
// value: A string specifying the interpolation method. Only 'bilinear' is
// supported for now.
// If not specified, defaults to "bilinear"
func CropAndResizeMethod(value string) CropAndResizeAttr {
	return func(m optionalAttr) {
		m["method"] = value
	}
}

// CropAndResizeExtrapolationValue sets the optional extrapolation_value attribute to value.
//
// value: Value used for extrapolation, when applicable.
// If not specified, defaults to 0
func CropAndResizeExtrapolationValue(value float32) CropAndResizeAttr {
	return func(m optionalAttr) {
		m["extrapolation_value"] = value
	}
}

// Extracts crops from the input image tensor and bilinearly resizes them (possibly
//
// with aspect ratio change) to a common output size specified by `crop_size`. This
// is more general than the `crop_to_bounding_box` op which extracts a fixed size
// slice from the input image and does not allow resizing or aspect ratio change.
//
// Returns a tensor with `crops` from the input `image` at positions defined at the
// bounding box locations in `boxes`. The cropped boxes are all resized (with
// bilinear interpolation) to a fixed `size = [crop_height, crop_width]`. The
// result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`.
//
// Arguments:
//	image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
// Both `image_height` and `image_width` need to be positive.
//	boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
// specifies the coordinates of a box in the `box_ind[i]` image and is specified
// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
// `[0, 1]` interval of normalized image height is mapped to
// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
// which case the sampled crop is an up-down flipped version of the original
// image. The width dimension is treated similarly. Normalized coordinates
// outside the `[0, 1]` range are allowed, in which case we use
// `extrapolation_value` to extrapolate the input image values.
//	box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
// The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
//	crop_size: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All
// cropped image patches are resized to this size. The aspect ratio of the image
// content is not preserved. Both `crop_height` and `crop_width` need to be
// positive.
//
// Returns A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
func CropAndResize(scope *Scope, image tf.Output, boxes tf.Output, box_ind tf.Output, crop_size tf.Output, optional ...CropAndResizeAttr) (crops tf.Output) {
	if scope.Err() != nil {
		return
	}
	attrs := map[string]interface{}{}
	for _, a := range optional {
		a(attrs)
	}
	opspec := tf.OpSpec{
		Type: "CropAndResize",
		Input: []tf.Input{
			image, boxes, box_ind, crop_size,
		},
		Attrs: attrs,
	}
	op := scope.AddOperation(opspec)
	return op.Output(0)
}