/
model.pb.go
823 lines (737 loc) · 33 KB
/
model.pb.go
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// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.31.0
// protoc (unknown)
// source: tensorflow/core/framework/model.proto
package framework
import (
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
reflect "reflect"
sync "sync"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// Class of a node in the performance model.
type NodeClass int32
const (
NodeClass_UNKNOWN NodeClass = 0
NodeClass_INTERLEAVE_MANY NodeClass = 1
NodeClass_ASYNC_INTERLEAVE_MANY NodeClass = 2
NodeClass_KNOWN_RATIO NodeClass = 3
NodeClass_ASYNC_KNOWN_RATIO NodeClass = 4
NodeClass_UNKNOWN_RATIO NodeClass = 5
NodeClass_ASYNC_UNKNOWN_RATIO NodeClass = 6
)
// Enum value maps for NodeClass.
var (
NodeClass_name = map[int32]string{
0: "UNKNOWN",
1: "INTERLEAVE_MANY",
2: "ASYNC_INTERLEAVE_MANY",
3: "KNOWN_RATIO",
4: "ASYNC_KNOWN_RATIO",
5: "UNKNOWN_RATIO",
6: "ASYNC_UNKNOWN_RATIO",
}
NodeClass_value = map[string]int32{
"UNKNOWN": 0,
"INTERLEAVE_MANY": 1,
"ASYNC_INTERLEAVE_MANY": 2,
"KNOWN_RATIO": 3,
"ASYNC_KNOWN_RATIO": 4,
"UNKNOWN_RATIO": 5,
"ASYNC_UNKNOWN_RATIO": 6,
}
)
func (x NodeClass) Enum() *NodeClass {
p := new(NodeClass)
*p = x
return p
}
func (x NodeClass) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (NodeClass) Descriptor() protoreflect.EnumDescriptor {
return file_tensorflow_core_framework_model_proto_enumTypes[0].Descriptor()
}
func (NodeClass) Type() protoreflect.EnumType {
return &file_tensorflow_core_framework_model_proto_enumTypes[0]
}
func (x NodeClass) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use NodeClass.Descriptor instead.
func (NodeClass) EnumDescriptor() ([]byte, []int) {
return file_tensorflow_core_framework_model_proto_rawDescGZIP(), []int{0}
}
// Algorithm used for model autotuning optimization.
type AutotuneAlgorithm int32
const (
AutotuneAlgorithm_DEFAULT AutotuneAlgorithm = 0
AutotuneAlgorithm_HILL_CLIMB AutotuneAlgorithm = 1
AutotuneAlgorithm_GRADIENT_DESCENT AutotuneAlgorithm = 2
AutotuneAlgorithm_MAX_PARALLELISM AutotuneAlgorithm = 3
AutotuneAlgorithm_STAGE_BASED AutotuneAlgorithm = 4
)
// Enum value maps for AutotuneAlgorithm.
var (
AutotuneAlgorithm_name = map[int32]string{
0: "DEFAULT",
1: "HILL_CLIMB",
2: "GRADIENT_DESCENT",
3: "MAX_PARALLELISM",
4: "STAGE_BASED",
}
AutotuneAlgorithm_value = map[string]int32{
"DEFAULT": 0,
"HILL_CLIMB": 1,
"GRADIENT_DESCENT": 2,
"MAX_PARALLELISM": 3,
"STAGE_BASED": 4,
}
)
func (x AutotuneAlgorithm) Enum() *AutotuneAlgorithm {
p := new(AutotuneAlgorithm)
*p = x
return p
}
func (x AutotuneAlgorithm) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (AutotuneAlgorithm) Descriptor() protoreflect.EnumDescriptor {
return file_tensorflow_core_framework_model_proto_enumTypes[1].Descriptor()
}
func (AutotuneAlgorithm) Type() protoreflect.EnumType {
return &file_tensorflow_core_framework_model_proto_enumTypes[1]
}
func (x AutotuneAlgorithm) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use AutotuneAlgorithm.Descriptor instead.
func (AutotuneAlgorithm) EnumDescriptor() ([]byte, []int) {
return file_tensorflow_core_framework_model_proto_rawDescGZIP(), []int{1}
}
// Protocol buffer representing the data used by the autotuning modeling
// framework.
type ModelProto struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Map of node IDs to nodes of this model.
Nodes map[int64]*ModelProto_Node `protobuf:"bytes,1,rep,name=nodes,proto3" json:"nodes,omitempty" protobuf_key:"varint,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// ID of the output node of this model.
Output int64 `protobuf:"varint,2,opt,name=output,proto3" json:"output,omitempty"`
// Counter for node IDs of this model.
IdCounter int64 `protobuf:"varint,3,opt,name=id_counter,json=idCounter,proto3" json:"id_counter,omitempty"`
OptimizationParams *ModelProto_OptimizationParams `protobuf:"bytes,5,opt,name=optimization_params,json=optimizationParams,proto3" json:"optimization_params,omitempty"`
}
func (x *ModelProto) Reset() {
*x = ModelProto{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_framework_model_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelProto) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelProto) ProtoMessage() {}
func (x *ModelProto) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_framework_model_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelProto.ProtoReflect.Descriptor instead.
func (*ModelProto) Descriptor() ([]byte, []int) {
return file_tensorflow_core_framework_model_proto_rawDescGZIP(), []int{0}
}
func (x *ModelProto) GetNodes() map[int64]*ModelProto_Node {
if x != nil {
return x.Nodes
}
return nil
}
func (x *ModelProto) GetOutput() int64 {
if x != nil {
return x.Output
}
return 0
}
func (x *ModelProto) GetIdCounter() int64 {
if x != nil {
return x.IdCounter
}
return 0
}
func (x *ModelProto) GetOptimizationParams() *ModelProto_OptimizationParams {
if x != nil {
return x.OptimizationParams
}
return nil
}
// General representation of a node in the model.
type ModelProto_Node struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Unique node ID.
Id int64 `protobuf:"varint,1,opt,name=id,proto3" json:"id,omitempty"`
// Human-readable name of the node.
Name string `protobuf:"bytes,2,opt,name=name,proto3" json:"name,omitempty"`
// An indication whether autotuning is enabled for this node.
Autotune bool `protobuf:"varint,3,opt,name=autotune,proto3" json:"autotune,omitempty"`
// The number of bytes stored in this node's buffer.
BufferedBytes int64 `protobuf:"varint,4,opt,name=buffered_bytes,json=bufferedBytes,proto3" json:"buffered_bytes,omitempty"`
// The number of elements stored in this node's buffer.
BufferedElements int64 `protobuf:"varint,5,opt,name=buffered_elements,json=bufferedElements,proto3" json:"buffered_elements,omitempty"`
// The number of bytes consumed by the node.
BytesConsumed int64 `protobuf:"varint,6,opt,name=bytes_consumed,json=bytesConsumed,proto3" json:"bytes_consumed,omitempty"`
// The number of bytes produced by the node.
BytesProduced int64 `protobuf:"varint,7,opt,name=bytes_produced,json=bytesProduced,proto3" json:"bytes_produced,omitempty"`
// The number of elements produced by the node.
NumElements int64 `protobuf:"varint,8,opt,name=num_elements,json=numElements,proto3" json:"num_elements,omitempty"`
// The aggregate processing time spent in this node in nanoseconds.
ProcessingTime int64 `protobuf:"varint,9,opt,name=processing_time,json=processingTime,proto3" json:"processing_time,omitempty"`
// An indication whether this node records metrics about produced and
// consumed elements.
RecordMetrics bool `protobuf:"varint,10,opt,name=record_metrics,json=recordMetrics,proto3" json:"record_metrics,omitempty"`
// Parameters of this node.
Parameters []*ModelProto_Node_Parameter `protobuf:"bytes,11,rep,name=parameters,proto3" json:"parameters,omitempty"`
// Statistic of inputs processing time history.
InputProcessingTimeSum float64 `protobuf:"fixed64,12,opt,name=input_processing_time_sum,json=inputProcessingTimeSum,proto3" json:"input_processing_time_sum,omitempty"`
InputProcessingTimeCount int64 `protobuf:"varint,13,opt,name=input_processing_time_count,json=inputProcessingTimeCount,proto3" json:"input_processing_time_count,omitempty"`
// IDs of inputs of this node.
Inputs []int64 `protobuf:"varint,14,rep,packed,name=inputs,proto3" json:"inputs,omitempty"`
// Class of this node.
NodeClass NodeClass `protobuf:"varint,15,opt,name=node_class,json=nodeClass,proto3,enum=tensorflow.data.model.NodeClass" json:"node_class,omitempty"`
// Ratio of input to output elements. This is only used by KNOWN_RATIO and
// ASYNC_KNOWN_RATIO nodes.
Ratio float64 `protobuf:"fixed64,16,opt,name=ratio,proto3" json:"ratio,omitempty"`
// Ratio identifies how many parallelism calls are introduced by one
// buffered element. This is only used by ASYNC_KNOWN_RATIO nodes.
MemoryRatio float64 `protobuf:"fixed64,17,opt,name=memory_ratio,json=memoryRatio,proto3" json:"memory_ratio,omitempty"`
}
func (x *ModelProto_Node) Reset() {
*x = ModelProto_Node{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_framework_model_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelProto_Node) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelProto_Node) ProtoMessage() {}
func (x *ModelProto_Node) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_framework_model_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelProto_Node.ProtoReflect.Descriptor instead.
func (*ModelProto_Node) Descriptor() ([]byte, []int) {
return file_tensorflow_core_framework_model_proto_rawDescGZIP(), []int{0, 0}
}
func (x *ModelProto_Node) GetId() int64 {
if x != nil {
return x.Id
}
return 0
}
func (x *ModelProto_Node) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *ModelProto_Node) GetAutotune() bool {
if x != nil {
return x.Autotune
}
return false
}
func (x *ModelProto_Node) GetBufferedBytes() int64 {
if x != nil {
return x.BufferedBytes
}
return 0
}
func (x *ModelProto_Node) GetBufferedElements() int64 {
if x != nil {
return x.BufferedElements
}
return 0
}
func (x *ModelProto_Node) GetBytesConsumed() int64 {
if x != nil {
return x.BytesConsumed
}
return 0
}
func (x *ModelProto_Node) GetBytesProduced() int64 {
if x != nil {
return x.BytesProduced
}
return 0
}
func (x *ModelProto_Node) GetNumElements() int64 {
if x != nil {
return x.NumElements
}
return 0
}
func (x *ModelProto_Node) GetProcessingTime() int64 {
if x != nil {
return x.ProcessingTime
}
return 0
}
func (x *ModelProto_Node) GetRecordMetrics() bool {
if x != nil {
return x.RecordMetrics
}
return false
}
func (x *ModelProto_Node) GetParameters() []*ModelProto_Node_Parameter {
if x != nil {
return x.Parameters
}
return nil
}
func (x *ModelProto_Node) GetInputProcessingTimeSum() float64 {
if x != nil {
return x.InputProcessingTimeSum
}
return 0
}
func (x *ModelProto_Node) GetInputProcessingTimeCount() int64 {
if x != nil {
return x.InputProcessingTimeCount
}
return 0
}
func (x *ModelProto_Node) GetInputs() []int64 {
if x != nil {
return x.Inputs
}
return nil
}
func (x *ModelProto_Node) GetNodeClass() NodeClass {
if x != nil {
return x.NodeClass
}
return NodeClass_UNKNOWN
}
func (x *ModelProto_Node) GetRatio() float64 {
if x != nil {
return x.Ratio
}
return 0
}
func (x *ModelProto_Node) GetMemoryRatio() float64 {
if x != nil {
return x.MemoryRatio
}
return 0
}
// Contains parameters of the model autotuning optimization.
type ModelProto_OptimizationParams struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Algorithm used for autotuning optimization.
Algorithm AutotuneAlgorithm `protobuf:"varint,1,opt,name=algorithm,proto3,enum=tensorflow.data.model.AutotuneAlgorithm" json:"algorithm,omitempty"`
// Number of available logical threads.
CpuBudget int64 `protobuf:"varint,2,opt,name=cpu_budget,json=cpuBudget,proto3" json:"cpu_budget,omitempty"`
// Amount of available memory in bytes.
RamBudget int64 `protobuf:"varint,3,opt,name=ram_budget,json=ramBudget,proto3" json:"ram_budget,omitempty"`
// Time between two consecutive `GetNext` calls to the iterator represented
// by the output node.
ModelInputTime float64 `protobuf:"fixed64,4,opt,name=model_input_time,json=modelInputTime,proto3" json:"model_input_time,omitempty"`
}
func (x *ModelProto_OptimizationParams) Reset() {
*x = ModelProto_OptimizationParams{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_framework_model_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelProto_OptimizationParams) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelProto_OptimizationParams) ProtoMessage() {}
func (x *ModelProto_OptimizationParams) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_framework_model_proto_msgTypes[3]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelProto_OptimizationParams.ProtoReflect.Descriptor instead.
func (*ModelProto_OptimizationParams) Descriptor() ([]byte, []int) {
return file_tensorflow_core_framework_model_proto_rawDescGZIP(), []int{0, 2}
}
func (x *ModelProto_OptimizationParams) GetAlgorithm() AutotuneAlgorithm {
if x != nil {
return x.Algorithm
}
return AutotuneAlgorithm_DEFAULT
}
func (x *ModelProto_OptimizationParams) GetCpuBudget() int64 {
if x != nil {
return x.CpuBudget
}
return 0
}
func (x *ModelProto_OptimizationParams) GetRamBudget() int64 {
if x != nil {
return x.RamBudget
}
return 0
}
func (x *ModelProto_OptimizationParams) GetModelInputTime() float64 {
if x != nil {
return x.ModelInputTime
}
return 0
}
// Represents a node parameter.
type ModelProto_Node_Parameter struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Human-readable name of the parameter.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Identifies the model value of the parameter. This can be different from
// the actual value (e.g. during optimization search).
Value float64 `protobuf:"fixed64,2,opt,name=value,proto3" json:"value,omitempty"`
// The actual value of the parameter.
StateValue float64 `protobuf:"fixed64,3,opt,name=state_value,json=stateValue,proto3" json:"state_value,omitempty"`
// Minimum value of the parameter.
Min float64 `protobuf:"fixed64,4,opt,name=min,proto3" json:"min,omitempty"`
// Maximum value of the parameter.
Max float64 `protobuf:"fixed64,5,opt,name=max,proto3" json:"max,omitempty"`
// Identifies whether the parameter should participate in autotuning.
Tunable bool `protobuf:"varint,6,opt,name=tunable,proto3" json:"tunable,omitempty"`
}
func (x *ModelProto_Node_Parameter) Reset() {
*x = ModelProto_Node_Parameter{}
if protoimpl.UnsafeEnabled {
mi := &file_tensorflow_core_framework_model_proto_msgTypes[4]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelProto_Node_Parameter) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelProto_Node_Parameter) ProtoMessage() {}
func (x *ModelProto_Node_Parameter) ProtoReflect() protoreflect.Message {
mi := &file_tensorflow_core_framework_model_proto_msgTypes[4]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelProto_Node_Parameter.ProtoReflect.Descriptor instead.
func (*ModelProto_Node_Parameter) Descriptor() ([]byte, []int) {
return file_tensorflow_core_framework_model_proto_rawDescGZIP(), []int{0, 0, 0}
}
func (x *ModelProto_Node_Parameter) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *ModelProto_Node_Parameter) GetValue() float64 {
if x != nil {
return x.Value
}
return 0
}
func (x *ModelProto_Node_Parameter) GetStateValue() float64 {
if x != nil {
return x.StateValue
}
return 0
}
func (x *ModelProto_Node_Parameter) GetMin() float64 {
if x != nil {
return x.Min
}
return 0
}
func (x *ModelProto_Node_Parameter) GetMax() float64 {
if x != nil {
return x.Max
}
return 0
}
func (x *ModelProto_Node_Parameter) GetTunable() bool {
if x != nil {
return x.Tunable
}
return false
}
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}
var (
file_tensorflow_core_framework_model_proto_rawDescOnce sync.Once
file_tensorflow_core_framework_model_proto_rawDescData = file_tensorflow_core_framework_model_proto_rawDesc
)
func file_tensorflow_core_framework_model_proto_rawDescGZIP() []byte {
file_tensorflow_core_framework_model_proto_rawDescOnce.Do(func() {
file_tensorflow_core_framework_model_proto_rawDescData = protoimpl.X.CompressGZIP(file_tensorflow_core_framework_model_proto_rawDescData)
})
return file_tensorflow_core_framework_model_proto_rawDescData
}
var file_tensorflow_core_framework_model_proto_enumTypes = make([]protoimpl.EnumInfo, 2)
var file_tensorflow_core_framework_model_proto_msgTypes = make([]protoimpl.MessageInfo, 5)
var file_tensorflow_core_framework_model_proto_goTypes = []interface{}{
(NodeClass)(0), // 0: tensorflow.data.model.NodeClass
(AutotuneAlgorithm)(0), // 1: tensorflow.data.model.AutotuneAlgorithm
(*ModelProto)(nil), // 2: tensorflow.data.model.ModelProto
(*ModelProto_Node)(nil), // 3: tensorflow.data.model.ModelProto.Node
nil, // 4: tensorflow.data.model.ModelProto.NodesEntry
(*ModelProto_OptimizationParams)(nil), // 5: tensorflow.data.model.ModelProto.OptimizationParams
(*ModelProto_Node_Parameter)(nil), // 6: tensorflow.data.model.ModelProto.Node.Parameter
}
var file_tensorflow_core_framework_model_proto_depIdxs = []int32{
4, // 0: tensorflow.data.model.ModelProto.nodes:type_name -> tensorflow.data.model.ModelProto.NodesEntry
5, // 1: tensorflow.data.model.ModelProto.optimization_params:type_name -> tensorflow.data.model.ModelProto.OptimizationParams
6, // 2: tensorflow.data.model.ModelProto.Node.parameters:type_name -> tensorflow.data.model.ModelProto.Node.Parameter
0, // 3: tensorflow.data.model.ModelProto.Node.node_class:type_name -> tensorflow.data.model.NodeClass
3, // 4: tensorflow.data.model.ModelProto.NodesEntry.value:type_name -> tensorflow.data.model.ModelProto.Node
1, // 5: tensorflow.data.model.ModelProto.OptimizationParams.algorithm:type_name -> tensorflow.data.model.AutotuneAlgorithm
6, // [6:6] is the sub-list for method output_type
6, // [6:6] is the sub-list for method input_type
6, // [6:6] is the sub-list for extension type_name
6, // [6:6] is the sub-list for extension extendee
0, // [0:6] is the sub-list for field type_name
}
func init() { file_tensorflow_core_framework_model_proto_init() }
func file_tensorflow_core_framework_model_proto_init() {
if File_tensorflow_core_framework_model_proto != nil {
return
}
if !protoimpl.UnsafeEnabled {
file_tensorflow_core_framework_model_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelProto); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_tensorflow_core_framework_model_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelProto_Node); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_tensorflow_core_framework_model_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelProto_OptimizationParams); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_tensorflow_core_framework_model_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelProto_Node_Parameter); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_tensorflow_core_framework_model_proto_rawDesc,
NumEnums: 2,
NumMessages: 5,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_tensorflow_core_framework_model_proto_goTypes,
DependencyIndexes: file_tensorflow_core_framework_model_proto_depIdxs,
EnumInfos: file_tensorflow_core_framework_model_proto_enumTypes,
MessageInfos: file_tensorflow_core_framework_model_proto_msgTypes,
}.Build()
File_tensorflow_core_framework_model_proto = out.File
file_tensorflow_core_framework_model_proto_rawDesc = nil
file_tensorflow_core_framework_model_proto_goTypes = nil
file_tensorflow_core_framework_model_proto_depIdxs = nil
}