Use op name rather than hard-coding Hann in window op doc strings. (o…

…nnx#4248)

And strip trailing spaces.

Signed-off-by: Gary Miguel <garymiguel@microsoft.com>

docs/Changelog.md

@@ -20845,7 +20845,7 @@ This version of the operator has been available since version 17 of the default
 
 <dl>
 <dt><tt>output</tt> (non-differentiable) : T2</dt>
-<dd>A Hann window with length: size. The output has the shape: [size].</dd>
+<dd>A Blackman window with length: size. The output has the shape: [size].</dd>
 </dl>
 
 #### Type Constraints
@@ -20929,7 +20929,7 @@ This version of the operator has been available since version 17 of the default
 
 <dl>
 <dt><tt>output</tt> (non-differentiable) : T2</dt>
-<dd>A Hann window with length: size. The output has the shape: [size].</dd>
+<dd>A Hamming window with length: size. The output has the shape: [size].</dd>
 </dl>
 
 #### Type Constraints

docs/Operators.md

@@ -2417,7 +2417,7 @@ This version of the operator has been available since version 17 of the default
 
 <dl>
 <dt><tt>output</tt> (non-differentiable) : T2</dt>
-<dd>A Hann window with length: size. The output has the shape: [size].</dd>
+<dd>A Blackman window with length: size. The output has the shape: [size].</dd>
 </dl>
 
 #### Type Constraints
@@ -8468,7 +8468,7 @@ This version of the operator has been available since version 17 of the default
 
 <dl>
 <dt><tt>output</tt> (non-differentiable) : T2</dt>
-<dd>A Hann window with length: size. The output has the shape: [size].</dd>
+<dd>A Hamming window with length: size. The output has the shape: [size].</dd>
 </dl>
 
 #### Type Constraints

onnx/defs/math/defs.cc

@@ -757,7 +757,7 @@ ONNX_OPERATOR_SET_SCHEMA(
         .TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
         .FunctionBody(R"ONNX(
           {
-            HS_X = HardSigmoid<alpha = 0.16666667163372, beta = 0.5>(X) 
+            HS_X = HardSigmoid<alpha = 0.16666667163372, beta = 0.5>(X)
             Y = Mul (X, HS_X)
           }
         )ONNX"));
@@ -1708,14 +1708,14 @@ ONNX_OPERATOR_SET_SCHEMA(
 
 static const char* QLinearMatMul_ver10_doc = R"DOC(
 Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
-It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, 
-and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). 
-For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 
-Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor 
-(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row 
-or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be 
-an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K] 
-for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may 
+It consumes two quantized input tensors, their scales and zero points, scale and zero point of output,
+and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).
+For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.
+Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor
+(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row
+or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be
+an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]
+for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may
 have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.
 Production must never overflow, and accumulation may overflow if and only if in 32 bits.
 )DOC";
@@ -2858,16 +2858,9 @@ Generates a {name} window as described in the paper https://ieeexplore.ieee.org/
         true,
         1,
         OpSchema::NonDifferentiable);
-    schema.Output(
-        0,
-        "output",
-        "A Hann window with length: size. "
-        "The output has the shape: [size].",
-        "T2",
-        OpSchema::Single,
-        true,
-        1,
-        OpSchema::NonDifferentiable);
+    std::string output_doc("A {name} window with length: size. The output has the shape: [size].");
+    ReplaceAll(output_doc, "{name}", name);
+    schema.Output(0, "output", output_doc, "T2", OpSchema::Single, true, 1, OpSchema::NonDifferentiable);
     schema.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
       // Update the output data type to the output_datatype
       auto output_datatype = getAttribute(ctx, "output_datatype", static_cast<int64_t>(TensorProto_DataType_FLOAT));
@@ -3017,7 +3010,7 @@ ONNX_OPERATOR_SET_SCHEMA(
 static const char* MelWeightMatrix_ver17_doc = R"DOC(
 Generate a MelWeightMatrix that can be used to re-weight a Tensor containing a linearly sampled frequency spectra (from DFT or STFT) into num_mel_bins frequency information based on the [lower_edge_hertz, upper_edge_hertz] range on the mel scale.
 This function defines the mel scale in terms of a frequency in hertz according to the following formula:
-    
+
     mel(f) = 2595 * log10(1 + f/700)
 
 In the returned matrix, all the triangles (filterbanks) have a peak value of 1.0.