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Go: Update generated wrapper functions for TensorFlow ops. 

PiperOrigin-RevId: 311478670
Change-Id: Ib8c15d5cba307629a0d8fc55e07efc401502899e
This commit is contained in:
A. Unique TensorFlower 2020-05-13 23:46:17 -07:00 committed by TensorFlower Gardener
parent 112288586d
commit 03f3e8153c
1 changed files with 65 additions and 59 deletions
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124
tensorflow/go/op/wrappers.go
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@ -4715,7 +4715,7 @@ type DenseCountSparseOutputAttr func(optionalAttr)
// DenseCountSparseOutputMinlength sets the optional minlength attribute to value.
//
// value: Minimum value to count. Can be set to -1 for no minimum.
// value: int32; minimum value to count. Can be set to -1 for no minimum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -4727,7 +4727,7 @@ func DenseCountSparseOutputMinlength(value int64) DenseCountSparseOutputAttr {
// DenseCountSparseOutputMaxlength sets the optional maxlength attribute to value.
//
// value: Maximum value to count. Can be set to -1 for no maximum.
// value: int32; maximum value to count. Can be set to -1 for no maximum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -4742,20 +4742,20 @@ func DenseCountSparseOutputMaxlength(value int64) DenseCountSparseOutputAttr {
// Counts the number of times each value occurs in the input.
//
// Arguments:
// values: Tensor containing data to count.
// weights: A Tensor of the same shape as indices containing per-index weight values. May
// also be the empty tensor if no weights are used.
// binary_output: Whether to output the number of occurrences of each value or 1.
// values: int32 or int64; Tensor containing data to count.
// weights: float32; Optional rank 1 Tensor (shape=[max_values]) with weights for each count value.
// binary_count: bool; whether to output the number of occurrences of each value or 1.
// output_type: dtype; dtype of the output values tensor.
//
// Returns:
// output_indices: Indices tensor for the resulting sparse tensor object.
// output_values: Values tensor for the resulting sparse tensor object.
// output_dense_shape: Shape tensor for the resulting sparse tensor object.
func DenseCountSparseOutput(scope *Scope, values tf.Output, weights tf.Output, binary_output bool, optional ...DenseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
// output_indices: int64; indices tensor for the resulting sparse tensor object.
// output_values: int64 or float32; values tensor for the resulting sparse tensor object.
// output_dense_shape: int64; shape tensor for the resulting sparse tensor object.
func DenseCountSparseOutput(scope *Scope, values tf.Output, weights tf.Output, binary_count bool, output_type tf.DataType, optional ...DenseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"binary_output": binary_output}
attrs := map[string]interface{}{"binary_count": binary_count, "output_type": output_type}
for _, a := range optional {
a(attrs)
}
@ -8607,7 +8607,7 @@ type RaggedCountSparseOutputAttr func(optionalAttr)
// RaggedCountSparseOutputMinlength sets the optional minlength attribute to value.
//
// value: Minimum value to count. Can be set to -1 for no minimum.
// value: int32; minimum value to count. Can be set to -1 for no minimum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -8619,7 +8619,7 @@ func RaggedCountSparseOutputMinlength(value int64) RaggedCountSparseOutputAttr {
// RaggedCountSparseOutputMaxlength sets the optional maxlength attribute to value.
//
// value: Maximum value to count. Can be set to -1 for no maximum.
// value: int32; maximum value to count. Can be set to -1 for no maximum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -8634,27 +8634,33 @@ func RaggedCountSparseOutputMaxlength(value int64) RaggedCountSparseOutputAttr {
// Counts the number of times each value occurs in the input.
//
// Arguments:
// splits: Tensor containing the row splits of the ragged tensor to count.
// values: Tensor containing values of the sparse tensor to count.
// weights: A Tensor of the same shape as indices containing per-index weight values.
// May also be the empty tensor if no weights are used.
// binary_output: Whether to output the number of occurrences of each value or 1.
// splits: int64; Tensor containing the row splits of the ragged tensor to count.
// values: int32 or int64; Tensor containing values of the sparse tensor to count.
// weights: float32; Optional rank 1 Tensor (shape=[max_values]) with weights for each count value.
// binary_count: bool; whether to output the number of occurrences of each value or 1.
// output_type: dtype; dtype of the output values tensor.
//
// Returns:
// output_indices: Indices tensor for the resulting sparse tensor object.
// output_values: Values tensor for the resulting sparse tensor object.
// output_dense_shape: Shape tensor for the resulting sparse tensor object.
// output_indices: int64; indices tensor for the resulting sparse tensor object.
// output_values: int64 or float32; values tensor for the resulting sparse tensor object.
// END
// }
// out_arg {
// name: "output_dense_shape"
// description: <<END
// int64; shape tensor for the resulting sparse tensor object.
// END
// }
// attr {
// name: "T"
// description: <<END
// Dtype of the input values tensor.
func RaggedCountSparseOutput(scope *Scope, splits tf.Output, values tf.Output, weights tf.Output, binary_output bool, optional ...RaggedCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
// dtype; dtype of the input values tensor.
// output_dense_shape
func RaggedCountSparseOutput(scope *Scope, splits tf.Output, values tf.Output, weights tf.Output, binary_count bool, output_type tf.DataType, optional ...RaggedCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"binary_output": binary_output}
attrs := map[string]interface{}{"binary_count": binary_count, "output_type": output_type}
for _, a := range optional {
a(attrs)
}
@ -12053,7 +12059,7 @@ func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBo
//
// 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}
// 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
@ -12064,7 +12070,7 @@ func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistorted
//
// value: The cropped area of the image must contain a fraction of the
// supplied image within this range.
// If not specified, defaults to {f:0.05 f:1}
// If not specified, defaults to {f:0.05 f:1}
func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
return func(m optionalAttr) {
m["area_range"] = value
@ -13700,7 +13706,7 @@ type SparseCountSparseOutputAttr func(optionalAttr)
// SparseCountSparseOutputMinlength sets the optional minlength attribute to value.
//
// value: Minimum value to count. Can be set to -1 for no minimum.
// value: int32; minimum value to count. Can be set to -1 for no minimum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -13712,7 +13718,7 @@ func SparseCountSparseOutputMinlength(value int64) SparseCountSparseOutputAttr {
// SparseCountSparseOutputMaxlength sets the optional maxlength attribute to value.
//
// value: Maximum value to count. Can be set to -1 for no maximum.
// value: int32; maximum value to count. Can be set to -1 for no maximum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -13727,22 +13733,22 @@ func SparseCountSparseOutputMaxlength(value int64) SparseCountSparseOutputAttr {
// Counts the number of times each value occurs in the input.
//
// Arguments:
// indices: Tensor containing the indices of the sparse tensor to count.
// values: Tensor containing values of the sparse tensor to count.
// dense_shape: Tensor containing the dense shape of the sparse tensor to count.
// weights: A Tensor of the same shape as indices containing per-index weight values.
// May also be the empty tensor if no weights are used.
// binary_output: Whether to output the number of occurrences of each value or 1.
// indices: int64; Tensor containing the indices of the sparse tensor to count.
// values: int32 or int64; Tensor containing values of the sparse tensor to count.
// dense_shape: int64; Tensor containing the dense shape of the sparse tensor to count.
// weights: float32; Optional rank 1 Tensor (shape=[max_values]) with weights for each count value.
// binary_count: bool; whether to output the number of occurrences of each value or 1.
// output_type: dtype; dtype of the output values tensor.
//
// Returns:
// output_indices: Indices tensor for the resulting sparse tensor object.
// output_values: Values tensor for the resulting sparse tensor object.
// output_dense_shape: Shape tensor for the resulting sparse tensor object.
func SparseCountSparseOutput(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, weights tf.Output, binary_output bool, optional ...SparseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
// output_indices: int64; indices tensor for the resulting sparse tensor object.
// output_values: int64 or float32; values tensor for the resulting sparse tensor object.
// output_dense_shape: int64; shape tensor for the resulting sparse tensor object.
func SparseCountSparseOutput(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, weights tf.Output, binary_count bool, output_type tf.DataType, optional ...SparseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"binary_output": binary_output}
attrs := map[string]interface{}{"binary_count": binary_count, "output_type": output_type}
for _, a := range optional {
a(attrs)
}
@ -18969,7 +18975,7 @@ func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2
//
// 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}
// If not specified, defaults to {f:0.75 f:1.33}
func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr {
return func(m optionalAttr) {
m["aspect_ratio_range"] = value
@ -18980,7 +18986,7 @@ func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistort
//
// value: The cropped area of the image must contain a fraction of the
// supplied image within this range.
// If not specified, defaults to {f:0.05 f:1}
// If not specified, defaults to {f:0.05 f:1}
func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr {
return func(m optionalAttr) {
m["area_range"] = value
@ -19384,7 +19390,7 @@ func ImageSummaryMaxImages(value int64) ImageSummaryAttr {
// 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}
// 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
@ -20455,7 +20461,7 @@ func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr {
return func(m optionalAttr) {
m["dilations"] = value
@ -21627,7 +21633,7 @@ func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr {
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -22335,7 +22341,7 @@ func Conv2DDataFormat(value string) Conv2DAttr {
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func Conv2DDilations(value []int64) Conv2DAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -22531,7 +22537,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType(value tf.DataTy
// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value.
//
// value: List of dilation values.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -22600,7 +22606,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluOutType(value tf.DataType) Quantized
// QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value.
//
// value: List of dilation values.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -22715,7 +22721,7 @@ func QuantizedDepthwiseConv2DWithBiasOutType(value tf.DataType) QuantizedDepthwi
// QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value.
//
// value: List of dilation values.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -22774,7 +22780,7 @@ func QuantizedDepthwiseConv2DOutType(value tf.DataType) QuantizedDepthwiseConv2D
// QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value.
//
// value: List of dilation values.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -22948,7 +22954,7 @@ func QuantizedConv2DPerChannelOutType(value tf.DataType) QuantizedConv2DPerChann
// QuantizedConv2DPerChannelDilations sets the optional dilations attribute to value.
//
// value: list of dilation values.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedConv2DPerChannelDilations(value []int64) QuantizedConv2DPerChannelAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -23325,7 +23331,7 @@ func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr {
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr {
return func(m optionalAttr) {
m["dilations"] = value
@ -25648,7 +25654,7 @@ func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksi
type Conv3DBackpropFilterAttr func(optionalAttr)
// Conv3DBackpropFilterDilations sets the optional dilations attribute to value.
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -25711,7 +25717,7 @@ func Conv3DDataFormat(value string) Conv3DAttr {
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DDilations(value []int64) Conv3DAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -25962,7 +25968,7 @@ func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dN
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -26446,7 +26452,7 @@ func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
// filter element on that dimension. The dimension order is determined by the
// value of `data_format`, see above for details. Dilations in the batch and
// depth dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -45534,7 +45540,7 @@ func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2d
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -47474,7 +47480,7 @@ func LoadTPUEmbeddingFTRLParameters(scope *Scope, parameters tf.Output, accumula
type Conv3DBackpropInputAttr func(optionalAttr)
// Conv3DBackpropInputDilations sets the optional dilations attribute to value.
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -47545,7 +47551,7 @@ func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
return func(m optionalAttr) {
m["dilations"] = value
@ -48534,7 +48540,7 @@ func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr {
// element on that dimension. The dimension order is determined by the value of
// `data_format`, see above for details. Dilations in the batch and depth
// dimensions must be 1.
// If not specified, defaults to {i:1 i:1 i:1 i:1}
// If not specified, defaults to {i:1 i:1 i:1 i:1}
func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr {
return func(m optionalAttr) {
m["dilations"] = value