Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 322461142 Change-Id: Idfc03fde2f11d9cf21ece08f252a6971a8955b85
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
145d21a90d
commit
4e4cfe7e65
@ -8508,99 +8508,6 @@ func IteratorGetNextSync(scope *Scope, iterator tf.Output, output_types []tf.Dat
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return components
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}
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// RaggedCountSparseOutputAttr is an optional argument to RaggedCountSparseOutput.
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type RaggedCountSparseOutputAttr func(optionalAttr)
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// RaggedCountSparseOutputMinlength sets the optional minlength attribute to value.
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//
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// value: Minimum value to count. Can be set to -1 for no minimum.
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// If not specified, defaults to -1
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//
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// REQUIRES: value >= -1
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func RaggedCountSparseOutputMinlength(value int64) RaggedCountSparseOutputAttr {
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return func(m optionalAttr) {
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m["minlength"] = value
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}
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}
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// RaggedCountSparseOutputMaxlength sets the optional maxlength attribute to value.
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//
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// value: Maximum value to count. Can be set to -1 for no maximum.
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// If not specified, defaults to -1
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//
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// REQUIRES: value >= -1
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func RaggedCountSparseOutputMaxlength(value int64) RaggedCountSparseOutputAttr {
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return func(m optionalAttr) {
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m["maxlength"] = value
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}
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}
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// Performs sparse-output bin counting for a ragged tensor input.
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//
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// Counts the number of times each value occurs in the input.
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//
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// Arguments:
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// splits: Tensor containing the row splits of the ragged tensor to count.
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// values: Tensor containing values of the sparse tensor to count.
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// weights: A Tensor of the same shape as indices containing per-index weight values.
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// May also be the empty tensor if no weights are used.
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// binary_output: Whether to output the number of occurrences of each value or 1.
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//
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// Returns:
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// output_indices: Indices tensor for the resulting sparse tensor object.
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// output_values: Values tensor for the resulting sparse tensor object.
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// output_dense_shape: Shape tensor for the resulting sparse tensor object.
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// END
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// }
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// attr {
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// name: "T"
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// description: <<END
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// Dtype of the input values tensor.
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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) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"binary_output": binary_output}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "RaggedCountSparseOutput",
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Input: []tf.Input{
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splits, values, weights,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0), op.Output(1), op.Output(2)
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}
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// Gets the next output from the given iterator .
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func IteratorGetNext(scope *Scope, iterator tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
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opspec := tf.OpSpec{
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Type: "IteratorGetNext",
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Input: []tf.Input{
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iterator,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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if scope.Err() != nil {
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return
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}
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var idx int
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var err error
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if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
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scope.UpdateErr("IteratorGetNext", err)
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return
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}
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return components
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}
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// Makes a new iterator from the given `dataset` and stores it in `iterator`.
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//
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// This operation may be executed multiple times. Each execution will reset the
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@ -11324,6 +11231,114 @@ func DynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged
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return op.Output(0)
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}
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// RaggedCountSparseOutputAttr is an optional argument to RaggedCountSparseOutput.
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type RaggedCountSparseOutputAttr func(optionalAttr)
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// RaggedCountSparseOutputMinlength sets the optional minlength attribute to value.
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//
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// value: Minimum value to count. Can be set to -1 for no minimum.
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// If not specified, defaults to -1
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//
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// REQUIRES: value >= -1
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func RaggedCountSparseOutputMinlength(value int64) RaggedCountSparseOutputAttr {
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return func(m optionalAttr) {
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m["minlength"] = value
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}
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}
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// RaggedCountSparseOutputMaxlength sets the optional maxlength attribute to value.
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//
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// value: Maximum value to count. Can be set to -1 for no maximum.
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// If not specified, defaults to -1
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//
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// REQUIRES: value >= -1
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func RaggedCountSparseOutputMaxlength(value int64) RaggedCountSparseOutputAttr {
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return func(m optionalAttr) {
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m["maxlength"] = value
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}
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}
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// Performs sparse-output bin counting for a ragged tensor input.
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//
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// Counts the number of times each value occurs in the input.
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//
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// Arguments:
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// splits: Tensor containing the row splits of the ragged tensor to count.
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// values: Tensor containing values of the sparse tensor to count.
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// weights: A Tensor of the same shape as indices containing per-index weight values.
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// May also be the empty tensor if no weights are used.
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// binary_output: Whether to output the number of occurrences of each value or 1.
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//
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// Returns:
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// output_indices: Indices tensor for the resulting sparse tensor object.
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// output_values: Values tensor for the resulting sparse tensor object.
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// output_dense_shape: Shape tensor for the resulting sparse tensor object.
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// END
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// }
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// attr {
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// name: "T"
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// description: <<END
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// Dtype of the input values tensor.
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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) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"binary_output": binary_output}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "RaggedCountSparseOutput",
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Input: []tf.Input{
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splits, values, weights,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0), op.Output(1), op.Output(2)
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}
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// Gets the next output from the given iterator .
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func IteratorGetNext(scope *Scope, iterator tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
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opspec := tf.OpSpec{
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Type: "IteratorGetNext",
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Input: []tf.Input{
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iterator,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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if scope.Err() != nil {
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return
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}
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var idx int
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var err error
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if components, idx, err = makeOutputList(op, idx, "components"); err != nil {
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scope.UpdateErr("IteratorGetNext", err)
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return
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}
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return components
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}
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// Computes the static batch size of a dataset sans partial batches.
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func ComputeBatchSize(scope *Scope, input_dataset tf.Output) (batch_size tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "ComputeBatchSize",
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Input: []tf.Input{
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input_dataset,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Uncompresses a compressed dataset element.
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func UncompressElement(scope *Scope, compressed tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) {
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if scope.Err() != nil {
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