Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 390275050
Change-Id: I30bb09b92dad3615e537673acca289b2a1d30a2e
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 0868ed4..9518a6b 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -33747,37 +33747,6 @@
return op.Output(0)
}
-// IgnoreErrorsDatasetAttr is an optional argument to IgnoreErrorsDataset.
-type IgnoreErrorsDatasetAttr func(optionalAttr)
-
-// IgnoreErrorsDatasetLogWarning sets the optional log_warning attribute to value.
-// If not specified, defaults to false
-func IgnoreErrorsDatasetLogWarning(value bool) IgnoreErrorsDatasetAttr {
- return func(m optionalAttr) {
- m["log_warning"] = value
- }
-}
-
-// Creates a dataset that contains the elements of `input_dataset` ignoring errors.
-func IgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...IgnoreErrorsDatasetAttr) (handle tf.Output) {
- if scope.Err() != nil {
- return
- }
- attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
- for _, a := range optional {
- a(attrs)
- }
- opspec := tf.OpSpec{
- Type: "IgnoreErrorsDataset",
- Input: []tf.Input{
- input_dataset,
- },
- Attrs: attrs,
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
// Deprecated. Use TensorArrayGradV3
//
// DEPRECATED at GraphDef version 26: Use TensorArrayWriteV3
@@ -36958,6 +36927,67 @@
return scope.AddOperation(opspec)
}
+// LoadTPUEmbeddingAdagradMomentumParametersAttr is an optional argument to LoadTPUEmbeddingAdagradMomentumParameters.
+type LoadTPUEmbeddingAdagradMomentumParametersAttr func(optionalAttr)
+
+// LoadTPUEmbeddingAdagradMomentumParametersTableId sets the optional table_id attribute to value.
+// If not specified, defaults to -1
+func LoadTPUEmbeddingAdagradMomentumParametersTableId(value int64) LoadTPUEmbeddingAdagradMomentumParametersAttr {
+ return func(m optionalAttr) {
+ m["table_id"] = value
+ }
+}
+
+// LoadTPUEmbeddingAdagradMomentumParametersTableName sets the optional table_name attribute to value.
+// If not specified, defaults to ""
+func LoadTPUEmbeddingAdagradMomentumParametersTableName(value string) LoadTPUEmbeddingAdagradMomentumParametersAttr {
+ return func(m optionalAttr) {
+ m["table_name"] = value
+ }
+}
+
+// LoadTPUEmbeddingAdagradMomentumParametersConfig sets the optional config attribute to value.
+// If not specified, defaults to ""
+func LoadTPUEmbeddingAdagradMomentumParametersConfig(value string) LoadTPUEmbeddingAdagradMomentumParametersAttr {
+ return func(m optionalAttr) {
+ m["config"] = value
+ }
+}
+
+// Load Adagrad Momentum embedding parameters.
+//
+// An op that loads optimization parameters into HBM for embedding. Must be
+// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+// embedding table configuration. For example, this op is used to install
+// parameters that are loaded from a checkpoint before a training loop is
+// executed.
+//
+// Arguments:
+// parameters: Value of parameters used in the Adagrad Momentum optimization algorithm.
+// accumulators: Value of accumulators used in the Adagrad Momentum optimization algorithm.
+// momenta: Value of momenta used in the Adagrad Momentum optimization algorithm.
+//
+//
+//
+// Returns the created operation.
+func LoadTPUEmbeddingAdagradMomentumParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, momenta tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradMomentumParametersAttr) (o *tf.Operation) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "LoadTPUEmbeddingAdagradMomentumParameters",
+ Input: []tf.Input{
+ parameters, accumulators, momenta,
+ },
+ Attrs: attrs,
+ }
+ return scope.AddOperation(opspec)
+}
+
// MaxPoolAttr is an optional argument to MaxPool.
type MaxPoolAttr func(optionalAttr)
@@ -39659,98 +39689,6 @@
return op.Output(0)
}
-// Gives a guarantee to the TF runtime that the input tensor is a constant.
-//
-// The runtime is then free to make optimizations based on this.
-//
-// Only accepts value typed tensors as inputs and rejects resource variable handles
-// as input.
-//
-// Returns the input tensor without modification.
-func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "GuaranteeConst",
- Input: []tf.Input{
- input,
- },
- }
- op := scope.AddOperation(opspec)
- return op.Output(0)
-}
-
-// Transforms a tf.Example proto (as a string) into typed tensors.
-//
-// Arguments:
-// serialized: A vector containing a batch of binary serialized Example protos.
-// dense_defaults: A list of Tensors (some may be empty), whose length matches
-// the length of `dense_keys`. 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.
-// num_sparse: The number of sparse features to be parsed from the example. This
-// must match the lengths of `sparse_keys` and `sparse_types`.
-// sparse_keys: A list of `num_sparse` strings.
-// The keys expected in the Examples' features associated with sparse values.
-// dense_keys: The keys expected in the Examples' features associated with dense
-// values.
-// sparse_types: A list of `num_sparse` types; the data types of data in each
-// Feature given in sparse_keys.
-// Currently the ParseSingleExample op supports DT_FLOAT (FloatList),
-// DT_INT64 (Int64List), and DT_STRING (BytesList).
-// dense_shapes: The shapes of data in each Feature given in dense_keys.
-// The length of this list must match the length of `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 (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1,
-// ..., DN), the shape of the output Tensor dense_values[j] will be (M,
-// D1, .., DN), where M is the number of blocks of elements of length
-// D1 * .... * DN, in the input.
-func ParseSingleExample(scope *Scope, serialized tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_keys []string, dense_keys []string, 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{}{"num_sparse": num_sparse, "sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes}
- opspec := tf.OpSpec{
- Type: "ParseSingleExample",
- Input: []tf.Input{
- serialized, 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("ParseSingleExample", err)
- return
- }
- if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
- scope.UpdateErr("ParseSingleExample", err)
- return
- }
- if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
- scope.UpdateErr("ParseSingleExample", err)
- return
- }
- if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
- scope.UpdateErr("ParseSingleExample", err)
- return
- }
- return sparse_indices, sparse_values, sparse_shapes, dense_values
-}
-
// StringToNumberAttr is an optional argument to StringToNumber.
type StringToNumberAttr func(optionalAttr)
@@ -40193,6 +40131,98 @@
return op.Output(0)
}
+// Gives a guarantee to the TF runtime that the input tensor is a constant.
+//
+// The runtime is then free to make optimizations based on this.
+//
+// Only accepts value typed tensors as inputs and rejects resource variable handles
+// as input.
+//
+// Returns the input tensor without modification.
+func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ opspec := tf.OpSpec{
+ Type: "GuaranteeConst",
+ Input: []tf.Input{
+ input,
+ },
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
+// Transforms a tf.Example proto (as a string) into typed tensors.
+//
+// Arguments:
+// serialized: A vector containing a batch of binary serialized Example protos.
+// dense_defaults: A list of Tensors (some may be empty), whose length matches
+// the length of `dense_keys`. 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.
+// num_sparse: The number of sparse features to be parsed from the example. This
+// must match the lengths of `sparse_keys` and `sparse_types`.
+// sparse_keys: A list of `num_sparse` strings.
+// The keys expected in the Examples' features associated with sparse values.
+// dense_keys: The keys expected in the Examples' features associated with dense
+// values.
+// sparse_types: A list of `num_sparse` types; the data types of data in each
+// Feature given in sparse_keys.
+// Currently the ParseSingleExample op supports DT_FLOAT (FloatList),
+// DT_INT64 (Int64List), and DT_STRING (BytesList).
+// dense_shapes: The shapes of data in each Feature given in dense_keys.
+// The length of this list must match the length of `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 (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1,
+// ..., DN), the shape of the output Tensor dense_values[j] will be (M,
+// D1, .., DN), where M is the number of blocks of elements of length
+// D1 * .... * DN, in the input.
+func ParseSingleExample(scope *Scope, serialized tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_keys []string, dense_keys []string, 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{}{"num_sparse": num_sparse, "sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes}
+ opspec := tf.OpSpec{
+ Type: "ParseSingleExample",
+ Input: []tf.Input{
+ serialized, 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("ParseSingleExample", err)
+ return
+ }
+ if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
+ return
+ }
+ if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
+ return
+ }
+ if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil {
+ scope.UpdateErr("ParseSingleExample", err)
+ return
+ }
+ return sparse_indices, sparse_values, sparse_shapes, dense_values
+}
+
// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate.
type ResourceScatterNdUpdateAttr func(optionalAttr)
@@ -41198,6 +41228,61 @@
return op.Output(0)
}
+// RetrieveTPUEmbeddingAdagradMomentumParametersAttr is an optional argument to RetrieveTPUEmbeddingAdagradMomentumParameters.
+type RetrieveTPUEmbeddingAdagradMomentumParametersAttr func(optionalAttr)
+
+// RetrieveTPUEmbeddingAdagradMomentumParametersTableId sets the optional table_id attribute to value.
+// If not specified, defaults to -1
+func RetrieveTPUEmbeddingAdagradMomentumParametersTableId(value int64) RetrieveTPUEmbeddingAdagradMomentumParametersAttr {
+ return func(m optionalAttr) {
+ m["table_id"] = value
+ }
+}
+
+// RetrieveTPUEmbeddingAdagradMomentumParametersTableName sets the optional table_name attribute to value.
+// If not specified, defaults to ""
+func RetrieveTPUEmbeddingAdagradMomentumParametersTableName(value string) RetrieveTPUEmbeddingAdagradMomentumParametersAttr {
+ return func(m optionalAttr) {
+ m["table_name"] = value
+ }
+}
+
+// RetrieveTPUEmbeddingAdagradMomentumParametersConfig sets the optional config attribute to value.
+// If not specified, defaults to ""
+func RetrieveTPUEmbeddingAdagradMomentumParametersConfig(value string) RetrieveTPUEmbeddingAdagradMomentumParametersAttr {
+ return func(m optionalAttr) {
+ m["config"] = value
+ }
+}
+
+// Retrieve Adagrad Momentum embedding parameters.
+//
+// An op that retrieves optimization parameters from embedding to host
+// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+// the correct embedding table configuration. For example, this op is
+// used to retrieve updated parameters before saving a checkpoint.
+//
+// Returns:
+// parameters: Parameter parameters updated by the Adagrad Momentum optimization algorithm.
+// accumulators: Parameter accumulators updated by the Adagrad Momentum optimization algorithm.
+// momenta: Parameter momenta updated by the Adagrad Momentum optimization algorithm.
+func RetrieveTPUEmbeddingAdagradMomentumParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdagradMomentumParametersAttr) (parameters tf.Output, accumulators tf.Output, momenta tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "RetrieveTPUEmbeddingAdagradMomentumParameters",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0), op.Output(1), op.Output(2)
+}
+
// Replaces the contents of the table with the specified keys and values.
//
// The tensor `keys` must be of the same type as the keys of the table.
@@ -47716,6 +47801,37 @@
return op.Output(0)
}
+// IgnoreErrorsDatasetAttr is an optional argument to IgnoreErrorsDataset.
+type IgnoreErrorsDatasetAttr func(optionalAttr)
+
+// IgnoreErrorsDatasetLogWarning sets the optional log_warning attribute to value.
+// If not specified, defaults to false
+func IgnoreErrorsDatasetLogWarning(value bool) IgnoreErrorsDatasetAttr {
+ return func(m optionalAttr) {
+ m["log_warning"] = value
+ }
+}
+
+// Creates a dataset that contains the elements of `input_dataset` ignoring errors.
+func IgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...IgnoreErrorsDatasetAttr) (handle tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "IgnoreErrorsDataset",
+ Input: []tf.Input{
+ input_dataset,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Computes gradients for SparseSegmentSum.
//
// Returns tensor "output" with same shape as grad, except for dimension 0 whose
