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Go: Update generated wrapper functions for TensorFlow ops.

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PiperOrigin-RevId: 309147560
Change-Id: Ic591ce76811bbd7841ac63f6f2aae75f95070280
This commit is contained in:
A. Unique TensorFlower 2020-04-29 19:46:26 -07:00 committed by TensorFlower Gardener
parent b5f954b27d
commit 2d80f55c77
1 changed files with 38 additions and 24 deletions
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18
tensorflow/go/op/wrappers.go
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@ -16989,6 +16989,17 @@ func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x
return op.Output(0), op.Output(1), op.Output(2)
}
// ShuffleAndRepeatDatasetAttr is an optional argument to ShuffleAndRepeatDataset.
type ShuffleAndRepeatDatasetAttr func(optionalAttr)
// ShuffleAndRepeatDatasetReshuffleEachIteration sets the optional reshuffle_each_iteration attribute to value.
// If not specified, defaults to true
func ShuffleAndRepeatDatasetReshuffleEachIteration(value bool) ShuffleAndRepeatDatasetAttr {
return func(m optionalAttr) {
m["reshuffle_each_iteration"] = value
}
}
// Creates a dataset that shuffles and repeats elements from `input_dataset`
//
// pseudorandomly.
@ -17006,11 +17017,14 @@ func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x
// should be repeated. The default is `-1`, which results in infinite repetition.
//
//
func ShuffleAndRepeatDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, seed tf.Output, seed2 tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
func ShuffleAndRepeatDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, seed tf.Output, seed2 tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ShuffleAndRepeatDatasetAttr) (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: "ShuffleAndRepeatDataset",
Input: []tf.Input{
@ -26814,7 +26828,7 @@ func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output)
//
// @tf.function
// def foo(x, y):
// return = mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])
// return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])
//
// graph_def = foo.get_concrete_function(tf.TensorSpec([10], tf.float32), tf.TensorSpec([10], tf.float32)).graph.as_graph_def()
// ```