Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 309147560 Change-Id: Ic591ce76811bbd7841ac63f6f2aae75f95070280
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@ -16989,6 +16989,17 @@ func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x
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return op.Output(0), op.Output(1), op.Output(2)
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}
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// ShuffleAndRepeatDatasetAttr is an optional argument to ShuffleAndRepeatDataset.
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type ShuffleAndRepeatDatasetAttr func(optionalAttr)
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// ShuffleAndRepeatDatasetReshuffleEachIteration sets the optional reshuffle_each_iteration attribute to value.
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// If not specified, defaults to true
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func ShuffleAndRepeatDatasetReshuffleEachIteration(value bool) ShuffleAndRepeatDatasetAttr {
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return func(m optionalAttr) {
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m["reshuffle_each_iteration"] = value
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}
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}
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// Creates a dataset that shuffles and repeats elements from `input_dataset`
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//
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// pseudorandomly.
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@ -17006,11 +17017,14 @@ func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x
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// should be repeated. The default is `-1`, which results in infinite repetition.
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//
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//
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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) {
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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) {
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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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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: "ShuffleAndRepeatDataset",
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Input: []tf.Input{
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@ -26814,7 +26828,7 @@ func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output)
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//
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// @tf.function
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// def foo(x, y):
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// return = mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])
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// return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])
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//
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// graph_def = foo.get_concrete_function(tf.TensorSpec([10], tf.float32), tf.TensorSpec([10], tf.float32)).graph.as_graph_def()
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// ```
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