Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 328372956 Change-Id: I763361a219ff3839a97f674120699170ed26b418
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A. Unique TensorFlower
TensorFlower Gardener
parent
65da7b87d6
commit
86f9d1fbae
@ -7748,98 +7748,6 @@ func PriorityQueueV2(scope *Scope, shapes []tf.Shape, optional ...PriorityQueueV
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return op.Output(0)
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}
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// Does nothing. Serves as a control trigger for scheduling.
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//
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// Only useful as a placeholder for control edges.
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//
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// Returns the created operation.
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func ControlTrigger(scope *Scope) (o *tf.Operation) {
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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: "ControlTrigger",
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}
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return scope.AddOperation(opspec)
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}
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// Interleave the values from the `data` tensors into a single tensor.
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//
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// Builds a merged tensor such that
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//
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// ```python
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// merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
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// ```
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//
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// For example, if each `indices[m]` is scalar or vector, we have
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//
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// ```python
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// # Scalar indices:
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// merged[indices[m], ...] = data[m][...]
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//
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// # Vector indices:
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// merged[indices[m][i], ...] = data[m][i, ...]
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// ```
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//
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// Each `data[i].shape` must start with the corresponding `indices[i].shape`,
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// and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we
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// must have `data[i].shape = indices[i].shape + constant`. In terms of this
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// `constant`, the output shape is
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//
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// merged.shape = [max(indices)] + constant
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//
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// Values may be merged in parallel, so if an index appears in both `indices[m][i]`
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// and `indices[n][j]`, the result may be invalid. This differs from the normal
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// DynamicStitch operator that defines the behavior in that case.
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//
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// For example:
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//
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// ```python
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// indices[0] = 6
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// indices[1] = [4, 1]
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// indices[2] = [[5, 2], [0, 3]]
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// data[0] = [61, 62]
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// data[1] = [[41, 42], [11, 12]]
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// data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
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// merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
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// [51, 52], [61, 62]]
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// ```
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//
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// This method can be used to merge partitions created by `dynamic_partition`
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// as illustrated on the following example:
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//
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// ```python
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// # Apply function (increments x_i) on elements for which a certain condition
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// # apply (x_i != -1 in this example).
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// x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
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// condition_mask=tf.not_equal(x,tf.constant(-1.))
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// partitioned_data = tf.dynamic_partition(
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// x, tf.cast(condition_mask, tf.int32) , 2)
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// partitioned_data[1] = partitioned_data[1] + 1.0
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// condition_indices = tf.dynamic_partition(
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// tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)
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// x = tf.dynamic_stitch(condition_indices, partitioned_data)
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// # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
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// # unchanged.
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// ```
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//
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// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
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// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicStitch.png" alt>
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// </div>
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func ParallelDynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged 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: "ParallelDynamicStitch",
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Input: []tf.Input{
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tf.OutputList(indices), tf.OutputList(data),
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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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// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
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//
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// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
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@ -15307,6 +15215,118 @@ func TensorListLength(scope *Scope, input_handle tf.Output) (length tf.Output) {
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return op.Output(0)
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}
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// Does nothing. Serves as a control trigger for scheduling.
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//
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// Only useful as a placeholder for control edges.
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//
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// Returns the created operation.
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func ControlTrigger(scope *Scope) (o *tf.Operation) {
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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: "ControlTrigger",
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}
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return scope.AddOperation(opspec)
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}
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// Interleave the values from the `data` tensors into a single tensor.
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//
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// Builds a merged tensor such that
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//
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// ```python
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// merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
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// ```
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//
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// For example, if each `indices[m]` is scalar or vector, we have
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//
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// ```python
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// # Scalar indices:
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// merged[indices[m], ...] = data[m][...]
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//
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// # Vector indices:
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// merged[indices[m][i], ...] = data[m][i, ...]
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// ```
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//
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// Each `data[i].shape` must start with the corresponding `indices[i].shape`,
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// and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we
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// must have `data[i].shape = indices[i].shape + constant`. In terms of this
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// `constant`, the output shape is
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//
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// merged.shape = [max(indices)] + constant
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//
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// Values may be merged in parallel, so if an index appears in both `indices[m][i]`
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// and `indices[n][j]`, the result may be invalid. This differs from the normal
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// DynamicStitch operator that defines the behavior in that case.
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//
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// For example:
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//
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// ```python
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// indices[0] = 6
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// indices[1] = [4, 1]
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// indices[2] = [[5, 2], [0, 3]]
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// data[0] = [61, 62]
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// data[1] = [[41, 42], [11, 12]]
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// data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
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// merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
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// [51, 52], [61, 62]]
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// ```
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//
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// This method can be used to merge partitions created by `dynamic_partition`
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// as illustrated on the following example:
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//
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// ```python
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// # Apply function (increments x_i) on elements for which a certain condition
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// # apply (x_i != -1 in this example).
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// x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
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// condition_mask=tf.not_equal(x,tf.constant(-1.))
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// partitioned_data = tf.dynamic_partition(
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// x, tf.cast(condition_mask, tf.int32) , 2)
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// partitioned_data[1] = partitioned_data[1] + 1.0
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// condition_indices = tf.dynamic_partition(
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// tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)
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// x = tf.dynamic_stitch(condition_indices, partitioned_data)
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// # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
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// # unchanged.
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// ```
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//
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// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
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// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicStitch.png" alt>
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// </div>
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func ParallelDynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged 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: "ParallelDynamicStitch",
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Input: []tf.Input{
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tf.OutputList(indices), tf.OutputList(data),
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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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// Returns a Tensor stack of all keys in a tensor map.
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//
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// input_handle: the input map
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// keys: the returned Tensor of all keys in the map
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func TensorMapStackKeys(scope *Scope, input_handle tf.Output, key_dtype tf.DataType) (keys 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{}{"key_dtype": key_dtype}
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opspec := tf.OpSpec{
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Type: "TensorMapStackKeys",
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Input: []tf.Input{
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input_handle,
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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)
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}
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// Returns whether the given key exists in the map.
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//
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// input_handle: the input map
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