tf.raw_ops.TensorScatterAdd Adds sparse updates to an existing tensor according to indices.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterAdd
tf.raw_ops.TensorScatterAdd(
    tensor, indices, updates, name=None
)
 This operation creates a new tensor by adding sparse updates to the passed in tensor. This operation is very similar to tf.scatter_nd_add, except that the updates are added onto an existing tensor (as opposed to a variable). If the memory for the existing tensor cannot be re-used, a copy is made and updated. indices is an integer tensor containing indices into a new tensor of shape tensor.shape. The last dimension of indices can be at most the rank of tensor.shape: indices.shape[-1] <= tensor.shape.rank
 The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = tensor.shape.rank) or slices (if indices.shape[-1] < tensor.shape.rank) along dimension indices.shape[-1] of tensor.shape. updates is a tensor with shape indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
 The simplest form of tensor_scatter_add is to add individual elements to a tensor by index. For example, say we want to add 4 elements in a rank-1 tensor with 8 elements. In Python, this scatter add operation would look like this: indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones([8], dtype=tf.int32)
updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
print(updated)
 The resulting tensor would look like this: [1, 12, 1, 11, 10, 1, 1, 13]
 We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values. In Python, this scatter add operation would look like this: indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                        [7, 7, 7, 7], [8, 8, 8, 8]],
                       [[5, 5, 5, 5], [6, 6, 6, 6],
                        [7, 7, 7, 7], [8, 8, 8, 8]]])
tensor = tf.ones([4, 4, 4],dtype=tf.int32)
updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
print(updated)
 The resulting tensor would look like this: [[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
 [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]
 Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.

 Args
  tensor   A Tensor. Tensor to copy/update.
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as tensor.


tf.raw_ops.TensorScatterMax  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterMax
tf.raw_ops.TensorScatterMax(
    tensor, indices, updates, name=None
)

 Args
  tensor   A Tensor. Tensor to update.
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as tensor.


tf.raw_ops.TensorScatterMin  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterMin
tf.raw_ops.TensorScatterMin(
    tensor, indices, updates, name=None
)

 Args
  tensor   A Tensor. Tensor to update.
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as tensor.


tf.raw_ops.TensorScatterSub Subtracts sparse updates from an existing tensor according to indices.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterSub
tf.raw_ops.TensorScatterSub(
    tensor, indices, updates, name=None
)
 This operation creates a new tensor by subtracting sparse updates from the passed in tensor. This operation is very similar to tf.scatter_nd_sub, except that the updates are subtracted from an existing tensor (as opposed to a variable). If the memory for the existing tensor cannot be re-used, a copy is made and updated. indices is an integer tensor containing indices into a new tensor of shape shape. The last dimension of indices can be at most the rank of shape: indices.shape[-1] <= shape.rank
 The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = shape.rank) or slices (if indices.shape[-1] < shape.rank) along dimension indices.shape[-1] of shape. updates is a tensor with shape indices.shape[:-1] + shape[indices.shape[-1]:]
 The simplest form of tensor_scatter_sub is to subtract individual elements from a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements. In Python, this scatter subtract operation would look like this: indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones([8], dtype=tf.int32)
updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
print(updated)
 The resulting tensor would look like this: [1, -10, 1, -9, -8, 1, 1, -11]
 We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values. In Python, this scatter add operation would look like this: indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                        [7, 7, 7, 7], [8, 8, 8, 8]],
                       [[5, 5, 5, 5], [6, 6, 6, 6],
                        [7, 7, 7, 7], [8, 8, 8, 8]]])
tensor = tf.ones([4, 4, 4],dtype=tf.int32)
updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
print(updated)
 The resulting tensor would look like this: [[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
 [[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]
 Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.

 Args
  tensor   A Tensor. Tensor to copy/update.
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as tensor.


tf.raw_ops.TensorScatterUpdate Scatter updates into an existing tensor according to indices.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterUpdate
tf.raw_ops.TensorScatterUpdate(
    tensor, indices, updates, name=None
)
 This operation creates a new tensor by applying sparse updates to the passed in tensor. This operation is very similar to tf.scatter_nd, except that the updates are scattered onto an existing tensor (as opposed to a zero-tensor). If the memory for the existing tensor cannot be re-used, a copy is made and updated. If indices contains duplicates, then we pick the last update for the index. If an out of bound index is found on CPU, an error is returned. Warning: There are some GPU specific semantics for this operation.  If an out of bound index is found, the index is ignored. The order in which updates are applied is nondeterministic, so the output will be nondeterministic if indices contains duplicates.  indices is an integer tensor containing indices into a new tensor of shape shape.
indices must have at least 2 axes: (num_updates, index_depth). The last axis of indices is how deep to index into tensor so this index depth must be less than the rank of tensor: indices.shape[-1] <= tensor.ndim
  if indices.shape[-1] = tensor.rank this Op indexes and updates scalar elements. if indices.shape[-1] < tensor.rank it indexes and updates slices of the input tensor. Each update has a rank of tensor.rank - indices.shape[-1]. The overall shape of updates is: indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
 For usage examples see the python tf.tensor_scatter_nd_update function

 Args
  tensor   A Tensor. Tensor to copy/update.
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as tensor.


tf.raw_ops.TensorSliceDataset Creates a dataset that emits each dim-0 slice of components once.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorSliceDataset
tf.raw_ops.TensorSliceDataset(
    components, output_shapes, name=None
)

 Args
  components   A list of Tensor objects.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorStridedSliceUpdate Assign value to the sliced l-value reference of input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorStridedSliceUpdate
tf.raw_ops.TensorStridedSliceUpdate(
    input, begin, end, strides, value, begin_mask=0, end_mask=0, ellipsis_mask=0,
    new_axis_mask=0, shrink_axis_mask=0, name=None
)
 The values of value are assigned to the positions in the tensor input that are selected by the slice parameters. The slice parameters begin end strides etc. work exactly as in StridedSlice. NOTE this op currently does not support broadcasting and so value's shape must be exactly the shape produced by the slice of input.

 Args
  input   A Tensor.
  begin   A Tensor. Must be one of the following types: int32, int64.
  end   A Tensor. Must have the same type as begin.
  strides   A Tensor. Must have the same type as begin.
  value   A Tensor. Must have the same type as input.
  begin_mask   An optional int. Defaults to 0.
  end_mask   An optional int. Defaults to 0.
  ellipsis_mask   An optional int. Defaults to 0.
  new_axis_mask   An optional int. Defaults to 0.
  shrink_axis_mask   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.TensorSummary Outputs a Summary protocol buffer with a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorSummary
tf.raw_ops.TensorSummary(
    tensor, description='', labels=[], display_name='',
    name=None
)
 This op is being phased out in favor of TensorSummaryV2, which lets callers pass a tag as well as a serialized SummaryMetadata proto string that contains plugin-specific data. We will keep this op to maintain backwards compatibility.

 Args
  tensor   A Tensor. A tensor to serialize.
  description   An optional string. Defaults to "". A json-encoded SummaryDescription proto.
  labels   An optional list of strings. Defaults to []. An unused list of strings.
  display_name   An optional string. Defaults to "". An unused string.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.TensorSummaryV2 Outputs a Summary protocol buffer with a tensor and per-plugin data.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorSummaryV2
tf.raw_ops.TensorSummaryV2(
    tag, tensor, serialized_summary_metadata, name=None
)

 Args
  tag   A Tensor of type string. A string attached to this summary. Used for organization in TensorBoard.
  tensor   A Tensor. A tensor to serialize.
  serialized_summary_metadata   A Tensor of type string. A serialized SummaryMetadata proto. Contains plugin data.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.TextLineDataset Creates a dataset that emits the lines of one or more text files.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TextLineDataset
tf.raw_ops.TextLineDataset(
    filenames, compression_type, buffer_size, name=None
)

 Args
  filenames   A Tensor of type string. A scalar or a vector containing the name(s) of the file(s) to be read.
  compression_type   A Tensor of type string. A scalar containing either (i) the empty string (no compression), (ii) "ZLIB", or (iii) "GZIP".
  buffer_size   A Tensor of type int64. A scalar containing the number of bytes to buffer.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TextLineReader A Reader that outputs the lines of a file delimited by '\n'.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TextLineReader
tf.raw_ops.TextLineReader(
    skip_header_lines=0, container='', shared_name='', name=None
)

 Args
  skip_header_lines   An optional int. Defaults to 0. Number of lines to skip from the beginning of every file.
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.TextLineReaderV2 A Reader that outputs the lines of a file delimited by '\n'.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TextLineReaderV2
tf.raw_ops.TextLineReaderV2(
    skip_header_lines=0, container='', shared_name='', name=None
)

 Args
  skip_header_lines   An optional int. Defaults to 0. Number of lines to skip from the beginning of every file.
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.TFRecordDataset Creates a dataset that emits the records from one or more TFRecord files.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TFRecordDataset
tf.raw_ops.TFRecordDataset(
    filenames, compression_type, buffer_size, name=None
)

 Args
  filenames   A Tensor of type string. A scalar or vector containing the name(s) of the file(s) to be read.
  compression_type   A Tensor of type string. A scalar containing either (i) the empty string (no compression), (ii) "ZLIB", or (iii) "GZIP".
  buffer_size   A Tensor of type int64. A scalar representing the number of bytes to buffer. A value of 0 means no buffering will be performed.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TFRecordReader A Reader that outputs the records from a TensorFlow Records file.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TFRecordReader
tf.raw_ops.TFRecordReader(
    container='', shared_name='', compression_type='',
    name=None
)

 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  compression_type   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.TFRecordReaderV2 A Reader that outputs the records from a TensorFlow Records file.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TFRecordReaderV2
tf.raw_ops.TFRecordReaderV2(
    container='', shared_name='', compression_type='',
    name=None
)

 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  compression_type   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.ThreadPoolDataset Creates a dataset that uses a custom thread pool to compute input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ThreadPoolDataset
tf.raw_ops.ThreadPoolDataset(
    input_dataset, thread_pool, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  thread_pool   A Tensor of type resource. A resource produced by the ThreadPoolHandle op.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ThreadPoolHandle Creates a dataset that uses a custom thread pool to compute input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ThreadPoolHandle
tf.raw_ops.ThreadPoolHandle(
    num_threads, display_name, max_intra_op_parallelism=1, container='',
    shared_name='', name=None
)

 Args
  num_threads   An int. The number of threads in the thread pool.
  display_name   A string. A human-readable name for the threads that may be visible in some visualizations. threadpool.
  max_intra_op_parallelism   An optional int. Defaults to 1. The maximum degree of parallelism to use within operations that execute on this threadpool.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.ThreadUnsafeUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ThreadUnsafeUnigramCandidateSampler
tf.raw_ops.ThreadUnsafeUnigramCandidateSampler(
    true_classes, num_true, num_sampled, unique, range_max, seed=0, seed2=0,
    name=None
)
 See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels.

 Args
  true_classes   A Tensor of type int64. A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
  num_true   An int that is >= 1. Number of true labels per context.
  num_sampled   An int that is >= 1. Number of candidates to randomly sample.
  unique   A bool. If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
  range_max   An int that is >= 1. The sampler will sample integers from the interval [0, range_max).
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count).     sampled_candidates   A Tensor of type int64.
  true_expected_count   A Tensor of type float32.
  sampled_expected_count   A Tensor of type float32.


tf.raw_ops.Tile Constructs a tensor by tiling a given tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Tile
tf.raw_ops.Tile(
    input, multiples, name=None
)
 This operation creates a new tensor by replicating input multiples times. The output tensor's i'th dimension has input.dims(i) * multiples[i] elements, and the values of input are replicated multiples[i] times along the 'i'th dimension. For example, tiling [a b c d] by [2] produces [a b c d a b c d].
a = tf.constant([[1,2,3],[4,5,6]], tf.int32)
b = tf.constant([1,2], tf.int32)
tf.tile(a, b)
<tf.Tensor: shape=(2, 6), dtype=int32, numpy=
array([[1, 2, 3, 1, 2, 3],
       [4, 5, 6, 4, 5, 6]], dtype=int32)>
c = tf.constant([2,1], tf.int32)
tf.tile(a, c)
<tf.Tensor: shape=(4, 3), dtype=int32, numpy=
array([[1, 2, 3],
       [4, 5, 6],
       [1, 2, 3],
       [4, 5, 6]], dtype=int32)>
d = tf.constant([2,2], tf.int32)
tf.tile(a, d)
<tf.Tensor: shape=(4, 6), dtype=int32, numpy=
array([[1, 2, 3, 1, 2, 3],
       [4, 5, 6, 4, 5, 6],
       [1, 2, 3, 1, 2, 3],
       [4, 5, 6, 4, 5, 6]], dtype=int32)>

 Args
  input   A Tensor. 1-D or higher.
  multiples   A Tensor. Must be one of the following types: int32, int64. 1-D. Length must be the same as the number of dimensions in input
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.TileGrad Returns the gradient of Tile.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TileGrad
tf.raw_ops.TileGrad(
    input, multiples, name=None
)
 Since Tile takes an input and repeats the input multiples times along each dimension, TileGrad takes in multiples and aggregates each repeated tile of input into output.

 Args
  input   A Tensor.
  multiples   A Tensor of type int32.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Timestamp Provides the time since epoch in seconds.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Timestamp
tf.raw_ops.Timestamp(
    name=None
)
 Returns the timestamp as a float64 for seconds since the Unix epoch.
Note: the timestamp is computed when the op is executed, not when it is added to the graph.

 Args
  name   A name for the operation (optional).

 Returns   A Tensor of type float64.


tf.raw_ops.ToBool Converts a tensor to a scalar predicate.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ToBool
tf.raw_ops.ToBool(
    input, name=None
)
 Converts a tensor to a scalar predicate with the following rules:  For 0D tensors, truthiness is determined by comparing against a "zero" value. For numerical types it is the obvious zero. For strings it is the empty string. For >0D tensors, truthiness is determined by looking at the number of elements. If has zero elements, then the result is false. Otherwise the result is true.  This matches the behavior of If and While for determining if a tensor counts as true/false for a branch condition.

 Args
  input   A Tensor.
  name   A name for the operation (optional).

 Returns   A Tensor of type bool.


tf.raw_ops.TopK Finds values and indices of the k largest elements for the last dimension.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TopK
tf.raw_ops.TopK(
    input, k, sorted=True, name=None
)
 If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j]. For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus, values.shape = indices.shape = input.shape[:-1] + [k]
 If two elements are equal, the lower-index element appears first. If k varies dynamically, use TopKV2 below.

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 1-D or higher with last dimension at least k.
  k   An int that is >= 0. Number of top elements to look for along the last dimension (along each row for matrices).
  sorted   An optional bool. Defaults to True. If true the resulting k elements will be sorted by the values in descending order.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (values, indices).     values   A Tensor. Has the same type as input.
  indices   A Tensor of type int32.


tf.raw_ops.TopKV2 Finds values and indices of the k largest elements for the last dimension.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TopKV2
tf.raw_ops.TopKV2(
    input, k, sorted=True, name=None
)
 If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j]. For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus, values.shape = indices.shape = input.shape[:-1] + [k]
 If two elements are equal, the lower-index element appears first.

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 1-D or higher with last dimension at least k.
  k   A Tensor of type int32. 0-D. Number of top elements to look for along the last dimension (along each row for matrices).
  sorted   An optional bool. Defaults to True. If true the resulting k elements will be sorted by the values in descending order.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (values, indices).     values   A Tensor. Has the same type as input.
  indices   A Tensor of type int32.


tf.raw_ops.TPUCompilationResult Returns the result of a TPU compilation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TPUCompilationResult
tf.raw_ops.TPUCompilationResult(
    name=None
)
 This operation returns the result of a TPU compilation as a serialized CompilationResultProto, which holds a status and an error message if an error occurred during compilation.

 Args
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.TPUEmbeddingActivations An op enabling differentiation of TPU Embeddings.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TPUEmbeddingActivations
tf.raw_ops.TPUEmbeddingActivations(
    embedding_variable, sliced_activations, table_id, lookup_id, name=None
)
 This op simply returns its first input, which is assumed to have been sliced from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of this op, and its first argument being a trainable Variable, enables automatic differentiation of graphs containing embeddings via the TPU Embedding Python libraries.

 Args
  embedding_variable   A Tensor of type float32. A trainable variable, enabling optimizers to find this op.
  sliced_activations   A Tensor of type float32. The embedding activations Tensor to return.
  table_id   An int that is >= 0. The id of the table in the embedding layer configuration from which these activations were computed.
  lookup_id   An int that is >= 0. Identifier of the set of embedding indices which produced these activations.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TPUOrdinalSelector A TPU core selector Op.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TPUOrdinalSelector
tf.raw_ops.TPUOrdinalSelector(
    name=None
)
 This Op produces a set of TPU cores (for warm-up) or a single TPU core (for regular inference) to execute the TPU program on. The output is consumed by TPUPartitionedCall.

 Args
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.TPUPartitionedCall Calls a function placed on a specified TPU device.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TPUPartitionedCall
tf.raw_ops.TPUPartitionedCall(
    args, device_ordinal, Tout, f, autotuner_thresh=0, name=None
)

 Args
  args   A list of Tensor objects. The arguments to the function.
  device_ordinal   A Tensor of type int32. The TPU device ordinal to run the function on.
  Tout   A list of tf.DTypes. The types of the outputs of the function.
  f   A function decorated with @Defun. The function to call.
  autotuner_thresh   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type Tout.


tf.raw_ops.TPUReplicatedInput Connects N inputs to an N-way replicated TPU computation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TPUReplicatedInput
tf.raw_ops.TPUReplicatedInput(
    inputs, is_mirrored_variable=False, index=-1, is_packed=False, name=None
)
 This operation holds a replicated input to a tpu.replicate() computation subgraph. Each replicated input has the same shape and type alongside the output. For example: %a = "tf.opA"()
%b = "tf.opB"()
%replicated_input = "tf.TPUReplicatedInput"(%a, %b)
%computation = "tf.Computation"(%replicated_input)
 The above computation has a replicated input of two replicas.

 Args
  inputs   A list of at least 1 Tensor objects with the same type.
  is_mirrored_variable   An optional bool. Defaults to False.
  index   An optional int. Defaults to -1.
  is_packed   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as inputs.


tf.raw_ops.TPUReplicatedOutput Connects N outputs from an N-way replicated TPU computation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TPUReplicatedOutput
tf.raw_ops.TPUReplicatedOutput(
    input, num_replicas, name=None
)
 This operation holds a replicated output from a tpu.replicate() computation subgraph. Each replicated output has the same shape and type alongside the input. For example: %computation = "tf.Computation"()
%replicated_output:2 = "tf.TPUReplicatedOutput"(%computation)
 The above computation has a replicated output of two replicas.

 Args
  input   A Tensor.
  num_replicas   An int that is >= 1.
  name   A name for the operation (optional).

 Returns   A list of num_replicas Tensor objects with the same type as input.


tf.raw_ops.TPUReplicateMetadata Metadata indicating how the TPU computation should be replicated.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TPUReplicateMetadata
tf.raw_ops.TPUReplicateMetadata(
    num_replicas, num_cores_per_replica=1, topology='', use_tpu=True,
    device_assignment=[], computation_shape=[], host_compute_core=[],
    padding_map=[], step_marker_location='STEP_MARK_AT_ENTRY',
    allow_soft_placement=False, use_spmd_for_xla_partitioning=False, name=None
)
 This operation holds the metadata common to operations of a tpu.replicate() computation subgraph.

 Args
  num_replicas   An int that is >= 0. Number of replicas of the computation
  num_cores_per_replica   An optional int. Defaults to 1. Number of cores per replica. Used for model parallelism.
  topology   An optional string. Defaults to "". TopologyProto indicating the topology of the TPU pod slice.
  use_tpu   An optional bool. Defaults to True. Whether to place the computation on the TPU.
  device_assignment   An optional list of ints. Defaults to []. The assignment of devices for the computation.
  computation_shape   An optional list of ints. Defaults to []. DEPRECATED. Use num_cores_per_replica instead.
  host_compute_core   An optional list of strings. Defaults to [].
  padding_map   An optional list of strings. Defaults to [].
  step_marker_location   An optional string. Defaults to "STEP_MARK_AT_ENTRY".
  allow_soft_placement   An optional bool. Defaults to False.
  use_spmd_for_xla_partitioning   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.Transpose Shuffle dimensions of x according to a permutation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Transpose
tf.raw_ops.Transpose(
    x, perm, name=None
)
 The output y has the same rank as x. The shapes of x and y satisfy: y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]

 Args
  x   A Tensor.
  perm   A Tensor. Must be one of the following types: int32, int64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.TridiagonalMatMul Calculate product with tridiagonal matrix.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TridiagonalMatMul
tf.raw_ops.TridiagonalMatMul(
    superdiag, maindiag, subdiag, rhs, name=None
)
 Calculates product of two matrices, where left matrix is a tridiagonal matrix.

 Args
  superdiag   A Tensor. Must be one of the following types: float64, float32, complex64, complex128. Tensor of shape [..., 1, M], representing superdiagonals of tri-diagonal matrices to the left of multiplication. Last element is ignored.
  maindiag   A Tensor. Must have the same type as superdiag. Tensor of shape [..., 1, M], representing main diagonals of tri-diagonal matrices to the left of multiplication.
  subdiag   A Tensor. Must have the same type as superdiag. Tensor of shape [..., 1, M], representing subdiagonals of tri-diagonal matrices to the left of multiplication. First element is ignored.
  rhs   A Tensor. Must have the same type as superdiag. Tensor of shape [..., M, N], representing MxN matrices to the right of multiplication.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as superdiag.


tf.raw_ops.TridiagonalSolve Solves tridiagonal systems of equations.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TridiagonalSolve
tf.raw_ops.TridiagonalSolve(
    diagonals, rhs, partial_pivoting=True, name=None
)
 Solves tridiagonal systems of equations. Supports batch dimensions and multiple right-hand sides per each left-hand side. On CPU, solution is computed via Gaussian elimination with or without partial pivoting, depending on partial_pivoting attribute. On GPU, Nvidia's cuSPARSE library is used: https://docs.nvidia.com/cuda/cusparse/index.html#gtsv Partial pivoting is not yet supported by XLA backends.

 Args
  diagonals   A Tensor. Must be one of the following types: float64, float32, complex64, complex128. Tensor of shape [..., 3, M] whose innermost 2 dimensions represent the tridiagonal matrices with three rows being the superdiagonal, diagonals, and subdiagonals, in order. The last element of the superdiagonal and the first element of the subdiagonal is ignored.
  rhs   A Tensor. Must have the same type as diagonals. Tensor of shape [..., M, K], representing K right-hand sides per each left-hand side.
  partial_pivoting   An optional bool. Defaults to True. Whether to apply partial pivoting. Partial pivoting makes the procedure more stable, but slower.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as diagonals.


tf.raw_ops.TruncateDiv Returns x / y element-wise for integer types.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TruncateDiv
tf.raw_ops.TruncateDiv(
    x, y, name=None
)
 Truncation designates that negative numbers will round fractional quantities toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different than Python semantics. See FloorDiv for a division function that matches Python Semantics.
Note: truncatediv supports broadcasting. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.TruncatedNormal Outputs random values from a truncated normal distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TruncatedNormal
tf.raw_ops.TruncatedNormal(
    shape, dtype, seed=0, seed2=0, name=None
)
 The generated values follow a normal distribution with mean 0 and standard deviation 1, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  dtype   A tf.DType from: tf.half, tf.bfloat16, tf.float32, tf.float64. The type of the output.
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
  seed2   An optional int. Defaults to 0. A second seed to avoid seed collision.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.TruncateMod Returns element-wise remainder of division. This emulates C semantics in that  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TruncateMod
tf.raw_ops.TruncateMod(
    x, y, name=None
)
 the result here is consistent with a truncating divide. E.g. truncate(x / y) * y + truncate_mod(x, y) = x.
Note: truncatemod supports broadcasting. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: int32, int64, bfloat16, half, float32, float64.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Unbatch Reverses the operation of Batch for a single output Tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Unbatch
tf.raw_ops.Unbatch(
    batched_tensor, batch_index, id, timeout_micros, container='',
    shared_name='', name=None
)
 An instance of Unbatch either receives an empty batched_tensor, in which case it asynchronously waits until the values become available from a concurrently running instance of Unbatch with the same container and shared_name, or receives a non-empty batched_tensor in which case it finalizes all other concurrently running instances and outputs its own element from the batch. batched_tensor: The possibly transformed output of Batch. The size of the first dimension should remain unchanged by the transformations for the operation to work. batch_index: The matching batch_index obtained from Batch. id: The id scalar emitted by Batch. unbatched_tensor: The Tensor corresponding to this execution. timeout_micros: Maximum amount of time (in microseconds) to wait to receive the batched input tensor associated with a given invocation of the op. container: Container to control resource sharing. shared_name: Instances of Unbatch with the same container and shared_name are assumed to possibly belong to the same batch. If left empty, the op name will be used as the shared name.

 Args
  batched_tensor   A Tensor.
  batch_index   A Tensor of type int64.
  id   A Tensor of type int64.
  timeout_micros   An int.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as batched_tensor.


tf.raw_ops.UnbatchDataset A dataset that splits the elements of its input into multiple elements.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnbatchDataset
tf.raw_ops.UnbatchDataset(
    input_dataset, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.UnbatchGrad Gradient of Unbatch.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnbatchGrad
tf.raw_ops.UnbatchGrad(
    original_input, batch_index, grad, id, container='',
    shared_name='', name=None
)
 Acts like Batch but using the given batch_index index of batching things as they become available. This ensures that the gradients are propagated back in the same session which did the forward pass. original_input: The input to the Unbatch operation this is the gradient of. batch_index: The batch_index given to the Unbatch operation this is the gradient of. grad: The downstream gradient. id: The id scalar emitted by Batch. batched_grad: The return value, either an empty tensor or the batched gradient. container: Container to control resource sharing. shared_name: Instances of UnbatchGrad with the same container and shared_name are assumed to possibly belong to the same batch. If left empty, the op name will be used as the shared name.

 Args
  original_input   A Tensor.
  batch_index   A Tensor of type int64.
  grad   A Tensor. Must have the same type as original_input.
  id   A Tensor of type int64.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as original_input.


tf.raw_ops.UncompressElement Uncompresses a compressed dataset element.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UncompressElement
tf.raw_ops.UncompressElement(
    compressed, output_types, output_shapes, name=None
)

 Args
  compressed   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type output_types.


tf.raw_ops.UnicodeDecode Decodes each string in input into a sequence of Unicode code points.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeDecode
tf.raw_ops.UnicodeDecode(
    input, input_encoding, errors='replace', replacement_char=65533,
    replace_control_characters=False, Tsplits=tf.dtypes.int64, name=None
)
 The character codepoints for all strings are returned using a single vector char_values, with strings expanded to characters in row-major order. The row_splits tensor indicates where the codepoints for each input string begin and end within the char_values tensor. In particular, the values for the ith string (in row-major order) are stored in the slice [row_splits[i]:row_splits[i+1]]. Thus:
char_values[row_splits[i]+j] is the Unicode codepoint for the jth character in the ith string (in row-major order).
row_splits[i+1] - row_splits[i] is the number of characters in the ith string (in row-major order).

 Args
  input   A Tensor of type string. The text to be decoded. Can have any shape. Note that the output is flattened to a vector of char values.
  input_encoding   A string. Text encoding of the input strings. This is any of the encodings supported by ICU ucnv algorithmic converters. Examples: "UTF-16", "US ASCII", "UTF-8".
  errors   An optional string from: "strict", "replace", "ignore". Defaults to "replace". Error handling policy when there is invalid formatting found in the input. The value of 'strict' will cause the operation to produce a InvalidArgument error on any invalid input formatting. A value of 'replace' (the default) will cause the operation to replace any invalid formatting in the input with the replacement_char codepoint. A value of 'ignore' will cause the operation to skip any invalid formatting in the input and produce no corresponding output character.
  replacement_char   An optional int. Defaults to 65533. The replacement character codepoint to be used in place of any invalid formatting in the input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character is 0xFFFD or U+65533.)
  replace_control_characters   An optional bool. Defaults to False. Whether to replace the C0 control characters (00-1F) with the replacement_char. Default is false.
  Tsplits   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (row_splits, char_values).     row_splits   A Tensor of type Tsplits.
  char_values   A Tensor of type int32.


tf.raw_ops.UnicodeDecodeWithOffsets Decodes each string in input into a sequence of Unicode code points.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeDecodeWithOffsets
tf.raw_ops.UnicodeDecodeWithOffsets(
    input, input_encoding, errors='replace', replacement_char=65533,
    replace_control_characters=False, Tsplits=tf.dtypes.int64, name=None
)
 The character codepoints for all strings are returned using a single vector char_values, with strings expanded to characters in row-major order. Similarly, the character start byte offsets are returned using a single vector char_to_byte_starts, with strings expanded in row-major order. The row_splits tensor indicates where the codepoints and start offsets for each input string begin and end within the char_values and char_to_byte_starts tensors. In particular, the values for the ith string (in row-major order) are stored in the slice [row_splits[i]:row_splits[i+1]]. Thus:
char_values[row_splits[i]+j] is the Unicode codepoint for the jth character in the ith string (in row-major order).
char_to_bytes_starts[row_splits[i]+j] is the start byte offset for the jth character in the ith string (in row-major order).
row_splits[i+1] - row_splits[i] is the number of characters in the ith string (in row-major order).

 Args
  input   A Tensor of type string. The text to be decoded. Can have any shape. Note that the output is flattened to a vector of char values.
  input_encoding   A string. Text encoding of the input strings. This is any of the encodings supported by ICU ucnv algorithmic converters. Examples: "UTF-16", "US ASCII", "UTF-8".
  errors   An optional string from: "strict", "replace", "ignore". Defaults to "replace". Error handling policy when there is invalid formatting found in the input. The value of 'strict' will cause the operation to produce a InvalidArgument error on any invalid input formatting. A value of 'replace' (the default) will cause the operation to replace any invalid formatting in the input with the replacement_char codepoint. A value of 'ignore' will cause the operation to skip any invalid formatting in the input and produce no corresponding output character.
  replacement_char   An optional int. Defaults to 65533. The replacement character codepoint to be used in place of any invalid formatting in the input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character is 0xFFFD or U+65533.)
  replace_control_characters   An optional bool. Defaults to False. Whether to replace the C0 control characters (00-1F) with the replacement_char. Default is false.
  Tsplits   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (row_splits, char_values, char_to_byte_starts).     row_splits   A Tensor of type Tsplits.
  char_values   A Tensor of type int32.
  char_to_byte_starts   A Tensor of type int64.


tf.raw_ops.UnicodeEncode Encode a tensor of ints into unicode strings.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeEncode
tf.raw_ops.UnicodeEncode(
    input_values, input_splits, output_encoding, errors='replace',
    replacement_char=65533, name=None
)
 Returns a vector of strings, where output[i] is constructed by encoding the Unicode codepoints in input_values[input_splits[i]:input_splits[i+1]] using output_encoding. Example: input_values = [72, 101, 108, 108, 111, 87, 111, 114, 108, 100]
input_splits = [0, 5, 10]
output_encoding = 'UTF-8'

output = ['Hello', 'World']

 Args
  input_values   A Tensor of type int32. A 1D tensor containing the unicode codepoints that should be encoded.
  input_splits   A Tensor. Must be one of the following types: int32, int64. A 1D tensor specifying how the unicode codepoints should be split into strings. In particular, output[i] is constructed by encoding the codepoints in the slice input_values[input_splits[i]:input_splits[i+1]].
  output_encoding   A string from: "UTF-8", "UTF-16-BE", "UTF-32-BE". Unicode encoding of the output strings. Valid encodings are: "UTF-8", "UTF-16-BE", and "UTF-32-BE".
  errors   An optional string from: "ignore", "replace", "strict". Defaults to "replace". Error handling policy when there is invalid formatting found in the input. The value of 'strict' will cause the operation to produce a InvalidArgument error on any invalid input formatting. A value of 'replace' (the default) will cause the operation to replace any invalid formatting in the input with the replacement_char codepoint. A value of 'ignore' will cause the operation to skip any invalid formatting in the input and produce no corresponding output character.
  replacement_char   An optional int. Defaults to 65533. The replacement character codepoint to be used in place of any invalid formatting in the input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character is 0xFFFD (U+65533).
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.UnicodeScript Determine the script codes of a given tensor of Unicode integer code points.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeScript
tf.raw_ops.UnicodeScript(
    input, name=None
)
 This operation converts Unicode code points to script codes corresponding to each code point. Script codes correspond to International Components for Unicode (ICU) UScriptCode values. See ICU project docs for more details on script codes. For an example, see the unicode strings guide on unicode scripts. Returns -1 (USCRIPT_INVALID_CODE) for invalid codepoints. Output shape will match input shape. Examples:
tf.strings.unicode_script([1, 31, 38])
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([0, 0, 0], dtype=int32)>

 Args
  input   A Tensor of type int32. A Tensor of int32 Unicode code points.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.UnicodeTranscode Transcode the input text from a source encoding to a destination encoding.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeTranscode
tf.raw_ops.UnicodeTranscode(
    input, input_encoding, output_encoding, errors='replace',
    replacement_char=65533, replace_control_characters=False, name=None
)
 The input is a string tensor of any shape. The output is a string tensor of the same shape containing the transcoded strings. Output strings are always valid unicode. If the input contains invalid encoding positions, the errors attribute sets the policy for how to deal with them. If the default error-handling policy is used, invalid formatting will be substituted in the output by the replacement_char. If the errors policy is to ignore, any invalid encoding positions in the input are skipped and not included in the output. If it set to strict then any invalid formatting will result in an InvalidArgument error. This operation can be used with output_encoding = input_encoding to enforce correct formatting for inputs even if they are already in the desired encoding. If the input is prefixed by a Byte Order Mark needed to determine encoding (e.g. if the encoding is UTF-16 and the BOM indicates big-endian), then that BOM will be consumed and not emitted into the output. If the input encoding is marked with an explicit endianness (e.g. UTF-16-BE), then the BOM is interpreted as a non-breaking-space and is preserved in the output (including always for UTF-8). The end result is that if the input is marked as an explicit endianness the transcoding is faithful to all codepoints in the source. If it is not marked with an explicit endianness, the BOM is not considered part of the string itself but as metadata, and so is not preserved in the output. Examples:
tf.strings.unicode_transcode(["Hello", "TensorFlow", "2.x"], "UTF-8", "UTF-16-BE")
<tf.Tensor: shape=(3,), dtype=string, numpy=
array([b'\x00H\x00e\x00l\x00l\x00o',
       b'\x00T\x00e\x00n\x00s\x00o\x00r\x00F\x00l\x00o\x00w',
       b'\x002\x00.\x00x'], dtype=object)>
tf.strings.unicode_transcode(["A", "B", "C"], "US ASCII", "UTF-8").numpy()
array([b'A', b'B', b'C'], dtype=object)

 Args
  input   A Tensor of type string. The text to be processed. Can have any shape.
  input_encoding   A string. Text encoding of the input strings. This is any of the encodings supported by ICU ucnv algorithmic converters. Examples: "UTF-16", "US ASCII", "UTF-8".
  output_encoding   A string from: "UTF-8", "UTF-16-BE", "UTF-32-BE". The unicode encoding to use in the output. Must be one of "UTF-8", "UTF-16-BE", "UTF-32-BE". Multi-byte encodings will be big-endian.
  errors   An optional string from: "strict", "replace", "ignore". Defaults to "replace". Error handling policy when there is invalid formatting found in the input. The value of 'strict' will cause the operation to produce a InvalidArgument error on any invalid input formatting. A value of 'replace' (the default) will cause the operation to replace any invalid formatting in the input with the replacement_char codepoint. A value of 'ignore' will cause the operation to skip any invalid formatting in the input and produce no corresponding output character.
  replacement_char   An optional int. Defaults to 65533. The replacement character codepoint to be used in place of any invalid formatting in the input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character is 0xFFFD or U+65533.) Note that for UTF-8, passing a replacement character expressible in 1 byte, such as ' ', will preserve string alignment to the source since invalid bytes will be replaced with a 1-byte replacement. For UTF-16-BE and UTF-16-LE, any 1 or 2 byte replacement character will preserve byte alignment to the source.

  replace_control_characters   An optional bool. Defaults to False. Whether to replace the C0 control characters (00-1F) with the replacement_char. Default is false.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.UniformCandidateSampler Generates labels for candidate sampling with a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UniformCandidateSampler
tf.raw_ops.UniformCandidateSampler(
    true_classes, num_true, num_sampled, unique, range_max, seed=0, seed2=0,
    name=None
)
 See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels.

 Args
  true_classes   A Tensor of type int64. A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
  num_true   An int that is >= 1. Number of true labels per context.
  num_sampled   An int that is >= 1. Number of candidates to randomly sample.
  unique   A bool. If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
  range_max   An int that is >= 1. The sampler will sample integers from the interval [0, range_max).
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count).     sampled_candidates   A Tensor of type int64.
  true_expected_count   A Tensor of type float32.
  sampled_expected_count   A Tensor of type float32.


tf.raw_ops.Unique Finds unique elements in a 1-D tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Unique
tf.raw_ops.Unique(
    x, out_idx=tf.dtypes.int32, name=None
)
 This operation returns a tensor y containing all of the unique elements of x sorted in the same order that they occur in x; x does not need to be sorted. This operation also returns a tensor idx the same size as x that contains the index of each value of x in the unique output y. In other words: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] Examples: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx = unique(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]

# tensor 'x' is [4, 5, 1, 2, 3, 3, 4, 5]
y, idx = unique(x)
y ==> [4, 5, 1, 2, 3]
idx ==> [0, 1, 2, 3, 4, 4, 0, 1]

 Args
  x   A Tensor. 1-D.
  out_idx   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (y, idx).     y   A Tensor. Has the same type as x.
  idx   A Tensor of type out_idx.


tf.raw_ops.UniqueDataset Creates a dataset that contains the unique elements of input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UniqueDataset
tf.raw_ops.UniqueDataset(
    input_dataset, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.UniqueV2 Finds unique elements along an axis of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UniqueV2
tf.raw_ops.UniqueV2(
    x, axis, out_idx=tf.dtypes.int32, name=None
)
 This operation either returns a tensor y containing unique elements along the axis of a tensor. The returned unique elements is sorted in the same order as they occur along axis in x. This operation also returns a tensor idx that is the same size as the number of the elements in x along the axis dimension. It contains the index in the unique output y. In other words, for an 1-D tensor x with `axis = None: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] For example: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx = unique(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
 For an 2-D tensor x with axis = 0: # tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx = unique(x, axis=0)
y ==> [[1, 0, 0],
       [2, 0, 0]]
idx ==> [0, 0, 1]
 For an 2-D tensor x with axis = 1: # tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx = unique(x, axis=1)
y ==> [[1, 0],
       [1, 0],
       [2, 0]]
idx ==> [0, 1, 1]

 Args
  x   A Tensor. A Tensor.
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: None). The axis of the Tensor to find the unique elements.
  out_idx   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (y, idx).     y   A Tensor. Has the same type as x.
  idx   A Tensor of type out_idx.


tf.raw_ops.UniqueWithCounts Finds unique elements in a 1-D tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UniqueWithCounts
tf.raw_ops.UniqueWithCounts(
    x, out_idx=tf.dtypes.int32, name=None
)
 This operation returns a tensor y containing all of the unique elements of x sorted in the same order that they occur in x. This operation also returns a tensor idx the same size as x that contains the index of each value of x in the unique output y. Finally, it returns a third tensor count that contains the count of each element of y in x. In other words: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] For example: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx, count = unique_with_counts(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
count ==> [2, 1, 3, 1, 2]

 Args
  x   A Tensor. 1-D.
  out_idx   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (y, idx, count).     y   A Tensor. Has the same type as x.
  idx   A Tensor of type out_idx.
  count   A Tensor of type out_idx.


tf.raw_ops.UniqueWithCountsV2 Finds unique elements along an axis of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UniqueWithCountsV2
tf.raw_ops.UniqueWithCountsV2(
    x, axis, out_idx=tf.dtypes.int32, name=None
)
 This operation either returns a tensor y containing unique elements along the axis of a tensor. The returned unique elements is sorted in the same order as they occur along axis in x. This operation also returns a tensor idx and a tensor count that are the same size as the number of the elements in x along the axis dimension. The idx contains the index in the unique output y and the count contains the count in the unique output y. In other words, for an 1-D tensor x with `axis = None: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] For example: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx, count = unique_with_counts(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
count ==> [2, 1, 3, 1, 2]
 For an 2-D tensor x with axis = 0: # tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx, count = unique_with_counts(x, axis=0)
y ==> [[1, 0, 0],
       [2, 0, 0]]
idx ==> [0, 0, 1]
count ==> [2, 1]
 For an 2-D tensor x with axis = 1: # tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx, count = unique_with_counts(x, axis=1)
y ==> [[1, 0],
       [1, 0],
       [2, 0]]
idx ==> [0, 1, 1]
count ==> [1, 2]

 Args
  x   A Tensor. A Tensor.
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: None). The axis of the Tensor to find the unique elements.
  out_idx   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (y, idx, count).     y   A Tensor. Has the same type as x.
  idx   A Tensor of type out_idx.
  count   A Tensor of type out_idx.


tf.raw_ops.Unpack Unpacks a given dimension of a rank-R tensor into num rank-(R-1) tensors.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Unpack
tf.raw_ops.Unpack(
    value, num, axis=0, name=None
)
 Unpacks num tensors from value by chipping it along the axis dimension. For example, given a tensor of shape (A, B, C, D); If axis == 0 then the i'th tensor in output is the slice value[i, :, :, :] and each tensor in output will have shape (B, C, D). (Note that the dimension unpacked along is gone, unlike split). If axis == 1 then the i'th tensor in output is the slice value[:, i, :, :] and each tensor in output will have shape (A, C, D). Etc. This is the opposite of pack.

 Args
  value   A Tensor. 1-D or higher, with axis dimension size equal to num.
  num   An int that is >= 0.
  axis   An optional int. Defaults to 0. Dimension along which to unpack. Negative values wrap around, so the valid range is [-R, R).
  name   A name for the operation (optional).

 Returns   A list of num Tensor objects with the same type as value.


tf.raw_ops.UnravelIndex Converts an array of flat indices into a tuple of coordinate arrays.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnravelIndex
tf.raw_ops.UnravelIndex(
    indices, dims, name=None
)
 Example: y = tf.unravel_index(indices=[2, 5, 7], dims=[3, 3])
# 'dims' represent a hypothetical (3, 3) tensor of indices:
# [[0, 1, *2*],
#  [3, 4, *5*],
#  [6, *7*, 8]]
# For each entry from 'indices', this operation returns
# its coordinates (marked with '*'), such as
# 2 ==> (0, 2)
# 5 ==> (1, 2)
# 7 ==> (2, 1)
y ==> [[0, 1, 2], [2, 2, 1]]

 Args
  indices   A Tensor. Must be one of the following types: int32, int64. An 0-D or 1-D int Tensor whose elements are indices into the flattened version of an array of dimensions dims.
  dims   A Tensor. Must have the same type as indices. An 1-D int Tensor. The shape of the array to use for unraveling indices.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as indices.
 Numpy Compatibility Equivalent to np.unravel_index


tf.raw_ops.UnsortedSegmentJoin Joins the elements of inputs based on segment_ids.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentJoin
tf.raw_ops.UnsortedSegmentJoin(
    inputs, segment_ids, num_segments, separator='', name=None
)
 Computes the string join along segments of a tensor. Given segment_ids with rank N and data with rank N+M: `output[i, k1...kM] = strings.join([data[j1...jN, k1...kM])`
 where the join is over all [j1...jN] such that segment_ids[j1...jN] = i. Strings are joined in row-major order. For example: inputs = [['Y', 'q', 'c'], ['Y', '6', '6'], ['p', 'G', 'a']]
output_array = string_ops.unsorted_segment_join(inputs=inputs,
                                                segment_ids=[1, 0, 1],
                                                num_segments=2,
                                                separator=':'))
# output_array ==> [['Y', '6', '6'], ['Y:p', 'q:G', 'c:a']]

inputs = ['this', 'is', 'a', 'test']
output_array = string_ops.unsorted_segment_join(inputs=inputs,
                                                segment_ids=[0, 0, 0, 0],
                                                num_segments=1,
                                                separator=':'))
# output_array ==> ['this:is:a:test']

 Args
  inputs   A Tensor of type string. The input to be joined.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape. Negative segment ids are not supported.
  num_segments   A Tensor. Must be one of the following types: int32, int64. A scalar.
  separator   An optional string. Defaults to "". The separator to use when joining.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.UnsortedSegmentMax   Computes the maximum along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentMax
tf.raw_ops.UnsortedSegmentMax(
    data, segment_ids, num_segments, name=None
)
 Read the section on segmentation for an explanation of segments. This operator is similar to the unsorted segment sum operator found (here). Instead of computing the sum over segments, it computes the maximum such that: \(output_i = \max_{j...} data[j...]\) where max is over tuples j... such that segment_ids[j...] == i. If the maximum is empty for a given segment ID i, it outputs the smallest possible value for the specific numeric type, output[i] = numeric_limits<T>::lowest(). If the given segment ID i is negative, then the corresponding value is dropped, and will not be included in the result.    For example: c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_max(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 4,  3, 3, 4],
#       [5,  6, 7, 8]]

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape.
  num_segments   A Tensor. Must be one of the following types: int32, int64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.UnsortedSegmentMin   Computes the minimum along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentMin
tf.raw_ops.UnsortedSegmentMin(
    data, segment_ids, num_segments, name=None
)
 Read the section on segmentation for an explanation of segments. This operator is similar to the unsorted segment sum operator found (here). Instead of computing the sum over segments, it computes the minimum such that: \(output_i = \min_{j...} data_[j...]\) where min is over tuples j... such that segment_ids[j...] == i. If the minimum is empty for a given segment ID i, it outputs the largest possible value for the specific numeric type, output[i] = numeric_limits<T>::max(). For example: c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_min(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 1,  2, 2, 1],
#       [5,  6, 7, 8]]
 If the given segment ID i is negative, then the corresponding value is dropped, and will not be included in the result.

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape.
  num_segments   A Tensor. Must be one of the following types: int32, int64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.UnsortedSegmentProd   Computes the product along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentProd
tf.raw_ops.UnsortedSegmentProd(
    data, segment_ids, num_segments, name=None
)
 Read the section on segmentation for an explanation of segments. This operator is similar to the unsorted segment sum operator found (here). Instead of computing the sum over segments, it computes the product of all entries belonging to a segment such that: \(output_i = \prod_{j...} data[j...]\) where the product is over tuples j... such that segment_ids[j...] == i. For example: c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_prod(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 4,  6, 6, 4],
#       [5,  6, 7, 8]]
 If there is no entry for a given segment ID i, it outputs 1. If the given segment ID i is negative, then the corresponding value is dropped, and will not be included in the result.

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape.
  num_segments   A Tensor. Must be one of the following types: int32, int64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.UnsortedSegmentSum   Computes the sum along segments of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentSum
tf.raw_ops.UnsortedSegmentSum(
    data, segment_ids, num_segments, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output[i] = \sum_{j...} data[j...]\) where the sum is over tuples j... such that segment_ids[j...] == i. Unlike SegmentSum, segment_ids need not be sorted and need not cover all values in the full range of valid values. If the sum is empty for a given segment ID i, output[i] = 0. If the given segment ID i is negative, the value is dropped and will not be added to the sum of the segment. num_segments should equal the number of distinct segment IDs.    c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_sum(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 5,  5, 5, 5],
#       [5,  6, 7, 8]]

 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape.
  num_segments   A Tensor. Must be one of the following types: int32, int64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.Unstage Op is similar to a lightweight Dequeue.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Unstage
tf.raw_ops.Unstage(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 The basic functionality is similar to dequeue with many fewer capabilities and options. This Op is optimized for performance.

 Args
  dtypes   A list of tf.DTypes that has length >= 1.
  capacity   An optional int that is >= 0. Defaults to 0.
  memory_limit   An optional int that is >= 0. Defaults to 0.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type dtypes.


tf.raw_ops.UnwrapDatasetVariant  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UnwrapDatasetVariant
tf.raw_ops.UnwrapDatasetVariant(
    input_handle, name=None
)

 Args
  input_handle   A Tensor of type variant.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.UpperBound Applies upper_bound(sorted_search_values, values) along each row.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.UpperBound
tf.raw_ops.UpperBound(
    sorted_inputs, values, out_type=tf.dtypes.int32, name=None
)
 Each set of rows with the same index in (sorted_inputs, values) is treated independently. The resulting row is the equivalent of calling np.searchsorted(sorted_inputs, values, side='right'). The result is not a global index to the entire Tensor, but rather just the index in the last dimension. A 2-D example: sorted_sequence = [[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]] values = [[2, 4, 9], [0, 2, 6]] result = UpperBound(sorted_sequence, values) result == [[1, 2, 4], [0, 2, 5]]

 Args
  sorted_inputs   A Tensor. 2-D Tensor where each row is ordered.
  values   A Tensor. Must have the same type as sorted_inputs. 2-D Tensor with the same numbers of rows as sorted_search_values. Contains the values that will be searched for in sorted_search_values.
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type out_type.


tf.raw_ops.VarHandleOp Creates a handle to a Variable resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.VarHandleOp
tf.raw_ops.VarHandleOp(
    dtype, shape, container='', shared_name='',
    allowed_devices=[], name=None
)

 Args
  dtype   A tf.DType. the type of this variable. Must agree with the dtypes of all ops using this variable.
  shape   A tf.TensorShape or list of ints. The (possibly partially specified) shape of this variable.
  container   An optional string. Defaults to "". the container this variable is placed in.
  shared_name   An optional string. Defaults to "". the name by which this variable is referred to.
  allowed_devices   An optional list of strings. Defaults to []. DEPRECATED. The allowed devices containing the resource variable. Set when the output ResourceHandle represents a per-replica/partitioned resource variable.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.Variable Use VariableV2 instead.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Variable
tf.raw_ops.Variable(
    shape, dtype, container='', shared_name='', name=None
)

 Args
  shape   A tf.TensorShape or list of ints.
  dtype   A tf.DType.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A mutable Tensor of type dtype.


tf.raw_ops.VariableShape Returns the shape of the variable pointed to by resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.VariableShape
tf.raw_ops.VariableShape(
    input, out_type=tf.dtypes.int32, name=None
)
 This operation returns a 1-D integer tensor representing the shape of input. For example: # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]

 Args
  input   A Tensor of type resource.
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type out_type.


tf.raw_ops.VariableV2 Holds state in the form of a tensor that persists across steps.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.VariableV2
tf.raw_ops.VariableV2(
    shape, dtype, container='', shared_name='', name=None
)
 Outputs a ref to the tensor state so it may be read or modified. about sharing states in tensorflow.

 Args
  shape   A tf.TensorShape or list of ints. The shape of the variable tensor.
  dtype   A tf.DType. The type of elements in the variable tensor.
  container   An optional string. Defaults to "". If non-empty, this variable is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this variable is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  name   A name for the operation (optional).

 Returns   A mutable Tensor of type dtype.


tf.raw_ops.VarIsInitializedOp Checks whether a resource handle-based variable has been initialized.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.VarIsInitializedOp
tf.raw_ops.VarIsInitializedOp(
    resource, name=None
)

 Args
  resource   A Tensor of type resource. the input resource handle.
  name   A name for the operation (optional).

 Returns   A Tensor of type bool.


tf.raw_ops.Where Returns locations of nonzero / true values in a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Where
tf.raw_ops.Where(
    condition, name=None
)
 This operation returns the coordinates of true elements in condition. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in condition. Indices are output in row-major order. For example: # 'input' tensor is [[True, False]
#                    [True, False]]
# 'input' has two true values, so output has two coordinates.
# 'input' has rank of 2, so coordinates have two indices.
where(input) ==> [[0, 0],
                  [1, 0]]

# `condition` tensor is [[[True, False]
#                     [True, False]]
#                    [[False, True]
#                     [False, True]]
#                    [[False, False]
#                     [False, True]]]
# 'input' has 5 true values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
                  [0, 1, 0],
                  [1, 0, 1],
                  [1, 1, 1],
                  [2, 1, 1]]

# `condition` tensor is [[[1.5,  0.0]
#                     [-0.5, 0.0]]
#                    [[0.0,  0.25]
#                     [0.0,  0.75]]
#                    [[0.0,  0.0]
#                     [0.0,  0.01]]]
# 'input' has 5 nonzero values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
                  [0, 1, 0],
                  [1, 0, 1],
                  [1, 1, 1],
                  [2, 1, 1]]

# `condition` tensor is [[[1.5 + 0.0j, 0.0  + 0.0j]
#                     [0.0 + 0.5j, 0.0  + 0.0j]]
#                    [[0.0 + 0.0j, 0.25 + 1.5j]
#                     [0.0 + 0.0j, 0.75 + 0.0j]]
#                    [[0.0 + 0.0j, 0.0  + 0.0j]
#                     [0.0 + 0.0j, 0.01 + 0.0j]]]
# 'input' has 5 nonzero magnitude values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
                  [0, 1, 0],
                  [1, 0, 1],
                  [1, 1, 1],
                  [2, 1, 1]]

 Args
  condition   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, bool.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.While output = input; While (Cond(output)) { output = Body(output) }  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.While
tf.raw_ops.While(
    input, cond, body, output_shapes=[], parallel_iterations=10, name=None
)

 Args
  input   A list of Tensor objects. A list of input tensors whose types are T.
  cond   A function decorated with @Defun. A function takes 'input' and returns a tensor. If the tensor is a scalar of non-boolean, the scalar is converted to a boolean according to the following rule: if the scalar is a numerical value, non-zero means True and zero means False; if the scalar is a string, non-empty means True and empty means False. If the tensor is not a scalar, non-emptiness means True and False otherwise.
  body   A function decorated with @Defun. A function that takes a list of tensors and returns another list of tensors. Both lists have the same types as specified by T.
  output_shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to [].
  parallel_iterations   An optional int. Defaults to 10.
  name   A name for the operation (optional).

 Returns   A list of Tensor objects. Has the same type as input.


tf.raw_ops.WholeFileReader A Reader that outputs the entire contents of a file as a value.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WholeFileReader
tf.raw_ops.WholeFileReader(
    container='', shared_name='', name=None
)
 To use, enqueue filenames in a Queue. The output of ReaderRead will be a filename (key) and the contents of that file (value).

 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.WholeFileReaderV2 A Reader that outputs the entire contents of a file as a value.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WholeFileReaderV2
tf.raw_ops.WholeFileReaderV2(
    container='', shared_name='', name=None
)
 To use, enqueue filenames in a Queue. The output of ReaderRead will be a filename (key) and the contents of that file (value).

 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.WindowDataset Combines (nests of) input elements into a dataset of (nests of) windows.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WindowDataset
tf.raw_ops.WindowDataset(
    input_dataset, size, shift, stride, drop_remainder, output_types, output_shapes,
    name=None
)
 A "window" is a finite dataset of flat elements of size size (or possibly fewer if there are not enough input elements to fill the window and drop_remainder evaluates to false). The shift argument determines the number of input elements by which the window moves on each iteration. The first element in the kth window will be element 1 + (k-1) * shift
 of the input dataset. In particular, the first element of the first window will always be the first element of the input dataset.  If the stride parameter is greater than 1, then each window will skip (stride - 1) input elements between each element that appears in the window. Output windows will still contain size elements regardless of the value of stride. The stride argument determines the stride of the input elements, and the shift argument determines the shift of the window. For example, letting {...} to represent a Dataset:
tf.data.Dataset.range(7).window(2) produces { {0, 1}, {2, 3}, {4, 5}, {6} }

tf.data.Dataset.range(7).window(3, 2, 1, True) produces { {0, 1, 2}, {2, 3, 4}, {4, 5, 6} }

tf.data.Dataset.range(7).window(3, 1, 2, True) produces { {0, 2, 4}, {1, 3, 5}, {2, 4, 6} }
  Note that when the window transformation is applied to a dataset of nested elements, it produces a dataset of nested windows. For example:
tf.data.Dataset.from_tensor_slices((range(4), range(4))).window(2) produces {({0, 1}, {0, 1}), ({2, 3}, {2, 3})}

tf.data.Dataset.from_tensor_slices({"a": range(4)}).window(2) produces { {"a": {0, 1} }, {"a": {2, 3} } }

 Args
  input_dataset   A Tensor of type variant.
  size   A Tensor of type int64. An integer scalar, representing the number of elements of the input dataset to combine into a window. Must be positive.
  shift   A Tensor of type int64. An integer scalar, representing the number of input elements by which the window moves in each iteration. Defaults to size. Must be positive.
  stride   A Tensor of type int64. An integer scalar, representing the stride of the input elements in the sliding window. Must be positive. The default value of 1 means "retain every input element".
  drop_remainder   A Tensor of type bool. A Boolean scalar, representing whether the last window should be dropped if its size is smaller than window_size.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.WorkerHeartbeat Worker heartbeat op.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WorkerHeartbeat
tf.raw_ops.WorkerHeartbeat(
    request, name=None
)
 Heartbeats may be sent periodically to indicate the coordinator is still active, to retrieve the current worker status and to expedite shutdown when necessary.

 Args
  request   A Tensor of type string. A string tensor containing a serialized WorkerHeartbeatRequest
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.WrapDatasetVariant  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WrapDatasetVariant
tf.raw_ops.WrapDatasetVariant(
    input_handle, name=None
)

 Args
  input_handle   A Tensor of type variant.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.WriteAudioSummary Writes an audio summary.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WriteAudioSummary
tf.raw_ops.WriteAudioSummary(
    writer, step, tag, tensor, sample_rate, max_outputs=3, name=None
)
 Writes encoded audio summary tensor at step with tag using summary writer. sample_rate is the audio sample rate is Hz.

 Args
  writer   A Tensor of type resource.
  step   A Tensor of type int64.
  tag   A Tensor of type string.
  tensor   A Tensor of type float32.
  sample_rate   A Tensor of type float32.
  max_outputs   An optional int that is >= 1. Defaults to 3.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.WriteFile Writes contents to the file at input filename. Creates file and recursively  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WriteFile
tf.raw_ops.WriteFile(
    filename, contents, name=None
)
 creates directory if not existing.

 Args
  filename   A Tensor of type string. scalar. The name of the file to which we write the contents.
  contents   A Tensor of type string. scalar. The content to be written to the output file.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.WriteGraphSummary Writes a graph summary.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WriteGraphSummary
tf.raw_ops.WriteGraphSummary(
    writer, step, tensor, name=None
)
 Writes TensorFlow graph tensor at step using summary writer.

 Args
  writer   A Tensor of type resource.
  step   A Tensor of type int64.
  tensor   A Tensor of type string.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.WriteHistogramSummary Writes a histogram summary.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WriteHistogramSummary
tf.raw_ops.WriteHistogramSummary(
    writer, step, tag, values, name=None
)
 Writes histogram values at step with tag using summary writer.

 Args
  writer   A Tensor of type resource.
  step   A Tensor of type int64.
  tag   A Tensor of type string.
  values   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.WriteImageSummary Writes an image summary.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WriteImageSummary
tf.raw_ops.WriteImageSummary(
    writer, step, tag, tensor, bad_color, max_images=3, name=None
)
 Writes image tensor at step with tag using summary writer. tensor is image with shape [height, width, channels].

 Args
  writer   A Tensor of type resource.
  step   A Tensor of type int64.
  tag   A Tensor of type string.
  tensor   A Tensor. Must be one of the following types: uint8, float32, half.
  bad_color   A Tensor of type uint8.
  max_images   An optional int that is >= 1. Defaults to 3.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.WriteRawProtoSummary Writes a serialized proto summary.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WriteRawProtoSummary
tf.raw_ops.WriteRawProtoSummary(
    writer, step, tensor, name=None
)
 Writes tensor, a serialized proto at step using summary writer.

 Args
  writer   A Tensor of type resource.
  step   A Tensor of type int64.
  tensor   A Tensor of type string.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.WriteScalarSummary Writes a scalar summary.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WriteScalarSummary
tf.raw_ops.WriteScalarSummary(
    writer, step, tag, value, name=None
)
 Writes scalar value at step with tag using summary writer.

 Args
  writer   A Tensor of type resource.
  step   A Tensor of type int64.
  tag   A Tensor of type string.
  value   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.WriteSummary Writes a tensor summary.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.WriteSummary
tf.raw_ops.WriteSummary(
    writer, step, tensor, tag, summary_metadata, name=None
)
 Writes tensor at step with tag using summary writer.

 Args
  writer   A Tensor of type resource.
  step   A Tensor of type int64.
  tensor   A Tensor.
  tag   A Tensor of type string.
  summary_metadata   A Tensor of type string.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.Xdivy Returns 0 if x == 0, and x / y otherwise, elementwise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Xdivy
tf.raw_ops.Xdivy(
    x, y, name=None
)

 Args
  x   A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Xlog1py Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Xlog1py
tf.raw_ops.Xlog1py(
    x, y, name=None
)

 Args
  x   A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Xlogy Returns 0 if x == 0, and x * log(y) otherwise, elementwise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Xlogy
tf.raw_ops.Xlogy(
    x, y, name=None
)

 Args
  x   A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.ZerosLike Returns a tensor of zeros with the same shape and type as x.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ZerosLike
tf.raw_ops.ZerosLike(
    x, name=None
)

 Args
  x   A Tensor. a tensor of type T.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Zeta   Compute the Hurwitz zeta function \(\zeta(x, q)\).  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Zeta
tf.raw_ops.Zeta(
    x, q, name=None
)
 The Hurwitz zeta function is defined as: \(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\)

 Args
  x   A Tensor. Must be one of the following types: float32, float64.
  q   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.ZipDataset Creates a dataset that zips together input_datasets.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ZipDataset
tf.raw_ops.ZipDataset(
    input_datasets, output_types, output_shapes, name=None
)
 The elements of the resulting dataset are created by zipping corresponding elements from each of the input datasets. The size of the resulting dataset will match the size of the smallest input dataset, and no error will be raised if input datasets have different sizes.

 Args
  input_datasets   A list of at least 1 Tensor objects with type variant. List of N variant Tensors representing datasets to be zipped together.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.realdiv Returns x / y element-wise for real types.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.realdiv
tf.realdiv(
    x, y, name=None
)
 If x and y are reals, this will return the floating-point division.
Note: Div supports broadcasting. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.