tf.raw_ops.StatelessRandomPoisson Outputs deterministic pseudorandom random numbers from a Poisson distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessRandomPoisson
tf.raw_ops.StatelessRandomPoisson(
    shape, seed, lam, dtype, name=None
)
 Outputs random values from a Poisson distribution. The outputs are a deterministic function of shape, seed, and lam.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  seed   A Tensor. Must be one of the following types: int32, int64. 2 seeds (shape [2]).
  lam   A Tensor. Must be one of the following types: half, float32, float64, int32, int64. The rate of the Poisson distribution. Shape must match the rightmost dimensions of shape.
  dtype   A tf.DType from: tf.half, tf.float32, tf.float64, tf.int32, tf.int64. The type of the output.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.StatelessRandomUniform Outputs deterministic pseudorandom random values from a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessRandomUniform
tf.raw_ops.StatelessRandomUniform(
    shape, seed, dtype=tf.dtypes.float32, name=None
)
 The generated values follow a uniform distribution in the range [0, 1). The lower bound 0 is included in the range, while the upper bound 1 is excluded. The outputs are a deterministic function of shape and seed.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  seed   A Tensor. Must be one of the following types: int32, int64. 2 seeds (shape [2]).
  dtype   An optional tf.DType from: tf.half, tf.bfloat16, tf.float32, tf.float64. Defaults to tf.float32. The type of the output.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.StatelessRandomUniformFullInt Outputs deterministic pseudorandom random integers from a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessRandomUniformFullInt
tf.raw_ops.StatelessRandomUniformFullInt(
    shape, seed, dtype=tf.dtypes.uint64, name=None
)
 The generated values are uniform integers covering the whole range of dtype. The outputs are a deterministic function of shape and seed.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  seed   A Tensor. Must be one of the following types: int32, int64, uint32, uint64. 2 seeds (shape [2]).
  dtype   An optional tf.DType from: tf.int32, tf.int64, tf.uint32, tf.uint64. Defaults to tf.uint64. The type of the output.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.StatelessRandomUniformFullIntV2 Outputs deterministic pseudorandom random integers from a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessRandomUniformFullIntV2
tf.raw_ops.StatelessRandomUniformFullIntV2(
    shape, key, counter, alg, dtype=tf.dtypes.uint64, name=None
)
 The generated values are uniform integers covering the whole range of dtype. The outputs are a deterministic function of shape, key, counter and alg.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  key   A Tensor of type uint64. Key for the counter-based RNG algorithm (shape uint64[1]).
  counter   A Tensor of type uint64. Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used.
  alg   A Tensor of type int32. The RNG algorithm (shape int32[]).
  dtype   An optional tf.DType from: tf.int32, tf.int64, tf.uint32, tf.uint64. Defaults to tf.uint64. The type of the output.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.StatelessRandomUniformInt Outputs deterministic pseudorandom random integers from a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessRandomUniformInt
tf.raw_ops.StatelessRandomUniformInt(
    shape, seed, minval, maxval, name=None
)
 The generated values follow a uniform distribution in the range [minval, maxval). The outputs are a deterministic function of shape, seed, minval, and maxval.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  seed   A Tensor. Must be one of the following types: int32, int64. 2 seeds (shape [2]).
  minval   A Tensor. Must be one of the following types: int32, int64. Minimum value (inclusive, scalar).
  maxval   A Tensor. Must have the same type as minval. Maximum value (exclusive, scalar).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as minval.


tf.raw_ops.StatelessRandomUniformIntV2 Outputs deterministic pseudorandom random integers from a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessRandomUniformIntV2
tf.raw_ops.StatelessRandomUniformIntV2(
    shape, key, counter, alg, minval, maxval, name=None
)
 The generated values follow a uniform distribution in the range [minval, maxval). The outputs are a deterministic function of shape, key, counter, alg, minval and maxval.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  key   A Tensor of type uint64. Key for the counter-based RNG algorithm (shape uint64[1]).
  counter   A Tensor of type uint64. Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used.
  alg   A Tensor of type int32. The RNG algorithm (shape int32[]).
  minval   A Tensor. Must be one of the following types: int32, int64, uint32, uint64. Minimum value (inclusive, scalar).
  maxval   A Tensor. Must have the same type as minval. Maximum value (exclusive, scalar).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as minval.


tf.raw_ops.StatelessRandomUniformV2 Outputs deterministic pseudorandom random values from a uniform distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessRandomUniformV2
tf.raw_ops.StatelessRandomUniformV2(
    shape, key, counter, alg, dtype=tf.dtypes.float32, name=None
)
 The generated values follow a uniform distribution in the range [0, 1). The lower bound 0 is included in the range, while the upper bound 1 is excluded. The outputs are a deterministic function of shape, key, counter and alg.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  key   A Tensor of type uint64. Key for the counter-based RNG algorithm (shape uint64[1]).
  counter   A Tensor of type uint64. Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used.
  alg   A Tensor of type int32. The RNG algorithm (shape int32[]).
  dtype   An optional tf.DType from: tf.half, tf.bfloat16, tf.float32, tf.float64. Defaults to tf.float32. The type of the output.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.StatelessSampleDistortedBoundingBox Generate a randomly distorted bounding box for an image deterministically.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessSampleDistortedBoundingBox
tf.raw_ops.StatelessSampleDistortedBoundingBox(
    image_size, bounding_boxes, min_object_covered, seed, aspect_ratio_range=[0.75,
    1.33], area_range=[0.05, 1], max_attempts=100,
    use_image_if_no_bounding_boxes=False, name=None
)
 Bounding box annotations are often supplied in addition to ground-truth labels in image recognition or object localization tasks. A common technique for training such a system is to randomly distort an image while preserving its content, i.e. data augmentation. This Op, given the same seed, deterministically outputs a randomly distorted localization of an object, i.e. bounding box, given an image_size, bounding_boxes and a series of constraints. The output of this Op is a single bounding box that may be used to crop the original image. The output is returned as 3 tensors: begin, size and bboxes. The first 2 tensors can be fed directly into tf.slice to crop the image. The latter may be supplied to tf.image.draw_bounding_boxes to visualize what the bounding box looks like. Bounding boxes are supplied and returned as [y_min, x_min, y_max, x_max]. The bounding box coordinates are floats in [0.0, 1.0] relative to the width and the height of the underlying image. The output of this Op is guaranteed to be the same given the same seed and is independent of how many times the function is called, and independent of global seed settings (e.g. tf.random.set_seed). Example usage:
image = np.array([[[1], [2], [3]], [[4], [5], [6]], [[7], [8], [9]]])
bbox = tf.constant(
  [0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
seed = (1, 2)
# Generate a single distorted bounding box.
bbox_begin, bbox_size, bbox_draw = (
  tf.image.stateless_sample_distorted_bounding_box(
    tf.shape(image), bounding_boxes=bbox, seed=seed))
# Employ the bounding box to distort the image.
tf.slice(image, bbox_begin, bbox_size)
<tf.Tensor: shape=(2, 2, 1), dtype=int64, numpy=
array([[[1],
        [2]],
       [[4],
        [5]]])>
# Draw the bounding box in an image summary.
colors = np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
tf.image.draw_bounding_boxes(
  tf.expand_dims(tf.cast(image, tf.float32),0), bbox_draw, colors)
<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
array([[[[1.],
         [1.],
         [3.]],
        [[1.],
         [1.],
         [6.]],
        [[7.],
         [8.],
         [9.]]]], dtype=float32)>
 Note that if no bounding box information is available, setting use_image_if_no_bounding_boxes = true will assume there is a single implicit bounding box covering the whole image. If use_image_if_no_bounding_boxes is false and no bounding boxes are supplied, an error is raised.

 Args
  image_size   A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64. 1-D, containing [height, width, channels].
  bounding_boxes   A Tensor of type float32. 3-D with shape [batch, N, 4] describing the N bounding boxes associated with the image.
  min_object_covered   A Tensor of type float32. The cropped area of the image must contain at least this fraction of any bounding box supplied. The value of this parameter should be non-negative. In the case of 0, the cropped area does not need to overlap any of the bounding boxes supplied.
  seed   A Tensor. Must be one of the following types: int32, int64. 1-D with shape [2]. The seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
  aspect_ratio_range   An optional list of floats. Defaults to [0.75, 1.33]. The cropped area of the image must have an aspect ratio = width / height within this range.
  area_range   An optional list of floats. Defaults to [0.05, 1]. The cropped area of the image must contain a fraction of the supplied image within this range.
  max_attempts   An optional int. Defaults to 100. Number of attempts at generating a cropped region of the image of the specified constraints. After max_attempts failures, return the entire image.
  use_image_if_no_bounding_boxes   An optional bool. Defaults to False. Controls behavior if no bounding boxes supplied. If true, assume an implicit bounding box covering the whole input. If false, raise an error.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (begin, size, bboxes).     begin   A Tensor. Has the same type as image_size.
  size   A Tensor. Has the same type as image_size.
  bboxes   A Tensor of type float32.


tf.raw_ops.StatelessTruncatedNormal Outputs deterministic pseudorandom values from a truncated normal distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessTruncatedNormal
tf.raw_ops.StatelessTruncatedNormal(
    shape, seed, dtype=tf.dtypes.float32, 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. The outputs are a deterministic function of shape and seed.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  seed   A Tensor. Must be one of the following types: int32, int64. 2 seeds (shape [2]).
  dtype   An optional tf.DType from: tf.half, tf.bfloat16, tf.float32, tf.float64. Defaults to tf.float32. The type of the output.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.StatelessTruncatedNormalV2 Outputs deterministic pseudorandom values from a truncated normal distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessTruncatedNormalV2
tf.raw_ops.StatelessTruncatedNormalV2(
    shape, key, counter, alg, dtype=tf.dtypes.float32, 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. The outputs are a deterministic function of shape, key, counter and alg.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
  key   A Tensor of type uint64. Key for the counter-based RNG algorithm (shape uint64[1]).
  counter   A Tensor of type uint64. Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used.
  alg   A Tensor of type int32. The RNG algorithm (shape int32[]).
  dtype   An optional tf.DType from: tf.half, tf.bfloat16, tf.float32, tf.float64. Defaults to tf.float32. The type of the output.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.StatelessWhile 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.StatelessWhile
tf.raw_ops.StatelessWhile(
    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. This should only be used when the while condition and body functions do not have stateful ops.

  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.StaticRegexFullMatch Check if the input matches the regex pattern.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StaticRegexFullMatch
tf.raw_ops.StaticRegexFullMatch(
    input, pattern, name=None
)
 The input is a string tensor of any shape. The pattern is the regular expression to be matched with every element of the input tensor. The boolean values (True or False) of the output tensor indicate if the input matches the regex pattern provided. The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)

 Args
  input   A Tensor of type string. A string tensor of the text to be processed.
  pattern   A string. The regular expression to match the input.
  name   A name for the operation (optional).

 Returns   A Tensor of type bool.


tf.raw_ops.StaticRegexReplace Replaces the match of pattern in input with rewrite.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StaticRegexReplace
tf.raw_ops.StaticRegexReplace(
    input, pattern, rewrite, replace_global=True, name=None
)
 It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)

 Args
  input   A Tensor of type string. The text to be processed.
  pattern   A string. The regular expression to match the input.
  rewrite   A string. The rewrite to be applied to the matched expression.
  replace_global   An optional bool. Defaults to True. If True, the replacement is global, otherwise the replacement is done only on the first match.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.StatsAggregatorHandle Creates a statistics manager resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatsAggregatorHandle
tf.raw_ops.StatsAggregatorHandle(
    container='', shared_name='', name=None
)

 Args
  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.StatsAggregatorHandleV2  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatsAggregatorHandleV2
tf.raw_ops.StatsAggregatorHandleV2(
    container='', shared_name='', name=None
)

 Args
  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.StatsAggregatorSetSummaryWriter Set a summary_writer_interface to record statistics using given stats_aggregator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatsAggregatorSetSummaryWriter
tf.raw_ops.StatsAggregatorSetSummaryWriter(
    stats_aggregator, summary, name=None
)

 Args
  stats_aggregator   A Tensor of type resource.
  summary   A Tensor of type resource.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.StatsAggregatorSummary Produces a summary of any statistics recorded by the given statistics manager.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StatsAggregatorSummary
tf.raw_ops.StatsAggregatorSummary(
    iterator, name=None
)

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

 Returns   A Tensor of type string.


tf.raw_ops.StopGradient Stops gradient computation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StopGradient
tf.raw_ops.StopGradient(
    input, name=None
)
 When executed in a graph, this op outputs its input tensor as-is. When building ops to compute gradients, this op prevents the contribution of its inputs to be taken into account. Normally, the gradient generator adds ops to a graph to compute the derivatives of a specified 'loss' by recursively finding out inputs that contributed to its computation. If you insert this op in the graph it inputs are masked from the gradient generator. They are not taken into account for computing gradients. This is useful any time you want to compute a value with TensorFlow but need to pretend that the value was a constant. Some examples include:  The EM algorithm where the M-step should not involve backpropagation through the output of the E-step. Contrastive divergence training of Boltzmann machines where, when differentiating the energy function, the training must not backpropagate through the graph that generated the samples from the model. Adversarial training, where no backprop should happen through the adversarial example generation process.

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

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.StridedSlice Return a strided slice from input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StridedSlice
tf.raw_ops.StridedSlice(
    input, begin, end, strides, begin_mask=0, end_mask=0, ellipsis_mask=0,
    new_axis_mask=0, shrink_axis_mask=0, name=None
)
 Note, most python users will want to use the Python Tensor.getitem or Variable.getitem rather than this op directly. The goal of this op is to produce a new tensor with a subset of the elements from the n dimensional input tensor. The subset is chosen using a sequence of m sparse range specifications encoded into the arguments of this function. Note, in some cases m could be equal to n, but this need not be the case. Each range specification entry can be one of the following:  An ellipsis (...). Ellipses are used to imply zero or more dimensions of full-dimension selection and are produced using ellipsis_mask. For example, foo[...] is the identity slice. A new axis. This is used to insert a new shape=1 dimension and is produced using new_axis_mask. For example, foo[:, ...] where foo is shape (3, 4) produces a (1, 3, 4) tensor. A range begin:end:stride. This is used to specify how much to choose from a given dimension. stride can be any integer but 0. begin is an integer which represents the index of the first value to select while end represents the index of the last value to select. The number of values selected in each dimension is end - begin if stride > 0 and begin - end if stride < 0. begin and end can be negative where -1 is the last element, -2 is the second to last. begin_mask controls whether to replace the explicitly given begin with an implicit effective value of 0 if stride > 0 and -1 if stride < 0. end_mask is analogous but produces the number required to create the largest open interval. For example, given a shape (3,) tensor foo[:], the effective begin and end are 0 and 3. Do not assume this is equivalent to foo[0:-1] which has an effective begin and end of 0 and 2. Another example is foo[-2::-1] which reverses the first dimension of a tensor while dropping the last two (in the original order elements). For example foo = [1,2,3,4]; foo[-2::-1] is [4,3]. A single index. This is used to keep only elements that have a given index. For example (foo[2, :] on a shape (5,6) tensor produces a shape (6,) tensor. This is encoded in begin and end and shrink_axis_mask.  Each conceptual range specification is encoded in the op's argument. This encoding is best understand by considering a non-trivial example. In particular, foo[1, 2:4, None, ..., :-3:-1, :] will be encoded as begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0)
end = [2, 4, x, x, -3, x]
strides = [1, 1, x, x, -1, 1]
begin_mask = 1<<4 | 1<<5 = 48
end_mask = 1<<5 = 32
ellipsis_mask = 1<<3 = 8
new_axis_mask = 1<<2 = 4
shrink_axis_mask = 1<<0 = 1
 In this case if foo.shape is (5, 5, 5, 5, 5, 5) the final shape of the slice becomes (2, 1, 5, 5, 2, 5). Let us walk step by step through each argument specification.  The first argument in the example slice is turned into begin = 1 and end = begin + 1 = 2. To disambiguate from the original spec 2:4 we also set the appropriate bit in shrink_axis_mask. 2:4 is contributes 2, 4, 1 to begin, end, and stride. All masks have zero bits contributed. None is a synonym for tf.newaxis. This means insert a dimension of size 1 dimension in the final shape. Dummy values are contributed to begin, end and stride, while the new_axis_mask bit is set. ... grab the full ranges from as many dimensions as needed to fully specify a slice for every dimension of the input shape. :-3:-1 shows the use of negative indices. A negative index i associated with a dimension that has shape s is converted to a positive index s + i. So -1 becomes s-1 (i.e. the last element). This conversion is done internally so begin, end and strides receive x, -3, and -1. The appropriate begin_mask bit is set to indicate the start range is the full range (ignoring the x). : indicates that the entire contents of the corresponding dimension is selected. This is equivalent to :: or 0::1. begin, end, and strides receive 0, 0, and 1, respectively. The appropriate bits in begin_mask and end_mask are also set.  Requirements: 0 != strides[i] for i in [0, m) ellipsis_mask must be a power of two (only one ellipsis)

 Args
  input   A Tensor.
  begin   A Tensor. Must be one of the following types: int32, int64. begin[k] specifies the offset into the kth range specification. The exact dimension this corresponds to will be determined by context. Out-of-bounds values will be silently clamped. If the kth bit of begin_mask then begin[k] is ignored and the full range of the appropriate dimension is used instead. Negative values causes indexing to start from the highest element e.g. If foo==[1,2,3] then foo[-1]==3.
  end   A Tensor. Must have the same type as begin. end[i] is like begin with the exception that end_mask is used to determine full ranges.
  strides   A Tensor. Must have the same type as begin. strides[i] specifies the increment in the ith specification after extracting a given element. Negative indices will reverse the original order. Out or range values are clamped to [0,dim[i]) if slice[i]>0 or [-1,dim[i]-1] if slice[i] < 0
  begin_mask   An optional int. Defaults to 0. a bitmask where a bit i being 1 means to ignore the begin value and instead use the largest interval possible. At runtime begin[i] will be replaced with [0, n-1) if stride[i] > 0 or [-1, n-1] if stride[i] < 0
  end_mask   An optional int. Defaults to 0. analogous to begin_mask
  ellipsis_mask   An optional int. Defaults to 0. a bitmask where bit i being 1 means the ith position is actually an ellipsis. One bit at most can be 1. If ellipsis_mask == 0, then an implicit ellipsis mask of 1 << (m+1) is provided. This means that foo[3:5] == foo[3:5, ...]. An ellipsis implicitly creates as many range specifications as necessary to fully specify the sliced range for every dimension. For example for a 4-dimensional tensor foo the slice foo[2, ..., 5:8] implies foo[2, :, :, 5:8].
  new_axis_mask   An optional int. Defaults to 0. a bitmask where bit i being 1 means the ith specification creates a new shape 1 dimension. For example foo[:4, tf.newaxis, :2] would produce a shape (4, 1, 2) tensor.
  shrink_axis_mask   An optional int. Defaults to 0. a bitmask where bit i implies that the ith specification should shrink the dimensionality. begin and end must imply a slice of size 1 in the dimension. For example in python one might do foo[:, 3, :] which would result in shrink_axis_mask being 2.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.StridedSliceAssign Assign value to the sliced l-value reference of ref.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StridedSliceAssign
tf.raw_ops.StridedSliceAssign(
    ref, 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 variable ref 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 ref.

 Args
  ref   A mutable 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 ref.
  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 mutable Tensor. Has the same type as ref.


tf.raw_ops.StridedSliceGrad Returns the gradient of StridedSlice.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StridedSliceGrad
tf.raw_ops.StridedSliceGrad(
    shape, begin, end, strides, dy, begin_mask=0, end_mask=0, ellipsis_mask=0,
    new_axis_mask=0, shrink_axis_mask=0, name=None
)
 Since StridedSlice cuts out pieces of its input which is size shape, its gradient will have the same shape (which is passed here as shape). The gradient will be zero in any element that the slice does not select. Arguments are the same as StridedSliceGrad with the exception that dy is the input gradient to be propagated and shape is the shape of StridedSlice's input.

 Args
  shape   A Tensor. Must be one of the following types: int32, int64.
  begin   A Tensor. Must have the same type as shape.
  end   A Tensor. Must have the same type as shape.
  strides   A Tensor. Must have the same type as shape.
  dy   A Tensor.
  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 dy.


tf.raw_ops.StringFormat Formats a string template using a list of tensors.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringFormat
tf.raw_ops.StringFormat(
    inputs, template='%s', placeholder='%s', summarize=3,
    name=None
)
 Formats a string template using a list of tensors, pretty-printing tensor summaries.

 Args
  inputs   A list of Tensor objects. The list of tensors to format into the placeholder string.
  template   An optional string. Defaults to "%s". A string, the template to format tensor summaries into.
  placeholder   An optional string. Defaults to "%s". A string, at each placeholder in the template a subsequent tensor summary will be inserted.
  summarize   An optional int. Defaults to 3. When formatting the tensor summaries print the first and last summarize entries of each tensor dimension.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.StringJoin Joins the strings in the given list of string tensors into one tensor;  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringJoin
tf.raw_ops.StringJoin(
    inputs, separator='', name=None
)
 with the given separator (default is an empty separator). Examples:
s = ["hello", "world", "tensorflow"]
tf.strings.join(s, " ")
<tf.Tensor: shape=(), dtype=string, numpy=b'hello world tensorflow'>

 Args
  inputs   A list of at least 1 Tensor objects with type string. A list of string tensors. The tensors must all have the same shape, or be scalars. Scalars may be mixed in; these will be broadcast to the shape of non-scalar inputs.
  separator   An optional string. Defaults to "". string, an optional join separator.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.StringLength String lengths of input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringLength
tf.raw_ops.StringLength(
    input, unit='BYTE', name=None
)
 Computes the length of each string given in the input tensor.
strings = tf.constant(['Hello','TensorFlow', '\U0001F642'])
tf.strings.length(strings).numpy() # default counts bytes
array([ 5, 10, 4], dtype=int32)
tf.strings.length(strings, unit="UTF8_CHAR").numpy()
array([ 5, 10, 1], dtype=int32)

 Args
  input   A Tensor of type string. The strings for which to compute the length for each element.
  unit   An optional string from: "BYTE", "UTF8_CHAR". Defaults to "BYTE". The unit that is counted to compute string length. One of: "BYTE" (for the number of bytes in each string) or "UTF8_CHAR" (for the number of UTF-8 encoded Unicode code points in each string). Results are undefined if unit=UTF8_CHAR and the input strings do not contain structurally valid UTF-8.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.StringLower Converts all uppercase characters into their respective lowercase replacements.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringLower
tf.raw_ops.StringLower(
    input, encoding='', name=None
)
 Example:
tf.strings.lower("CamelCase string and ALL CAPS")
<tf.Tensor: shape=(), dtype=string, numpy=b'camelcase string and all caps'>

 Args
  input   A Tensor of type string.
  encoding   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.StringNGrams Creates ngrams from ragged string data.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringNGrams
tf.raw_ops.StringNGrams(
    data, data_splits, separator, ngram_widths, left_pad, right_pad, pad_width,
    preserve_short_sequences, name=None
)
 This op accepts a ragged tensor with 1 ragged dimension containing only strings and outputs a ragged tensor with 1 ragged dimension containing ngrams of that string, joined along the innermost axis.

 Args
  data   A Tensor of type string. The values tensor of the ragged string tensor to make ngrams out of. Must be a 1D string tensor.
  data_splits   A Tensor. Must be one of the following types: int32, int64. The splits tensor of the ragged string tensor to make ngrams out of.
  separator   A string. The string to append between elements of the token. Use "" for no separator.
  ngram_widths   A list of ints. The sizes of the ngrams to create.
  left_pad   A string. The string to use to pad the left side of the ngram sequence. Only used if pad_width != 0.
  right_pad   A string. The string to use to pad the right side of the ngram sequence. Only used if pad_width != 0.
  pad_width   An int. The number of padding elements to add to each side of each sequence. Note that padding will never be greater than 'ngram_widths'-1 regardless of this value. If pad_width=-1, then add max(ngram_widths)-1 elements.
  preserve_short_sequences   A bool.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (ngrams, ngrams_splits).     ngrams   A Tensor of type string.
  ngrams_splits   A Tensor. Has the same type as data_splits.


tf.raw_ops.StringSplit Split elements of input based on delimiter into a SparseTensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringSplit
tf.raw_ops.StringSplit(
    input, delimiter, skip_empty=True, name=None
)
 Let N be the size of source (typically N will be the batch size). Split each element of input based on delimiter and return a SparseTensor containing the splitted tokens. Empty tokens are ignored. delimiter can be empty, or a string of split characters. If delimiter is an empty string, each element of input is split into individual single-byte character strings, including splitting of UTF-8 multibyte sequences. Otherwise every character of delimiter is a potential split point. For example: N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output will be indices = [0, 0; 0, 1; 1, 0; 1, 1; 1, 2] shape = [2, 3] values = ['hello', 'world', 'a', 'b', 'c']

 Args
  input   A Tensor of type string. 1-D. Strings to split.
  delimiter   A Tensor of type string. 0-D. Delimiter characters (bytes), or empty string.
  skip_empty   An optional bool. Defaults to True. A bool. If True, skip the empty strings from the result.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (indices, values, shape).     indices   A Tensor of type int64.
  values   A Tensor of type string.
  shape   A Tensor of type int64.


tf.raw_ops.StringSplitV2 Split elements of source based on sep into a SparseTensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringSplitV2
tf.raw_ops.StringSplitV2(
    input, sep, maxsplit=-1, name=None
)
 Let N be the size of source (typically N will be the batch size). Split each element of source based on sep and return a SparseTensor containing the split tokens. Empty tokens are ignored. For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', then the output will be st.indices = [0, 0;
              0, 1;
              1, 0;
              1, 1;
              1, 2]
st.shape = [2, 3]
st.values = ['hello', 'world', 'a', 'b', 'c']
 If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings. For example, source of "1<>2<><>3" and sep of "<>" returns ["1", "2", "", "3"]. If sep is None or an empty string, consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the startor end if the string has leading or trailing whitespace. Note that the above mentioned behavior matches python's str.split.

 Args
  input   A Tensor of type string. 1-D string Tensor, the strings to split.
  sep   A Tensor of type string. 0-D string Tensor, the delimiter character.
  maxsplit   An optional int. Defaults to -1. An int. If maxsplit > 0, limit of the split of the result.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (indices, values, shape).     indices   A Tensor of type int64.
  values   A Tensor of type string.
  shape   A Tensor of type int64.


tf.raw_ops.StringStrip Strip leading and trailing whitespaces from the Tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringStrip
tf.raw_ops.StringStrip(
    input, name=None
)

 Args
  input   A Tensor of type string. A string Tensor of any shape.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.StringToHashBucket Converts each string in the input Tensor to its hash mod by a number of buckets.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringToHashBucket
tf.raw_ops.StringToHashBucket(
    string_tensor, num_buckets, name=None
)
 The hash function is deterministic on the content of the string within the process. Note that the hash function may change from time to time. This functionality will be deprecated and it's recommended to use tf.string_to_hash_bucket_fast() or tf.string_to_hash_bucket_strong().

 Args
  string_tensor   A Tensor of type string.
  num_buckets   An int that is >= 1. The number of buckets.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.StringToHashBucketFast Converts each string in the input Tensor to its hash mod by a number of buckets.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringToHashBucketFast
tf.raw_ops.StringToHashBucketFast(
    input, num_buckets, name=None
)
 The hash function is deterministic on the content of the string within the process and will never change. However, it is not suitable for cryptography. This function may be used when CPU time is scarce and inputs are trusted or unimportant. There is a risk of adversaries constructing inputs that all hash to the same bucket. To prevent this problem, use a strong hash function with tf.string_to_hash_bucket_strong. Examples:
tf.strings.to_hash_bucket_fast(["Hello", "TensorFlow", "2.x"], 3).numpy()
array([0, 2, 2])

 Args
  input   A Tensor of type string. The strings to assign a hash bucket.
  num_buckets   An int that is >= 1. The number of buckets.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.StringToHashBucketStrong Converts each string in the input Tensor to its hash mod by a number of buckets.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringToHashBucketStrong
tf.raw_ops.StringToHashBucketStrong(
    input, num_buckets, key, name=None
)
 The hash function is deterministic on the content of the string within the process. The hash function is a keyed hash function, where attribute key defines the key of the hash function. key is an array of 2 elements. A strong hash is important when inputs may be malicious, e.g. URLs with additional components. Adversaries could try to make their inputs hash to the same bucket for a denial-of-service attack or to skew the results. A strong hash can be used to make it difficult to find inputs with a skewed hash value distribution over buckets. This requires that the hash function is seeded by a high-entropy (random) "key" unknown to the adversary. The additional robustness comes at a cost of roughly 4x higher compute time than tf.string_to_hash_bucket_fast. Examples:
tf.strings.to_hash_bucket_strong(["Hello", "TF"], 3, [1, 2]).numpy()
array([2, 0])

 Args
  input   A Tensor of type string. The strings to assign a hash bucket.
  num_buckets   An int that is >= 1. The number of buckets.
  key   A list of ints. The key used to seed the hash function, passed as a list of two uint64 elements.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.StringToNumber Converts each string in the input Tensor to the specified numeric type.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringToNumber
tf.raw_ops.StringToNumber(
    string_tensor, out_type=tf.dtypes.float32, name=None
)
 (Note that int32 overflow results in an error while float overflow results in a rounded value.) Example:
strings = ["5.0", "3.0", "7.0"]
tf.strings.to_number(strings)
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([5., 3., 7.], dtype=float32)>

 Args
  string_tensor   A Tensor of type string.
  out_type   An optional tf.DType from: tf.float32, tf.float64, tf.int32, tf.int64. Defaults to tf.float32. The numeric type to interpret each string in string_tensor as.
  name   A name for the operation (optional).

 Returns   A Tensor of type out_type.


tf.raw_ops.StringUpper Converts all lowercase characters into their respective uppercase replacements.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.StringUpper
tf.raw_ops.StringUpper(
    input, encoding='', name=None
)
 Example:
tf.strings.upper("CamelCase string and ALL CAPS")
<tf.Tensor: shape=(), dtype=string, numpy=b'CAMELCASE STRING AND ALL CAPS'>

 Args
  input   A Tensor of type string.
  encoding   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.Sub Returns x - y element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Sub
tf.raw_ops.Sub(
    x, y, name=None
)

Note: Subtract 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, uint32.
  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.Substr Return substrings from Tensor of strings.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Substr
tf.raw_ops.Substr(
    input, pos, len, unit='BYTE', name=None
)
 For each string in the input Tensor, creates a substring starting at index pos with a total length of len. If len defines a substring that would extend beyond the length of the input string, or if len is negative, then as many characters as possible are used. A negative pos indicates distance within the string backwards from the end. If pos specifies an index which is out of range for any of the input strings, then an InvalidArgumentError is thrown. pos and len must have the same shape, otherwise a ValueError is thrown on Op creation.
Note: Substr supports broadcasting up to two dimensions. More about broadcasting here
 Examples Using scalar pos and len: input = [b'Hello', b'World']
position = 1
length = 3

output = [b'ell', b'orl']
 Using pos and len with same shape as input: input = [[b'ten', b'eleven', b'twelve'],
         [b'thirteen', b'fourteen', b'fifteen'],
         [b'sixteen', b'seventeen', b'eighteen']]
position = [[1, 2, 3],
            [1, 2, 3],
            [1, 2, 3]]
length =   [[2, 3, 4],
            [4, 3, 2],
            [5, 5, 5]]

output = [[b'en', b'eve', b'lve'],
          [b'hirt', b'urt', b'te'],
          [b'ixtee', b'vente', b'hteen']]
 Broadcasting pos and len onto input: input = [[b'ten', b'eleven', b'twelve'],
         [b'thirteen', b'fourteen', b'fifteen'],
         [b'sixteen', b'seventeen', b'eighteen'],
         [b'nineteen', b'twenty', b'twentyone']]
position = [1, 2, 3]
length =   [1, 2, 3]

output = [[b'e', b'ev', b'lve'],
          [b'h', b'ur', b'tee'],
          [b'i', b've', b'hte'],
          [b'i', b'en', b'nty']]
 Broadcasting input onto pos and len: input = b'thirteen'
position = [1, 5, 7]
length =   [3, 2, 1]

output = [b'hir', b'ee', b'n']

 Raises
ValueError: If the first argument cannot be converted to a Tensor of dtype string.
InvalidArgumentError: If indices are out of range.
ValueError: If pos and len are not the same shape.

 Args
  input   A Tensor of type string. Tensor of strings
  pos   A Tensor. Must be one of the following types: int32, int64. Scalar defining the position of first character in each substring
  len   A Tensor. Must have the same type as pos. Scalar defining the number of characters to include in each substring
  unit   An optional string from: "BYTE", "UTF8_CHAR". Defaults to "BYTE". The unit that is used to create the substring. One of: "BYTE" (for defining position and length by bytes) or "UTF8_CHAR" (for the UTF-8 encoded Unicode code points). The default is "BYTE". Results are undefined if unit=UTF8_CHAR and the input strings do not contain structurally valid UTF-8.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.Sum Computes the sum of elements across dimensions of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Sum
tf.raw_ops.Sum(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.

 Args
  input   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. The tensor to reduce.
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.SummaryWriter  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SummaryWriter
tf.raw_ops.SummaryWriter(
    shared_name='', container='', name=None
)

 Args
  shared_name   An optional string. Defaults to "".
  container   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.Svd Computes the singular value decompositions of one or more matrices.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Svd
tf.raw_ops.Svd(
    input, compute_uv=True, full_matrices=False, name=None
)
 Computes the SVD of each inner matrix in input such that input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :]) # a is a tensor containing a batch of matrices.
# s is a tensor of singular values for each matrix.
# u is the tensor containing the left singular vectors for each matrix.
# v is the tensor containing the right singular vectors for each matrix.
s, u, v = svd(a)
s, _, _ = svd(a, compute_uv=False)

 Args
  input   A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. A tensor of shape [..., M, N] whose inner-most 2 dimensions form matrices of size [M, N]. Let P be the minimum of M and N.
  compute_uv   An optional bool. Defaults to True. If true, left and right singular vectors will be computed and returned in u and v, respectively. If false, u and v are not set and should never referenced.
  full_matrices   An optional bool. Defaults to False. If true, compute full-sized u and v. If false (the default), compute only the leading P singular vectors. Ignored if compute_uv is False.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (s, u, v).     s   A Tensor. Has the same type as input.
  u   A Tensor. Has the same type as input.
  v   A Tensor. Has the same type as input.


tf.raw_ops.Switch Forwards data to the output port determined by pred.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Switch
tf.raw_ops.Switch(
    data, pred, name=None
)
 If pred is true, the data input is forwarded to output_true. Otherwise, the data goes to output_false. See also RefSwitch and Merge.

 Args
  data   A Tensor. The tensor to be forwarded to the appropriate output.
  pred   A Tensor of type bool. A scalar that specifies which output port will receive data.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output_false, output_true).     output_false   A Tensor. Has the same type as data.
  output_true   A Tensor. Has the same type as data.


tf.raw_ops.SymbolicGradient Computes the gradient function for function f via backpropagation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.SymbolicGradient
tf.raw_ops.SymbolicGradient(
    input, Tout, f, name=None
)

 Args
  input   A list of Tensor objects. a list of input tensors of size N + M;
  Tout   A list of tf.DTypes that has length >= 1. the type list for the input list.
  f   A function decorated with @Defun. The function we want to compute the gradient for. The function 'f' must be a numerical function which takes N inputs and produces M outputs. Its gradient function 'g', which is computed by this SymbolicGradient op is a function taking N + M inputs and produces N outputs. I.e. if we have (y1, y2, ..., y_M) = f(x1, x2, ..., x_N), then, g is (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N, dL/dy1, dL/dy2, ..., dL/dy_M), where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the loss function). dL/dx_i is the partial derivative of L with respect to x_i. (Needs some math expert to say the comment above better.)

  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type Tout.


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

 Args
  input_dataset   A Tensor of type variant.
  count   A Tensor of type int64. A scalar representing the number of elements from the input_dataset that should be taken. A value of -1 indicates that all of input_dataset is taken.
  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.TakeManySparseFromTensorsMap Read SparseTensors from a SparseTensorsMap and concatenate them.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TakeManySparseFromTensorsMap
tf.raw_ops.TakeManySparseFromTensorsMap(
    sparse_handles, dtype, container='', shared_name='',
    name=None
)
 The input sparse_handles must be an int64 matrix of shape [N, 1] where N is the minibatch size and the rows correspond to the output handles of AddSparseToTensorsMap or AddManySparseToTensorsMap. The ranks of the original SparseTensor objects that went into the given input ops must all match. When the final SparseTensor is created, it has rank one higher than the ranks of the incoming SparseTensor objects (they have been concatenated along a new row dimension on the left). The output SparseTensor object's shape values for all dimensions but the first are the max across the input SparseTensor objects' shape values for the corresponding dimensions. Its first shape value is N, the minibatch size. The input SparseTensor objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run SparseReorder to restore index ordering. For example, if the handles represent an input, which is a [2, 3] matrix representing two original SparseTensor objects: index = [ 0]
        [10]
        [20]
values = [1, 2, 3]
shape = [50]
 and index = [ 2]
        [10]
values = [4, 5]
shape = [30]
 then the final SparseTensor will be: index = [0  0]
        [0 10]
        [0 20]
        [1  2]
        [1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]

 Args
  sparse_handles   A Tensor of type int64. 1-D, The N serialized SparseTensor objects. Shape: [N].
  dtype   A tf.DType. The dtype of the SparseTensor objects stored in the SparseTensorsMap.
  container   An optional string. Defaults to "". The container name for the SparseTensorsMap read by this op.
  shared_name   An optional string. Defaults to "". The shared name for the SparseTensorsMap read by this op. It should not be blank; rather the shared_name or unique Operation name of the Op that created the original SparseTensorsMap should be used.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (sparse_indices, sparse_values, sparse_shape).     sparse_indices   A Tensor of type int64.
  sparse_values   A Tensor of type dtype.
  sparse_shape   A Tensor of type int64.


tf.raw_ops.TakeWhileDataset Creates a dataset that stops iteration when predicate` is false.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TakeWhileDataset
tf.raw_ops.TakeWhileDataset(
    input_dataset, other_arguments, predicate, output_types, output_shapes,
    name=None
)
 The predicate function must return a scalar boolean and accept the following arguments:  One tensor for each component of an element of input_dataset. One tensor for each value in other_arguments.

 Args
  input_dataset   A Tensor of type variant.
  other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for predicate.
  predicate   A function decorated with @Defun. A function returning a scalar boolean.
  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.Tan Computes tan of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Tan
tf.raw_ops.Tan(
    x, name=None
)
 Given an input tensor, this function computes tangent of every element in the tensor. Input range is (-inf, inf) and output range is (-inf, inf). If input lies outside the boundary, nan is returned. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])
tf.math.tan(x) ==> [nan 0.45231566 -0.5463025 1.5574077 2.572152 -1.7925274 0.32097113 nan]

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

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Tanh Computes hyperbolic tangent of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Tanh
tf.raw_ops.Tanh(
    x, name=None
)
 Given an input tensor, this function computes hyperbolic tangent of every element in the tensor. Input range is [-inf, inf] and output range is [-1,1].
x = tf.constant([-float("inf"), -5, -0.5, 1, 1.2, 2, 3, float("inf")])
tf.math.tanh(x)
  <tf.Tensor: shape=(8,), dtype=float32, numpy=
  array([-1.        , -0.99990916, -0.46211717,  0.7615942 ,  0.8336547 ,
          0.9640276 ,  0.9950547 ,  1.        ], dtype=float32)>

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.TanhGrad Computes the gradient for the tanh of x wrt its input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TanhGrad
tf.raw_ops.TanhGrad(
    y, dy, name=None
)
 Specifically, grad = dy * (1 - y*y), where y = tanh(x), and dy is the corresponding input gradient.

 Args
  y   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  dy   A Tensor. Must have the same type as y.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as y.


tf.raw_ops.TemporaryVariable Returns a tensor that may be mutated, but only persists within a single step.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TemporaryVariable
tf.raw_ops.TemporaryVariable(
    shape, dtype, var_name='', name=None
)
 This is an experimental op for internal use only and it is possible to use this op in unsafe ways. DO NOT USE unless you fully understand the risks. It is the caller's responsibility to ensure that 'ref' is eventually passed to a matching 'DestroyTemporaryVariable' op after all other uses have completed. Outputs a ref to the tensor state so it may be read or modified. E.g. var = state_ops._temporaryvariable([1, 2], types.float) var_name = var.op.name var = state_ops.assign(var, [[4.0, 5.0]]) var = state_ops.assign_add(var, [[6.0, 7.0]]) final = state_ops._destroy_temporary_variable(var, var_name=var_name)

 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.
  var_name   An optional string. Defaults to "". Overrides the name used for the temporary variable resource. Default value is the name of the 'TemporaryVariable' op (which is guaranteed unique).
  name   A name for the operation (optional).

 Returns   A mutable Tensor of type dtype.


tf.raw_ops.TensorArray  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArray
tf.raw_ops.TensorArray(
    size, dtype, dynamic_size=False, clear_after_read=True,
    tensor_array_name='', element_shape=None, name=None
)

 Args
  size   A Tensor of type int32.
  dtype   A tf.DType.
  dynamic_size   An optional bool. Defaults to False.
  clear_after_read   An optional bool. Defaults to True.
  tensor_array_name   An optional string. Defaults to "".
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


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

 Args
  handle   A Tensor of type mutable string.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.TensorArrayCloseV2 Deprecated. Use TensorArrayCloseV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayCloseV2
tf.raw_ops.TensorArrayCloseV2(
    handle, name=None
)

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

 Returns   The created Operation.


tf.raw_ops.TensorArrayCloseV3 Delete the TensorArray from its resource container.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayCloseV3
tf.raw_ops.TensorArrayCloseV3(
    handle, name=None
)
 This enables the user to close and release the resource in the middle of a step/run.

 Args
  handle   A Tensor of type resource. The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.TensorArrayConcat  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayConcat
tf.raw_ops.TensorArrayConcat(
    handle, flow_in, dtype, element_shape_except0=None, name=None
)

 Args
  handle   A Tensor of type mutable string.
  flow_in   A Tensor of type float32.
  dtype   A tf.DType.
  element_shape_except0   An optional tf.TensorShape or list of ints. Defaults to None.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (value, lengths).     value   A Tensor of type dtype.
  lengths   A Tensor of type int64.


tf.raw_ops.TensorArrayConcatV2 Deprecated. Use TensorArrayConcatV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayConcatV2
tf.raw_ops.TensorArrayConcatV2(
    handle, flow_in, dtype, element_shape_except0=None, name=None
)

 Args
  handle   A Tensor of type string.
  flow_in   A Tensor of type float32.
  dtype   A tf.DType.
  element_shape_except0   An optional tf.TensorShape or list of ints. Defaults to None.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (value, lengths).     value   A Tensor of type dtype.
  lengths   A Tensor of type int64.


tf.raw_ops.TensorArrayConcatV3 Concat the elements from the TensorArray into value value.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayConcatV3
tf.raw_ops.TensorArrayConcatV3(
    handle, flow_in, dtype, element_shape_except0=None, name=None
)
 Takes T elements of shapes (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...)
 and concatenates them into a Tensor of shape: (n0 + n1 + ... + n(T-1) x d0 x d1 x ...) All elements must have the same shape (excepting the first dimension).

 Args
  handle   A Tensor of type resource. The handle to a TensorArray.
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  dtype   A tf.DType. The type of the elem that is returned.
  element_shape_except0   An optional tf.TensorShape or list of ints. Defaults to None. The expected shape of an element, if known, excluding the first dimension. Used to validate the shapes of TensorArray elements. If this shape is not fully specified, concatenating zero-size TensorArrays is an error.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (value, lengths).     value   A Tensor of type dtype.
  lengths   A Tensor of type int64.


tf.raw_ops.TensorArrayGather  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGather
tf.raw_ops.TensorArrayGather(
    handle, indices, flow_in, dtype, element_shape=None, name=None
)

 Args
  handle   A Tensor of type mutable string.
  indices   A Tensor of type int32.
  flow_in   A Tensor of type float32.
  dtype   A tf.DType.
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.TensorArrayGatherV2 Deprecated. Use TensorArrayGatherV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGatherV2
tf.raw_ops.TensorArrayGatherV2(
    handle, indices, flow_in, dtype, element_shape=None, name=None
)

 Args
  handle   A Tensor of type string.
  indices   A Tensor of type int32.
  flow_in   A Tensor of type float32.
  dtype   A tf.DType.
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.TensorArrayGatherV3 Gather specific elements from the TensorArray into output value.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGatherV3
tf.raw_ops.TensorArrayGatherV3(
    handle, indices, flow_in, dtype, element_shape=None, name=None
)
 All elements selected by indices must have the same shape.

 Args
  handle   A Tensor of type resource. The handle to a TensorArray.
  indices   A Tensor of type int32. The locations in the TensorArray from which to read tensor elements.
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  dtype   A tf.DType. The type of the elem that is returned.
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None. The expected shape of an element, if known. Used to validate the shapes of TensorArray elements. If this shape is not fully specified, gathering zero-size TensorArrays is an error.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.TensorArrayGrad  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGrad
tf.raw_ops.TensorArrayGrad(
    handle, flow_in, source, name=None
)

 Args
  handle   A Tensor of type string.
  flow_in   A Tensor of type float32.
  source   A string.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.TensorArrayGradV2 Deprecated. Use TensorArrayGradV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGradV2
tf.raw_ops.TensorArrayGradV2(
    handle, flow_in, source, name=None
)

 Args
  handle   A Tensor of type string.
  flow_in   A Tensor of type float32.
  source   A string.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.TensorArrayGradV3 Creates a TensorArray for storing the gradients of values in the given handle.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGradV3
tf.raw_ops.TensorArrayGradV3(
    handle, flow_in, source, name=None
)
 If the given TensorArray gradient already exists, returns a reference to it. Locks the size of the original TensorArray by disabling its dynamic size flag. A note about the input flow_in: The handle flow_in forces the execution of the gradient lookup to occur only after certain other operations have occurred. For example, when the forward TensorArray is dynamically sized, writes to this TensorArray may resize the object. The gradient TensorArray is statically sized based on the size of the forward TensorArray when this operation executes. Furthermore, the size of the forward TensorArray is frozen by this call. As a result, the flow is used to ensure that the call to generate the gradient TensorArray only happens after all writes are executed. In the case of dynamically sized TensorArrays, gradient computation should only be performed on read operations that have themselves been chained via flow to occur only after all writes have executed. That way the final size of the forward TensorArray is known when this operation is called. A note about the source attribute: TensorArray gradient calls use an accumulator TensorArray object. If multiple gradients are calculated and run in the same session, the multiple gradient nodes may accidentally flow through the same accumulator TensorArray. This double counts and generally breaks the TensorArray gradient flow. The solution is to identify which gradient call this particular TensorArray gradient is being called in. This is performed by identifying a unique string (e.g. "gradients", "gradients_1", ...) from the input gradient Tensor's name. This string is used as a suffix when creating the TensorArray gradient object here (the attribute source). The attribute source is added as a suffix to the forward TensorArray's name when performing the creation / lookup, so that each separate gradient calculation gets its own TensorArray accumulator.

 Args
  handle   A Tensor of type resource. The handle to the forward TensorArray.
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  source   A string. The gradient source string, used to decide which gradient TensorArray to return.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (grad_handle, flow_out).     grad_handle   A Tensor of type resource.
  flow_out   A Tensor of type float32.


tf.raw_ops.TensorArrayGradWithShape Creates a TensorArray for storing multiple gradients of values in the given handle.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGradWithShape
tf.raw_ops.TensorArrayGradWithShape(
    handle, flow_in, shape_to_prepend, source, name=None
)
 Similar to TensorArrayGradV3. However it creates an accumulator with an expanded shape compared to the input TensorArray whose gradient is being computed. This enables multiple gradients for the same TensorArray to be calculated using the same accumulator.

 Args
  handle   A Tensor of type resource. The handle to the forward TensorArray.
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  shape_to_prepend   A Tensor of type int32. An int32 vector representing a shape. Elements in the gradient accumulator will have shape which is this shape_to_prepend value concatenated with shape of the elements in the TensorArray corresponding to the input handle.
  source   A string. The gradient source string, used to decide which gradient TensorArray to return.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (grad_handle, flow_out).     grad_handle   A Tensor of type resource.
  flow_out   A Tensor of type float32.


tf.raw_ops.TensorArrayPack  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayPack
tf.raw_ops.TensorArrayPack(
    handle, flow_in, dtype, element_shape=None, name=None
)

 Args
  handle   A Tensor of type mutable string.
  flow_in   A Tensor of type float32.
  dtype   A tf.DType.
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.TensorArrayRead  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayRead
tf.raw_ops.TensorArrayRead(
    handle, index, flow_in, dtype, name=None
)

 Args
  handle   A Tensor of type mutable string.
  index   A Tensor of type int32.
  flow_in   A Tensor of type float32.
  dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.TensorArrayReadV2 Deprecated. Use TensorArrayReadV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayReadV2
tf.raw_ops.TensorArrayReadV2(
    handle, index, flow_in, dtype, name=None
)

 Args
  handle   A Tensor of type string.
  index   A Tensor of type int32.
  flow_in   A Tensor of type float32.
  dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.TensorArrayReadV3 Read an element from the TensorArray into output value.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayReadV3
tf.raw_ops.TensorArrayReadV3(
    handle, index, flow_in, dtype, name=None
)

 Args
  handle   A Tensor of type resource. The handle to a TensorArray.
  index   A Tensor of type int32.
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  dtype   A tf.DType. The type of the elem that is returned.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.TensorArrayScatter  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayScatter
tf.raw_ops.TensorArrayScatter(
    handle, indices, value, flow_in, name=None
)

 Args
  handle   A Tensor of type mutable string.
  indices   A Tensor of type int32.
  value   A Tensor.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorArrayScatterV2 Deprecated. Use TensorArrayScatterV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayScatterV2
tf.raw_ops.TensorArrayScatterV2(
    handle, indices, value, flow_in, name=None
)

 Args
  handle   A Tensor of type string.
  indices   A Tensor of type int32.
  value   A Tensor.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorArrayScatterV3 Scatter the data from the input value into specific TensorArray elements.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayScatterV3
tf.raw_ops.TensorArrayScatterV3(
    handle, indices, value, flow_in, name=None
)
 indices must be a vector, its length must match the first dim of value.

 Args
  handle   A Tensor of type resource. The handle to a TensorArray.
  indices   A Tensor of type int32. The locations at which to write the tensor elements.
  value   A Tensor. The concatenated tensor to write to the TensorArray.
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


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

 Args
  handle   A Tensor of type mutable string.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.TensorArraySizeV2 Deprecated. Use TensorArraySizeV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArraySizeV2
tf.raw_ops.TensorArraySizeV2(
    handle, flow_in, name=None
)

 Args
  handle   A Tensor of type string.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.TensorArraySizeV3 Get the current size of the TensorArray.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArraySizeV3
tf.raw_ops.TensorArraySizeV3(
    handle, flow_in, name=None
)

 Args
  handle   A Tensor of type resource. The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.TensorArraySplit  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArraySplit
tf.raw_ops.TensorArraySplit(
    handle, value, lengths, flow_in, name=None
)

 Args
  handle   A Tensor of type mutable string.
  value   A Tensor.
  lengths   A Tensor of type int64.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorArraySplitV2 Deprecated. Use TensorArraySplitV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArraySplitV2
tf.raw_ops.TensorArraySplitV2(
    handle, value, lengths, flow_in, name=None
)

 Args
  handle   A Tensor of type string.
  value   A Tensor.
  lengths   A Tensor of type int64.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorArraySplitV3 Split the data from the input value into TensorArray elements.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArraySplitV3
tf.raw_ops.TensorArraySplitV3(
    handle, value, lengths, flow_in, name=None
)
 Assuming that lengths takes on values (n0, n1, ..., n(T-1)) and that value has shape (n0 + n1 + ... + n(T-1) x d0 x d1 x ...), this splits values into a TensorArray with T tensors. TensorArray index t will be the subtensor of values with starting position (n0 + n1 + ... + n(t-1), 0, 0, ...) and having size nt x d0 x d1 x ...

 Args
  handle   A Tensor of type resource. The handle to a TensorArray.
  value   A Tensor. The concatenated tensor to write to the TensorArray.
  lengths   A Tensor of type int64. The vector of lengths, how to split the rows of value into the TensorArray.
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorArrayUnpack  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayUnpack
tf.raw_ops.TensorArrayUnpack(
    handle, value, flow_in, name=None
)

 Args
  handle   A Tensor of type mutable string.
  value   A Tensor.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorArrayV2 Deprecated. Use TensorArrayV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayV2
tf.raw_ops.TensorArrayV2(
    size, dtype, element_shape=None, dynamic_size=False, clear_after_read=True,
    tensor_array_name='', name=None
)

 Args
  size   A Tensor of type int32.
  dtype   A tf.DType.
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.
  dynamic_size   An optional bool. Defaults to False.
  clear_after_read   An optional bool. Defaults to True.
  tensor_array_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.TensorArrayV3 An array of Tensors of given size.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayV3
tf.raw_ops.TensorArrayV3(
    size, dtype, element_shape=None, dynamic_size=False, clear_after_read=True,
    identical_element_shapes=False, tensor_array_name='', name=None
)
 Write data via Write and read via Read or Pack.

 Args
  size   A Tensor of type int32. The size of the array.
  dtype   A tf.DType. The type of the elements on the tensor_array.
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None. The expected shape of an element, if known. Used to validate the shapes of TensorArray elements. If this shape is not fully specified, gathering zero-size TensorArrays is an error.
  dynamic_size   An optional bool. Defaults to False. A boolean that determines whether writes to the TensorArray are allowed to grow the size. By default, this is not allowed.
  clear_after_read   An optional bool. Defaults to True. If true (default), Tensors in the TensorArray are cleared after being read. This disables multiple read semantics but allows early release of memory.
  identical_element_shapes   An optional bool. Defaults to False. If true (default is false), then all elements in the TensorArray will be expected to have have identical shapes. This allows certain behaviors, like dynamically checking for consistent shapes on write, and being able to fill in properly shaped zero tensors on stack -- even if the element_shape attribute is not fully defined.
  tensor_array_name   An optional string. Defaults to "". Overrides the name used for the temporary tensor_array resource. Default value is the name of the 'TensorArray' op (which is guaranteed unique).
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (handle, flow).     handle   A Tensor of type resource.
  flow   A Tensor of type float32.


tf.raw_ops.TensorArrayWrite  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayWrite
tf.raw_ops.TensorArrayWrite(
    handle, index, value, flow_in, name=None
)

 Args
  handle   A Tensor of type mutable string.
  index   A Tensor of type int32.
  value   A Tensor.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorArrayWriteV2 Deprecated. Use TensorArrayGradV3  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayWriteV2
tf.raw_ops.TensorArrayWriteV2(
    handle, index, value, flow_in, name=None
)

 Args
  handle   A Tensor of type string.
  index   A Tensor of type int32.
  value   A Tensor.
  flow_in   A Tensor of type float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorArrayWriteV3 Push an element onto the tensor_array.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayWriteV3
tf.raw_ops.TensorArrayWriteV3(
    handle, index, value, flow_in, name=None
)

 Args
  handle   A Tensor of type resource. The handle to a TensorArray.
  index   A Tensor of type int32. The position to write to inside the TensorArray.
  value   A Tensor. The tensor to write to the TensorArray.
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.TensorDataset Creates a dataset that emits components as a tuple of tensors once.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorDataset
tf.raw_ops.TensorDataset(
    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.TensorListConcat Concats all tensors in the list along the 0th dimension.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListConcat
tf.raw_ops.TensorListConcat(
    input_handle, element_dtype, element_shape=None, name=None
)
 Requires that all tensors have the same shape except the first dimension. input_handle: The input list. tensor: The concated result. lengths: Output tensor containing sizes of the 0th dimension of tensors in the list, used for computing the gradient.

 Args
  input_handle   A Tensor of type variant.
  element_dtype   A tf.DType.
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (tensor, lengths).     tensor   A Tensor of type element_dtype.
  lengths   A Tensor of type int64.


tf.raw_ops.TensorListConcatLists  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListConcatLists
tf.raw_ops.TensorListConcatLists(
    input_a, input_b, element_dtype, name=None
)

 Args
  input_a   A Tensor of type variant.
  input_b   A Tensor of type variant.
  element_dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListConcatV2 Concats all tensors in the list along the 0th dimension.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListConcatV2
tf.raw_ops.TensorListConcatV2(
    input_handle, element_shape, leading_dims, element_dtype, name=None
)
 Requires that all tensors have the same shape except the first dimension. input_handle: The input list. element_shape: The shape of the uninitialized elements in the list. If the first dimension is not -1, it is assumed that all list elements have the same leading dim. leading_dims: The list of leading dims of uninitialized list elements. Used if the leading dim of input_handle.element_shape or the element_shape input arg is not already set. tensor: The concated result. lengths: Output tensor containing sizes of the 0th dimension of tensors in the list, used for computing the gradient.

 Args
  input_handle   A Tensor of type variant.
  element_shape   A Tensor. Must be one of the following types: int32, int64.
  leading_dims   A Tensor of type int64.
  element_dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (tensor, lengths).     tensor   A Tensor of type element_dtype.
  lengths   A Tensor of type int64.


tf.raw_ops.TensorListElementShape The shape of the elements of the given list, as a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListElementShape
tf.raw_ops.TensorListElementShape(
    input_handle, shape_type, name=None
)
 input_handle: the list element_shape: the shape of elements of the list

 Args
  input_handle   A Tensor of type variant.
  shape_type   A tf.DType from: tf.int32, tf.int64.
  name   A name for the operation (optional).

 Returns   A Tensor of type shape_type.


tf.raw_ops.TensorListFromTensor Creates a TensorList which, when stacked, has the value of tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListFromTensor
tf.raw_ops.TensorListFromTensor(
    tensor, element_shape, name=None
)
 Each tensor in the result list corresponds to one row of the input tensor. tensor: The input tensor. output_handle: The list.

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

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListGather Creates a Tensor by indexing into the TensorList.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListGather
tf.raw_ops.TensorListGather(
    input_handle, indices, element_shape, element_dtype, name=None
)
 Each row in the produced Tensor corresponds to the element in the TensorList specified by the given index (see tf.gather). input_handle: The input tensor list. indices: The indices used to index into the list. values: The tensor.

 Args
  input_handle   A Tensor of type variant.
  indices   A Tensor of type int32.
  element_shape   A Tensor of type int32.
  element_dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A Tensor of type element_dtype.


tf.raw_ops.TensorListGetItem Returns the item in the list with the given index.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListGetItem
tf.raw_ops.TensorListGetItem(
    input_handle, index, element_shape, element_dtype, name=None
)
 input_handle: the list index: the position in the list from which an element will be retrieved item: the element at that position

 Args
  input_handle   A Tensor of type variant.
  index   A Tensor of type int32.
  element_shape   A Tensor of type int32.
  element_dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A Tensor of type element_dtype.


tf.raw_ops.TensorListLength Returns the number of tensors in the input tensor list.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListLength
tf.raw_ops.TensorListLength(
    input_handle, name=None
)
 input_handle: the input list length: the number of tensors in the list

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

 Returns   A Tensor of type int32.


tf.raw_ops.TensorListPopBack Returns the last element of the input list as well as a list with all but that element.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListPopBack
tf.raw_ops.TensorListPopBack(
    input_handle, element_shape, element_dtype, name=None
)
 Fails if the list is empty. input_handle: the input list tensor: the withdrawn last element of the list element_dtype: the type of elements in the list element_shape: the shape of the output tensor

 Args
  input_handle   A Tensor of type variant.
  element_shape   A Tensor of type int32.
  element_dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output_handle, tensor).     output_handle   A Tensor of type variant.
  tensor   A Tensor of type element_dtype.


tf.raw_ops.TensorListPushBack Returns a list which has the passed-in Tensor as last element and the other elements of the given list in input_handle.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListPushBack
tf.raw_ops.TensorListPushBack(
    input_handle, tensor, name=None
)
 tensor: The tensor to put on the list. input_handle: The old list. output_handle: A list with the elements of the old list followed by tensor. element_dtype: the type of elements in the list. element_shape: a shape compatible with that of elements in the list.

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

 Returns   A Tensor of type variant.


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

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

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListReserve List of the given size with empty elements.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListReserve
tf.raw_ops.TensorListReserve(
    element_shape, num_elements, element_dtype, name=None
)
 element_shape: the shape of the future elements of the list num_elements: the number of elements to reserve handle: the output list element_dtype: the desired type of elements in the list.

 Args
  element_shape   A Tensor. Must be one of the following types: int32, int64.
  num_elements   A Tensor of type int32.
  element_dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListResize Resizes the list.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListResize
tf.raw_ops.TensorListResize(
    input_handle, size, name=None
)
 input_handle: the input list size: size of the output list

 Args
  input_handle   A Tensor of type variant.
  size   A Tensor of type int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListScatter Creates a TensorList by indexing into a Tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListScatter
tf.raw_ops.TensorListScatter(
    tensor, indices, element_shape, name=None
)
 Each member of the TensorList corresponds to one row of the input tensor, specified by the given index (see tf.gather). tensor: The input tensor. indices: The indices used to index into the list. element_shape: The shape of the elements in the list (can be less specified than the shape of the tensor). output_handle: The TensorList.

 Args
  tensor   A Tensor.
  indices   A Tensor of type int32.
  element_shape   A Tensor. Must be one of the following types: int32, int64.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListScatterIntoExistingList Scatters tensor at indices in an input list.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListScatterIntoExistingList
tf.raw_ops.TensorListScatterIntoExistingList(
    input_handle, tensor, indices, name=None
)
 Each member of the TensorList corresponds to one row of the input tensor, specified by the given index (see tf.gather). input_handle: The list to scatter into. tensor: The input tensor. indices: The indices used to index into the list. output_handle: The TensorList.

 Args
  input_handle   A Tensor of type variant.
  tensor   A Tensor.
  indices   A Tensor of type int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListScatterV2 Creates a TensorList by indexing into a Tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListScatterV2
tf.raw_ops.TensorListScatterV2(
    tensor, indices, element_shape, num_elements, name=None
)
 Each member of the TensorList corresponds to one row of the input tensor, specified by the given index (see tf.gather). tensor: The input tensor. indices: The indices used to index into the list. element_shape: The shape of the elements in the list (can be less specified than the shape of the tensor). num_elements: The size of the output list. Must be large enough to accommodate the largest index in indices. If -1, the list is just large enough to include the largest index in indices. output_handle: The TensorList.

 Args
  tensor   A Tensor.
  indices   A Tensor of type int32.
  element_shape   A Tensor. Must be one of the following types: int32, int64.
  num_elements   A Tensor of type int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListSetItem Sets the index-th position of the list to contain the given tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListSetItem
tf.raw_ops.TensorListSetItem(
    input_handle, index, item, name=None
)
 input_handle: the list index: the position in the list to which the tensor will be assigned item: the element to be assigned to that position output_handle: the new list, with the element in the proper position

 Args
  input_handle   A Tensor of type variant.
  index   A Tensor of type int32.
  item   A Tensor.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListSplit Splits a tensor into a list.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListSplit
tf.raw_ops.TensorListSplit(
    tensor, element_shape, lengths, name=None
)
 list[i] corresponds to lengths[i] tensors from the input tensor. The tensor must have rank at least 1 and contain exactly sum(lengths) elements. tensor: The input tensor. element_shape: A shape compatible with that of elements in the tensor. lengths: Vector of sizes of the 0th dimension of tensors in the list. output_handle: The list.

 Args
  tensor   A Tensor.
  element_shape   A Tensor. Must be one of the following types: int32, int64.
  lengths   A Tensor of type int64.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.TensorListStack Stacks all tensors in the list.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListStack
tf.raw_ops.TensorListStack(
    input_handle, element_shape, element_dtype, num_elements=-1, name=None
)
 Requires that all tensors have the same shape. input_handle: the input list tensor: the gathered result num_elements: optional. If not -1, the number of elements in the list.

 Args
  input_handle   A Tensor of type variant.
  element_shape   A Tensor of type int32.
  element_dtype   A tf.DType.
  num_elements   An optional int. Defaults to -1.
  name   A name for the operation (optional).

 Returns   A Tensor of type element_dtype.