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

 


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


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

 


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


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


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


 Returns   A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count).     sampled_candidates   A Tensor of type int64.  
  true_expected_count   A Tensor of type float32.  
  sampled_expected_count   A Tensor of type float32.
tf.raw_ops.Tile Constructs a tensor by tiling a given tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Tile  
tf.raw_ops.Tile(
    input, multiples, name=None
)
 This operation creates a new tensor by replicating input multiples times. The output tensor's i'th dimension has input.dims(i) * multiples[i] elements, and the values of input are replicated multiples[i] times along the 'i'th dimension. For example, tiling [a b c d] by [2] produces [a b c d a b c d]. 
a = tf.constant([[1,2,3],[4,5,6]], tf.int32)
b = tf.constant([1,2], tf.int32)
tf.tile(a, b)
<tf.Tensor: shape=(2, 6), dtype=int32, numpy=
array([[1, 2, 3, 1, 2, 3],
       [4, 5, 6, 4, 5, 6]], dtype=int32)>
c = tf.constant([2,1], tf.int32)
tf.tile(a, c)
<tf.Tensor: shape=(4, 3), dtype=int32, numpy=
array([[1, 2, 3],
       [4, 5, 6],
       [1, 2, 3],
       [4, 5, 6]], dtype=int32)>
d = tf.constant([2,2], tf.int32)
tf.tile(a, d)
<tf.Tensor: shape=(4, 6), dtype=int32, numpy=
array([[1, 2, 3, 1, 2, 3],
       [4, 5, 6, 4, 5, 6],
       [1, 2, 3, 1, 2, 3],
       [4, 5, 6, 4, 5, 6]], dtype=int32)>

 


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


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


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


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

 


 Args
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float64.
tf.raw_ops.ToBool Converts a tensor to a scalar predicate.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ToBool  
tf.raw_ops.ToBool(
    input, name=None
)
 Converts a tensor to a scalar predicate with the following rules:  For 0D tensors, truthiness is determined by comparing against a "zero" value. For numerical types it is the obvious zero. For strings it is the empty string. For >0D tensors, truthiness is determined by looking at the number of elements. If has zero elements, then the result is false. Otherwise the result is true.  This matches the behavior of If and While for determining if a tensor counts as true/false for a branch condition.
 


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


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


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


 Returns   A tuple of Tensor objects (values, indices).     values   A Tensor. Has the same type as input.  
  indices   A Tensor of type int32.
tf.raw_ops.TopKV2 Finds values and indices of the k largest elements for the last dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TopKV2  
tf.raw_ops.TopKV2(
    input, k, sorted=True, name=None
)
 If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j]. For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus, values.shape = indices.shape = input.shape[:-1] + [k]
 If two elements are equal, the lower-index element appears first.
 


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


 Returns   A tuple of Tensor objects (values, indices).     values   A Tensor. Has the same type as input.  
  indices   A Tensor of type int32.
tf.raw_ops.TPUCompilationResult Returns the result of a TPU compilation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TPUCompilationResult  
tf.raw_ops.TPUCompilationResult(
    name=None
)
 This operation returns the result of a TPU compilation as a serialized CompilationResultProto, which holds a status and an error message if an error occurred during compilation.
 


 Args
  name   A name for the operation (optional).   
 


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


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


 Returns   A Tensor of type float32.
tf.raw_ops.TPUOrdinalSelector A TPU core selector Op.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TPUOrdinalSelector  
tf.raw_ops.TPUOrdinalSelector(
    name=None
)
 This Op produces a set of TPU cores (for warm-up) or a single TPU core (for regular inference) to execute the TPU program on. The output is consumed by TPUPartitionedCall.
 


 Args
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.TPUPartitionedCall Calls a function placed on a specified TPU device.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TPUPartitionedCall  
tf.raw_ops.TPUPartitionedCall(
    args, device_ordinal, Tout, f, autotuner_thresh=0, name=None
)

 


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


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


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


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


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


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


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


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


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


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


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


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


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


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

 


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


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


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


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

 


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


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


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


 Returns   A Tensor. Has the same type as batched_tensor.