neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ChooseFastestBranchDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ChooseFastestBranchDataset  
tf.raw_ops.ChooseFastestBranchDataset(
    input_dataset, ratio_numerator, ratio_denominator, other_arguments,
    num_elements_per_branch, branches, other_arguments_lengths, output_types,
    output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  ratio_numerator   A Tensor of type int64.  
  ratio_denominator   A Tensor of type int64.  
  other_arguments   A list of Tensor objects.  
  num_elements_per_branch   An int that is >= 1.  
  branches   A list of functions decorated with @Defun that has length >= 1.  
  other_arguments_lengths   A list of ints that has length >= 1.  
  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.ChooseFastestDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ChooseFastestDataset  
tf.raw_ops.ChooseFastestDataset(
    input_datasets, num_experiments, output_types, output_shapes, name=None
)

 


 Args
  input_datasets   A list of at least 2 Tensor objects with type variant.  
  num_experiments   An int.  
  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.ClipByValue Clips tensor values to a specified min and max.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ClipByValue  
tf.raw_ops.ClipByValue(
    t, clip_value_min, clip_value_max, name=None
)
 Given a tensor t, this operation returns a tensor of the same type and shape as t with its values clipped to clip_value_min and clip_value_max. Any values less than clip_value_min are set to clip_value_min. Any values greater than clip_value_max are set to clip_value_max.
 


 Args
  t   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. A Tensor.  
  clip_value_min   A Tensor. Must have the same type as t. A 0-D (scalar) Tensor, or a Tensor with the same shape as t. The minimum value to clip by.  
  clip_value_max   A Tensor. Must have the same type as t. A 0-D (scalar) Tensor, or a Tensor with the same shape as t. The maximum value to clip by.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as t.
tf.raw_ops.CloseSummaryWriter  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CloseSummaryWriter  
tf.raw_ops.CloseSummaryWriter(
    writer, name=None
)

 


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


 Returns   The created Operation.
tf.raw_ops.CollectiveBcastRecv Receives a tensor value broadcast from another device.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CollectiveBcastRecv  
tf.raw_ops.CollectiveBcastRecv(
    T, group_size, group_key, instance_key, shape,
    communication_hint='auto', timeout_seconds=0, name=None
)

 


 Args
  T   A tf.DType from: tf.bool, tf.float32, tf.half, tf.float64, tf.int32, tf.int64.  
  group_size   An int.  
  group_key   An int.  
  instance_key   An int.  
  shape   A tf.TensorShape or list of ints.  
  communication_hint   An optional string. Defaults to "auto".  
  timeout_seconds   An optional float. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type T.
tf.raw_ops.CollectiveBcastSend Broadcasts a tensor value to one or more other devices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CollectiveBcastSend  
tf.raw_ops.CollectiveBcastSend(
    input, group_size, group_key, instance_key, shape,
    communication_hint='auto', timeout_seconds=0, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: bool, float32, half, float64, int32, int64.  
  group_size   An int.  
  group_key   An int.  
  instance_key   An int.  
  shape   A tf.TensorShape or list of ints.  
  communication_hint   An optional string. Defaults to "auto".  
  timeout_seconds   An optional float. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CollectiveGather Mutually accumulates multiple tensors of identical type and shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CollectiveGather  
tf.raw_ops.CollectiveGather(
    input, group_size, group_key, instance_key, shape,
    communication_hint='auto', timeout_seconds=0, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, half, float64, int32, int64.  
  group_size   An int.  
  group_key   An int.  
  instance_key   An int.  
  shape   A tf.TensorShape or list of ints.  
  communication_hint   An optional string. Defaults to "auto".  
  timeout_seconds   An optional float. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CollectiveGatherV2 Mutually accumulates multiple tensors of identical type and shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CollectiveGatherV2  
tf.raw_ops.CollectiveGatherV2(
    input, group_size, group_key, instance_key, communication_hint='auto',
    timeout_seconds=0, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, half, float64, int32, int64.  
  group_size   A Tensor of type int32.  
  group_key   A Tensor of type int32.  
  instance_key   A Tensor of type int32.  
  communication_hint   An optional string. Defaults to "auto".  
  timeout_seconds   An optional float. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CollectivePermute An Op to permute tensors across replicated TPU instances.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CollectivePermute  
tf.raw_ops.CollectivePermute(
    input, source_target_pairs, name=None
)
 Each instance supplies its own input. For example, suppose there are 4 TPU instances: [A, B, C, D]. Passing source_target_pairs=[[0,1],[1,2],[2,3],[3,0]] gets the outputs: [D, A, B, C].
 


 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 local input to be permuted. Currently only supports float and bfloat16.  
  source_target_pairs   A Tensor of type int32. A tensor with shape [num_pairs, 2].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CollectiveReduce Mutually reduces multiple tensors of identical type and shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CollectiveReduce  
tf.raw_ops.CollectiveReduce(
    input, group_size, group_key, instance_key, merge_op, final_op, subdiv_offsets,
    wait_for=[], communication_hint='auto', timeout_seconds=0, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, half, float64, int32, int64.  
  group_size   An int.  
  group_key   An int.  
  instance_key   An int.  
  merge_op   A string from: "Min", "Max", "Mul", "Add".  
  final_op   A string from: "Id", "Div".  
  subdiv_offsets   A list of ints.  
  wait_for   An optional list of ints. Defaults to [].  
  communication_hint   An optional string. Defaults to "auto".  
  timeout_seconds   An optional float. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CollectiveReduceV2 Mutually reduces multiple tensors of identical type and shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CollectiveReduceV2  
tf.raw_ops.CollectiveReduceV2(
    input, group_size, group_key, instance_key, merge_op, final_op,
    communication_hint='auto', timeout_seconds=0, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, half, float64, int32, int64.  
  group_size   A Tensor of type int32.  
  group_key   A Tensor of type int32.  
  instance_key   A Tensor of type int32.  
  merge_op   A string from: "Min", "Max", "Mul", "Add".  
  final_op   A string from: "Id", "Div".  
  communication_hint   An optional string. Defaults to "auto".  
  timeout_seconds   An optional float. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CompareAndBitpack Compare values of input to threshold and pack resulting bits into a uint8.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CompareAndBitpack  
tf.raw_ops.CompareAndBitpack(
    input, threshold, name=None
)
 Each comparison returns a boolean true (if input_value > threshold) or and false otherwise. This operation is useful for Locality-Sensitive-Hashing (LSH) and other algorithms that use hashing approximations of cosine and L2 distances; codes can be generated from an input via: codebook_size = 50
codebook_bits = codebook_size * 32
codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
                           dtype=x.dtype,
                           initializer=tf.orthogonal_initializer())
codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
codes = tf.bitcast(codes, tf.int32)  # go from uint8 to int32
# now codes has shape x.shape[:-1] + [codebook_size]


Note: Currently, the innermost dimension of the tensor must be divisible by 8.
 Given an input shaped [s0, s1, ..., s_n], the output is a uint8 tensor shaped [s0, s1, ..., s_n / 8].
 


 Args
  input   A Tensor. Must be one of the following types: bool, half, float32, float64, int8, int16, int32, int64. Values to compare against threshold and bitpack.  
  threshold   A Tensor. Must have the same type as input. Threshold to compare against.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type uint8.
tf.raw_ops.Complex   Converts two real numbers to a complex number.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Complex  
tf.raw_ops.Complex(
    real, imag, Tout=tf.dtypes.complex64, name=None
)
 Given a tensor real representing the real part of a complex number, and a tensor imag representing the imaginary part of a complex number, this operation returns complex numbers elementwise of the form \(a + bj\), where a represents the real part and b represents the imag part. The input tensors real and imag must have the same shape. For example: # tensor 'real' is [2.25, 3.25]
# tensor `imag` is [4.75, 5.75]
tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]

 


 Args
  real   A Tensor. Must be one of the following types: float32, float64.  
  imag   A Tensor. Must have the same type as real.  
  Tout   An optional tf.DType from: tf.complex64, tf.complex128. Defaults to tf.complex64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Tout.
tf.raw_ops.ComplexAbs   Computes the complex absolute value of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ComplexAbs  
tf.raw_ops.ComplexAbs(
    x, Tout=tf.dtypes.float32, name=None
)
 Given a tensor x of complex numbers, this operation returns a tensor of type float or double that is the absolute value of each element in x. All elements in x must be complex numbers of the form \(a + bj\). The absolute value is computed as \( \sqrt{a^2 + b^2}\).
 


 Args
  x   A Tensor. Must be one of the following types: complex64, complex128.  
  Tout   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Tout.
tf.raw_ops.CompressElement Compresses a dataset element.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CompressElement  
tf.raw_ops.CompressElement(
    components, name=None
)

 


 Args
  components   A list of Tensor objects.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.ComputeAccidentalHits Computes the ids of the positions in sampled_candidates that match true_labels.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ComputeAccidentalHits  
tf.raw_ops.ComputeAccidentalHits(
    true_classes, sampled_candidates, num_true, seed=0, seed2=0, name=None
)
 When doing log-odds NCE, the result of this op should be passed through a SparseToDense op, then added to the logits of the sampled candidates. This has the effect of 'removing' the sampled labels that match the true labels by making the classifier sure that they are sampled labels.
 


 Args
  true_classes   A Tensor of type int64. The true_classes output of UnpackSparseLabels.  
  sampled_candidates   A Tensor of type int64. The sampled_candidates output of CandidateSampler.  
  num_true   An int. Number of true labels per context.  
  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 (indices, ids, weights).     indices   A Tensor of type int32.  
  ids   A Tensor of type int64.  
  weights   A Tensor of type float32.
tf.raw_ops.ComputeBatchSize Computes the static batch size of a dataset sans partial batches.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ComputeBatchSize  
tf.raw_ops.ComputeBatchSize(
    input_dataset, name=None
)

 


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


 Returns   A Tensor of type int64.
tf.raw_ops.Concat Concatenates tensors along one dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Concat  
tf.raw_ops.Concat(
    concat_dim, values, name=None
)

 


 Args
  concat_dim   A Tensor of type int32. 0-D. The dimension along which to concatenate. Must be in the range [0, rank(values)).  
  values   A list of at least 2 Tensor objects with the same type. The N Tensors to concatenate. Their ranks and types must match, and their sizes must match in all dimensions except concat_dim.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as values.
tf.raw_ops.ConcatenateDataset Creates a dataset that concatenates input_dataset with another_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ConcatenateDataset  
tf.raw_ops.ConcatenateDataset(
    input_dataset, another_dataset, output_types, output_shapes, name=None
)

 


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


 Returns   A Tensor of type variant.
tf.raw_ops.ConcatOffset Computes offsets of concat inputs within its output.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ConcatOffset  
tf.raw_ops.ConcatOffset(
    concat_dim, shape, name=None
)
 For example: # 'x' is [2, 2, 7]
# 'y' is [2, 3, 7]
# 'z' is [2, 5, 7]
concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0]
 This is typically used by gradient computations for a concat operation.
 


 Args
  concat_dim   A Tensor of type int32. The dimension along which to concatenate.  
  shape   A list of at least 2 Tensor objects with type int32. The N int32 vectors representing shape of tensors being concatenated.  
  name   A name for the operation (optional).   
 


 Returns   A list with the same length as shape of Tensor objects with type int32.
tf.raw_ops.ConcatV2 Concatenates tensors along one dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ConcatV2  
tf.raw_ops.ConcatV2(
    values, axis, name=None
)

 


 Args
  values   A list of at least 2 Tensor objects with the same type. List of N Tensors to concatenate. Their ranks and types must match, and their sizes must match in all dimensions except concat_dim.  
  axis   A Tensor. Must be one of the following types: int32, int64. 0-D. The dimension along which to concatenate. Must be in the range [-rank(values), rank(values)).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as values.
tf.raw_ops.ConditionalAccumulator A conditional accumulator for aggregating gradients.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ConditionalAccumulator  
tf.raw_ops.ConditionalAccumulator(
    dtype, shape, container='', shared_name='',
    reduction_type='MEAN', name=None
)
 The accumulator accepts gradients marked with local_step greater or equal to the most recent global_step known to the accumulator. The average can be extracted from the accumulator, provided sufficient gradients have been accumulated. Extracting the average automatically resets the aggregate to 0, and increments the global_step recorded by the accumulator.
 


 Args
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The type of the value being accumulated.  
  shape   A tf.TensorShape or list of ints. The shape of the values, can be [], in which case shape is unknown.  
  container   An optional string. Defaults to "". If non-empty, this accumulator is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this accumulator will be shared under the given name across multiple sessions.  
  reduction_type   An optional string from: "MEAN", "SUM". Defaults to "MEAN".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.ConfigureDistributedTPU Sets up the centralized structures for a distributed TPU system.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ConfigureDistributedTPU  
tf.raw_ops.ConfigureDistributedTPU(
    embedding_config='', tpu_embedding_config='',
    is_global_init=False, enable_whole_mesh_compilations=False,
    compilation_failure_closes_chips=True, name=None
)

 


 Args
  embedding_config   An optional string. Defaults to "". Reserved. Do not use.  
  tpu_embedding_config   An optional string. Defaults to "". Serialized tensorflow.tpu.TPUEmbeddingConfiguration that describes the embedding lookups of the program.  
  is_global_init   An optional bool. Defaults to False. Reserved. Do not use.  
  enable_whole_mesh_compilations   An optional bool. Defaults to False.  
  compilation_failure_closes_chips   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.ConfigureTPUEmbedding Sets up TPUEmbedding in a distributed TPU system.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ConfigureTPUEmbedding  
tf.raw_ops.ConfigureTPUEmbedding(
    config, name=None
)

 


 Args
  config   A string. Serialized tensorflow.tpu.TPUEmbeddingConfiguration that describes the embedding lookups of the program.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.Conj   Returns the complex conjugate of a complex number.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conj  
tf.raw_ops.Conj(
    input, name=None
)
 Given a tensor input of complex numbers, this operation returns a tensor of complex numbers that are the complex conjugate of each element in input. The complex numbers in input must be of the form \(a + bj\), where a is the real part and b is the imaginary part. The complex conjugate returned by this operation is of the form \(a - bj\). For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]

 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128, variant.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.ConjugateTranspose Shuffle dimensions of x according to a permutation and conjugate the result.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ConjugateTranspose  
tf.raw_ops.ConjugateTranspose(
    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] y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])
 


 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.Const Returns a constant tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Const  
tf.raw_ops.Const(
    value, dtype, name=None
)

 


 Args
  value   A tf.TensorProto. Attr value is the tensor to return.  
  dtype   A tf.DType.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.ConsumeMutexLock This op consumes a lock created by MutexLock.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ConsumeMutexLock  
tf.raw_ops.ConsumeMutexLock(
    mutex_lock, name=None
)
 This op exists to consume a tensor created by MutexLock (other than direct control dependencies). It should be the only that consumes the tensor, and will raise an error if it is not. Its only purpose is to keep the mutex lock tensor alive until it is consumed by this op. 
Note: This operation must run on the same device as its input. This may be enforced via the colocate_with mechanism.

 


 Args
  mutex_lock   A Tensor of type variant. A tensor returned by MutexLock.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ControlTrigger Does nothing. Serves as a control trigger for scheduling.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ControlTrigger  
tf.raw_ops.ControlTrigger(
    name=None
)
 Only useful as a placeholder for control edges.
 


 Args
  name   A name for the operation (optional).   
 


 Returns   The created Operation.