neulab/docprompting-conala huggingface dataset:
tf.raw_ops.RaggedTensorToVariant Encodes a RaggedTensor into a variant Tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedTensorToVariant  
tf.raw_ops.RaggedTensorToVariant(
    rt_nested_splits, rt_dense_values, batched_input, name=None
)
 Encodes the given RaggedTensor and returns a variant Tensor. If batched_input is True, then input RaggedTensor is unbatched along the zero-th dimension, each component RaggedTensor is encoded into a scalar variant Tensor, and these are stacked to return a 1-D variant Tensor. If batched_input is False, then the input RaggedTensor is encoded as is and a scalar variant Tensor is returned. A RaggedTensor is encoded by first creating a 1-D variant Tensor with ragged_rank + 1 elements, containing the splits and values Tensors of the RaggedTensor. Then the 1-D variant Tensor is wrapped in a scalar variant Tensor. See RaggedTensorFromVariant for the corresponding decoding logic.
 


 Args
  rt_nested_splits   A list of Tensor objects with the same type in: int32, int64. A list of one or more Tensors representing the splits of the input RaggedTensor.  
  rt_dense_values   A Tensor. A Tensor representing the values of the input RaggedTensor.  
  batched_input   A bool. A bool denoting whether the input is a batched RaggedTensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.RaggedTensorToVariantGradient Helper used to compute the gradient for RaggedTensorToVariant.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedTensorToVariantGradient  
tf.raw_ops.RaggedTensorToVariantGradient(
    encoded_ragged_grad, row_splits, dense_values_shape, Tvalues, name=None
)
 Computes the gradient for the dense_values input to the RaggedTensorToVariant op, given the variant-encoded ragged gradients of the outputs, along with the outer row-splits and the shape of the dense-values that were provided as inputs to the RaggedTensorToVariant op.
 


 Args
  encoded_ragged_grad   A Tensor of type variant. A variant Tensor containing encoded RaggedTensor gradients.  
  row_splits   A Tensor. Must be one of the following types: int32, int64. Outermost row-splits that were used as input to the RaggedTensorToVariant op.  
  dense_values_shape   A Tensor of type int32. Shape of the dense_values that was used as an input to the RaggedTensorToVariant op.  
  Tvalues   A tf.DType.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Tvalues.
tf.raw_ops.RandomCrop Randomly crop image.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomCrop  
tf.raw_ops.RandomCrop(
    image, size, seed=0, seed2=0, name=None
)
 size is a 1-D int64 tensor with 2 elements representing the crop height and width. The values must be non negative. This Op picks a random location in image and crops a height by width rectangle from that location. The random location is picked so the cropped area will fit inside the original image.
 


 Args
  image   A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64, float32, float64. 3-D of shape [height, width, channels].  
  size   A Tensor of type int64. 1-D of length 2 containing: crop_height, crop_width..  
  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 Tensor. Has the same type as image.
tf.raw_ops.RandomDataset Creates a Dataset that returns pseudorandom numbers.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomDataset  
tf.raw_ops.RandomDataset(
    seed, seed2, output_types, output_shapes, name=None
)
 Creates a Dataset that returns a stream of uniformly distributed pseudorandom 64-bit signed integers. In the TensorFlow Python API, you can instantiate this dataset via the class tf.data.experimental.RandomDataset. Instances of this dataset are also created as a result of the hoist_random_uniform static optimization. Whether this optimization is performed is determined by the experimental_optimization.hoist_random_uniform option of tf.data.Options.
 


 Args
  seed   A Tensor of type int64. A scalar seed for the random number generator. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.  
  seed2   A Tensor of type int64. A second scalar seed to avoid seed collision.  
  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.RandomGamma Outputs random values from the Gamma distribution(s) described by alpha.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomGamma  
tf.raw_ops.RandomGamma(
    shape, alpha, seed=0, seed2=0, name=None
)
 This op uses the algorithm by Marsaglia et al. to acquire samples via transformation-rejection from pairs of uniform and normal random variables. See http://dl.acm.org/citation.cfm?id=358414
 


 Args
  shape   A Tensor. Must be one of the following types: int32, int64. 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in alpha.  
  alpha   A Tensor. Must be one of the following types: half, float32, float64. A tensor in which each scalar is a "shape" parameter describing the associated gamma distribution.  
  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. Has the same type as alpha.
tf.raw_ops.RandomGammaGrad Computes the derivative of a Gamma random sample w.r.t. alpha.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomGammaGrad  
tf.raw_ops.RandomGammaGrad(
    alpha, sample, name=None
)

 


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


 Returns   A Tensor. Has the same type as alpha.
tf.raw_ops.RandomPoisson Use RandomPoissonV2 instead.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomPoisson  
tf.raw_ops.RandomPoisson(
    shape, rate, seed=0, seed2=0, name=None
)

 


 Args
  shape   A Tensor. Must be one of the following types: int32, int64.  
  rate   A Tensor. Must be one of the following types: half, float32, float64.  
  seed   An optional int. Defaults to 0.  
  seed2   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as rate.
tf.raw_ops.RandomPoissonV2 Outputs random values from the Poisson distribution(s) described by rate.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomPoissonV2  
tf.raw_ops.RandomPoissonV2(
    shape, rate, seed=0, seed2=0, dtype=tf.dtypes.int64, name=None
)
 This op uses two algorithms, depending on rate. If rate >= 10, then the algorithm by Hormann is used to acquire samples via transformation-rejection. See http://www.sciencedirect.com/science/article/pii/0167668793909974 Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform random variables. See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Addison Wesley
 


 Args
  shape   A Tensor. Must be one of the following types: int32, int64. 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate.  
  rate   A Tensor. Must be one of the following types: half, float32, float64, int32, int64. A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution.  
  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.  
  dtype   An optional tf.DType from: tf.half, tf.float32, tf.float64, tf.int32, tf.int64. Defaults to tf.int64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.RandomShuffle Randomly shuffles a tensor along its first dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomShuffle  
tf.raw_ops.RandomShuffle(
    value, seed=0, seed2=0, name=None
)
 The tensor is shuffled along dimension 0, such that each value[j] is mapped to one and only one output[i]. For example, a mapping that might occur for a 3x2 tensor is: [[1, 2],       [[5, 6],
 [3, 4],  ==>   [1, 2],
 [5, 6]]        [3, 4]]

 


 Args
  value   A Tensor. The tensor to be shuffled.  
  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. Has the same type as value.
tf.raw_ops.RandomShuffleQueue A queue that randomizes the order of elements.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomShuffleQueue  
tf.raw_ops.RandomShuffleQueue(
    component_types, shapes=[], capacity=-1, min_after_dequeue=0, seed=0, seed2=0,
    container='', shared_name='', name=None
)

 


 Args
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a value.  
  shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time.  
  capacity   An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.  
  min_after_dequeue   An optional int. Defaults to 0. Dequeue will block unless there would be this many elements after the dequeue or the queue is closed. This ensures a minimum level of mixing of elements.  
  seed   An optional int. Defaults to 0. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.  
  seed2   An optional int. Defaults to 0. A second seed to avoid seed collision.  
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.RandomShuffleQueueV2 A queue that randomizes the order of elements.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomShuffleQueueV2  
tf.raw_ops.RandomShuffleQueueV2(
    component_types, shapes=[], capacity=-1, min_after_dequeue=0, seed=0, seed2=0,
    container='', shared_name='', name=None
)

 


 Args
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a value.  
  shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time.  
  capacity   An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.  
  min_after_dequeue   An optional int. Defaults to 0. Dequeue will block unless there would be this many elements after the dequeue or the queue is closed. This ensures a minimum level of mixing of elements.  
  seed   An optional int. Defaults to 0. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.  
  seed2   An optional int. Defaults to 0. A second seed to avoid seed collision.  
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.RandomStandardNormal Outputs random values from a normal distribution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomStandardNormal  
tf.raw_ops.RandomStandardNormal(
    shape, dtype, seed=0, seed2=0, name=None
)
 The generated values will have mean 0 and standard deviation 1.
 


 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.RandomUniform Outputs random values from a uniform distribution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomUniform  
tf.raw_ops.RandomUniform(
    shape, dtype, seed=0, seed2=0, 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.
 


 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.RandomUniformInt Outputs random integers from a uniform distribution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RandomUniformInt  
tf.raw_ops.RandomUniformInt(
    shape, minval, maxval, seed=0, seed2=0, name=None
)
 The generated values are uniform integers in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded. The random integers are slightly biased unless maxval - minval is an exact power of two. The bias is small for values of maxval - minval significantly smaller than the range of the output (either 2^32 or 2^64).
 


 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.  
  minval   A Tensor. Must be one of the following types: int32, int64. 0-D. Inclusive lower bound on the generated integers.  
  maxval   A Tensor. Must have the same type as minval. 0-D. Exclusive upper bound on the generated integers.  
  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. Has the same type as minval.
tf.raw_ops.Range Creates a sequence of numbers.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Range  
tf.raw_ops.Range(
    start, limit, delta, name=None
)
 This operation creates a sequence of numbers that begins at start and extends by increments of delta up to but not including limit. For example: # 'start' is 3
# 'limit' is 18
# 'delta' is 3
tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]

 


 Args
  start   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64. 0-D (scalar). First entry in the sequence.  
  limit   A Tensor. Must have the same type as start. 0-D (scalar). Upper limit of sequence, exclusive.  
  delta   A Tensor. Must have the same type as start. 0-D (scalar). Optional. Default is 1. Number that increments start.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as start.
tf.raw_ops.RangeDataset Creates a dataset with a range of values. Corresponds to python's xrange.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RangeDataset  
tf.raw_ops.RangeDataset(
    start, stop, step, output_types, output_shapes, name=None
)

 


 Args
  start   A Tensor of type int64. corresponds to start in python's xrange().  
  stop   A Tensor of type int64. corresponds to stop in python's xrange().  
  step   A Tensor of type int64. corresponds to step in python's xrange().  
  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.Rank Returns the rank of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Rank  
tf.raw_ops.Rank(
    input, name=None
)
 This operation returns an integer representing the rank of input. For example: # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
# shape of tensor 't' is [2, 2, 3]
rank(t) ==> 3


Note: The rank of a tensor is not the same as the rank of a matrix. The rank of a tensor is the number of indices required to uniquely select each element of the tensor. Rank is also known as "order", "degree", or "ndims."

 


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


 Returns   A Tensor of type int32.
tf.raw_ops.ReaderNumRecordsProduced Returns the number of records this Reader has produced.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderNumRecordsProduced  
tf.raw_ops.ReaderNumRecordsProduced(
    reader_handle, name=None
)
 This is the same as the number of ReaderRead executions that have succeeded.
 


 Args
  reader_handle   A Tensor of type mutable string. Handle to a Reader.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.ReaderNumRecordsProducedV2 Returns the number of records this Reader has produced.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderNumRecordsProducedV2  
tf.raw_ops.ReaderNumRecordsProducedV2(
    reader_handle, name=None
)
 This is the same as the number of ReaderRead executions that have succeeded.
 


 Args
  reader_handle   A Tensor of type resource. Handle to a Reader.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.ReaderNumWorkUnitsCompleted Returns the number of work units this Reader has finished processing.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderNumWorkUnitsCompleted  
tf.raw_ops.ReaderNumWorkUnitsCompleted(
    reader_handle, name=None
)

 


 Args
  reader_handle   A Tensor of type mutable string. Handle to a Reader.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.ReaderNumWorkUnitsCompletedV2 Returns the number of work units this Reader has finished processing.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderNumWorkUnitsCompletedV2  
tf.raw_ops.ReaderNumWorkUnitsCompletedV2(
    reader_handle, name=None
)

 


 Args
  reader_handle   A Tensor of type resource. Handle to a Reader.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.ReaderRead Returns the next record (key, value pair) produced by a Reader.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderRead  
tf.raw_ops.ReaderRead(
    reader_handle, queue_handle, name=None
)
 Will dequeue from the input queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file).
 


 Args
  reader_handle   A Tensor of type mutable string. Handle to a Reader.  
  queue_handle   A Tensor of type mutable string. Handle to a Queue, with string work items.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (key, value).     key   A Tensor of type string.  
  value   A Tensor of type string.
tf.raw_ops.ReaderReadUpTo Returns up to num_records (key, value) pairs produced by a Reader.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderReadUpTo  
tf.raw_ops.ReaderReadUpTo(
    reader_handle, queue_handle, num_records, name=None
)
 Will dequeue from the input queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than num_records even before the last batch.
 


 Args
  reader_handle   A Tensor of type mutable string. Handle to a Reader.  
  queue_handle   A Tensor of type mutable string. Handle to a Queue, with string work items.  
  num_records   A Tensor of type int64. number of records to read from Reader.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (keys, values).     keys   A Tensor of type string.  
  values   A Tensor of type string.
tf.raw_ops.ReaderReadUpToV2 Returns up to num_records (key, value) pairs produced by a Reader.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderReadUpToV2  
tf.raw_ops.ReaderReadUpToV2(
    reader_handle, queue_handle, num_records, name=None
)
 Will dequeue from the input queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than num_records even before the last batch.
 


 Args
  reader_handle   A Tensor of type resource. Handle to a Reader.  
  queue_handle   A Tensor of type resource. Handle to a Queue, with string work items.  
  num_records   A Tensor of type int64. number of records to read from Reader.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (keys, values).     keys   A Tensor of type string.  
  values   A Tensor of type string.