neulab/docprompting-conala huggingface dataset:
tf.raw_ops.QueueDequeueUpToV2 Dequeues n tuples of one or more tensors from the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueDequeueUpToV2  
tf.raw_ops.QueueDequeueUpToV2(
    handle, n, component_types, timeout_ms=-1, name=None
)
 This operation is not supported by all queues. If a queue does not support DequeueUpTo, then an Unimplemented error is returned. If the queue is closed and there are more than 0 but less than n elements remaining, then instead of returning an OutOfRange error like QueueDequeueMany, less than n elements are returned immediately. If the queue is closed and there are 0 elements left in the queue, then an OutOfRange error is returned just like in QueueDequeueMany. Otherwise the behavior is identical to QueueDequeueMany: This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple.
 


 Args
  handle   A Tensor of type resource. The handle to a queue.  
  n   A Tensor of type int32. The number of tuples to dequeue.  
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a tuple.  
  timeout_ms   An optional int. Defaults to -1. If the queue has fewer than n elements, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type component_types.
tf.raw_ops.QueueDequeueV2 Dequeues a tuple of one or more tensors from the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueDequeueV2  
tf.raw_ops.QueueDequeueV2(
    handle, component_types, timeout_ms=-1, name=None
)
 This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple. N.B. If the queue is empty, this operation will block until an element has been dequeued (or 'timeout_ms' elapses, if specified).
 


 Args
  handle   A Tensor of type resource. The handle to a queue.  
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a tuple.  
  timeout_ms   An optional int. Defaults to -1. If the queue is empty, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type component_types.
tf.raw_ops.QueueEnqueue Enqueues a tuple of one or more tensors in the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueEnqueue  
tf.raw_ops.QueueEnqueue(
    handle, components, timeout_ms=-1, name=None
)
 The components input has k elements, which correspond to the components of tuples stored in the given queue. N.B. If the queue is full, this operation will block until the given element has been enqueued (or 'timeout_ms' elapses, if specified).
 


 Args
  handle   A Tensor of type mutable string. The handle to a queue.  
  components   A list of Tensor objects. One or more tensors from which the enqueued tensors should be taken.  
  timeout_ms   An optional int. Defaults to -1. If the queue is full, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.QueueEnqueueMany Enqueues zero or more tuples of one or more tensors in the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueEnqueueMany  
tf.raw_ops.QueueEnqueueMany(
    handle, components, timeout_ms=-1, name=None
)
 This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tuple components must have the same size in the 0th dimension. The components input has k elements, which correspond to the components of tuples stored in the given queue. N.B. If the queue is full, this operation will block until the given elements have been enqueued (or 'timeout_ms' elapses, if specified).
 


 Args
  handle   A Tensor of type mutable string. The handle to a queue.  
  components   A list of Tensor objects. One or more tensors from which the enqueued tensors should be taken.  
  timeout_ms   An optional int. Defaults to -1. If the queue is too full, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.QueueEnqueueManyV2 Enqueues zero or more tuples of one or more tensors in the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueEnqueueManyV2  
tf.raw_ops.QueueEnqueueManyV2(
    handle, components, timeout_ms=-1, name=None
)
 This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tuple components must have the same size in the 0th dimension. The components input has k elements, which correspond to the components of tuples stored in the given queue. N.B. If the queue is full, this operation will block until the given elements have been enqueued (or 'timeout_ms' elapses, if specified).
 


 Args
  handle   A Tensor of type resource. The handle to a queue.  
  components   A list of Tensor objects. One or more tensors from which the enqueued tensors should be taken.  
  timeout_ms   An optional int. Defaults to -1. If the queue is too full, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.QueueEnqueueV2 Enqueues a tuple of one or more tensors in the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueEnqueueV2  
tf.raw_ops.QueueEnqueueV2(
    handle, components, timeout_ms=-1, name=None
)
 The components input has k elements, which correspond to the components of tuples stored in the given queue. N.B. If the queue is full, this operation will block until the given element has been enqueued (or 'timeout_ms' elapses, if specified).
 


 Args
  handle   A Tensor of type resource. The handle to a queue.  
  components   A list of Tensor objects. One or more tensors from which the enqueued tensors should be taken.  
  timeout_ms   An optional int. Defaults to -1. If the queue is full, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.QueueIsClosed Returns true if queue is closed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueIsClosed  
tf.raw_ops.QueueIsClosed(
    handle, name=None
)
 This operation returns true if the queue is closed and false if the queue is open.
 


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


 Returns   A Tensor of type bool.
tf.raw_ops.QueueIsClosedV2 Returns true if queue is closed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueIsClosedV2  
tf.raw_ops.QueueIsClosedV2(
    handle, name=None
)
 This operation returns true if the queue is closed and false if the queue is open.
 


 Args
  handle   A Tensor of type resource. The handle to a queue.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.QueueSize Computes the number of elements in the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueSize  
tf.raw_ops.QueueSize(
    handle, name=None
)

 


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


 Returns   A Tensor of type int32.
tf.raw_ops.QueueSizeV2 Computes the number of elements in the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueSizeV2  
tf.raw_ops.QueueSizeV2(
    handle, name=None
)

 


 Args
  handle   A Tensor of type resource. The handle to a queue.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.RaggedBincount Counts the number of occurrences of each value in an integer array.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedBincount  
tf.raw_ops.RaggedBincount(
    splits, values, size, weights, binary_output=False, name=None
)
 Outputs a vector with length size and the same dtype as weights. If weights are empty, then index i stores the number of times the value i is counted in arr. If weights are non-empty, then index i stores the sum of the value in weights at each index where the corresponding value in arr is i. Values in arr outside of the range [0, size) are ignored.
 


 Args
  splits   A Tensor of type int64. 1D int64 Tensor.  
  values   A Tensor. Must be one of the following types: int32, int64. 2D int Tensor.  
  size   A Tensor. Must have the same type as values. non-negative int scalar Tensor.  
  weights   A Tensor. Must be one of the following types: int32, int64, float32, float64. is an int32, int64, float32, or float64 Tensor with the same shape as input, or a length-0 Tensor, in which case it acts as all weights equal to 1.  
  binary_output   An optional bool. Defaults to False. bool; Whether the kernel should count the appearance or number of occurrences.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as weights.
tf.raw_ops.RaggedCountSparseOutput Performs sparse-output bin counting for a ragged tensor input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedCountSparseOutput  
tf.raw_ops.RaggedCountSparseOutput(
    splits, values, weights, binary_output, minlength=-1, maxlength=-1, name=None
)
 Counts the number of times each value occurs in the input.
 


 Args
  splits   A Tensor of type int64. Tensor containing the row splits of the ragged tensor to count.  
  values   A Tensor. Must be one of the following types: int32, int64. Tensor containing values of the sparse tensor to count.  
  weights   A Tensor. Must be one of the following types: int32, int64, float32, float64. A Tensor of the same shape as indices containing per-index weight values. May also be the empty tensor if no weights are used.  
  binary_output   A bool. Whether to output the number of occurrences of each value or 1.  
  minlength   An optional int that is >= -1. Defaults to -1. Minimum value to count. Can be set to -1 for no minimum.  
  maxlength   An optional int that is >= -1. Defaults to -1. Maximum value to count. Can be set to -1 for no maximum.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_indices, output_values, output_dense_shape).     output_indices   A Tensor of type int64.  
  output_values   A Tensor. Has the same type as weights.  
  output_dense_shape   A Tensor of type int64.
tf.raw_ops.RaggedCross Generates a feature cross from a list of tensors, and returns it as a  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedCross  
tf.raw_ops.RaggedCross(
    ragged_values, ragged_row_splits, sparse_indices, sparse_values, sparse_shape,
    dense_inputs, input_order, hashed_output, num_buckets, hash_key,
    out_values_type, out_row_splits_type, name=None
)
 RaggedTensor. See tf.ragged.cross for more details. Args: ragged_values: A list of Tensor objects with types from: int64, string. The values tensor for each RaggedTensor input. ragged_row_splits: A list of Tensor objects with types from: int32, int64. The row_splits tensor for each RaggedTensor input. sparse_indices: A list of Tensor objects with type int64. The indices tensor for each SparseTensor input. sparse_values: A list of Tensor objects with types from: int64, string. The values tensor for each SparseTensor input. sparse_shape: A list with the same length as sparse_indices of Tensor objects with type int64. The dense_shape tensor for each SparseTensor input. dense_inputs: A list of Tensor objects with types from: int64, string. The tf.Tensor inputs. input_order: A string. String specifying the tensor type for each input. The ith character in this string specifies the type of the ith input, and is one of: 'R' (ragged), 'D' (dense), or 'S' (sparse). This attr is used to ensure that the crossed values are combined in the order of the inputs from the call to tf.ragged.cross. hashed_output: A bool. num_buckets: An int that is >= 0. hash_key: An int. out_values_type: A tf.DType from: tf.int64, tf.string. out_row_splits_type: A tf.DType from: tf.int32, tf.int64. name: A name for the operation (optional). Returns: A tuple of Tensor objects (output_values, output_row_splits). output_values: A `Tensor` of type `out_values_type`.
output_row_splits: A `Tensor` of type `out_row_splits_type`.
tf.raw_ops.RaggedGather Gather ragged slices from params axis 0 according to indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedGather  
tf.raw_ops.RaggedGather(
    params_nested_splits, params_dense_values, indices, OUTPUT_RAGGED_RANK,
    name=None
)
 Outputs a RaggedTensor output composed from output_dense_values and output_nested_splits, such that: output.shape = indices.shape + params.shape[1:]
output.ragged_rank = indices.shape.ndims + params.ragged_rank
output[i...j, d0...dn] = params[indices[i...j], d0...dn]
 where  
params = ragged.from_nested_row_splits(params_dense_values, params_nested_splits) provides the values that should be gathered. 
indices ia a dense tensor with dtype int32 or int64, indicating which values should be gathered. 
output = ragged.from_nested_row_splits(output_dense_values, output_nested_splits) is the output tensor.  (Note: This c++ op is used to implement the higher-level python tf.ragged.gather op, which also supports ragged indices.)
 


 Args
  params_nested_splits   A list of at least 1 Tensor objects with the same type in: int32, int64. The nested_row_splits tensors that define the row-partitioning for the params RaggedTensor input.  
  params_dense_values   A Tensor. The flat_values for the params RaggedTensor. There was a terminology change at the python level from dense_values to flat_values, so dense_values is the deprecated name.  
  indices   A Tensor. Must be one of the following types: int32, int64. Indices in the outermost dimension of params of the values that should be gathered.  
  OUTPUT_RAGGED_RANK   An int that is >= 0. The ragged rank of the output RaggedTensor. output_nested_splits will contain this number of row_splits tensors. This value should equal indices.shape.ndims + params.ragged_rank - 1.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_nested_splits, output_dense_values).     output_nested_splits   A list of OUTPUT_RAGGED_RANK Tensor objects with the same type as params_nested_splits.  
  output_dense_values   A Tensor. Has the same type as params_dense_values.
tf.raw_ops.RaggedRange Returns a RaggedTensor containing the specified sequences of numbers.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedRange  
tf.raw_ops.RaggedRange(
    starts, limits, deltas, Tsplits=tf.dtypes.int64, name=None
)
 Returns a RaggedTensor result composed from rt_dense_values and rt_nested_splits, such that result[i] = range(starts[i], limits[i], deltas[i]). (rt_nested_splits, rt_dense_values) = ragged_range(
      starts=[2, 5, 8], limits=[3, 5, 12], deltas=1)
result = tf.ragged.from_row_splits(rt_dense_values, rt_nested_splits)
print(result)
<tf.RaggedTensor [[2], [], [8, 9, 10, 11]] >
 The input tensors starts, limits, and deltas may be scalars or vectors. The vector inputs must all have the same size. Scalar inputs are broadcast to match the size of the vector inputs.
 


 Args
  starts   A Tensor. Must be one of the following types: bfloat16, float32, float64, int32, int64. The starts of each range.  
  limits   A Tensor. Must have the same type as starts. The limits of each range.  
  deltas   A Tensor. Must have the same type as starts. The deltas of each range.  
  Tsplits   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (rt_nested_splits, rt_dense_values).     rt_nested_splits   A Tensor of type Tsplits.  
  rt_dense_values   A Tensor. Has the same type as starts.
tf.raw_ops.RaggedTensorFromVariant Decodes a variant Tensor into a RaggedTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedTensorFromVariant  
tf.raw_ops.RaggedTensorFromVariant(
    encoded_ragged, input_ragged_rank, output_ragged_rank, Tvalues,
    Tsplits=tf.dtypes.int64, name=None
)
 Decodes the given variant Tensor and returns a RaggedTensor. The input could be a scalar, meaning it encodes a single RaggedTensor with ragged_rank output_ragged_rank. It could also have an arbitrary rank, in which case each element is decoded into a RaggedTensor with ragged_rank input_ragged_rank and these are then stacked according to the input shape to output a single RaggedTensor with ragged_rank output_ragged_rank. Each variant element in the input Tensor is decoded by retrieving from the element a 1-D variant Tensor with input_ragged_rank + 1 Tensors, corresponding to the splits and values of the decoded RaggedTensor. If input_ragged_rank is -1, then it is inferred as output_ragged_rank - rank(encoded_ragged). See RaggedTensorToVariant for the corresponding encoding logic.
 


 Args
  encoded_ragged   A Tensor of type variant. A variant Tensor containing encoded RaggedTensors.  
  input_ragged_rank   An int that is >= -1. The ragged rank of each encoded RaggedTensor component in the input. If set to -1, this is inferred as output_ragged_rank - rank(encoded_ragged)  
  output_ragged_rank   An int that is >= 0. The expected ragged rank of the output RaggedTensor. The following must hold: output_ragged_rank = rank(encoded_ragged) + input_ragged_rank.  
  Tvalues   A tf.DType.  
  Tsplits   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_nested_splits, output_dense_values).     output_nested_splits   A list of output_ragged_rank Tensor objects with type Tsplits.  
  output_dense_values   A Tensor of type Tvalues.
tf.raw_ops.RaggedTensorToSparse Converts a RaggedTensor into a SparseTensor with the same values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedTensorToSparse  
tf.raw_ops.RaggedTensorToSparse(
    rt_nested_splits, rt_dense_values, name=None
)
 input=ragged.from_nested_row_splits(rt_dense_values, rt_nested_splits) output=SparseTensor(indices=sparse_indices, values=sparse_values, dense_shape=sparse_dense_shape)
 


 Args
  rt_nested_splits   A list of at least 1 Tensor objects with the same type in: int32, int64. The row_splits for the RaggedTensor.  
  rt_dense_values   A Tensor. The flat_values for the RaggedTensor.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (sparse_indices, sparse_values, sparse_dense_shape).     sparse_indices   A Tensor of type int64.  
  sparse_values   A Tensor. Has the same type as rt_dense_values.  
  sparse_dense_shape   A Tensor of type int64.
tf.raw_ops.RaggedTensorToTensor Create a dense tensor from a ragged tensor, possibly altering its shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RaggedTensorToTensor  
tf.raw_ops.RaggedTensorToTensor(
    shape, values, default_value, row_partition_tensors, row_partition_types,
    name=None
)
 The ragged_to_dense op creates a dense tensor from a list of row partition tensors, a value vector, and default values. If the shape is unspecified, the minimal shape required to contain all the elements in the ragged tensor (the natural shape) will be used. If some dimensions are left unspecified, then the size of the natural shape is used in that dimension. The default_value will be broadcast to the output shape. After that, the values from the ragged tensor overwrite the default values. Note that the default_value must have less dimensions than the value. The row partition tensors are in the order of the dimensions. At present, the types can be:  "ROW_SPLITS": the row_splits tensor from the ragged tensor. "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor. "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it is preceded by "FIRST_DIM_SIZE". 
 


 Args
  shape   A Tensor. Must be one of the following types: int64, int32. The desired shape of the the output tensor. If left unspecified (empty), the minimal shape required to contain all the elements in the ragged tensor (the natural shape) will be used. If some dimensions are left unspecified, then the size of the natural shape is used in that dimension. Note that dense dimensions cannot be modified by the shape argument. Trying to change the size of a dense dimension will cause the op to fail. Examples: natural shape: [4, 5, 6] shape: -1 output shape: [4, 5, 6] natural shape: [4, 5, 6] shape: [3, -1, 2] output shape: [3, 5, 2] natural shape: [4, 5, 6] shape: [3, 7, 2] output shape: [3, 7, 2] 
 
  values   A Tensor. A 1D tensor representing the values of the ragged tensor.  
  default_value   A Tensor. Must have the same type as values. The default_value when the shape is larger than the ragged tensor. The default_value is broadcast until it is the shape of the output tensor, and then overwritten by values in the ragged tensor. The default value must be compatible with this broadcast operation, and must have fewer dimensions than the value tensor.  
  row_partition_tensors   A list of at least 1 Tensor objects with the same type in: int64, int32.  
  row_partition_types   A list of strings. The types of the row partition tensors. At present, these can be:  "ROW_SPLITS": the row_splits tensor from the ragged tensor. "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor. "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it is preceeded by "FIRST_DIM_SIZE". The tensors are in the order of the dimensions. 

 
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as values.