neulab/docprompting-conala huggingface dataset:
tf.raw_ops.OptimizeDatasetV2 Creates a dataset by applying related optimizations to input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OptimizeDatasetV2  
tf.raw_ops.OptimizeDatasetV2(
    input_dataset, optimizations_enabled, optimizations_disabled,
    optimizations_default, output_types, output_shapes, optimization_configs=[],
    name=None
)
 Creates a dataset by applying related optimizations to input_dataset.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.  
  optimizations_enabled   A Tensor of type string. A tf.string vector tf.Tensor identifying user enabled optimizations.  
  optimizations_disabled   A Tensor of type string. A tf.string vector tf.Tensor identifying user disabled optimizations.  
  optimizations_default   A Tensor of type string. A tf.string vector tf.Tensor identifying optimizations by default.  
  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.  
  optimization_configs   An optional list of strings. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.OptionalFromValue Constructs an Optional variant from a tuple of tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OptionalFromValue  
tf.raw_ops.OptionalFromValue(
    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.OptionalGetValue Returns the value stored in an Optional variant or raises an error if none exists.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OptionalGetValue  
tf.raw_ops.OptionalGetValue(
    optional, output_types, output_shapes, name=None
)

 


 Args
  optional   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 list of Tensor objects of type output_types.
tf.raw_ops.OptionalHasValue Returns true if and only if the given Optional variant has a value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OptionalHasValue  
tf.raw_ops.OptionalHasValue(
    optional, name=None
)

 


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


 Returns   A Tensor of type bool.
tf.raw_ops.OptionalNone Creates an Optional variant with no value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OptionalNone  
tf.raw_ops.OptionalNone(
    name=None
)

 


 Args
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.OrderedMapClear Op removes all elements in the underlying container.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapClear  
tf.raw_ops.OrderedMapClear(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)

 


 Args
  dtypes   A list of tf.DTypes.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.OrderedMapIncompleteSize Op returns the number of incomplete elements in the underlying container.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapIncompleteSize  
tf.raw_ops.OrderedMapIncompleteSize(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)

 


 Args
  dtypes   A list of tf.DTypes.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  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 int32.
tf.raw_ops.OrderedMapPeek Op peeks at the values at the specified key. If the  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapPeek  
tf.raw_ops.OrderedMapPeek(
    key, indices, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 underlying container does not contain this key this op will block until it does. This Op is optimized for performance.
 


 Args
  key   A Tensor of type int64.  
  indices   A Tensor of type int32.  
  dtypes   A list of tf.DTypes that has length >= 1.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type dtypes.
tf.raw_ops.OrderedMapSize Op returns the number of elements in the underlying container.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapSize  
tf.raw_ops.OrderedMapSize(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)

 


 Args
  dtypes   A list of tf.DTypes.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  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 int32.
tf.raw_ops.OrderedMapStage Stage (key, values) in the underlying container which behaves like a ordered  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapStage  
tf.raw_ops.OrderedMapStage(
    key, indices, values, dtypes, capacity=0, memory_limit=0,
    container='', shared_name='', name=None
)
 associative container. Elements are ordered by key.
 


 Args
  key   A Tensor of type int64. int64  
  indices   A Tensor of type int32.  
  values   A list of Tensor objects. a list of tensors dtypes A list of data types that inserted values should adhere to.  
  dtypes   A list of tf.DTypes.  
  capacity   An optional int that is >= 0. Defaults to 0. Maximum number of elements in the Staging Area. If > 0, inserts on the container will block when the capacity is reached.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  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 "". It is necessary to match this name to the matching Unstage Op.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.OrderedMapUnstage Op removes and returns the values associated with the key  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapUnstage  
tf.raw_ops.OrderedMapUnstage(
    key, indices, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 from the underlying container. If the underlying container does not contain this key, the op will block until it does.
 


 Args
  key   A Tensor of type int64.  
  indices   A Tensor of type int32.  
  dtypes   A list of tf.DTypes that has length >= 1.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type dtypes.
tf.raw_ops.OrderedMapUnstageNoKey Op removes and returns the (key, value) element with the smallest  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapUnstageNoKey  
tf.raw_ops.OrderedMapUnstageNoKey(
    indices, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 key from the underlying container. If the underlying container does not contain elements, the op will block until it does.
 


 Args
  indices   A Tensor of type int32.  
  dtypes   A list of tf.DTypes that has length >= 1.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (key, values).     key   A Tensor of type int64.  
  values   A list of Tensor objects of type dtypes.
tf.raw_ops.OutfeedDequeue Retrieves a single tensor from the computation outfeed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedDequeue  
tf.raw_ops.OutfeedDequeue(
    dtype, shape, device_ordinal=-1, name=None
)
 This operation will block indefinitely until data is available.
 


 Args
  dtype   A tf.DType. The type of elements in the tensor.  
  shape   A tf.TensorShape or list of ints. The shape of the tensor.  
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.OutfeedDequeueTuple Retrieve multiple values from the computation outfeed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedDequeueTuple  
tf.raw_ops.OutfeedDequeueTuple(
    dtypes, shapes, device_ordinal=-1, name=None
)
 This operation will block indefinitely until data is available. Output i corresponds to XLA tuple element i.
 


 Args
  dtypes   A list of tf.DTypes that has length >= 1. The element types of each element in outputs.  
  shapes   A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in outputs.  
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type dtypes.
tf.raw_ops.OutfeedDequeueTupleV2 Retrieve multiple values from the computation outfeed. Device ordinal is a  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedDequeueTupleV2  
tf.raw_ops.OutfeedDequeueTupleV2(
    device_ordinal, dtypes, shapes, name=None
)
 tensor allowing dynamic outfeed. This operation will block indefinitely until data is available. Output i corresponds to XLA tuple element i. Args: device_ordinal: A Tensor of type int32. An int scalar tensor, representing the TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. dtypes: A list of tf.DTypes that has length >= 1. The element types of each element in outputs. shapes: A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in outputs. name: A name for the operation (optional). Returns: A list of Tensor objects of type dtypes.
tf.raw_ops.OutfeedDequeueV2 Retrieves a single tensor from the computation outfeed. Device ordinal is a  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedDequeueV2  
tf.raw_ops.OutfeedDequeueV2(
    device_ordinal, dtype, shape, name=None
)
 tensor allowing dynamic outfeed. This operation will block indefinitely until data is available. Args: device_ordinal: A Tensor of type int32. An int scalar tensor, representing the TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. dtype: A tf.DType. The type of elements in the tensor. shape: A tf.TensorShape or list of ints. The shape of the tensor. name: A name for the operation (optional). Returns: A Tensor of type dtype.
tf.raw_ops.OutfeedEnqueue Enqueue a Tensor on the computation outfeed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedEnqueue  
tf.raw_ops.OutfeedEnqueue(
    input, name=None
)

 


 Args
  input   A Tensor. A tensor that will be inserted into the outfeed queue.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.OutfeedEnqueueTuple Enqueue multiple Tensor values on the computation outfeed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedEnqueueTuple  
tf.raw_ops.OutfeedEnqueueTuple(
    inputs, name=None
)

 


 Args
  inputs   A list of Tensor objects. A list of tensors that will be inserted into the outfeed queue as an XLA tuple.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.Pack Packs a list of N rank-R tensors into one rank-(R+1) tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Pack  
tf.raw_ops.Pack(
    values, axis=0, name=None
)
 Packs the N tensors in values into a tensor with rank one higher than each tensor in values, by packing them along the axis dimension. Given a list of tensors of shape (A, B, C); if axis == 0 then the output tensor will have the shape (N, A, B, C). if axis == 1 then the output tensor will have the shape (A, N, B, C). Etc. For example: # 'x' is [1, 4]
# 'y' is [2, 5]
# 'z' is [3, 6]
pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
 This is the opposite of unpack.
 


 Args
  values   A list of at least 1 Tensor objects with the same type. Must be of same shape and type.  
  axis   An optional int. Defaults to 0. Dimension along which to pack. Negative values wrap around, so the valid range is [-(R+1), R+1).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as values.
tf.raw_ops.Pad Pads a tensor with zeros.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Pad  
tf.raw_ops.Pad(
    input, paddings, name=None
)
 This operation pads a input with zeros according to the paddings you specify. paddings is an integer tensor with shape [Dn, 2], where n is the rank of input. For each dimension D of input, paddings[D, 0] indicates how many zeros to add before the contents of input in that dimension, and paddings[D, 1] indicates how many zeros to add after the contents of input in that dimension. The padded size of each dimension D of the output is: paddings(D, 0) + input.dim_size(D) + paddings(D, 1) For example: # 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
                      [0, 0, 1, 1, 0, 0]
                      [0, 0, 2, 2, 0, 0]
                      [0, 0, 0, 0, 0, 0]]

 


 Args
  input   A Tensor.  
  paddings   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 input.
tf.raw_ops.PaddedBatchDataset Creates a dataset that batches and pads batch_size elements from the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PaddedBatchDataset  
tf.raw_ops.PaddedBatchDataset(
    input_dataset, batch_size, padded_shapes, padding_values, output_shapes,
    name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  batch_size   A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch.  
  padded_shapes   A list of at least 1 Tensor objects with type int64. A list of int64 tensors representing the desired padded shapes of the corresponding output components. These shapes may be partially specified, using -1 to indicate that a particular dimension should be padded to the maximum size of all batch elements.  
  padding_values   A list of Tensor objects. A list of scalars containing the padding value to use for each of the outputs.  
  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.PaddedBatchDatasetV2 Creates a dataset that batches and pads batch_size elements from the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PaddedBatchDatasetV2  
tf.raw_ops.PaddedBatchDatasetV2(
    input_dataset, batch_size, padded_shapes, padding_values, drop_remainder,
    output_shapes, parallel_copy=False, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  batch_size   A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch.  
  padded_shapes   A list of at least 1 Tensor objects with type int64. A list of int64 tensors representing the desired padded shapes of the corresponding output components. These shapes may be partially specified, using -1 to indicate that a particular dimension should be padded to the maximum size of all batch elements.  
  padding_values   A list of Tensor objects. A list of scalars containing the padding value to use for each of the outputs.  
  drop_remainder   A Tensor of type bool. A scalar representing whether the last batch should be dropped in case its size is smaller than desired.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  parallel_copy   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.PaddingFIFOQueue A queue that produces elements in first-in first-out order.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PaddingFIFOQueue  
tf.raw_ops.PaddingFIFOQueue(
    component_types, shapes=[], capacity=-1, container='',
    shared_name='', name=None
)
 Variable-size shapes are allowed by setting the corresponding shape dimensions to 0 in the shape attr. In this case DequeueMany will pad up to the maximum size of any given element in the minibatch. See below for details.
 


 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. Shapes of fixed rank but variable size are allowed by setting any shape dimension to -1. In this case, the inputs' shape may vary along the given dimension, and DequeueMany will pad the given dimension with zeros up to the maximum shape of all elements in the given batch. If the length of this attr is 0, different queue elements may have different ranks and shapes, but 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.  
  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.PaddingFIFOQueueV2 A queue that produces elements in first-in first-out order.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PaddingFIFOQueueV2  
tf.raw_ops.PaddingFIFOQueueV2(
    component_types, shapes=[], capacity=-1, container='',
    shared_name='', name=None
)
 Variable-size shapes are allowed by setting the corresponding shape dimensions to 0 in the shape attr. In this case DequeueMany will pad up to the maximum size of any given element in the minibatch. See below for details.
 


 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. Shapes of fixed rank but variable size are allowed by setting any shape dimension to -1. In this case, the inputs' shape may vary along the given dimension, and DequeueMany will pad the given dimension with zeros up to the maximum shape of all elements in the given batch. If the length of this attr is 0, different queue elements may have different ranks and shapes, but 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.  
  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.PadV2 Pads a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PadV2  
tf.raw_ops.PadV2(
    input, paddings, constant_values, name=None
)
 This operation pads input according to the paddings and constant_values you specify. paddings is an integer tensor with shape [Dn, 2], where n is the rank of input. For each dimension D of input, paddings[D, 0] indicates how many padding values to add before the contents of input in that dimension, and paddings[D, 1] indicates how many padding values to add after the contents of input in that dimension. constant_values is a scalar tensor of the same type as input that indicates the value to use for padding input. The padded size of each dimension D of the output is: paddings(D, 0) + input.dim_size(D) + paddings(D, 1) For example: # 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# 'constant_values' is 0
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
                      [0, 0, 1, 1, 0, 0]
                      [0, 0, 2, 2, 0, 0]
                      [0, 0, 0, 0, 0, 0]]

 


 Args
  input   A Tensor.  
  paddings   A Tensor. Must be one of the following types: int32, int64.  
  constant_values   A Tensor. Must have the same type as input.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.