neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ParseSingleExample Transforms a tf.Example proto (as a string) into typed tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ParseSingleExample  
tf.raw_ops.ParseSingleExample(
    serialized, dense_defaults, num_sparse, sparse_keys, dense_keys, sparse_types,
    dense_shapes, name=None
)

 


 Args
  serialized   A Tensor of type string. A vector containing a batch of binary serialized Example protos.  
  dense_defaults   A list of Tensor objects with types from: float32, int64, string. A list of Tensors (some may be empty), whose length matches the length of dense_keys. dense_defaults[j] provides default values when the example's feature_map lacks dense_key[j]. If an empty Tensor is provided for dense_defaults[j], then the Feature dense_keys[j] is required. The input type is inferred from dense_defaults[j], even when it's empty. If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, then the shape of dense_defaults[j] must match that of dense_shapes[j]. If dense_shapes[j] has an undefined major dimension (variable strides dense feature), dense_defaults[j] must contain a single element: the padding element.  
  num_sparse   An int that is >= 0. The number of sparse features to be parsed from the example. This must match the lengths of sparse_keys and sparse_types.  
  sparse_keys   A list of strings. A list of num_sparse strings. The keys expected in the Examples' features associated with sparse values.  
  dense_keys   A list of strings. The keys expected in the Examples' features associated with dense values.  
  sparse_types   A list of tf.DTypes from: tf.float32, tf.int64, tf.string. A list of num_sparse types; the data types of data in each Feature given in sparse_keys. Currently the ParseSingleExample op supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList).  
  dense_shapes   A list of shapes (each a tf.TensorShape or list of ints). The shapes of data in each Feature given in dense_keys. The length of this list must match the length of dense_keys. The number of elements in the Feature corresponding to dense_key[j] must always equal dense_shapes[j].NumEntries(). If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output Tensor dense_values[j] will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1, ..., DN), the shape of the output Tensor dense_values[j] will be (M, D1, .., DN), where M is the number of blocks of elements of length D1 * .... * DN, in the input.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (sparse_indices, sparse_values, sparse_shapes, dense_values).     sparse_indices   A list of num_sparse Tensor objects with type int64.  
  sparse_values   A list of Tensor objects of type sparse_types.  
  sparse_shapes   A list of num_sparse Tensor objects with type int64.  
  dense_values   A list of Tensor objects. Has the same type as dense_defaults.
tf.raw_ops.ParseTensor Transforms a serialized tensorflow.TensorProto proto into a Tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ParseTensor  
tf.raw_ops.ParseTensor(
    serialized, out_type, name=None
)

 


 Args
  serialized   A Tensor of type string. A scalar string containing a serialized TensorProto proto.  
  out_type   A tf.DType. The type of the serialized tensor. The provided type must match the type of the serialized tensor and no implicit conversion will take place.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type out_type.
tf.raw_ops.PartitionedCall returns f(inputs), where f's body is placed and partitioned.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PartitionedCall  
tf.raw_ops.PartitionedCall(
    args, Tout, f, config='', config_proto='',
    executor_type='', name=None
)

 


 Args
  args   A list of Tensor objects. A list of input tensors.  
  Tout   A list of tf.DTypes. A list of output types.  
  f   A function decorated with @Defun. A function that takes 'args', a list of tensors, and returns 'output', another list of tensors. Input and output types are specified by 'Tin' and 'Tout'. The function body of f will be placed and partitioned across devices, setting this op apart from the regular Call op.  
  config   An optional string. Defaults to "".  
  config_proto   An optional string. Defaults to "".  
  executor_type   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.Placeholder A placeholder op for a value that will be fed into the computation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Placeholder  
tf.raw_ops.Placeholder(
    dtype, shape=None, name=None
)
 N.B. This operation will fail with an error if it is executed. It is intended as a way to represent a value that will always be fed, and to provide attrs that enable the fed value to be checked at runtime.
 


 Args
  dtype   A tf.DType. The type of elements in the tensor.  
  shape   An optional tf.TensorShape or list of ints. Defaults to None. (Optional) The shape of the tensor. If the shape has 0 dimensions, the shape is unconstrained.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.PlaceholderV2 A placeholder op for a value that will be fed into the computation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PlaceholderV2  
tf.raw_ops.PlaceholderV2(
    dtype, shape, name=None
)
 N.B. This operation will fail with an error if it is executed. It is intended as a way to represent a value that will always be fed, and to provide attrs that enable the fed value to be checked at runtime.
 


 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. The shape can be any partially-specified shape. To be unconstrained, pass in a shape with unknown rank.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.PlaceholderWithDefault A placeholder op that passes through input when its output is not fed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PlaceholderWithDefault  
tf.raw_ops.PlaceholderWithDefault(
    input, shape, name=None
)

 


 Args
  input   A Tensor. The default value to produce when output is not fed.  
  shape   A tf.TensorShape or list of ints. The (possibly partial) shape of the tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Polygamma   Compute the polygamma function \(\psi^{(n)}(x)\).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Polygamma  
tf.raw_ops.Polygamma(
    a, x, name=None
)
 The polygamma function is defined as: \(\psi^{(a)}(x) = \frac{d^a}{dx^a} \psi(x)\) where \(\psi(x)\) is the digamma function. The polygamma function is defined only for non-negative integer orders \a\.
 


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


 Returns   A Tensor. Has the same type as a.
tf.raw_ops.PopulationCount Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PopulationCount  
tf.raw_ops.PopulationCount(
    x, name=None
)
 For each entry in x, calculates the number of 1 (on) bits in the binary representation of that entry. 
Note: It is more efficient to first tf.bitcast your tensors into int32 or int64 and perform the bitcount on the result, than to feed in 8- or 16-bit inputs and then aggregate the resulting counts.

 


 Args
  x   A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, uint32, uint64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type uint8.
tf.raw_ops.Pow   Computes the power of one value to another.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Pow  
tf.raw_ops.Pow(
    x, y, name=None
)
 Given a tensor x and a tensor y, this operation computes \(x^y\) for corresponding elements in x and y. For example: # tensor 'x' is [[2, 2]], [3, 3]]
# tensor 'y' is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]

 


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


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.PrefetchDataset Creates a dataset that asynchronously prefetches elements from input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PrefetchDataset  
tf.raw_ops.PrefetchDataset(
    input_dataset, buffer_size, output_types, output_shapes, slack_period=0,
    legacy_autotune=True, buffer_size_min=0, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  buffer_size   A Tensor of type int64. The maximum number of elements to buffer in an iterator over this dataset.  
  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.  
  slack_period   An optional int. Defaults to 0.  
  legacy_autotune   An optional bool. Defaults to True.  
  buffer_size_min   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.Prelinearize An op which linearizes one Tensor value to an opaque variant tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Prelinearize  
tf.raw_ops.Prelinearize(
    input, shape=[], layout=[], name=None
)

 


 Args
  input   A Tensor. A tensor that will be linearized.  
  shape   An optional tf.TensorShape or list of ints. Defaults to []. The shape of the tensor.  
  layout   An optional list of ints. Defaults to []. A vector holding the requested layout in minor-to-major sequence. If a layout attribute is passed but its values are all -1 the layout will be computed by the infeed operation.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.PrelinearizeTuple An op which linearizes multiple Tensor values to an opaque variant tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PrelinearizeTuple  
tf.raw_ops.PrelinearizeTuple(
    inputs, shapes, layouts=[], name=None
)

 


 Args
  inputs   A list of Tensor objects. A list of tensors that will be provided using the infeed mechanism.  
  shapes   A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in inputs.  
  layouts   An optional list of ints. Defaults to []. A vector holding the requested layout in minor-to-major sequence for all the tuple shapes in the order the shapes appear in the "shapes" input. The layout elements for a sub-shape can be set to -1 in which case the corresponding layout will be computed by the infeed operation.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.PreventGradient An identity op that triggers an error if a gradient is requested.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PreventGradient  
tf.raw_ops.PreventGradient(
    input, message='', name=None
)
 When executed in a graph, this op outputs its input tensor as-is. When building ops to compute gradients, the TensorFlow gradient system will return an error when trying to lookup the gradient of this op, because no gradient must ever be registered for this function. This op exists to prevent subtle bugs from silently returning unimplemented gradients in some corner cases.
 


 Args
  input   A Tensor. any tensor.  
  message   An optional string. Defaults to "". Will be printed in the error when anyone tries to differentiate this operation.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Print Prints a list of tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Print  
tf.raw_ops.Print(
    input, data, message='', first_n=-1, summarize=3, name=None
)
 Passes input through to output and prints data when evaluating.
 


 Args
  input   A Tensor. The tensor passed to output  
  data   A list of Tensor objects. A list of tensors to print out when op is evaluated.  
  message   An optional string. Defaults to "". A string, prefix of the error message.  
  first_n   An optional int. Defaults to -1. Only log first_n number of times. -1 disables logging.  
  summarize   An optional int. Defaults to 3. Only print this many entries of each tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.PrintV2 Prints a string scalar.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PrintV2  
tf.raw_ops.PrintV2(
    input, output_stream='stderr', end='\n', name=None
)
 Prints a string scalar to the desired output_stream.
 


 Args
  input   A Tensor of type string. The string scalar to print.  
  output_stream   An optional string. Defaults to "stderr". A string specifying the output stream or logging level to print to.  
  end   An optional string. Defaults to "\n".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.PriorityQueue A queue that produces elements sorted by the first component value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PriorityQueue  
tf.raw_ops.PriorityQueue(
    shapes, component_types=[], capacity=-1, container='',
    shared_name='', name=None
)
 Note that the PriorityQueue requires the first component of any element to be a scalar int64, in addition to the other elements declared by component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue and DequeueMany) on a PriorityQueue will all require (resp. output) one extra entry in their input (resp. output) lists.
 


 Args
  shapes   A list of shapes (each a tf.TensorShape or list of ints). 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.  
  component_types   An optional list of tf.DTypes. Defaults to []. The type of each component in a value.  
  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.PriorityQueueV2 A queue that produces elements sorted by the first component value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PriorityQueueV2  
tf.raw_ops.PriorityQueueV2(
    shapes, component_types=[], capacity=-1, container='',
    shared_name='', name=None
)
 Note that the PriorityQueue requires the first component of any element to be a scalar int64, in addition to the other elements declared by component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue and DequeueMany) on a PriorityQueue will all require (resp. output) one extra entry in their input (resp. output) lists.
 


 Args
  shapes   A list of shapes (each a tf.TensorShape or list of ints). 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.  
  component_types   An optional list of tf.DTypes. Defaults to []. The type of each component in a value.  
  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.PrivateThreadPoolDataset Creates a dataset that uses a custom thread pool to compute input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PrivateThreadPoolDataset  
tf.raw_ops.PrivateThreadPoolDataset(
    input_dataset, num_threads, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  num_threads   A Tensor of type int64. Identifies the number of threads to use for the private threadpool.  
  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.Prod Computes the product of elements across dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Prod  
tf.raw_ops.Prod(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.
 


 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 tensor to reduce.  
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).  
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.PyFunc Invokes a python function to compute func(input)->output.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PyFunc  
tf.raw_ops.PyFunc(
    input, token, Tout, name=None
)
 This operation is considered stateful. For a stateless version, see PyFuncStateless.
 


 Args
  input   A list of Tensor objects. List of Tensors that will provide input to the Op.  
  token   A string. A token representing a registered python function in this address space.  
  Tout   A list of tf.DTypes. Data types of the outputs from the op. The length of the list specifies the number of outputs.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.PyFuncStateless A stateless version of PyFunc.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.PyFuncStateless  
tf.raw_ops.PyFuncStateless(
    input, token, Tout, name=None
)

 


 Args
  input   A list of Tensor objects.  
  token   A string.  
  Tout   A list of tf.DTypes.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.Qr Computes the QR decompositions of one or more matrices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Qr  
tf.raw_ops.Qr(
    input, full_matrices=False, name=None
)
 Computes the QR decomposition of each inner matrix in tensor such that tensor[..., :, :] = q[..., :, :] * r[..., :,:]) Currently, the gradient for the QR decomposition is well-defined only when the first P columns of the inner matrix are linearly independent, where P is the minimum of M and N, the 2 inner-most dimmensions of tensor. # a is a tensor.
# q is a tensor of orthonormal matrices.
# r is a tensor of upper triangular matrices.
q, r = qr(a)
q_full, r_full = qr(a, full_matrices=True)

 


 Args
  input   A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. A tensor of shape [..., M, N] whose inner-most 2 dimensions form matrices of size [M, N]. Let P be the minimum of M and N.  
  full_matrices   An optional bool. Defaults to False. If true, compute full-sized q and r. If false (the default), compute only the leading P columns of q.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (q, r).     q   A Tensor. Has the same type as input.  
  r   A Tensor. Has the same type as input.
tf.raw_ops.QuantizeAndDequantize Use QuantizeAndDequantizeV2 instead.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizeAndDequantize  
tf.raw_ops.QuantizeAndDequantize(
    input, signed_input=True, num_bits=8, range_given=False, input_min=0,
    input_max=0, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  signed_input   An optional bool. Defaults to True.  
  num_bits   An optional int. Defaults to 8.  
  range_given   An optional bool. Defaults to False.  
  input_min   An optional float. Defaults to 0.  
  input_max   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.QuantizeAndDequantizeV3 Quantizes then dequantizes a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizeAndDequantizeV3  
tf.raw_ops.QuantizeAndDequantizeV3(
    input, input_min, input_max, num_bits, signed_input=True, range_given=True,
    narrow_range=False, axis=-1, name=None
)
 This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a tensor, so its value can change during training.
 


 Args
  input   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  input_min   A Tensor. Must have the same type as input.  
  input_max   A Tensor. Must have the same type as input.  
  num_bits   A Tensor of type int32.  
  signed_input   An optional bool. Defaults to True.  
  range_given   An optional bool. Defaults to True.  
  narrow_range   An optional bool. Defaults to False.  
  axis   An optional int. Defaults to -1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.