neulab/docprompting-conala huggingface dataset:
tf.raw_ops.UnsortedSegmentMax   Computes the maximum along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentMax  
tf.raw_ops.UnsortedSegmentMax(
    data, segment_ids, num_segments, name=None
)
 Read the section on segmentation for an explanation of segments. This operator is similar to the unsorted segment sum operator found (here). Instead of computing the sum over segments, it computes the maximum such that: \(output_i = \max_{j...} data[j...]\) where max is over tuples j... such that segment_ids[j...] == i. If the maximum is empty for a given segment ID i, it outputs the smallest possible value for the specific numeric type, output[i] = numeric_limits<T>::lowest(). If the given segment ID i is negative, then the corresponding value is dropped, and will not be included in the result.    For example: c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_max(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 4,  3, 3, 4],
#       [5,  6, 7, 8]]

 


 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape.  
  num_segments   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 data.
tf.raw_ops.UnsortedSegmentMin   Computes the minimum along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentMin  
tf.raw_ops.UnsortedSegmentMin(
    data, segment_ids, num_segments, name=None
)
 Read the section on segmentation for an explanation of segments. This operator is similar to the unsorted segment sum operator found (here). Instead of computing the sum over segments, it computes the minimum such that: \(output_i = \min_{j...} data_[j...]\) where min is over tuples j... such that segment_ids[j...] == i. If the minimum is empty for a given segment ID i, it outputs the largest possible value for the specific numeric type, output[i] = numeric_limits<T>::max(). For example: c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_min(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 1,  2, 2, 1],
#       [5,  6, 7, 8]]
 If the given segment ID i is negative, then the corresponding value is dropped, and will not be included in the result.
 


 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape.  
  num_segments   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 data.
tf.raw_ops.UnsortedSegmentProd   Computes the product along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentProd  
tf.raw_ops.UnsortedSegmentProd(
    data, segment_ids, num_segments, name=None
)
 Read the section on segmentation for an explanation of segments. This operator is similar to the unsorted segment sum operator found (here). Instead of computing the sum over segments, it computes the product of all entries belonging to a segment such that: \(output_i = \prod_{j...} data[j...]\) where the product is over tuples j... such that segment_ids[j...] == i. For example: c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_prod(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 4,  6, 6, 4],
#       [5,  6, 7, 8]]
 If there is no entry for a given segment ID i, it outputs 1. If the given segment ID i is negative, then the corresponding value is dropped, and will not be included in the result.
 


 Args
  data   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.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape.  
  num_segments   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 data.
tf.raw_ops.UnsortedSegmentSum   Computes the sum along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentSum  
tf.raw_ops.UnsortedSegmentSum(
    data, segment_ids, num_segments, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output[i] = \sum_{j...} data[j...]\) where the sum is over tuples j... such that segment_ids[j...] == i. Unlike SegmentSum, segment_ids need not be sorted and need not cover all values in the full range of valid values. If the sum is empty for a given segment ID i, output[i] = 0. If the given segment ID i is negative, the value is dropped and will not be added to the sum of the segment. num_segments should equal the number of distinct segment IDs.    c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_sum(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 5,  5, 5, 5],
#       [5,  6, 7, 8]]

 


 Args
  data   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.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape.  
  num_segments   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 data.
tf.raw_ops.Unstage Op is similar to a lightweight Dequeue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Unstage  
tf.raw_ops.Unstage(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 The basic functionality is similar to dequeue with many fewer capabilities and options. This Op is optimized for performance.
 


 Args
  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.UnwrapDatasetVariant  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnwrapDatasetVariant  
tf.raw_ops.UnwrapDatasetVariant(
    input_handle, name=None
)

 


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


 Returns   A Tensor of type variant.
tf.raw_ops.UpperBound Applies upper_bound(sorted_search_values, values) along each row.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UpperBound  
tf.raw_ops.UpperBound(
    sorted_inputs, values, out_type=tf.dtypes.int32, name=None
)
 Each set of rows with the same index in (sorted_inputs, values) is treated independently. The resulting row is the equivalent of calling np.searchsorted(sorted_inputs, values, side='right'). The result is not a global index to the entire Tensor, but rather just the index in the last dimension. A 2-D example: sorted_sequence = [[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]] values = [[2, 4, 9], [0, 2, 6]] result = UpperBound(sorted_sequence, values) result == [[1, 2, 4], [0, 2, 5]]
 


 Args
  sorted_inputs   A Tensor. 2-D Tensor where each row is ordered.  
  values   A Tensor. Must have the same type as sorted_inputs. 2-D Tensor with the same numbers of rows as sorted_search_values. Contains the values that will be searched for in sorted_search_values.  
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type out_type.
tf.raw_ops.VarHandleOp Creates a handle to a Variable resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.VarHandleOp  
tf.raw_ops.VarHandleOp(
    dtype, shape, container='', shared_name='',
    allowed_devices=[], name=None
)

 


 Args
  dtype   A tf.DType. the type of this variable. Must agree with the dtypes of all ops using this variable.  
  shape   A tf.TensorShape or list of ints. The (possibly partially specified) shape of this variable.  
  container   An optional string. Defaults to "". the container this variable is placed in.  
  shared_name   An optional string. Defaults to "". the name by which this variable is referred to.  
  allowed_devices   An optional list of strings. Defaults to []. DEPRECATED. The allowed devices containing the resource variable. Set when the output ResourceHandle represents a per-replica/partitioned resource variable.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.Variable Use VariableV2 instead.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Variable  
tf.raw_ops.Variable(
    shape, dtype, container='', shared_name='', name=None
)

 


 Args
  shape   A tf.TensorShape or list of ints.  
  dtype   A tf.DType.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor of type dtype.
tf.raw_ops.VariableShape Returns the shape of the variable pointed to by resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.VariableShape  
tf.raw_ops.VariableShape(
    input, out_type=tf.dtypes.int32, name=None
)
 This operation returns a 1-D integer tensor representing the shape of input. For example: # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]

 


 Args
  input   A Tensor of type resource.  
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type out_type.
tf.raw_ops.VariableV2 Holds state in the form of a tensor that persists across steps.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.VariableV2  
tf.raw_ops.VariableV2(
    shape, dtype, container='', shared_name='', name=None
)
 Outputs a ref to the tensor state so it may be read or modified. about sharing states in tensorflow.
 


 Args
  shape   A tf.TensorShape or list of ints. The shape of the variable tensor.  
  dtype   A tf.DType. The type of elements in the variable tensor.  
  container   An optional string. Defaults to "". If non-empty, this variable is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this variable is named in the given bucket with this shared_name. Otherwise, the node name is used instead.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor of type dtype.
tf.raw_ops.VarIsInitializedOp Checks whether a resource handle-based variable has been initialized.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.VarIsInitializedOp  
tf.raw_ops.VarIsInitializedOp(
    resource, name=None
)

 


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


 Returns   A Tensor of type bool.
tf.raw_ops.Where Returns locations of nonzero / true values in a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Where  
tf.raw_ops.Where(
    condition, name=None
)
 This operation returns the coordinates of true elements in condition. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in condition. Indices are output in row-major order. For example: # 'input' tensor is [[True, False]
#                    [True, False]]
# 'input' has two true values, so output has two coordinates.
# 'input' has rank of 2, so coordinates have two indices.
where(input) ==> [[0, 0],
                  [1, 0]]

# `condition` tensor is [[[True, False]
#                     [True, False]]
#                    [[False, True]
#                     [False, True]]
#                    [[False, False]
#                     [False, True]]]
# 'input' has 5 true values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
                  [0, 1, 0],
                  [1, 0, 1],
                  [1, 1, 1],
                  [2, 1, 1]]

# `condition` tensor is [[[1.5,  0.0]
#                     [-0.5, 0.0]]
#                    [[0.0,  0.25]
#                     [0.0,  0.75]]
#                    [[0.0,  0.0]
#                     [0.0,  0.01]]]
# 'input' has 5 nonzero values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
                  [0, 1, 0],
                  [1, 0, 1],
                  [1, 1, 1],
                  [2, 1, 1]]

# `condition` tensor is [[[1.5 + 0.0j, 0.0  + 0.0j]
#                     [0.0 + 0.5j, 0.0  + 0.0j]]
#                    [[0.0 + 0.0j, 0.25 + 1.5j]
#                     [0.0 + 0.0j, 0.75 + 0.0j]]
#                    [[0.0 + 0.0j, 0.0  + 0.0j]
#                     [0.0 + 0.0j, 0.01 + 0.0j]]]
# 'input' has 5 nonzero magnitude values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
                  [0, 1, 0],
                  [1, 0, 1],
                  [1, 1, 1],
                  [2, 1, 1]]

 


 Args
  condition   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, bool.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.While output = input; While (Cond(output)) { output = Body(output) }  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.While  
tf.raw_ops.While(
    input, cond, body, output_shapes=[], parallel_iterations=10, name=None
)

 


 Args
  input   A list of Tensor objects. A list of input tensors whose types are T.  
  cond   A function decorated with @Defun. A function takes 'input' and returns a tensor. If the tensor is a scalar of non-boolean, the scalar is converted to a boolean according to the following rule: if the scalar is a numerical value, non-zero means True and zero means False; if the scalar is a string, non-empty means True and empty means False. If the tensor is not a scalar, non-emptiness means True and False otherwise.  
  body   A function decorated with @Defun. A function that takes a list of tensors and returns another list of tensors. Both lists have the same types as specified by T.  
  output_shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to [].  
  parallel_iterations   An optional int. Defaults to 10.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects. Has the same type as input.
tf.raw_ops.WholeFileReader A Reader that outputs the entire contents of a file as a value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WholeFileReader  
tf.raw_ops.WholeFileReader(
    container='', shared_name='', name=None
)
 To use, enqueue filenames in a Queue. The output of ReaderRead will be a filename (key) and the contents of that file (value).
 


 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.WholeFileReaderV2 A Reader that outputs the entire contents of a file as a value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WholeFileReaderV2  
tf.raw_ops.WholeFileReaderV2(
    container='', shared_name='', name=None
)
 To use, enqueue filenames in a Queue. The output of ReaderRead will be a filename (key) and the contents of that file (value).
 


 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.WindowDataset Combines (nests of) input elements into a dataset of (nests of) windows.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WindowDataset  
tf.raw_ops.WindowDataset(
    input_dataset, size, shift, stride, drop_remainder, output_types, output_shapes,
    name=None
)
 A "window" is a finite dataset of flat elements of size size (or possibly fewer if there are not enough input elements to fill the window and drop_remainder evaluates to false). The shift argument determines the number of input elements by which the window moves on each iteration. The first element in the kth window will be element 1 + (k-1) * shift
 of the input dataset. In particular, the first element of the first window will always be the first element of the input dataset.  If the stride parameter is greater than 1, then each window will skip (stride - 1) input elements between each element that appears in the window. Output windows will still contain size elements regardless of the value of stride. The stride argument determines the stride of the input elements, and the shift argument determines the shift of the window. For example, letting {...} to represent a Dataset:  
tf.data.Dataset.range(7).window(2) produces { {0, 1}, {2, 3}, {4, 5}, {6} }
 
tf.data.Dataset.range(7).window(3, 2, 1, True) produces { {0, 1, 2}, {2, 3, 4}, {4, 5, 6} }
 
tf.data.Dataset.range(7).window(3, 1, 2, True) produces { {0, 2, 4}, {1, 3, 5}, {2, 4, 6} }
  Note that when the window transformation is applied to a dataset of nested elements, it produces a dataset of nested windows. For example:  
tf.data.Dataset.from_tensor_slices((range(4), range(4))).window(2) produces {({0, 1}, {0, 1}), ({2, 3}, {2, 3})}
 
tf.data.Dataset.from_tensor_slices({"a": range(4)}).window(2) produces { {"a": {0, 1} }, {"a": {2, 3} } }
 
 


 Args
  input_dataset   A Tensor of type variant.  
  size   A Tensor of type int64. An integer scalar, representing the number of elements of the input dataset to combine into a window. Must be positive.  
  shift   A Tensor of type int64. An integer scalar, representing the number of input elements by which the window moves in each iteration. Defaults to size. Must be positive.  
  stride   A Tensor of type int64. An integer scalar, representing the stride of the input elements in the sliding window. Must be positive. The default value of 1 means "retain every input element".  
  drop_remainder   A Tensor of type bool. A Boolean scalar, representing whether the last window should be dropped if its size is smaller than window_size.  
  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.WorkerHeartbeat Worker heartbeat op.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WorkerHeartbeat  
tf.raw_ops.WorkerHeartbeat(
    request, name=None
)
 Heartbeats may be sent periodically to indicate the coordinator is still active, to retrieve the current worker status and to expedite shutdown when necessary.
 


 Args
  request   A Tensor of type string. A string tensor containing a serialized WorkerHeartbeatRequest  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.WrapDatasetVariant  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WrapDatasetVariant  
tf.raw_ops.WrapDatasetVariant(
    input_handle, name=None
)

 


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


 Returns   A Tensor of type variant.
tf.raw_ops.WriteAudioSummary Writes an audio summary.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WriteAudioSummary  
tf.raw_ops.WriteAudioSummary(
    writer, step, tag, tensor, sample_rate, max_outputs=3, name=None
)
 Writes encoded audio summary tensor at step with tag using summary writer. sample_rate is the audio sample rate is Hz.
 


 Args
  writer   A Tensor of type resource.  
  step   A Tensor of type int64.  
  tag   A Tensor of type string.  
  tensor   A Tensor of type float32.  
  sample_rate   A Tensor of type float32.  
  max_outputs   An optional int that is >= 1. Defaults to 3.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.WriteFile Writes contents to the file at input filename. Creates file and recursively  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WriteFile  
tf.raw_ops.WriteFile(
    filename, contents, name=None
)
 creates directory if not existing.
 


 Args
  filename   A Tensor of type string. scalar. The name of the file to which we write the contents.  
  contents   A Tensor of type string. scalar. The content to be written to the output file.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.WriteGraphSummary Writes a graph summary.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WriteGraphSummary  
tf.raw_ops.WriteGraphSummary(
    writer, step, tensor, name=None
)
 Writes TensorFlow graph tensor at step using summary writer.
 


 Args
  writer   A Tensor of type resource.  
  step   A Tensor of type int64.  
  tensor   A Tensor of type string.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.WriteHistogramSummary Writes a histogram summary.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.WriteHistogramSummary  
tf.raw_ops.WriteHistogramSummary(
    writer, step, tag, values, name=None
)
 Writes histogram values at step with tag using summary writer.
 


 Args
  writer   A Tensor of type resource.  
  step   A Tensor of type int64.  
  tag   A Tensor of type string.  
  values   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.