neulab/docprompting-conala huggingface dataset:
tf.raw_ops.UnbatchDataset A dataset that splits the elements of its input into multiple elements.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnbatchDataset  
tf.raw_ops.UnbatchDataset(
    input_dataset, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   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 Tensor of type variant.
tf.raw_ops.UnbatchGrad Gradient of Unbatch.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnbatchGrad  
tf.raw_ops.UnbatchGrad(
    original_input, batch_index, grad, id, container='',
    shared_name='', name=None
)
 Acts like Batch but using the given batch_index index of batching things as they become available. This ensures that the gradients are propagated back in the same session which did the forward pass. original_input: The input to the Unbatch operation this is the gradient of. batch_index: The batch_index given to the Unbatch operation this is the gradient of. grad: The downstream gradient. id: The id scalar emitted by Batch. batched_grad: The return value, either an empty tensor or the batched gradient. container: Container to control resource sharing. shared_name: Instances of UnbatchGrad with the same container and shared_name are assumed to possibly belong to the same batch. If left empty, the op name will be used as the shared name.
 


 Args
  original_input   A Tensor.  
  batch_index   A Tensor of type int64.  
  grad   A Tensor. Must have the same type as original_input.  
  id   A Tensor of type int64.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as original_input.
tf.raw_ops.UncompressElement Uncompresses a compressed dataset element.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UncompressElement  
tf.raw_ops.UncompressElement(
    compressed, output_types, output_shapes, name=None
)

 


 Args
  compressed   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.UnicodeDecode Decodes each string in input into a sequence of Unicode code points.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeDecode  
tf.raw_ops.UnicodeDecode(
    input, input_encoding, errors='replace', replacement_char=65533,
    replace_control_characters=False, Tsplits=tf.dtypes.int64, name=None
)
 The character codepoints for all strings are returned using a single vector char_values, with strings expanded to characters in row-major order. The row_splits tensor indicates where the codepoints for each input string begin and end within the char_values tensor. In particular, the values for the ith string (in row-major order) are stored in the slice [row_splits[i]:row_splits[i+1]]. Thus:  
char_values[row_splits[i]+j] is the Unicode codepoint for the jth character in the ith string (in row-major order). 
row_splits[i+1] - row_splits[i] is the number of characters in the ith string (in row-major order). 
 


 Args
  input   A Tensor of type string. The text to be decoded. Can have any shape. Note that the output is flattened to a vector of char values.  
  input_encoding   A string. Text encoding of the input strings. This is any of the encodings supported by ICU ucnv algorithmic converters. Examples: "UTF-16", "US ASCII", "UTF-8".  
  errors   An optional string from: "strict", "replace", "ignore". Defaults to "replace". Error handling policy when there is invalid formatting found in the input. The value of 'strict' will cause the operation to produce a InvalidArgument error on any invalid input formatting. A value of 'replace' (the default) will cause the operation to replace any invalid formatting in the input with the replacement_char codepoint. A value of 'ignore' will cause the operation to skip any invalid formatting in the input and produce no corresponding output character.  
  replacement_char   An optional int. Defaults to 65533. The replacement character codepoint to be used in place of any invalid formatting in the input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character is 0xFFFD or U+65533.)  
  replace_control_characters   An optional bool. Defaults to False. Whether to replace the C0 control characters (00-1F) with the replacement_char. Default is false.  
  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 (row_splits, char_values).     row_splits   A Tensor of type Tsplits.  
  char_values   A Tensor of type int32.
tf.raw_ops.UnicodeDecodeWithOffsets Decodes each string in input into a sequence of Unicode code points.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeDecodeWithOffsets  
tf.raw_ops.UnicodeDecodeWithOffsets(
    input, input_encoding, errors='replace', replacement_char=65533,
    replace_control_characters=False, Tsplits=tf.dtypes.int64, name=None
)
 The character codepoints for all strings are returned using a single vector char_values, with strings expanded to characters in row-major order. Similarly, the character start byte offsets are returned using a single vector char_to_byte_starts, with strings expanded in row-major order. The row_splits tensor indicates where the codepoints and start offsets for each input string begin and end within the char_values and char_to_byte_starts tensors. In particular, the values for the ith string (in row-major order) are stored in the slice [row_splits[i]:row_splits[i+1]]. Thus:  
char_values[row_splits[i]+j] is the Unicode codepoint for the jth character in the ith string (in row-major order). 
char_to_bytes_starts[row_splits[i]+j] is the start byte offset for the jth character in the ith string (in row-major order). 
row_splits[i+1] - row_splits[i] is the number of characters in the ith string (in row-major order). 
 


 Args
  input   A Tensor of type string. The text to be decoded. Can have any shape. Note that the output is flattened to a vector of char values.  
  input_encoding   A string. Text encoding of the input strings. This is any of the encodings supported by ICU ucnv algorithmic converters. Examples: "UTF-16", "US ASCII", "UTF-8".  
  errors   An optional string from: "strict", "replace", "ignore". Defaults to "replace". Error handling policy when there is invalid formatting found in the input. The value of 'strict' will cause the operation to produce a InvalidArgument error on any invalid input formatting. A value of 'replace' (the default) will cause the operation to replace any invalid formatting in the input with the replacement_char codepoint. A value of 'ignore' will cause the operation to skip any invalid formatting in the input and produce no corresponding output character.  
  replacement_char   An optional int. Defaults to 65533. The replacement character codepoint to be used in place of any invalid formatting in the input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character is 0xFFFD or U+65533.)  
  replace_control_characters   An optional bool. Defaults to False. Whether to replace the C0 control characters (00-1F) with the replacement_char. Default is false.  
  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 (row_splits, char_values, char_to_byte_starts).     row_splits   A Tensor of type Tsplits.  
  char_values   A Tensor of type int32.  
  char_to_byte_starts   A Tensor of type int64.
tf.raw_ops.UnicodeEncode Encode a tensor of ints into unicode strings.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeEncode  
tf.raw_ops.UnicodeEncode(
    input_values, input_splits, output_encoding, errors='replace',
    replacement_char=65533, name=None
)
 Returns a vector of strings, where output[i] is constructed by encoding the Unicode codepoints in input_values[input_splits[i]:input_splits[i+1]] using output_encoding. Example: input_values = [72, 101, 108, 108, 111, 87, 111, 114, 108, 100]
input_splits = [0, 5, 10]
output_encoding = 'UTF-8'

output = ['Hello', 'World']

 


 Args
  input_values   A Tensor of type int32. A 1D tensor containing the unicode codepoints that should be encoded.  
  input_splits   A Tensor. Must be one of the following types: int32, int64. A 1D tensor specifying how the unicode codepoints should be split into strings. In particular, output[i] is constructed by encoding the codepoints in the slice input_values[input_splits[i]:input_splits[i+1]].  
  output_encoding   A string from: "UTF-8", "UTF-16-BE", "UTF-32-BE". Unicode encoding of the output strings. Valid encodings are: "UTF-8", "UTF-16-BE", and "UTF-32-BE".  
  errors   An optional string from: "ignore", "replace", "strict". Defaults to "replace". Error handling policy when there is invalid formatting found in the input. The value of 'strict' will cause the operation to produce a InvalidArgument error on any invalid input formatting. A value of 'replace' (the default) will cause the operation to replace any invalid formatting in the input with the replacement_char codepoint. A value of 'ignore' will cause the operation to skip any invalid formatting in the input and produce no corresponding output character.  
  replacement_char   An optional int. Defaults to 65533. The replacement character codepoint to be used in place of any invalid formatting in the input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character is 0xFFFD (U+65533).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.UnicodeScript Determine the script codes of a given tensor of Unicode integer code points.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnicodeScript  
tf.raw_ops.UnicodeScript(
    input, name=None
)
 This operation converts Unicode code points to script codes corresponding to each code point. Script codes correspond to International Components for Unicode (ICU) UScriptCode values. See ICU project docs for more details on script codes. For an example, see the unicode strings guide on unicode scripts. Returns -1 (USCRIPT_INVALID_CODE) for invalid codepoints. Output shape will match input shape. Examples: 
tf.strings.unicode_script([1, 31, 38])
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([0, 0, 0], dtype=int32)>

 


 Args
  input   A Tensor of type int32. A Tensor of int32 Unicode code points.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.UniformCandidateSampler Generates labels for candidate sampling with a uniform distribution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UniformCandidateSampler  
tf.raw_ops.UniformCandidateSampler(
    true_classes, num_true, num_sampled, unique, range_max, seed=0, seed2=0,
    name=None
)
 See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels.
 


 Args
  true_classes   A Tensor of type int64. A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.  
  num_true   An int that is >= 1. Number of true labels per context.  
  num_sampled   An int that is >= 1. Number of candidates to randomly sample.  
  unique   A bool. If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.  
  range_max   An int that is >= 1. The sampler will sample integers from the interval [0, range_max).  
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.  
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count).     sampled_candidates   A Tensor of type int64.  
  true_expected_count   A Tensor of type float32.  
  sampled_expected_count   A Tensor of type float32.
tf.raw_ops.Unique Finds unique elements in a 1-D tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Unique  
tf.raw_ops.Unique(
    x, out_idx=tf.dtypes.int32, name=None
)
 This operation returns a tensor y containing all of the unique elements of x sorted in the same order that they occur in x; x does not need to be sorted. This operation also returns a tensor idx the same size as x that contains the index of each value of x in the unique output y. In other words: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] Examples: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx = unique(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]

# tensor 'x' is [4, 5, 1, 2, 3, 3, 4, 5]
y, idx = unique(x)
y ==> [4, 5, 1, 2, 3]
idx ==> [0, 1, 2, 3, 4, 4, 0, 1]

 


 Args
  x   A Tensor. 1-D.  
  out_idx   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (y, idx).     y   A Tensor. Has the same type as x.  
  idx   A Tensor of type out_idx.
tf.raw_ops.UniqueDataset Creates a dataset that contains the unique elements of input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UniqueDataset  
tf.raw_ops.UniqueDataset(
    input_dataset, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   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 Tensor of type variant.
tf.raw_ops.UniqueV2 Finds unique elements along an axis of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UniqueV2  
tf.raw_ops.UniqueV2(
    x, axis, out_idx=tf.dtypes.int32, name=None
)
 This operation either returns a tensor y containing unique elements along the axis of a tensor. The returned unique elements is sorted in the same order as they occur along axis in x. This operation also returns a tensor idx that is the same size as the number of the elements in x along the axis dimension. It contains the index in the unique output y. In other words, for an 1-D tensor x with `axis = None: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] For example: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx = unique(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
 For an 2-D tensor x with axis = 0: # tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx = unique(x, axis=0)
y ==> [[1, 0, 0],
       [2, 0, 0]]
idx ==> [0, 0, 1]
 For an 2-D tensor x with axis = 1: # tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx = unique(x, axis=1)
y ==> [[1, 0],
       [1, 0],
       [2, 0]]
idx ==> [0, 1, 1]

 


 Args
  x   A Tensor. A Tensor.  
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: None). The axis of the Tensor to find the unique elements.  
  out_idx   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (y, idx).     y   A Tensor. Has the same type as x.  
  idx   A Tensor of type out_idx.
tf.raw_ops.UniqueWithCounts Finds unique elements in a 1-D tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UniqueWithCounts  
tf.raw_ops.UniqueWithCounts(
    x, out_idx=tf.dtypes.int32, name=None
)
 This operation returns a tensor y containing all of the unique elements of x sorted in the same order that they occur in x. This operation also returns a tensor idx the same size as x that contains the index of each value of x in the unique output y. Finally, it returns a third tensor count that contains the count of each element of y in x. In other words: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] For example: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx, count = unique_with_counts(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
count ==> [2, 1, 3, 1, 2]

 


 Args
  x   A Tensor. 1-D.  
  out_idx   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (y, idx, count).     y   A Tensor. Has the same type as x.  
  idx   A Tensor of type out_idx.  
  count   A Tensor of type out_idx.
tf.raw_ops.UniqueWithCountsV2 Finds unique elements along an axis of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UniqueWithCountsV2  
tf.raw_ops.UniqueWithCountsV2(
    x, axis, out_idx=tf.dtypes.int32, name=None
)
 This operation either returns a tensor y containing unique elements along the axis of a tensor. The returned unique elements is sorted in the same order as they occur along axis in x. This operation also returns a tensor idx and a tensor count that are the same size as the number of the elements in x along the axis dimension. The idx contains the index in the unique output y and the count contains the count in the unique output y. In other words, for an 1-D tensor x with `axis = None: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] For example: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx, count = unique_with_counts(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
count ==> [2, 1, 3, 1, 2]
 For an 2-D tensor x with axis = 0: # tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx, count = unique_with_counts(x, axis=0)
y ==> [[1, 0, 0],
       [2, 0, 0]]
idx ==> [0, 0, 1]
count ==> [2, 1]
 For an 2-D tensor x with axis = 1: # tensor 'x' is [[1, 0, 0],
#                [1, 0, 0],
#                [2, 0, 0]]
y, idx, count = unique_with_counts(x, axis=1)
y ==> [[1, 0],
       [1, 0],
       [2, 0]]
idx ==> [0, 1, 1]
count ==> [1, 2]

 


 Args
  x   A Tensor. A Tensor.  
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: None). The axis of the Tensor to find the unique elements.  
  out_idx   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (y, idx, count).     y   A Tensor. Has the same type as x.  
  idx   A Tensor of type out_idx.  
  count   A Tensor of type out_idx.
tf.raw_ops.Unpack Unpacks a given dimension of a rank-R tensor into num rank-(R-1) tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Unpack  
tf.raw_ops.Unpack(
    value, num, axis=0, name=None
)
 Unpacks num tensors from value by chipping it along the axis dimension. For example, given a tensor of shape (A, B, C, D); If axis == 0 then the i'th tensor in output is the slice value[i, :, :, :] and each tensor in output will have shape (B, C, D). (Note that the dimension unpacked along is gone, unlike split). If axis == 1 then the i'th tensor in output is the slice value[:, i, :, :] and each tensor in output will have shape (A, C, D). Etc. This is the opposite of pack.
 


 Args
  value   A Tensor. 1-D or higher, with axis dimension size equal to num.  
  num   An int that is >= 0.  
  axis   An optional int. Defaults to 0. Dimension along which to unpack. Negative values wrap around, so the valid range is [-R, R).  
  name   A name for the operation (optional).   
 


 Returns   A list of num Tensor objects with the same type as value.
tf.raw_ops.UnravelIndex Converts an array of flat indices into a tuple of coordinate arrays.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnravelIndex  
tf.raw_ops.UnravelIndex(
    indices, dims, name=None
)
 Example: y = tf.unravel_index(indices=[2, 5, 7], dims=[3, 3])
# 'dims' represent a hypothetical (3, 3) tensor of indices:
# [[0, 1, *2*],
#  [3, 4, *5*],
#  [6, *7*, 8]]
# For each entry from 'indices', this operation returns
# its coordinates (marked with '*'), such as
# 2 ==> (0, 2)
# 5 ==> (1, 2)
# 7 ==> (2, 1)
y ==> [[0, 1, 2], [2, 2, 1]]

 


 Args
  indices   A Tensor. Must be one of the following types: int32, int64. An 0-D or 1-D int Tensor whose elements are indices into the flattened version of an array of dimensions dims.  
  dims   A Tensor. Must have the same type as indices. An 1-D int Tensor. The shape of the array to use for unraveling indices.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as indices.  
 Numpy Compatibility Equivalent to np.unravel_index
tf.raw_ops.UnsortedSegmentJoin Joins the elements of inputs based on segment_ids.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.UnsortedSegmentJoin  
tf.raw_ops.UnsortedSegmentJoin(
    inputs, segment_ids, num_segments, separator='', name=None
)
 Computes the string join along segments of a tensor. Given segment_ids with rank N and data with rank N+M: `output[i, k1...kM] = strings.join([data[j1...jN, k1...kM])`
 where the join is over all [j1...jN] such that segment_ids[j1...jN] = i. Strings are joined in row-major order. For example: inputs = [['Y', 'q', 'c'], ['Y', '6', '6'], ['p', 'G', 'a']]
output_array = string_ops.unsorted_segment_join(inputs=inputs,
                                                segment_ids=[1, 0, 1],
                                                num_segments=2,
                                                separator=':'))
# output_array ==> [['Y', '6', '6'], ['Y:p', 'q:G', 'c:a']]


inputs = ['this', 'is', 'a', 'test']
output_array = string_ops.unsorted_segment_join(inputs=inputs,
                                                segment_ids=[0, 0, 0, 0],
                                                num_segments=1,
                                                separator=':'))
# output_array ==> ['this:is:a:test']

 


 Args
  inputs   A Tensor of type string. The input to be joined.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape. Negative segment ids are not supported.  
  num_segments   A Tensor. Must be one of the following types: int32, int64. A scalar.  
  separator   An optional string. Defaults to "". The separator to use when joining.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.