neulab/docprompting-conala huggingface dataset:
tf.raw_ops.InplaceUpdate Updates specified rows 'i' with values 'v'.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InplaceUpdate  
tf.raw_ops.InplaceUpdate(
    x, i, v, name=None
)
 Computes x[i, :] = v; return x. Originally this function is mutative however for compilation we make this operation create / operate on a copy of x.
 


 Args
  x   A Tensor. A tensor of type T.  
  i   A Tensor of type int32. A vector. Indices into the left-most dimension of x.  
  v   A Tensor. Must have the same type as x. A Tensor of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.InterleaveDataset Creates a dataset that applies f to the outputs of input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InterleaveDataset  
tf.raw_ops.InterleaveDataset(
    input_dataset, other_arguments, cycle_length, block_length, f, output_types,
    output_shapes, name=None
)
 Unlike MapDataset, the f in InterleaveDataset is expected to return a Dataset variant, and InterleaveDataset will flatten successive results into a single Dataset. Unlike FlatMapDataset, InterleaveDataset will interleave sequences of up to block_length consecutive elements from cycle_length input elements.
 


 Args
  input_dataset   A Tensor of type variant.  
  other_arguments   A list of Tensor objects.  
  cycle_length   A Tensor of type int64.  
  block_length   A Tensor of type int64.  
  f   A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.  
  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.InTopK   Says whether the targets are in the top K predictions.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InTopK  
tf.raw_ops.InTopK(
    predictions, targets, k, name=None
)
 This outputs a batch_size bool array, an entry out[i] is true if the prediction for the target class is among the top k predictions among all predictions for example i. Note that the behavior of InTopK differs from the TopK op in its handling of ties; if multiple classes have the same prediction value and straddle the top-k boundary, all of those classes are considered to be in the top k. More formally, let \(predictions_i\) be the predictions for all classes for example i, \(targets_i\) be the target class for example i, \(out_i\) be the output for example i,  $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ 
 


 Args
  predictions   A Tensor of type float32. A batch_size x classes tensor.  
  targets   A Tensor. Must be one of the following types: int32, int64. A batch_size vector of class ids.  
  k   An int. Number of top elements to look at for computing precision.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.InTopKV2   Says whether the targets are in the top K predictions.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InTopKV2  
tf.raw_ops.InTopKV2(
    predictions, targets, k, name=None
)
 This outputs a batch_size bool array, an entry out[i] is true if the prediction for the target class is among the top k predictions among all predictions for example i. Note that the behavior of InTopK differs from the TopK op in its handling of ties; if multiple classes have the same prediction value and straddle the top-k boundary, all of those classes are considered to be in the top k. More formally, let \(predictions_i\) be the predictions for all classes for example i, \(targets_i\) be the target class for example i, \(out_i\) be the output for example i,  $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ 
 


 Args
  predictions   A Tensor of type float32. A batch_size x classes tensor.  
  targets   A Tensor. Must be one of the following types: int32, int64. A batch_size vector of class ids.  
  k   A Tensor. Must have the same type as targets. Number of top elements to look at for computing precision.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.Inv   Computes the reciprocal of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Inv  
tf.raw_ops.Inv(
    x, name=None
)
 I.e., \(y = 1 / x\).
 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Invert Invert (flip) each bit of supported types; for example, type uint8 value 01010101 becomes 10101010.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Invert  
tf.raw_ops.Invert(
    x, name=None
)
 Flip each bit of supported types. For example, type int8 (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101. This operation is performed on each element of the tensor argument x. Example: import tensorflow as tf
from tensorflow.python.ops import bitwise_ops

# flip 2 (00000010) to -3 (11111101)
tf.assert_equal(-3, bitwise_ops.invert(2))

dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
              dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]

inputs = [0, 5, 3, 14]
for dtype in dtype_list:
  # Because of issues with negative numbers, let's test this indirectly.
  # 1. invert(a) and a = 0
  # 2. invert(a) or a = invert(0)
  input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
  not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
                                      input_tensor, bitwise_ops.invert(input_tensor)),
                                    bitwise_ops.bitwise_or(
                                      input_tensor, bitwise_ops.invert(input_tensor)),
                                    bitwise_ops.invert(
                                      tf.constant(0, dtype=dtype))]

  expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
  tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)

  expected = tf.cast([not_0] * 4, tf.float32)
  tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)

  # For unsigned dtypes let's also check the result directly.
  if dtype.is_unsigned:
    inverted = bitwise_ops.invert(input_tensor)
    expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
    tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))

 


 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. Has the same type as x.
tf.raw_ops.InvertPermutation Computes the inverse permutation of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InvertPermutation  
tf.raw_ops.InvertPermutation(
    x, name=None
)
 This operation computes the inverse of an index permutation. It takes a 1-D integer tensor x, which represents the indices of a zero-based array, and swaps each value with its index position. In other words, for an output tensor y and an input tensor x, this operation computes the following: y[x[i]] = i for i in [0, 1, ..., len(x) - 1] The values must include 0. There can be no duplicate values or negative values. For example: # tensor `x` is [3, 4, 0, 2, 1]
invert_permutation(x) ==> [2, 4, 3, 0, 1]

 


 Args
  x   A Tensor. Must be one of the following types: int32, int64. 1-D.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.InvGrad Computes the gradient for the inverse of x wrt its input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InvGrad  
tf.raw_ops.InvGrad(
    y, dy, name=None
)
 Specifically, grad = -dy * y*y, where y = 1/x, and dy is the corresponding input gradient.
 


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


 Returns   A Tensor. Has the same type as y.
tf.raw_ops.IRFFT Inverse real-valued fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IRFFT  
tf.raw_ops.IRFFT(
    input, fft_length, Treal=tf.dtypes.float32, name=None
)
 Computes the inverse 1-dimensional discrete Fourier transform of a real-valued signal over the inner-most dimension of input. The inner-most dimension of input is assumed to be the result of RFFT: the fft_length / 2 + 1 unique components of the DFT of a real-valued signal. If fft_length is not provided, it is computed from the size of the inner-most dimension of input (fft_length = 2 * (inner - 1)). If the FFT length used to compute input is odd, it should be provided since it cannot be inferred properly. Along the axis IRFFT is computed on, if fft_length / 2 + 1 is smaller than the corresponding dimension of input, the dimension is cropped. If it is larger, the dimension is padded with zeros.
 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.  
  fft_length   A Tensor of type int32. An int32 tensor of shape [1]. The FFT length.  
  Treal   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Treal.
tf.raw_ops.IRFFT2D Inverse 2D real-valued fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IRFFT2D  
tf.raw_ops.IRFFT2D(
    input, fft_length, Treal=tf.dtypes.float32, name=None
)
 Computes the inverse 2-dimensional discrete Fourier transform of a real-valued signal over the inner-most 2 dimensions of input. The inner-most 2 dimensions of input are assumed to be the result of RFFT2D: The inner-most dimension contains the fft_length / 2 + 1 unique components of the DFT of a real-valued signal. If fft_length is not provided, it is computed from the size of the inner-most 2 dimensions of input. If the FFT length used to compute input is odd, it should be provided since it cannot be inferred properly. Along each axis IRFFT2D is computed on, if fft_length (or fft_length / 2 + 1 for the inner-most dimension) is smaller than the corresponding dimension of input, the dimension is cropped. If it is larger, the dimension is padded with zeros.
 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.  
  fft_length   A Tensor of type int32. An int32 tensor of shape [2]. The FFT length for each dimension.  
  Treal   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Treal.
tf.raw_ops.IRFFT3D Inverse 3D real-valued fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IRFFT3D  
tf.raw_ops.IRFFT3D(
    input, fft_length, Treal=tf.dtypes.float32, name=None
)
 Computes the inverse 3-dimensional discrete Fourier transform of a real-valued signal over the inner-most 3 dimensions of input. The inner-most 3 dimensions of input are assumed to be the result of RFFT3D: The inner-most dimension contains the fft_length / 2 + 1 unique components of the DFT of a real-valued signal. If fft_length is not provided, it is computed from the size of the inner-most 3 dimensions of input. If the FFT length used to compute input is odd, it should be provided since it cannot be inferred properly. Along each axis IRFFT3D is computed on, if fft_length (or fft_length / 2 + 1 for the inner-most dimension) is smaller than the corresponding dimension of input, the dimension is cropped. If it is larger, the dimension is padded with zeros.
 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.  
  fft_length   A Tensor of type int32. An int32 tensor of shape [3]. The FFT length for each dimension.  
  Treal   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Treal.
tf.raw_ops.IsBoostedTreesEnsembleInitialized Checks whether a tree ensemble has been initialized.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IsBoostedTreesEnsembleInitialized  
tf.raw_ops.IsBoostedTreesEnsembleInitialized(
    tree_ensemble_handle, name=None
)

 


 Args
  tree_ensemble_handle   A Tensor of type resource. Handle to the tree ensemble resource.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.IsBoostedTreesQuantileStreamResourceInitialized Checks whether a quantile stream has been initialized.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IsBoostedTreesQuantileStreamResourceInitialized  
tf.raw_ops.IsBoostedTreesQuantileStreamResourceInitialized(
    quantile_stream_resource_handle, name=None
)
 An Op that checks if quantile stream resource is initialized.
 


 Args
  quantile_stream_resource_handle   A Tensor of type resource. resource; The reference to quantile stream resource handle.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.IsFinite Returns which elements of x are finite.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IsFinite  
tf.raw_ops.IsFinite(
    x, name=None
)
 Example: x = tf.constant([5.0, 4.8, 6.8, np.inf, np.nan])
tf.math.is_finite(x) ==> [True, True, True, False, False]

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.  
 Numpy Compatibility Equivalent to np.isfinite
tf.raw_ops.IsInf Returns which elements of x are Inf.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IsInf  
tf.raw_ops.IsInf(
    x, name=None
)
 Example: x = tf.constant([5.0, np.inf, 6.8, np.inf])
tf.math.is_inf(x) ==> [False, True, False, True]

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.  
 Numpy Compatibility Equivalent to np.isinf
tf.raw_ops.IsNan Returns which elements of x are NaN.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IsNan  
tf.raw_ops.IsNan(
    x, name=None
)
 Example: x = tf.constant([5.0, np.nan, 6.8, np.nan, np.inf])
tf.math.is_nan(x) ==> [False, True, False, True, False]

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.  
 Numpy Compatibility Equivalent to np.isnan
tf.raw_ops.IsotonicRegression Solves a batch of isotonic regression problems.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IsotonicRegression  
tf.raw_ops.IsotonicRegression(
    input, output_dtype=tf.dtypes.float32, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. A (batch_size, dim)-tensor holding a batch of inputs.  
  output_dtype   An optional tf.DType from: tf.half, tf.bfloat16, tf.float32, tf.float64. Defaults to tf.float32. Dtype of output.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, segments).     output   A Tensor of type output_dtype.  
  segments   A Tensor of type int32.
tf.raw_ops.IsVariableInitialized Checks whether a tensor has been initialized.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IsVariableInitialized  
tf.raw_ops.IsVariableInitialized(
    ref, name=None
)
 Outputs boolean scalar indicating whether the tensor has been initialized.
 


 Args
  ref   A mutable Tensor. Should be from a Variable node. May be uninitialized.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.Iterator A container for an iterator resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Iterator  
tf.raw_ops.Iterator(
    shared_name, container, output_types, output_shapes, name=None
)

 


 Args
  shared_name   A string.  
  container   A string.  
  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 resource.
tf.raw_ops.IteratorFromStringHandle Converts the given string representing a handle to an iterator to a resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IteratorFromStringHandle  
tf.raw_ops.IteratorFromStringHandle(
    string_handle, output_types=[], output_shapes=[], name=None
)

 


 Args
  string_handle   A Tensor of type string. A string representation of the given handle.  
  output_types   An optional list of tf.DTypes. Defaults to []. If specified, defines the type of each tuple component in an element produced by the resulting iterator.  
  output_shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. If specified, defines the shape of each tuple component in an element produced by the resulting iterator.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.IteratorFromStringHandleV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IteratorFromStringHandleV2  
tf.raw_ops.IteratorFromStringHandleV2(
    string_handle, output_types=[], output_shapes=[], name=None
)

 


 Args
  string_handle   A Tensor of type string.  
  output_types   An optional list of tf.DTypes. Defaults to [].  
  output_shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.IteratorGetDevice Returns the name of the device on which resource has been placed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IteratorGetDevice  
tf.raw_ops.IteratorGetDevice(
    resource, name=None
)

 


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


 Returns   A Tensor of type string.
tf.raw_ops.IteratorGetNext Gets the next output from the given iterator .  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IteratorGetNext  
tf.raw_ops.IteratorGetNext(
    iterator, output_types, output_shapes, name=None
)

 


 Args
  iterator   A Tensor of type resource.  
  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.IteratorGetNextAsOptional Gets the next output from the given iterator as an Optional variant.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IteratorGetNextAsOptional  
tf.raw_ops.IteratorGetNextAsOptional(
    iterator, output_types, output_shapes, name=None
)

 


 Args
  iterator   A Tensor of type resource.  
  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.IteratorGetNextSync Gets the next output from the given iterator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IteratorGetNextSync  
tf.raw_ops.IteratorGetNextSync(
    iterator, output_types, output_shapes, name=None
)
 This operation is a synchronous version IteratorGetNext. It should only be used in situations where the iterator does not block the calling thread, or where the calling thread is not a member of the thread pool used to execute parallel operations (e.g. in eager mode).
 


 Args
  iterator   A Tensor of type resource.  
  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.IteratorToStringHandle Converts the given resource_handle representing an iterator to a string.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IteratorToStringHandle  
tf.raw_ops.IteratorToStringHandle(
    resource_handle, name=None
)

 


 Args
  resource_handle   A Tensor of type resource. A handle to an iterator resource.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.IteratorV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IteratorV2  
tf.raw_ops.IteratorV2(
    shared_name, container, output_types, output_shapes, name=None
)

 


 Args
  shared_name   A string.  
  container   A string.  
  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 resource.
tf.raw_ops.L2Loss L2 Loss.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.L2Loss  
tf.raw_ops.L2Loss(
    t, name=None
)
 Computes half the L2 norm of a tensor without the sqrt: output = sum(t ** 2) / 2

 


 Args
  t   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Typically 2-D, but may have any dimensions.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as t.
tf.raw_ops.LatencyStatsDataset Records the latency of producing input_dataset elements in a StatsAggregator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LatencyStatsDataset  
tf.raw_ops.LatencyStatsDataset(
    input_dataset, tag, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  tag   A Tensor of type string.  
  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.