neulab/docprompting-conala huggingface dataset:
tf.raw_ops.AccumulatorTakeGradient Extracts the average gradient in the given ConditionalAccumulator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AccumulatorTakeGradient  
tf.raw_ops.AccumulatorTakeGradient(
    handle, num_required, dtype, name=None
)
 The op blocks until sufficient (i.e., more than num_required) gradients have been accumulated. If the accumulator has already aggregated more than num_required gradients, it returns the average of the accumulated gradients. Also automatically increments the recorded global_step in the accumulator by 1, and resets the aggregate to 0.
 


 Args
  handle   A Tensor of type mutable string. The handle to an accumulator.  
  num_required   A Tensor of type int32. Number of gradients required before we return an aggregate.  
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The data type of accumulated gradients. Needs to correspond to the type of the accumulator.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.Acos Computes acos of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Acos  
tf.raw_ops.Acos(
    x, name=None
)
 Provided an input tensor, the tf.math.acos operation returns the inverse cosine of each element of the tensor. If y = tf.math.cos(x) then, x = tf.math.acos(y). Input range is [-1, 1] and the output has a range of [0, pi].
 


 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.Acosh Computes inverse hyperbolic cosine of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Acosh  
tf.raw_ops.Acosh(
    x, name=None
)
 Given an input tensor, the function computes inverse hyperbolic cosine of every element. Input range is [1, inf]. It returns nan if the input lies outside the range. x = tf.constant([-2, -0.5, 1, 1.2, 200, 10000, float("inf")])
tf.math.acosh(x) ==> [nan nan 0. 0.62236255 5.9914584 9.903487 inf]

 


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


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Add Returns x + y element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Add  
tf.raw_ops.Add(
    x, y, name=None
)
 
Note: math.add supports broadcasting. AddN does not. More about broadcasting here
 Given two input tensors, the tf.add operation computes the sum for every element in the tensor. Both input and output have a range (-inf, inf).
 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string.  
  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.AddManySparseToTensorsMap Add an N-minibatch SparseTensor to a SparseTensorsMap, return N handles.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AddManySparseToTensorsMap  
tf.raw_ops.AddManySparseToTensorsMap(
    sparse_indices, sparse_values, sparse_shape, container='',
    shared_name='', name=None
)
 A SparseTensor of rank R is represented by three tensors: sparse_indices, sparse_values, and sparse_shape, where sparse_indices.shape[1] == sparse_shape.shape[0] == R An N-minibatch of SparseTensor objects is represented as a SparseTensor having a first sparse_indices column taking values between [0, N), where the minibatch size N == sparse_shape[0]. The input SparseTensor must have rank R greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the SparseTensor must be sorted in increasing order of this first dimension. The stored SparseTensor objects pointed to by each row of the output sparse_handles will have rank R-1. The SparseTensor values can then be read out as part of a minibatch by passing the given keys as vector elements to TakeManySparseFromTensorsMap. To ensure the correct SparseTensorsMap is accessed, ensure that the same container and shared_name are passed to that Op. If no shared_name is provided here, instead use the name of the Operation created by calling AddManySparseToTensorsMap as the shared_name passed to TakeManySparseFromTensorsMap. Ensure the Operations are colocated.
 


 Args
  sparse_indices   A Tensor of type int64. 2-D. The indices of the minibatch SparseTensor. sparse_indices[:, 0] must be ordered values in [0, N).  
  sparse_values   A Tensor. 1-D. The values of the minibatch SparseTensor.  
  sparse_shape   A Tensor of type int64. 1-D. The shape of the minibatch SparseTensor. The minibatch size N == sparse_shape[0].  
  container   An optional string. Defaults to "". The container name for the SparseTensorsMap created by this op.  
  shared_name   An optional string. Defaults to "". The shared name for the SparseTensorsMap created by this op. If blank, the new Operation's unique name is used.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.AddN Add all input tensors element wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AddN  
tf.raw_ops.AddN(
    inputs, name=None
)
 Inputs must be of same size and shape. x = [9, 7, 10]
tf.math.add_n(x) ==> 26

 


 Args
  inputs   A list of at least 1 Tensor objects with the same type in: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, variant.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as inputs.
tf.raw_ops.AddSparseToTensorsMap Add a SparseTensor to a SparseTensorsMap return its handle.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AddSparseToTensorsMap  
tf.raw_ops.AddSparseToTensorsMap(
    sparse_indices, sparse_values, sparse_shape, container='',
    shared_name='', name=None
)
 A SparseTensor is represented by three tensors: sparse_indices, sparse_values, and sparse_shape. This operator takes the given SparseTensor and adds it to a container object (a SparseTensorsMap). A unique key within this container is generated in the form of an int64, and this is the value that is returned. The SparseTensor can then be read out as part of a minibatch by passing the key as a vector element to TakeManySparseFromTensorsMap. To ensure the correct SparseTensorsMap is accessed, ensure that the same container and shared_name are passed to that Op. If no shared_name is provided here, instead use the name of the Operation created by calling AddSparseToTensorsMap as the shared_name passed to TakeManySparseFromTensorsMap. Ensure the Operations are colocated.
 


 Args
  sparse_indices   A Tensor of type int64. 2-D. The indices of the SparseTensor.  
  sparse_values   A Tensor. 1-D. The values of the SparseTensor.  
  sparse_shape   A Tensor of type int64. 1-D. The shape of the SparseTensor.  
  container   An optional string. Defaults to "". The container name for the SparseTensorsMap created by this op.  
  shared_name   An optional string. Defaults to "". The shared name for the SparseTensorsMap created by this op. If blank, the new Operation's unique name is used.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.AddV2 Returns x + y element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AddV2  
tf.raw_ops.AddV2(
    x, y, name=None
)
 
Note: Add supports broadcasting. AddN does not. More about broadcasting here

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, int16, uint32, 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.AdjustContrast Deprecated. Disallowed in GraphDef version >= 2.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AdjustContrast  
tf.raw_ops.AdjustContrast(
    images, contrast_factor, min_value, max_value, name=None
)

 


 Args
  images   A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64, float32, float64.  
  contrast_factor   A Tensor of type float32.  
  min_value   A Tensor of type float32.  
  max_value   A Tensor of type float32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.AdjustContrastv2 Adjust the contrast of one or more images.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AdjustContrastv2  
tf.raw_ops.AdjustContrastv2(
    images, contrast_factor, name=None
)
 images is a tensor of at least 3 dimensions. The last 3 dimensions are interpreted as [height, width, channels]. The other dimensions only represent a collection of images, such as [batch, height, width, channels]. Contrast is adjusted independently for each channel of each image. For each channel, the Op first computes the mean of the image pixels in the channel and then adjusts each component of each pixel to (x - mean) * contrast_factor + mean.
 


 Args
  images   A Tensor. Must be one of the following types: half, float32. Images to adjust. At least 3-D.  
  contrast_factor   A Tensor of type float32. A float multiplier for adjusting contrast.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.AdjustHue Adjust the hue of one or more images.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AdjustHue  
tf.raw_ops.AdjustHue(
    images, delta, name=None
)
 images is a tensor of at least 3 dimensions. The last dimension is interpreted as channels, and must be three. The input image is considered in the RGB colorspace. Conceptually, the RGB colors are first mapped into HSV. A delta is then applied all the hue values, and then remapped back to RGB colorspace.
 


 Args
  images   A Tensor. Must be one of the following types: half, float32. Images to adjust. At least 3-D.  
  delta   A Tensor of type float32. A float delta to add to the hue.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.AdjustSaturation Adjust the saturation of one or more images.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AdjustSaturation  
tf.raw_ops.AdjustSaturation(
    images, scale, name=None
)
 images is a tensor of at least 3 dimensions. The last dimension is interpreted as channels, and must be three. The input image is considered in the RGB colorspace. Conceptually, the RGB colors are first mapped into HSV. A scale is then applied all the saturation values, and then remapped back to RGB colorspace.
 


 Args
  images   A Tensor. Must be one of the following types: half, float32. Images to adjust. At least 3-D.  
  scale   A Tensor of type float32. A float scale to add to the saturation.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.All Computes the "logical and" of elements across dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.All  
tf.raw_ops.All(
    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 of type bool. 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 of type bool.
tf.raw_ops.AllCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AllCandidateSampler  
tf.raw_ops.AllCandidateSampler(
    true_classes, num_true, num_sampled, unique, 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 produce.  
  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.  
  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.AllToAll An Op to exchange data across TPU replicas.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AllToAll  
tf.raw_ops.AllToAll(
    input, group_assignment, concat_dimension, split_dimension, split_count,
    name=None
)
 On each replica, the input is split into split_count blocks along split_dimension and send to the other replicas given group_assignment. After receiving split_count - 1 blocks from other replicas, we concatenate the blocks along concat_dimension as the output. For example, suppose there are 2 TPU replicas: replica 0 receives input: [[A, B]] replica 1 receives input: [[C, D]] group_assignment=[[0, 1]] concat_dimension=0 split_dimension=1 split_count=2 replica 0's output: [[A], [C]] replica 1's output: [[B], [D]]
 


 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, bool. The local input to the sum.  
  group_assignment   A Tensor of type int32. An int32 tensor with shape [num_groups, num_replicas_per_group]. group_assignment[i] represents the replica ids in the ith subgroup.  
  concat_dimension   An int. The dimension number to concatenate.  
  split_dimension   An int. The dimension number to split.  
  split_count   An int. The number of splits, this number must equal to the sub-group size(group_assignment.get_shape()[1])  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Angle   Returns the argument of a complex number.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Angle  
tf.raw_ops.Angle(
    input, Tout=tf.dtypes.float32, name=None
)
 Given a tensor input of complex numbers, this operation returns a tensor of type float that is the argument of each element in input. All elements in input must be complex numbers of the form \(a + bj\), where a is the real part and b is the imaginary part. The argument returned by this operation is of the form \(atan2(b, a)\). For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
tf.angle(input) ==> [2.0132, 1.056]

 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128.  
  Tout   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 Tout.  
 Numpy Compatibility Equivalent to np.angle.
tf.raw_ops.AnonymousIterator A container for an iterator resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousIterator  
tf.raw_ops.AnonymousIterator(
    output_types, output_shapes, name=None
)

 


 Args
  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.AnonymousIteratorV2 A container for an iterator resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousIteratorV2  
tf.raw_ops.AnonymousIteratorV2(
    output_types, output_shapes, name=None
)

 


 Args
  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 tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.  
  deleter   A Tensor of type variant.
tf.raw_ops.AnonymousMemoryCache  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousMemoryCache  
tf.raw_ops.AnonymousMemoryCache(
    name=None
)

 


 Args
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.  
  deleter   A Tensor of type variant.
tf.raw_ops.AnonymousMultiDeviceIterator A container for a multi device iterator resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousMultiDeviceIterator  
tf.raw_ops.AnonymousMultiDeviceIterator(
    devices, output_types, output_shapes, name=None
)

 


 Args
  devices   A list of strings that has length >= 1.  
  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 tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.  
  deleter   A Tensor of type variant.
tf.raw_ops.AnonymousRandomSeedGenerator  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousRandomSeedGenerator  
tf.raw_ops.AnonymousRandomSeedGenerator(
    seed, seed2, name=None
)

 


 Args
  seed   A Tensor of type int64.  
  seed2   A Tensor of type int64.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.  
  deleter   A Tensor of type variant.
tf.raw_ops.AnonymousSeedGenerator  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousSeedGenerator  
tf.raw_ops.AnonymousSeedGenerator(
    seed, seed2, reshuffle, name=None
)

 


 Args
  seed   A Tensor of type int64.  
  seed2   A Tensor of type int64.  
  reshuffle   A Tensor of type bool.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (handle, deleter).     handle   A Tensor of type resource.  
  deleter   A Tensor of type variant.
tf.raw_ops.Any Computes the "logical or" of elements across dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Any  
tf.raw_ops.Any(
    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 of type bool. 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 of type bool.
tf.raw_ops.ApplyAdadelta Update '*var' according to the adadelta scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ApplyAdadelta  
tf.raw_ops.ApplyAdadelta(
    var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None
)
 accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update;
 


 Args
  var   A mutable 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. Should be from a Variable().  
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().  
  accum_update   A mutable Tensor. Must have the same type as var. Should be from a Variable().  
  lr   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.  
  rho   A Tensor. Must have the same type as var. Decay factor. Must be a scalar.  
  epsilon   A Tensor. Must have the same type as var. Constant factor. Must be a scalar.  
  grad   A Tensor. Must have the same type as var. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as var.
tf.raw_ops.ApplyAdagrad Update '*var' according to the adagrad scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ApplyAdagrad  
tf.raw_ops.ApplyAdagrad(
    var, accum, lr, grad, use_locking=False, update_slots=True, name=None
)
 accum += grad * grad var -= lr * grad * (1 / sqrt(accum))
 


 Args
  var   A mutable 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. Should be from a Variable().  
  accum   A mutable Tensor. Must have the same type as var. Should be from a Variable().  
  lr   A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.  
  grad   A Tensor. Must have the same type as var. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  update_slots   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as var.