neulab/docprompting-conala huggingface dataset:
tf.raw_ops.NcclBroadcast Sends input to all devices that are connected to the output.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NcclBroadcast  
tf.raw_ops.NcclBroadcast(
    input, shape, name=None
)
 Sends input to all devices that are connected to the output. The graph should be constructed so that all ops connected to the output have a valid device assignment, and the op itself is assigned one of these devices. input: The input to the broadcast. output: The same as input. shape: The shape of the input tensor.
 


 Args
  input   A Tensor. Must be one of the following types: half, float32, float64, int32, int64.  
  shape   A tf.TensorShape or list of ints.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.NcclReduce Reduces input from num_devices using reduction to a single device.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NcclReduce  
tf.raw_ops.NcclReduce(
    input, reduction, name=None
)
 Reduces input from num_devices using reduction to a single device. The graph should be constructed so that all inputs have a valid device assignment, and the op itself is assigned one of these devices. input: The input to the reduction. data: the value of the reduction across all num_devices devices. reduction: the reduction operation to perform.
 


 Args
  input   A list of at least 1 Tensor objects with the same type in: half, float32, float64, int32, int64.  
  reduction   A string from: "min", "max", "prod", "sum".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Ndtri  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Ndtri  
tf.raw_ops.Ndtri(
    x, name=None
)

 


 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. Has the same type as x.
tf.raw_ops.Neg   Computes numerical negative value element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Neg  
tf.raw_ops.Neg(
    x, name=None
)
 I.e., \(y = -x\).
 


 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.NextAfter Returns the next representable value of x1 in the direction of x2, element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NextAfter  
tf.raw_ops.NextAfter(
    x1, x2, name=None
)
 This operation returns the same result as the C++ std::nextafter function. It can also return a subnormal number.
 


 Args
  x1   A Tensor. Must be one of the following types: float64, float32.  
  x2   A Tensor. Must have the same type as x1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x1.  
 Cpp Compatibility Equivalent to C++ std::nextafter function.
tf.raw_ops.NextIteration Makes its input available to the next iteration.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NextIteration  
tf.raw_ops.NextIteration(
    data, name=None
)

 


 Args
  data   A Tensor. The tensor to be made available to the next iteration.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.NonDeterministicInts Non-deterministically generates some integers.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NonDeterministicInts  
tf.raw_ops.NonDeterministicInts(
    shape, dtype=tf.dtypes.int64, name=None
)
 This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results.
 


 Args
  shape   A Tensor. The shape of the output tensor.  
  dtype   An optional tf.DType. Defaults to tf.int64. The type of the output.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.NonMaxSuppression Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppression  
tf.raw_ops.NonMaxSuppression(
    boxes, scores, max_output_size, iou_threshold=0.5, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system. Note that this algorithm is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression( boxes, scores, max_output_size, iou_threshold) selected_boxes = tf.gather(boxes, selected_indices)
 


 Args
  boxes   A Tensor of type float32. A 2-D float tensor of shape [num_boxes, 4].  
  scores   A Tensor of type float32. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).  
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.  
  iou_threshold   An optional float. Defaults to 0.5. A float representing the threshold for deciding whether boxes overlap too much with respect to IOU.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.NonMaxSuppressionV2 Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionV2  
tf.raw_ops.NonMaxSuppressionV2(
    boxes, scores, max_output_size, iou_threshold, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system. Note that this algorithm is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold) selected_boxes = tf.gather(boxes, selected_indices)
 


 Args
  boxes   A Tensor. Must be one of the following types: half, float32. A 2-D float tensor of shape [num_boxes, 4].  
  scores   A Tensor. Must have the same type as boxes. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).  
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.  
  iou_threshold   A Tensor. Must be one of the following types: half, float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.NonMaxSuppressionV3 Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionV3  
tf.raw_ops.NonMaxSuppressionV3(
    boxes, scores, max_output_size, iou_threshold, score_threshold, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices)
 


 Args
  boxes   A Tensor. Must be one of the following types: half, float32. A 2-D float tensor of shape [num_boxes, 4].  
  scores   A Tensor. Must have the same type as boxes. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).  
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.  
  iou_threshold   A Tensor. Must be one of the following types: half, float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.  
  score_threshold   A Tensor. Must have the same type as iou_threshold. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.NonMaxSuppressionV4 Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionV4  
tf.raw_ops.NonMaxSuppressionV4(
    boxes, scores, max_output_size, iou_threshold, score_threshold,
    pad_to_max_output_size=False, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices)
 


 Args
  boxes   A Tensor. Must be one of the following types: half, float32. A 2-D float tensor of shape [num_boxes, 4].  
  scores   A Tensor. Must have the same type as boxes. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).  
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.  
  iou_threshold   A Tensor. Must be one of the following types: half, float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.  
  score_threshold   A Tensor. Must have the same type as iou_threshold. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.  
  pad_to_max_output_size   An optional bool. Defaults to False. If true, the output selected_indices is padded to be of length max_output_size. Defaults to false.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (selected_indices, valid_outputs).     selected_indices   A Tensor of type int32.  
  valid_outputs   A Tensor of type int32.
tf.raw_ops.NonMaxSuppressionWithOverlaps Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionWithOverlaps  
tf.raw_ops.NonMaxSuppressionWithOverlaps(
    overlaps, scores, max_output_size, overlap_threshold, score_threshold, name=None
)
 pruning away boxes that have high overlaps with previously selected boxes. Bounding boxes with score less than score_threshold are removed. N-by-n overlap values are supplied as square matrix, which allows for defining a custom overlap criterium (eg. intersection over union, intersection over area, etc.). The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_with_overlaps( overlaps, scores, max_output_size, overlap_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices)
 


 Args
  overlaps   A Tensor of type float32. A 2-D float tensor of shape [num_boxes, num_boxes] representing the n-by-n box overlap values.  
  scores   A Tensor of type float32. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).  
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.  
  overlap_threshold   A Tensor of type float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too.  
  score_threshold   A Tensor of type float32. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.NonSerializableDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NonSerializableDataset  
tf.raw_ops.NonSerializableDataset(
    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.NoOp Does nothing. Only useful as a placeholder for control edges.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NoOp  
tf.raw_ops.NoOp(
    name=None
)

 


 Args
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.NotEqual Returns the truth value of (x != y) element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NotEqual  
tf.raw_ops.NotEqual(
    x, y, incompatible_shape_error=True, name=None
)
 
Note: NotEqual supports broadcasting. More about broadcasting here

 


 Args
  x   A Tensor.  
  y   A Tensor. Must have the same type as x.  
  incompatible_shape_error   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.NthElement Finds values of the n-th order statistic for the last dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.NthElement  
tf.raw_ops.NthElement(
    input, n, reverse=False, name=None
)
 If the input is a vector (rank-1), finds the entries which is the nth-smallest value in the vector and outputs their values as scalar tensor. For matrices (resp. higher rank input), computes the entries which is the nth-smallest value in each row (resp. vector along the last dimension). Thus, values.shape = input.shape[:-1]

 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 1-D or higher with last dimension at least n+1.  
  n   A Tensor of type int32. 0-D. Position of sorted vector to select along the last dimension (along each row for matrices). Valid range of n is [0, input.shape[:-1])  
  reverse   An optional bool. Defaults to False. When set to True, find the nth-largest value in the vector and vice versa.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.OneHot Returns a one-hot tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OneHot  
tf.raw_ops.OneHot(
    indices, depth, on_value, off_value, axis=-1, name=None
)
 The locations represented by indices in indices take value on_value, while all other locations take value off_value. If the input indices is rank N, the output will have rank N+1, The new axis is created at dimension axis (default: the new axis is appended at the end). If indices is a scalar the output shape will be a vector of length depth. If indices is a vector of length features, the output shape will be: features x depth if axis == -1
depth x features if axis == 0
 If indices is a matrix (batch) with shape [batch, features], the output shape will be: batch x features x depth if axis == -1
batch x depth x features if axis == 1
depth x batch x features if axis == 0
 Examples Suppose that indices = [0, 2, -1, 1]
depth = 3
on_value = 5.0
off_value = 0.0
axis = -1
 Then output is [4 x 3]: output =
  [5.0 0.0 0.0]  // one_hot(0)
  [0.0 0.0 5.0]  // one_hot(2)
  [0.0 0.0 0.0]  // one_hot(-1)
  [0.0 5.0 0.0]  // one_hot(1)
 Suppose that indices = [0, 2, -1, 1]
depth = 3
on_value = 0.0
off_value = 3.0
axis = 0
 Then output is [3 x 4]: output =
  [0.0 3.0 3.0 3.0]
  [3.0 3.0 3.0 0.0]
  [3.0 3.0 3.0 3.0]
  [3.0 0.0 3.0 3.0]
//  ^                one_hot(0)
//      ^            one_hot(2)
//          ^        one_hot(-1)
//              ^    one_hot(1)
 Suppose that indices = [[0, 2], [1, -1]]
depth = 3
on_value = 1.0
off_value = 0.0
axis = -1
 Then output is [2 x 2 x 3]: output =
  [
    [1.0, 0.0, 0.0]  // one_hot(0)
    [0.0, 0.0, 1.0]  // one_hot(2)
  ][
    [0.0, 1.0, 0.0]  // one_hot(1)
    [0.0, 0.0, 0.0]  // one_hot(-1)
  ]

 


 Args
  indices   A Tensor. Must be one of the following types: uint8, int32, int64. A tensor of indices.  
  depth   A Tensor of type int32. A scalar defining the depth of the one hot dimension.  
  on_value   A Tensor. A scalar defining the value to fill in output when indices[j] = i.  
  off_value   A Tensor. Must have the same type as on_value. A scalar defining the value to fill in output when indices[j] != i.  
  axis   An optional int. Defaults to -1. The axis to fill (default: -1, a new inner-most axis).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as on_value.
tf.raw_ops.OneShotIterator Makes a "one-shot" iterator that can be iterated only once.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OneShotIterator  
tf.raw_ops.OneShotIterator(
    dataset_factory, output_types, output_shapes, container='',
    shared_name='', name=None
)
 A one-shot iterator bundles the logic for defining the dataset and the state of the iterator in a single op, which allows simple input pipelines to be defined without an additional initialization ("MakeIterator") step. One-shot iterators have the following limitations:  They do not support parameterization: all logic for creating the underlying dataset must be bundled in the dataset_factory function. They are not resettable. Once a one-shot iterator reaches the end of its underlying dataset, subsequent "IteratorGetNext" operations on that iterator will always produce an OutOfRange error.  For greater flexibility, use "Iterator" and "MakeIterator" to define an iterator using an arbitrary subgraph, which may capture tensors (including fed values) as parameters, and which may be reset multiple times by rerunning "MakeIterator".
 


 Args
  dataset_factory   A function decorated with @Defun. A function of type () -> DT_VARIANT, where the returned DT_VARIANT is a dataset.  
  output_types   A list of tf.DTypes that has length >= 1.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.OnesLike Returns a tensor of ones with the same shape and type as x.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OnesLike  
tf.raw_ops.OnesLike(
    x, name=None
)

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, uint8, int16, uint16, int32, int64, complex64, complex128, bool. a tensor of type T.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.OptimizeDataset Creates a dataset by applying optimizations to input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.OptimizeDataset  
tf.raw_ops.OptimizeDataset(
    input_dataset, optimizations, output_types, output_shapes,
    optimization_configs=[], name=None
)
 Creates a dataset by applying optimizations to input_dataset.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.  
  optimizations   A Tensor of type string. A tf.string vector tf.Tensor identifying optimizations to use.  
  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.  
  optimization_configs   An optional list of strings. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.