neulab/docprompting-conala huggingface dataset:
tf.raw_ops.SparseReduceSumSparse Computes the sum of elements across dimensions of a SparseTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseReduceSumSparse  
tf.raw_ops.SparseReduceSumSparse(
    input_indices, input_values, input_shape, reduction_axes, keep_dims=False,
    name=None
)
 This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_sum(). In contrast to SparseReduceSum, this Op returns a SparseTensor. Reduces sp_input along the dimensions given in reduction_axes. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_axes. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_axes has no entries, all dimensions are reduced, and a tensor with a single element is returned. Additionally, the axes can be negative, which are interpreted according to the indexing rules in Python.
 


 Args
  input_indices   A Tensor of type int64. 2-D. N x R matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering.  
  input_values   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. 1-D. N non-empty values corresponding to input_indices.  
  input_shape   A Tensor of type int64. 1-D. Shape of the input SparseTensor.  
  reduction_axes   A Tensor of type int32. 1-D. Length-K vector containing the reduction axes.  
  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 tuple of Tensor objects (output_indices, output_values, output_shape).     output_indices   A Tensor of type int64.  
  output_values   A Tensor. Has the same type as input_values.  
  output_shape   A Tensor of type int64.
tf.raw_ops.SparseReorder Reorders a SparseTensor into the canonical, row-major ordering.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseReorder  
tf.raw_ops.SparseReorder(
    input_indices, input_values, input_shape, name=None
)
 Note that by convention, all sparse ops preserve the canonical ordering along increasing dimension number. The only time ordering can be violated is during manual manipulation of the indices and values vectors to add entries. Reordering does not affect the shape of the SparseTensor. If the tensor has rank R and N non-empty values, input_indices has shape [N, R], input_values has length N, and input_shape has length R.
 


 Args
  input_indices   A Tensor of type int64. 2-D. N x R matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering.  
  input_values   A Tensor. 1-D. N non-empty values corresponding to input_indices.  
  input_shape   A Tensor of type int64. 1-D. Shape of the input SparseTensor.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_indices, output_values).     output_indices   A Tensor of type int64.  
  output_values   A Tensor. Has the same type as input_values.
tf.raw_ops.SparseReshape Reshapes a SparseTensor to represent values in a new dense shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseReshape  
tf.raw_ops.SparseReshape(
    input_indices, input_shape, new_shape, name=None
)
 This operation has the same semantics as reshape on the represented dense tensor. The input_indices are recomputed based on the requested new_shape. If one component of new_shape is the special value -1, the size of that dimension is computed so that the total dense size remains constant. At most one component of new_shape can be -1. The number of dense elements implied by new_shape must be the same as the number of dense elements originally implied by input_shape. Reshaping does not affect the order of values in the SparseTensor. If the input tensor has rank R_in and N non-empty values, and new_shape has length R_out, then input_indices has shape [N, R_in], input_shape has length R_in, output_indices has shape [N, R_out], and output_shape has length R_out.
 


 Args
  input_indices   A Tensor of type int64. 2-D. N x R_in matrix with the indices of non-empty values in a SparseTensor.  
  input_shape   A Tensor of type int64. 1-D. R_in vector with the input SparseTensor's dense shape.  
  new_shape   A Tensor of type int64. 1-D. R_out vector with the requested new dense shape.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_indices, output_shape).     output_indices   A Tensor of type int64.  
  output_shape   A Tensor of type int64.
tf.raw_ops.SparseSegmentMean Computes the mean along sparse segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSegmentMean  
tf.raw_ops.SparseSegmentMean(
    data, indices, segment_ids, name=None
)
 See tf.sparse.segment_sum for usage examples. Like SegmentMean, but segment_ids can have rank less than data's first dimension, selecting a subset of dimension 0, specified by indices.
 


 Args
  data   A Tensor. Must be one of the following types: bfloat16, float32, float64.  
  indices   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Has same rank as segment_ids.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Values should be sorted and can be repeated.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.SparseSegmentMeanGrad Computes gradients for SparseSegmentMean.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSegmentMeanGrad  
tf.raw_ops.SparseSegmentMeanGrad(
    grad, indices, segment_ids, output_dim0, name=None
)
 Returns tensor "output" with same shape as grad, except for dimension 0 whose value is output_dim0.
 


 Args
  grad   A Tensor. Must be one of the following types: float32, float64. gradient propagated to the SparseSegmentMean op.  
  indices   A Tensor. Must be one of the following types: int32, int64. indices passed to the corresponding SparseSegmentMean op.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. segment_ids passed to the corresponding SparseSegmentMean op.  
  output_dim0   A Tensor of type int32. dimension 0 of "data" passed to SparseSegmentMean op.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as grad.
tf.raw_ops.SparseSegmentMeanWithNumSegments Computes the mean along sparse segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSegmentMeanWithNumSegments  
tf.raw_ops.SparseSegmentMeanWithNumSegments(
    data, indices, segment_ids, num_segments, name=None
)
 Like SparseSegmentMean, but allows missing ids in segment_ids. If an id is missing, the output tensor at that position will be zeroed. Read the section on segmentation for an explanation of segments.
 


 Args
  data   A Tensor. Must be one of the following types: bfloat16, float32, float64.  
  indices   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Has same rank as segment_ids.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Values should be sorted and can be repeated.  
  num_segments   A Tensor. Must be one of the following types: int32, int64. Should equal the number of distinct segment IDs.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.SparseSegmentSqrtN Computes the sum along sparse segments of a tensor divided by the sqrt of N.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSegmentSqrtN  
tf.raw_ops.SparseSegmentSqrtN(
    data, indices, segment_ids, name=None
)
 N is the size of the segment being reduced. See tf.sparse.segment_sum for usage examples.
 


 Args
  data   A Tensor. Must be one of the following types: bfloat16, float32, float64.  
  indices   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Has same rank as segment_ids.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Values should be sorted and can be repeated.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.SparseSegmentSqrtNGrad Computes gradients for SparseSegmentSqrtN.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSegmentSqrtNGrad  
tf.raw_ops.SparseSegmentSqrtNGrad(
    grad, indices, segment_ids, output_dim0, name=None
)
 Returns tensor "output" with same shape as grad, except for dimension 0 whose value is output_dim0.
 


 Args
  grad   A Tensor. Must be one of the following types: float32, float64. gradient propagated to the SparseSegmentSqrtN op.  
  indices   A Tensor. Must be one of the following types: int32, int64. indices passed to the corresponding SparseSegmentSqrtN op.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. segment_ids passed to the corresponding SparseSegmentSqrtN op.  
  output_dim0   A Tensor of type int32. dimension 0 of "data" passed to SparseSegmentSqrtN op.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as grad.
tf.raw_ops.SparseSegmentSqrtNWithNumSegments Computes the sum along sparse segments of a tensor divided by the sqrt of N.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSegmentSqrtNWithNumSegments  
tf.raw_ops.SparseSegmentSqrtNWithNumSegments(
    data, indices, segment_ids, num_segments, name=None
)
 N is the size of the segment being reduced. Like SparseSegmentSqrtN, but allows missing ids in segment_ids. If an id is missing, the output tensor at that position will be zeroed. Read the section on segmentation for an explanation of segments.
 


 Args
  data   A Tensor. Must be one of the following types: bfloat16, float32, float64.  
  indices   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Has same rank as segment_ids.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Values should be sorted and can be repeated.  
  num_segments   A Tensor. Must be one of the following types: int32, int64. Should equal the number of distinct segment IDs.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.SparseSegmentSum Computes the sum along sparse segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSegmentSum  
tf.raw_ops.SparseSegmentSum(
    data, indices, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Like SegmentSum, but segment_ids can have rank less than data's first dimension, selecting a subset of dimension 0, specified by indices. For example: c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])

# Select two rows, one segment.
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
# => [[0 0 0 0]]

# Select two rows, two segment.
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
# => [[ 1  2  3  4]
#     [-1 -2 -3 -4]]

# Select all rows, two segments.
tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
# => [[0 0 0 0]
#     [5 6 7 8]]

# Which is equivalent to:
tf.segment_sum(c, tf.constant([0, 0, 1]))

 


 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  indices   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Has same rank as segment_ids.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Values should be sorted and can be repeated.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.SparseSegmentSumWithNumSegments Computes the sum along sparse segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSegmentSumWithNumSegments  
tf.raw_ops.SparseSegmentSumWithNumSegments(
    data, indices, segment_ids, num_segments, name=None
)
 Like SparseSegmentSum, but allows missing ids in segment_ids. If an id is missing, the output tensor at that position will be zeroed. Read the section on segmentation for an explanation of segments. For example: c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])

tf.sparse_segment_sum_with_num_segments(
    c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3)
# => [[0 0 0 0]
#     [0 0 0 0]
#     [0 0 0 0]]

tf.sparse_segment_sum_with_num_segments(c,
                                        tf.constant([0, 1]),
                                        tf.constant([0, 2],
                                        num_segments=4))
# => [[ 1  2  3  4]
#     [ 0  0  0  0]
#     [-1 -2 -3 -4]
#     [ 0  0  0  0]]

 


 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  indices   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Has same rank as segment_ids.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Values should be sorted and can be repeated.  
  num_segments   A Tensor. Must be one of the following types: int32, int64. Should equal the number of distinct segment IDs.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.SparseSlice Slice a SparseTensor based on the start and size.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSlice  
tf.raw_ops.SparseSlice(
    indices, values, shape, start, size, name=None
)
 For example, if the input is input_tensor = shape = [2, 7]
[    a   d e  ]
[b c          ]
 Graphically the output tensors are: sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
[    a  ]
[b c    ]

sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
[ d e  ]
[      ]

 


 Args
  indices   A Tensor of type int64. 2-D tensor represents the indices of the sparse tensor.  
  values   A Tensor. 1-D tensor represents the values of the sparse tensor.  
  shape   A Tensor of type int64. 1-D. tensor represents the shape of the sparse tensor.  
  start   A Tensor of type int64. 1-D. tensor represents the start of the slice.  
  size   A Tensor of type int64. 1-D. tensor represents the size of the slice. output indices: A list of 1-D tensors represents the indices of the output sparse tensors.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_indices, output_values, output_shape).     output_indices   A Tensor of type int64.  
  output_values   A Tensor. Has the same type as values.  
  output_shape   A Tensor of type int64.
tf.raw_ops.SparseSliceGrad The gradient operator for the SparseSlice op.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSliceGrad  
tf.raw_ops.SparseSliceGrad(
    backprop_val_grad, input_indices, input_start, output_indices, name=None
)
 This op takes in the upstream gradient w.r.t. non-empty values of the sliced SparseTensor, and outputs the gradients w.r.t. the non-empty values of input SparseTensor.
 


 Args
  backprop_val_grad   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. 1-D. The gradient with respect to the non-empty values of the sliced SparseTensor.  
  input_indices   A Tensor of type int64. 2-D. The indices of the input SparseTensor.  
  input_start   A Tensor of type int64. 1-D. tensor represents the start of the slice.  
  output_indices   A Tensor of type int64. 2-D. The indices of the sliced SparseTensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as backprop_val_grad.
tf.raw_ops.SparseSoftmax Applies softmax to a batched N-D SparseTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSoftmax  
tf.raw_ops.SparseSoftmax(
    sp_indices, sp_values, sp_shape, name=None
)
 The inputs represent an N-D SparseTensor with logical shape [..., B, C] (where N >= 2), and with indices sorted in the canonical lexicographic order. This op is equivalent to applying the normal tf.nn.softmax() to each innermost logical submatrix with shape [B, C], but with the catch that the implicitly zero elements do not participate. Specifically, the algorithm is equivalent to the following: (1) Applies tf.nn.softmax() to a densified view of each innermost submatrix with shape [B, C], along the size-C dimension; (2) Masks out the original implicitly-zero locations; (3) Renormalizes the remaining elements. Hence, the SparseTensor result has exactly the same non-zero indices and shape.
 


 Args
  sp_indices   A Tensor of type int64. 2-D. NNZ x R matrix with the indices of non-empty values in a SparseTensor, in canonical ordering.  
  sp_values   A Tensor. Must be one of the following types: float32, float64. 1-D. NNZ non-empty values corresponding to sp_indices.  
  sp_shape   A Tensor of type int64. 1-D. Shape of the input SparseTensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as sp_values.
tf.raw_ops.SparseSoftmaxCrossEntropyWithLogits Computes softmax cross entropy cost and gradients to backpropagate.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSoftmaxCrossEntropyWithLogits  
tf.raw_ops.SparseSoftmaxCrossEntropyWithLogits(
    features, labels, name=None
)
 Unlike SoftmaxCrossEntropyWithLogits, this operation does not accept a matrix of label probabilities, but rather a single label per row of features. This label is considered to have probability 1.0 for the given row. Inputs are the logits, not probabilities.
 


 Args
  features   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. batch_size x num_classes matrix  
  labels   A Tensor. Must be one of the following types: int32, int64. batch_size vector with values in [0, num_classes). This is the label for the given minibatch entry.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (loss, backprop).     loss   A Tensor. Has the same type as features.  
  backprop   A Tensor. Has the same type as features.
tf.raw_ops.SparseSparseMaximum Returns the element-wise max of two SparseTensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSparseMaximum  
tf.raw_ops.SparseSparseMaximum(
    a_indices, a_values, a_shape, b_indices, b_values, b_shape, name=None
)
 Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
 


 Args
  a_indices   A Tensor of type int64. 2-D. N x R matrix with the indices of non-empty values in a SparseTensor, in the canonical lexicographic ordering.  
  a_values   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 1-D. N non-empty values corresponding to a_indices.  
  a_shape   A Tensor of type int64. 1-D. Shape of the input SparseTensor.  
  b_indices   A Tensor of type int64. counterpart to a_indices for the other operand.  
  b_values   A Tensor. Must have the same type as a_values. counterpart to a_values for the other operand; must be of the same dtype.  
  b_shape   A Tensor of type int64. counterpart to a_shape for the other operand; the two shapes must be equal.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_indices, output_values).     output_indices   A Tensor of type int64.  
  output_values   A Tensor. Has the same type as a_values.
tf.raw_ops.SparseSparseMinimum Returns the element-wise min of two SparseTensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSparseMinimum  
tf.raw_ops.SparseSparseMinimum(
    a_indices, a_values, a_shape, b_indices, b_values, b_shape, name=None
)
 Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
 


 Args
  a_indices   A Tensor of type int64. 2-D. N x R matrix with the indices of non-empty values in a SparseTensor, in the canonical lexicographic ordering.  
  a_values   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. 1-D. N non-empty values corresponding to a_indices.  
  a_shape   A Tensor of type int64. 1-D. Shape of the input SparseTensor.  
  b_indices   A Tensor of type int64. counterpart to a_indices for the other operand.  
  b_values   A Tensor. Must have the same type as a_values. counterpart to a_values for the other operand; must be of the same dtype.  
  b_shape   A Tensor of type int64. counterpart to a_shape for the other operand; the two shapes must be equal.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_indices, output_values).     output_indices   A Tensor of type int64.  
  output_values   A Tensor. Has the same type as a_values.
tf.raw_ops.SparseSplit Split a SparseTensor into num_split tensors along one dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseSplit  
tf.raw_ops.SparseSplit(
    split_dim, indices, values, shape, num_split, name=None
)
 If the shape[split_dim] is not an integer multiple of num_split. Slices [0 : shape[split_dim] % num_split] gets one extra dimension. For example, if split_dim = 1 and num_split = 2 and the input is input_tensor = shape = [2, 7]
[    a   d e  ]
[b c          ]
 Graphically the output tensors are: output_tensor[0] = shape = [2, 4]
[    a  ]
[b c    ]

output_tensor[1] = shape = [2, 3]
[ d e  ]
[      ]

 


 Args
  split_dim   A Tensor of type int64. 0-D. The dimension along which to split. Must be in the range [0, rank(shape)).  
  indices   A Tensor of type int64. 2-D tensor represents the indices of the sparse tensor.  
  values   A Tensor. 1-D tensor represents the values of the sparse tensor.  
  shape   A Tensor of type int64. 1-D. tensor represents the shape of the sparse tensor. output indices: A list of 1-D tensors represents the indices of the output sparse tensors.  
  num_split   An int that is >= 1. The number of ways to split.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_indices, output_values, output_shape).     output_indices   A list of num_split Tensor objects with type int64.  
  output_values   A list of num_split Tensor objects with the same type as values.  
  output_shape   A list of num_split Tensor objects with type int64.