neulab/docprompting-conala huggingface dataset:
tf.raw_ops.SparseTensorDenseAdd Adds up a SparseTensor and a dense Tensor, producing a dense Tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseTensorDenseAdd  
tf.raw_ops.SparseTensorDenseAdd(
    a_indices, a_values, a_shape, b, name=None
)
 This Op does not require a_indices be sorted in standard lexicographic order.
 


 Args
  a_indices   A Tensor. Must be one of the following types: int32, int64. 2-D. The indices of the SparseTensor, with shape [nnz, ndims].  
  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. The values of the SparseTensor, with shape [nnz].  
  a_shape   A Tensor. Must have the same type as a_indices. 1-D. The shape of the SparseTensor, with shape [ndims].  
  b   A Tensor. Must have the same type as a_values. ndims-D Tensor. With shape a_shape.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as a_values.
tf.raw_ops.SparseTensorDenseMatMul Multiply SparseTensor (of rank 2) "A" by dense matrix "B".  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseTensorDenseMatMul  
tf.raw_ops.SparseTensorDenseMatMul(
    a_indices, a_values, a_shape, b, adjoint_a=False, adjoint_b=False, name=None
)
 No validity checking is performed on the indices of A. However, the following input format is recommended for optimal behavior: if adjoint_a == false: A should be sorted in lexicographically increasing order. Use SparseReorder if you're not sure. if adjoint_a == true: A should be sorted in order of increasing dimension 1 (i.e., "column major" order instead of "row major" order).
 


 Args
  a_indices   A Tensor. Must be one of the following types: int32, int64. 2-D. The indices of the SparseTensor, size [nnz, 2] Matrix.  
  a_values   A Tensor. 1-D. The values of the SparseTensor, size [nnz] Vector.  
  a_shape   A Tensor of type int64. 1-D. The shape of the SparseTensor, size [2] Vector.  
  b   A Tensor. Must have the same type as a_values. 2-D. A dense Matrix.  
  adjoint_a   An optional bool. Defaults to False. Use the adjoint of A in the matrix multiply. If A is complex, this is transpose(conj(A)). Otherwise it's transpose(A).  
  adjoint_b   An optional bool. Defaults to False. Use the adjoint of B in the matrix multiply. If B is complex, this is transpose(conj(B)). Otherwise it's transpose(B).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as a_values.
tf.raw_ops.SparseTensorSliceDataset Creates a dataset that splits a SparseTensor into elements row-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseTensorSliceDataset  
tf.raw_ops.SparseTensorSliceDataset(
    indices, values, dense_shape, name=None
)

 


 Args
  indices   A Tensor of type int64.  
  values   A Tensor.  
  dense_shape   A Tensor of type int64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.SparseTensorToCSRSparseMatrix Converts a SparseTensor to a (possibly batched) CSRSparseMatrix.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseTensorToCSRSparseMatrix  
tf.raw_ops.SparseTensorToCSRSparseMatrix(
    indices, values, dense_shape, name=None
)

 


 Args
  indices   A Tensor of type int64. SparseTensor indices.  
  values   A Tensor. Must be one of the following types: float32, float64, complex64, complex128. SparseTensor values.  
  dense_shape   A Tensor of type int64. SparseTensor dense shape.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.SparseToDense Converts a sparse representation into a dense tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseToDense  
tf.raw_ops.SparseToDense(
    sparse_indices, output_shape, sparse_values, default_value,
    validate_indices=True, name=None
)
 Builds an array dense with shape output_shape such that # If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)

# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]

# If sparse_indices is an n by d matrix, then for each i in [0, n)
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
 All other values in dense are set to default_value. If sparse_values is a scalar, all sparse indices are set to this single value. Indices should be sorted in lexicographic order, and indices must not contain any repeats. If validate_indices is true, these properties are checked during execution.
 


 Args
  sparse_indices   A Tensor. Must be one of the following types: int32, int64. 0-D, 1-D, or 2-D. sparse_indices[i] contains the complete index where sparse_values[i] will be placed.  
  output_shape   A Tensor. Must have the same type as sparse_indices. 1-D. Shape of the dense output tensor.  
  sparse_values   A Tensor. 1-D. Values corresponding to each row of sparse_indices, or a scalar value to be used for all sparse indices.  
  default_value   A Tensor. Must have the same type as sparse_values. Scalar value to set for indices not specified in sparse_indices.  
  validate_indices   An optional bool. Defaults to True. If true, indices are checked to make sure they are sorted in lexicographic order and that there are no repeats.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as sparse_values.
tf.raw_ops.SparseToSparseSetOperation Applies set operation along last dimension of 2 SparseTensor inputs.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SparseToSparseSetOperation  
tf.raw_ops.SparseToSparseSetOperation(
    set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape,
    set_operation, validate_indices=True, name=None
)
 See SetOperationOp::SetOperationFromContext for values of set_operation. If validate_indices is True, SparseToSparseSetOperation validates the order and range of set1 and set2 indices. Input set1 is a SparseTensor represented by set1_indices, set1_values, and set1_shape. For set1 ranked n, 1st n-1 dimensions must be the same as set2. Dimension n contains values in a set, duplicates are allowed but ignored. Input set2 is a SparseTensor represented by set2_indices, set2_values, and set2_shape. For set2 ranked n, 1st n-1 dimensions must be the same as set1. Dimension n contains values in a set, duplicates are allowed but ignored. If validate_indices is True, this op validates the order and range of set1 and set2 indices. Output result is a SparseTensor represented by result_indices, result_values, and result_shape. For set1 and set2 ranked n, this has rank n and the same 1st n-1 dimensions as set1 and set2. The nth dimension contains the result of set_operation applied to the corresponding [0...n-1] dimension of set.
 


 Args
  set1_indices   A Tensor of type int64. 2D Tensor, indices of a SparseTensor. Must be in row-major order.  
  set1_values   A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, string. 1D Tensor, values of a SparseTensor. Must be in row-major order.  
  set1_shape   A Tensor of type int64. 1D Tensor, shape of a SparseTensor. set1_shape[0...n-1] must be the same as set2_shape[0...n-1], set1_shape[n] is the max set size across 0...n-1 dimensions.  
  set2_indices   A Tensor of type int64. 2D Tensor, indices of a SparseTensor. Must be in row-major order.  
  set2_values   A Tensor. Must have the same type as set1_values. 1D Tensor, values of a SparseTensor. Must be in row-major order.  
  set2_shape   A Tensor of type int64. 1D Tensor, shape of a SparseTensor. set2_shape[0...n-1] must be the same as set1_shape[0...n-1], set2_shape[n] is the max set size across 0...n-1 dimensions.  
  set_operation   A string.  
  validate_indices   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (result_indices, result_values, result_shape).     result_indices   A Tensor of type int64.  
  result_values   A Tensor. Has the same type as set1_values.  
  result_shape   A Tensor of type int64.
tf.raw_ops.Spence  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Spence  
tf.raw_ops.Spence(
    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.Split Splits a tensor into num_split tensors along one dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Split  
tf.raw_ops.Split(
    axis, value, num_split, name=None
)

 


 Args
  axis   A Tensor of type int32. 0-D. The dimension along which to split. Must be in the range [-rank(value), rank(value)).  
  value   A Tensor. The tensor to split.  
  num_split   An int that is >= 1. The number of ways to split. Must evenly divide value.shape[split_dim].  
  name   A name for the operation (optional).   
 


 Returns   A list of num_split Tensor objects with the same type as value.
tf.raw_ops.SplitV Splits a tensor into num_split tensors along one dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SplitV  
tf.raw_ops.SplitV(
    value, size_splits, axis, num_split, name=None
)

 


 Args
  value   A Tensor. The tensor to split.  
  size_splits   A Tensor. Must be one of the following types: int32, int64. list containing the sizes of each output tensor along the split dimension. Must sum to the dimension of value along split_dim. Can contain one -1 indicating that dimension is to be inferred.  
  axis   A Tensor of type int32. 0-D. The dimension along which to split. Must be in the range [-rank(value), rank(value)).  
  num_split   An int that is >= 1.  
  name   A name for the operation (optional).   
 


 Returns   A list of num_split Tensor objects with the same type as value.
tf.raw_ops.SqlDataset Creates a dataset that executes a SQL query and emits rows of the result set.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SqlDataset  
tf.raw_ops.SqlDataset(
    driver_name, data_source_name, query, output_types, output_shapes, name=None
)

 


 Args
  driver_name   A Tensor of type string. The database type. Currently, the only supported type is 'sqlite'.  
  data_source_name   A Tensor of type string. A connection string to connect to the database.  
  query   A Tensor of type string. A SQL query to execute.  
  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.Sqrt   Computes square root of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Sqrt  
tf.raw_ops.Sqrt(
    x, name=None
)
 I.e., \(y = \sqrt{x} = x^{1/2}\).
 


 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.SqrtGrad Computes the gradient for the sqrt of x wrt its input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SqrtGrad  
tf.raw_ops.SqrtGrad(
    y, dy, name=None
)
 Specifically, grad = dy * 0.5 / y, where y = sqrt(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.Square   Computes square of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Square  
tf.raw_ops.Square(
    x, name=None
)
 I.e., \(y = x * x = x^2\). 
tf.math.square([-2., 0., 3.])
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([4., 0., 9.], dtype=float32)>

 


 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.SquaredDifference Returns conj(x - y)(x - y) element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SquaredDifference  
tf.raw_ops.SquaredDifference(
    x, y, name=None
)
 
Note: math.squared_difference supports broadcasting. More about broadcasting here

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, 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.Squeeze Removes dimensions of size 1 from the shape of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Squeeze  
tf.raw_ops.Squeeze(
    input, axis=[], name=None
)
 Given a tensor input, this operation returns a tensor of the same type with all dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, you can remove specific size 1 dimensions by specifying axis. For example: # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
 Or, to remove specific size 1 dimensions: # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]

 


 Args
  input   A Tensor. The input to squeeze.  
  axis   An optional list of ints. Defaults to []. If specified, only squeezes the dimensions listed. The dimension index starts at 0. It is an error to squeeze a dimension that is not 1. Must be in the range [-rank(input), rank(input)).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Stack Deprecated, use StackV2.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Stack  
tf.raw_ops.Stack(
    elem_type, stack_name='', name=None
)

 


 Args
  elem_type   A tf.DType.  
  stack_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.StackClose Deprecated, use StackCloseV2.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StackClose  
tf.raw_ops.StackClose(
    handle, name=None
)

 


 Args
  handle   A Tensor of type mutable string.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.StackCloseV2 Delete the stack from its resource container.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StackCloseV2  
tf.raw_ops.StackCloseV2(
    handle, name=None
)

 


 Args
  handle   A Tensor of type resource. The handle to a stack.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.StackPop Deprecated, use StackPopV2.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StackPop  
tf.raw_ops.StackPop(
    handle, elem_type, name=None
)

 


 Args
  handle   A Tensor of type mutable string.  
  elem_type   A tf.DType.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type elem_type.
tf.raw_ops.StackPopV2 Pop the element at the top of the stack.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StackPopV2  
tf.raw_ops.StackPopV2(
    handle, elem_type, name=None
)

 


 Args
  handle   A Tensor of type resource. The handle to a stack.  
  elem_type   A tf.DType. The type of the elem that is popped.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type elem_type.
tf.raw_ops.StackPush Deprecated, use StackPushV2.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StackPush  
tf.raw_ops.StackPush(
    handle, elem, swap_memory=False, name=None
)

 


 Args
  handle   A Tensor of type mutable string.  
  elem   A Tensor.  
  swap_memory   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as elem.
tf.raw_ops.StackPushV2 Push an element onto the stack.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StackPushV2  
tf.raw_ops.StackPushV2(
    handle, elem, swap_memory=False, name=None
)

 


 Args
  handle   A Tensor of type resource. The handle to a stack.  
  elem   A Tensor. The tensor to be pushed onto the stack.  
  swap_memory   An optional bool. Defaults to False. Swap elem to CPU. Default to false.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as elem.
tf.raw_ops.StackV2 A stack that produces elements in first-in last-out order.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StackV2  
tf.raw_ops.StackV2(
    max_size, elem_type, stack_name='', name=None
)

 


 Args
  max_size   A Tensor of type int32. The maximum size of the stack if non-negative. If negative, the stack size is unlimited.  
  elem_type   A tf.DType. The type of the elements on the stack.  
  stack_name   An optional string. Defaults to "". Overrides the name used for the temporary stack resource. Default value is the name of the 'Stack' op (which is guaranteed unique).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.Stage Stage values similar to a lightweight Enqueue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Stage  
tf.raw_ops.Stage(
    values, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 The basic functionality of this Op is similar to a queue with many fewer capabilities and options. This Op is optimized for performance.
 


 Args
  values   A list of Tensor objects. a list of tensors dtypes A list of data types that inserted values should adhere to.  
  capacity   An optional int that is >= 0. Defaults to 0. Maximum number of elements in the Staging Area. If > 0, inserts on the container will block when the capacity is reached.  
  memory_limit   An optional int that is >= 0. Defaults to 0. The maximum number of bytes allowed for Tensors in the Staging Area. If > 0, inserts will block until sufficient space is available.  
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". It is necessary to match this name to the matching Unstage Op.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.StageClear Op removes all elements in the underlying container.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StageClear  
tf.raw_ops.StageClear(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)

 


 Args
  dtypes   A list of tf.DTypes.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.StagePeek Op peeks at the values at the specified index. If the  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StagePeek  
tf.raw_ops.StagePeek(
    index, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 underlying container does not contain sufficient elements this op will block until it does. This Op is optimized for performance.
 


 Args
  index   A Tensor of type int32.  
  dtypes   A list of tf.DTypes that has length >= 1.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type dtypes.
tf.raw_ops.StageSize Op returns the number of elements in the underlying container.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StageSize  
tf.raw_ops.StageSize(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)

 


 Args
  dtypes   A list of tf.DTypes.  
  capacity   An optional int that is >= 0. Defaults to 0.  
  memory_limit   An optional int that is >= 0. Defaults to 0.  
  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 int32.
tf.raw_ops.StatefulPartitionedCall returns f(inputs), where f's body is placed and partitioned.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StatefulPartitionedCall  
tf.raw_ops.StatefulPartitionedCall(
    args, Tout, f, config='', config_proto='',
    executor_type='', name=None
)

 


 Args
  args   A list of Tensor objects. A list of input tensors.  
  Tout   A list of tf.DTypes. A list of output types.  
  f   A function decorated with @Defun. A function that takes 'args', a list of tensors, and returns 'output', another list of tensors. Input and output types are specified by 'Tin' and 'Tout'. The function body of f will be placed and partitioned across devices, setting this op apart from the regular Call op. This op is stateful.  
  config   An optional string. Defaults to "".  
  config_proto   An optional string. Defaults to "".  
  executor_type   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.StatefulRandomBinomial  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StatefulRandomBinomial  
tf.raw_ops.StatefulRandomBinomial(
    resource, algorithm, shape, counts, probs, dtype=tf.dtypes.int64, name=None
)

 


 Args
  resource   A Tensor of type resource.  
  algorithm   A Tensor of type int64.  
  shape   A Tensor. Must be one of the following types: int32, int64.  
  counts   A Tensor. Must be one of the following types: half, float32, float64, int32, int64.  
  probs   A Tensor. Must have the same type as counts.  
  dtype   An optional tf.DType from: tf.half, tf.float32, tf.float64, tf.int32, tf.int64. Defaults to tf.int64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.