neulab/docprompting-conala huggingface dataset:
tf.raw_ops.Sub Returns x - y element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Sub  
tf.raw_ops.Sub(
    x, y, name=None
)
 
Note: Subtract supports broadcasting. More about broadcasting here

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128, uint32.  
  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.Substr Return substrings from Tensor of strings.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Substr  
tf.raw_ops.Substr(
    input, pos, len, unit='BYTE', name=None
)
 For each string in the input Tensor, creates a substring starting at index pos with a total length of len. If len defines a substring that would extend beyond the length of the input string, or if len is negative, then as many characters as possible are used. A negative pos indicates distance within the string backwards from the end. If pos specifies an index which is out of range for any of the input strings, then an InvalidArgumentError is thrown. pos and len must have the same shape, otherwise a ValueError is thrown on Op creation. 
Note: Substr supports broadcasting up to two dimensions. More about broadcasting here
 Examples Using scalar pos and len: input = [b'Hello', b'World']
position = 1
length = 3

output = [b'ell', b'orl']
 Using pos and len with same shape as input: input = [[b'ten', b'eleven', b'twelve'],
         [b'thirteen', b'fourteen', b'fifteen'],
         [b'sixteen', b'seventeen', b'eighteen']]
position = [[1, 2, 3],
            [1, 2, 3],
            [1, 2, 3]]
length =   [[2, 3, 4],
            [4, 3, 2],
            [5, 5, 5]]

output = [[b'en', b'eve', b'lve'],
          [b'hirt', b'urt', b'te'],
          [b'ixtee', b'vente', b'hteen']]
 Broadcasting pos and len onto input: input = [[b'ten', b'eleven', b'twelve'],
         [b'thirteen', b'fourteen', b'fifteen'],
         [b'sixteen', b'seventeen', b'eighteen'],
         [b'nineteen', b'twenty', b'twentyone']]
position = [1, 2, 3]
length =   [1, 2, 3]

output = [[b'e', b'ev', b'lve'],
          [b'h', b'ur', b'tee'],
          [b'i', b've', b'hte'],
          [b'i', b'en', b'nty']]
 Broadcasting input onto pos and len: input = b'thirteen'
position = [1, 5, 7]
length =   [3, 2, 1]

output = [b'hir', b'ee', b'n']

 


 Raises    
ValueError: If the first argument cannot be converted to a Tensor of dtype string. 
InvalidArgumentError: If indices are out of range. 
ValueError: If pos and len are not the same shape. 

 

 


 Args
  input   A Tensor of type string. Tensor of strings  
  pos   A Tensor. Must be one of the following types: int32, int64. Scalar defining the position of first character in each substring  
  len   A Tensor. Must have the same type as pos. Scalar defining the number of characters to include in each substring  
  unit   An optional string from: "BYTE", "UTF8_CHAR". Defaults to "BYTE". The unit that is used to create the substring. One of: "BYTE" (for defining position and length by bytes) or "UTF8_CHAR" (for the UTF-8 encoded Unicode code points). The default is "BYTE". Results are undefined if unit=UTF8_CHAR and the input strings do not contain structurally valid UTF-8.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.Sum Computes the sum of elements across dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Sum  
tf.raw_ops.Sum(
    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. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. 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. Has the same type as input.
tf.raw_ops.SummaryWriter  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SummaryWriter  
tf.raw_ops.SummaryWriter(
    shared_name='', container='', name=None
)

 


 Args
  shared_name   An optional string. Defaults to "".  
  container   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.Svd Computes the singular value decompositions of one or more matrices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Svd  
tf.raw_ops.Svd(
    input, compute_uv=True, full_matrices=False, name=None
)
 Computes the SVD of each inner matrix in input such that input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :]) # a is a tensor containing a batch of matrices.
# s is a tensor of singular values for each matrix.
# u is the tensor containing the left singular vectors for each matrix.
# v is the tensor containing the right singular vectors for each matrix.
s, u, v = svd(a)
s, _, _ = svd(a, compute_uv=False)

 


 Args
  input   A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. A tensor of shape [..., M, N] whose inner-most 2 dimensions form matrices of size [M, N]. Let P be the minimum of M and N.  
  compute_uv   An optional bool. Defaults to True. If true, left and right singular vectors will be computed and returned in u and v, respectively. If false, u and v are not set and should never referenced.  
  full_matrices   An optional bool. Defaults to False. If true, compute full-sized u and v. If false (the default), compute only the leading P singular vectors. Ignored if compute_uv is False.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (s, u, v).     s   A Tensor. Has the same type as input.  
  u   A Tensor. Has the same type as input.  
  v   A Tensor. Has the same type as input.
tf.raw_ops.Switch Forwards data to the output port determined by pred.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Switch  
tf.raw_ops.Switch(
    data, pred, name=None
)
 If pred is true, the data input is forwarded to output_true. Otherwise, the data goes to output_false. See also RefSwitch and Merge.
 


 Args
  data   A Tensor. The tensor to be forwarded to the appropriate output.  
  pred   A Tensor of type bool. A scalar that specifies which output port will receive data.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_false, output_true).     output_false   A Tensor. Has the same type as data.  
  output_true   A Tensor. Has the same type as data.
tf.raw_ops.SymbolicGradient Computes the gradient function for function f via backpropagation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SymbolicGradient  
tf.raw_ops.SymbolicGradient(
    input, Tout, f, name=None
)

 


 Args
  input   A list of Tensor objects. a list of input tensors of size N + M;  
  Tout   A list of tf.DTypes that has length >= 1. the type list for the input list.  
  f   A function decorated with @Defun. The function we want to compute the gradient for. The function 'f' must be a numerical function which takes N inputs and produces M outputs. Its gradient function 'g', which is computed by this SymbolicGradient op is a function taking N + M inputs and produces N outputs. I.e. if we have (y1, y2, ..., y_M) = f(x1, x2, ..., x_N), then, g is (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N, dL/dy1, dL/dy2, ..., dL/dy_M), where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the loss function). dL/dx_i is the partial derivative of L with respect to x_i. (Needs some math expert to say the comment above better.) 
 
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.TakeDataset Creates a dataset that contains count elements from the input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TakeDataset  
tf.raw_ops.TakeDataset(
    input_dataset, count, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  count   A Tensor of type int64. A scalar representing the number of elements from the input_dataset that should be taken. A value of -1 indicates that all of input_dataset is taken.  
  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.TakeManySparseFromTensorsMap Read SparseTensors from a SparseTensorsMap and concatenate them.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TakeManySparseFromTensorsMap  
tf.raw_ops.TakeManySparseFromTensorsMap(
    sparse_handles, dtype, container='', shared_name='',
    name=None
)
 The input sparse_handles must be an int64 matrix of shape [N, 1] where N is the minibatch size and the rows correspond to the output handles of AddSparseToTensorsMap or AddManySparseToTensorsMap. The ranks of the original SparseTensor objects that went into the given input ops must all match. When the final SparseTensor is created, it has rank one higher than the ranks of the incoming SparseTensor objects (they have been concatenated along a new row dimension on the left). The output SparseTensor object's shape values for all dimensions but the first are the max across the input SparseTensor objects' shape values for the corresponding dimensions. Its first shape value is N, the minibatch size. The input SparseTensor objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run SparseReorder to restore index ordering. For example, if the handles represent an input, which is a [2, 3] matrix representing two original SparseTensor objects: index = [ 0]
        [10]
        [20]
values = [1, 2, 3]
shape = [50]
 and index = [ 2]
        [10]
values = [4, 5]
shape = [30]
 then the final SparseTensor will be: index = [0  0]
        [0 10]
        [0 20]
        [1  2]
        [1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]

 


 Args
  sparse_handles   A Tensor of type int64. 1-D, The N serialized SparseTensor objects. Shape: [N].  
  dtype   A tf.DType. The dtype of the SparseTensor objects stored in the SparseTensorsMap.  
  container   An optional string. Defaults to "". The container name for the SparseTensorsMap read by this op.  
  shared_name   An optional string. Defaults to "". The shared name for the SparseTensorsMap read by this op. It should not be blank; rather the shared_name or unique Operation name of the Op that created the original SparseTensorsMap should be used.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (sparse_indices, sparse_values, sparse_shape).     sparse_indices   A Tensor of type int64.  
  sparse_values   A Tensor of type dtype.  
  sparse_shape   A Tensor of type int64.
tf.raw_ops.TakeWhileDataset Creates a dataset that stops iteration when predicate` is false.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TakeWhileDataset  
tf.raw_ops.TakeWhileDataset(
    input_dataset, other_arguments, predicate, output_types, output_shapes,
    name=None
)
 The predicate function must return a scalar boolean and accept the following arguments:  One tensor for each component of an element of input_dataset. One tensor for each value in other_arguments. 
 


 Args
  input_dataset   A Tensor of type variant.  
  other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for predicate.  
  predicate   A function decorated with @Defun. A function returning a scalar boolean.  
  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.Tan Computes tan of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Tan  
tf.raw_ops.Tan(
    x, name=None
)
 Given an input tensor, this function computes tangent of every element in the tensor. Input range is (-inf, inf) and output range is (-inf, inf). If input lies outside the boundary, nan is returned. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])
tf.math.tan(x) ==> [nan 0.45231566 -0.5463025 1.5574077 2.572152 -1.7925274 0.32097113 nan]

 


 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.Tanh Computes hyperbolic tangent of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Tanh  
tf.raw_ops.Tanh(
    x, name=None
)
 Given an input tensor, this function computes hyperbolic tangent of every element in the tensor. Input range is [-inf, inf] and output range is [-1,1]. 
x = tf.constant([-float("inf"), -5, -0.5, 1, 1.2, 2, 3, float("inf")])
tf.math.tanh(x)
  <tf.Tensor: shape=(8,), dtype=float32, numpy=
  array([-1.        , -0.99990916, -0.46211717,  0.7615942 ,  0.8336547 ,
          0.9640276 ,  0.9950547 ,  1.        ], dtype=float32)>
  

 


 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.TanhGrad Computes the gradient for the tanh of x wrt its input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TanhGrad  
tf.raw_ops.TanhGrad(
    y, dy, name=None
)
 Specifically, grad = dy * (1 - y*y), where y = tanh(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.TemporaryVariable Returns a tensor that may be mutated, but only persists within a single step.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TemporaryVariable  
tf.raw_ops.TemporaryVariable(
    shape, dtype, var_name='', name=None
)
 This is an experimental op for internal use only and it is possible to use this op in unsafe ways. DO NOT USE unless you fully understand the risks. It is the caller's responsibility to ensure that 'ref' is eventually passed to a matching 'DestroyTemporaryVariable' op after all other uses have completed. Outputs a ref to the tensor state so it may be read or modified. E.g. var = state_ops._temporaryvariable([1, 2], types.float) var_name = var.op.name var = state_ops.assign(var, [[4.0, 5.0]]) var = state_ops.assign_add(var, [[6.0, 7.0]]) final = state_ops._destroy_temporary_variable(var, var_name=var_name)
 


 Args
  shape   A tf.TensorShape or list of ints. The shape of the variable tensor.  
  dtype   A tf.DType. The type of elements in the variable tensor.  
  var_name   An optional string. Defaults to "". Overrides the name used for the temporary variable resource. Default value is the name of the 'TemporaryVariable' op (which is guaranteed unique).  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor of type dtype.
tf.raw_ops.TensorArray  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArray  
tf.raw_ops.TensorArray(
    size, dtype, dynamic_size=False, clear_after_read=True,
    tensor_array_name='', element_shape=None, name=None
)

 


 Args
  size   A Tensor of type int32.  
  dtype   A tf.DType.  
  dynamic_size   An optional bool. Defaults to False.  
  clear_after_read   An optional bool. Defaults to True.  
  tensor_array_name   An optional string. Defaults to "".  
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.TensorArrayClose  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayClose  
tf.raw_ops.TensorArrayClose(
    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.TensorArrayCloseV2 Deprecated. Use TensorArrayCloseV3  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayCloseV2  
tf.raw_ops.TensorArrayCloseV2(
    handle, name=None
)

 


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


 Returns   The created Operation.
tf.raw_ops.TensorArrayCloseV3 Delete the TensorArray from its resource container.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayCloseV3  
tf.raw_ops.TensorArrayCloseV3(
    handle, name=None
)
 This enables the user to close and release the resource in the middle of a step/run.
 


 Args
  handle   A Tensor of type resource. The handle to a TensorArray (output of TensorArray or TensorArrayGrad).  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.TensorArrayConcat  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayConcat  
tf.raw_ops.TensorArrayConcat(
    handle, flow_in, dtype, element_shape_except0=None, name=None
)

 


 Args
  handle   A Tensor of type mutable string.  
  flow_in   A Tensor of type float32.  
  dtype   A tf.DType.  
  element_shape_except0   An optional tf.TensorShape or list of ints. Defaults to None.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (value, lengths).     value   A Tensor of type dtype.  
  lengths   A Tensor of type int64.
tf.raw_ops.TensorArrayConcatV2 Deprecated. Use TensorArrayConcatV3  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayConcatV2  
tf.raw_ops.TensorArrayConcatV2(
    handle, flow_in, dtype, element_shape_except0=None, name=None
)

 


 Args
  handle   A Tensor of type string.  
  flow_in   A Tensor of type float32.  
  dtype   A tf.DType.  
  element_shape_except0   An optional tf.TensorShape or list of ints. Defaults to None.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (value, lengths).     value   A Tensor of type dtype.  
  lengths   A Tensor of type int64.
tf.raw_ops.TensorArrayConcatV3 Concat the elements from the TensorArray into value value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayConcatV3  
tf.raw_ops.TensorArrayConcatV3(
    handle, flow_in, dtype, element_shape_except0=None, name=None
)
 Takes T elements of shapes (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...)
 and concatenates them into a Tensor of shape: (n0 + n1 + ... + n(T-1) x d0 x d1 x ...) All elements must have the same shape (excepting the first dimension).
 


 Args
  handle   A Tensor of type resource. The handle to a TensorArray.  
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.  
  dtype   A tf.DType. The type of the elem that is returned.  
  element_shape_except0   An optional tf.TensorShape or list of ints. Defaults to None. The expected shape of an element, if known, excluding the first dimension. Used to validate the shapes of TensorArray elements. If this shape is not fully specified, concatenating zero-size TensorArrays is an error.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (value, lengths).     value   A Tensor of type dtype.  
  lengths   A Tensor of type int64.
tf.raw_ops.TensorArrayGather  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGather  
tf.raw_ops.TensorArrayGather(
    handle, indices, flow_in, dtype, element_shape=None, name=None
)

 


 Args
  handle   A Tensor of type mutable string.  
  indices   A Tensor of type int32.  
  flow_in   A Tensor of type float32.  
  dtype   A tf.DType.  
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.TensorArrayGatherV2 Deprecated. Use TensorArrayGatherV3  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGatherV2  
tf.raw_ops.TensorArrayGatherV2(
    handle, indices, flow_in, dtype, element_shape=None, name=None
)

 


 Args
  handle   A Tensor of type string.  
  indices   A Tensor of type int32.  
  flow_in   A Tensor of type float32.  
  dtype   A tf.DType.  
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.TensorArrayGatherV3 Gather specific elements from the TensorArray into output value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGatherV3  
tf.raw_ops.TensorArrayGatherV3(
    handle, indices, flow_in, dtype, element_shape=None, name=None
)
 All elements selected by indices must have the same shape.
 


 Args
  handle   A Tensor of type resource. The handle to a TensorArray.  
  indices   A Tensor of type int32. The locations in the TensorArray from which to read tensor elements.  
  flow_in   A Tensor of type float32. A float scalar that enforces proper chaining of operations.  
  dtype   A tf.DType. The type of the elem that is returned.  
  element_shape   An optional tf.TensorShape or list of ints. Defaults to None. The expected shape of an element, if known. Used to validate the shapes of TensorArray elements. If this shape is not fully specified, gathering zero-size TensorArrays is an error.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.TensorArrayGrad  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGrad  
tf.raw_ops.TensorArrayGrad(
    handle, flow_in, source, name=None
)

 


 Args
  handle   A Tensor of type string.  
  flow_in   A Tensor of type float32.  
  source   A string.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.TensorArrayGradV2 Deprecated. Use TensorArrayGradV3  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGradV2  
tf.raw_ops.TensorArrayGradV2(
    handle, flow_in, source, name=None
)

 


 Args
  handle   A Tensor of type string.  
  flow_in   A Tensor of type float32.  
  source   A string.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.