neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ScatterUpdate Applies sparse updates to a variable reference.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterUpdate  
tf.raw_ops.ScatterUpdate(
    ref, indices, updates, use_locking=True, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = updates[...]

# Vector indices (for each i)
ref[indices[i], ...] = updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. If values in ref is to be updated more than once, because there are duplicate entries in indices, the order at which the updates happen for each value is undefined. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].    See also tf.batch_scatter_update and tf.scatter_nd_update.
 


 Args
  ref   A mutable Tensor. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   A Tensor. Must have the same type as ref. A tensor of updated values to store in ref.  
  use_locking   An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.SdcaFprint Computes fingerprints of the input strings.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SdcaFprint  
tf.raw_ops.SdcaFprint(
    input, name=None
)

 


 Args
  input   A Tensor of type string. vector of strings to compute fingerprints on.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.SdcaShrinkL1 Applies L1 regularization shrink step on the parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SdcaShrinkL1  
tf.raw_ops.SdcaShrinkL1(
    weights, l1, l2, name=None
)

 


 Args
  weights   A list of Tensor objects with type mutable float32. a list of vectors where each value is the weight associated with a feature group.  
  l1   A float. Symmetric l1 regularization strength.  
  l2   A float. Symmetric l2 regularization strength. Should be a positive float.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.SegmentMax   Computes the maximum along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentMax  
tf.raw_ops.SegmentMax(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \max_j(data_j)\) where max is over j such that segment_ids[j] == i. If the max is empty for a given segment ID i, output[i] = 0.    For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_max(c, tf.constant([0, 0, 1]))
# ==> [[4, 3, 3, 4],
#      [5, 6, 7, 8]]

 


 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. 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.SegmentMean   Computes the mean along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentMean  
tf.raw_ops.SegmentMean(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \frac{\sum_j data_j}{N}\) where mean is over j such that segment_ids[j] == i and N is the total number of values summed. If the mean is empty for a given segment ID i, output[i] = 0.    For example: c = tf.constant([[1.0,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_mean(c, tf.constant([0, 0, 1]))
# ==> [[2.5, 2.5, 2.5, 2.5],
#      [5, 6, 7, 8]]

 


 Args
  data   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.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. 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.SegmentMin   Computes the minimum along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentMin  
tf.raw_ops.SegmentMin(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \min_j(data_j)\) where min is over j such that segment_ids[j] == i. If the min is empty for a given segment ID i, output[i] = 0.    For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_min(c, tf.constant([0, 0, 1]))
# ==> [[1, 2, 2, 1],
#      [5, 6, 7, 8]]

 


 Args
  data   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. 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.SegmentProd   Computes the product along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentProd  
tf.raw_ops.SegmentProd(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \prod_j data_j\) where the product is over j such that segment_ids[j] == i. If the product is empty for a given segment ID i, output[i] = 1.    For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_prod(c, tf.constant([0, 0, 1]))
# ==> [[4, 6, 6, 4],
#      [5, 6, 7, 8]]

 


 Args
  data   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.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. 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.SegmentSum   Computes the sum along segments of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SegmentSum  
tf.raw_ops.SegmentSum(
    data, segment_ids, name=None
)
 Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \sum_j data_j\) where sum is over j such that segment_ids[j] == i. If the sum is empty for a given segment ID i, output[i] = 0.    For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_sum(c, tf.constant([0, 0, 1]))
# ==> [[5, 5, 5, 5],
#      [5, 6, 7, 8]]

 


 Args
  data   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.  
  segment_ids   A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. 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.Select Selects elements from x or y, depending on condition.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Select  
tf.raw_ops.Select(
    condition, x, y, name=None
)
 The x, and y tensors must all have the same shape, and the output will also have that shape. The condition tensor must be a scalar if x and y are scalars. If x and y are vectors or higher rank, then condition must be either a scalar, a vector with size matching the first dimension of x, or must have the same shape as x. The condition tensor acts as a mask that chooses, based on the value at each element, whether the corresponding element / row in the output should be taken from x (if true) or y (if false). If condition is a vector and x and y are higher rank matrices, then it chooses which row (outer dimension) to copy from x and y. If condition has the same shape as x and y, then it chooses which element to copy from x and y. For example: # 'condition' tensor is [[True,  False]
#                        [False, True]]
# 't' is [[1, 2],
#         [3, 4]]
# 'e' is [[5, 6],
#         [7, 8]]
select(condition, t, e)  # => [[1, 6], [7, 4]]


# 'condition' tensor is [True, False]
# 't' is [[1, 2],
#         [3, 4]]
# 'e' is [[5, 6],
#         [7, 8]]
select(condition, t, e) ==> [[1, 2],
                             [7, 8]]


 


 Args
  condition   A Tensor of type bool.  
  x   A Tensor which may have the same shape as condition. If condition is rank 1, x may have higher rank, but its first dimension must match the size of condition.  
  y   A Tensor with the same type and shape as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as t.
tf.raw_ops.SelectV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SelectV2  
tf.raw_ops.SelectV2(
    condition, t, e, name=None
)

 


 Args
  condition   A Tensor of type bool.  
  t   A Tensor.  
  e   A Tensor. Must have the same type as t.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as t.
tf.raw_ops.SelfAdjointEig Computes the Eigen Decomposition of a batch of square self-adjoint matrices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SelfAdjointEig  
tf.raw_ops.SelfAdjointEig(
    input, name=None
)
 The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices, with the same constraints as the single matrix SelfAdjointEig. The result is a [..., M+1, M] matrix with [..., 0,:] containing the eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. The eigenvalues are sorted in non-decreasing order.
 


 Args
  input   A Tensor. Must be one of the following types: float64, float32, half. Shape is [..., M, M].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.SelfAdjointEigV2 Computes the eigen decomposition of one or more square self-adjoint matrices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SelfAdjointEigV2  
tf.raw_ops.SelfAdjointEigV2(
    input, compute_v=True, name=None
)
 Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in input such that input[..., :, :] = v[..., :, :] * diag(e[..., :]). The eigenvalues are sorted in non-decreasing order. # a is a tensor.
# e is a tensor of eigenvalues.
# v is a tensor of eigenvectors.
e, v = self_adjoint_eig(a)
e = self_adjoint_eig(a, compute_v=False)

 


 Args
  input   A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. Tensor input of shape [N, N].  
  compute_v   An optional bool. Defaults to True. If True then eigenvectors will be computed and returned in v. Otherwise, only the eigenvalues will be computed.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (e, v).     e   A Tensor. Has the same type as input.  
  v   A Tensor. Has the same type as input.
tf.raw_ops.Selu Computes scaled exponential linear: scale * alpha * (exp(features) - 1)  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Selu  
tf.raw_ops.Selu(
    features, name=None
)
 if < 0, scale * features otherwise. To be used together with initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN'). For correct dropout, use tf.contrib.nn.alpha_dropout. See Self-Normalizing Neural Networks
 


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


 Returns   A Tensor. Has the same type as features.
tf.raw_ops.SeluGrad Computes gradients for the scaled exponential linear (Selu) operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SeluGrad  
tf.raw_ops.SeluGrad(
    gradients, outputs, name=None
)

 


 Args
  gradients   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding Selu operation.  
  outputs   A Tensor. Must have the same type as gradients. The outputs of the corresponding Selu operation.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as gradients.
tf.raw_ops.Send Sends the named tensor from send_device to recv_device.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Send  
tf.raw_ops.Send(
    tensor, tensor_name, send_device, send_device_incarnation, recv_device,
    client_terminated=False, name=None
)

 


 Args
  tensor   A Tensor. The tensor to send.  
  tensor_name   A string. The name of the tensor to send.  
  send_device   A string. The name of the device sending the tensor.  
  send_device_incarnation   An int. The current incarnation of send_device.  
  recv_device   A string. The name of the device receiving the tensor.  
  client_terminated   An optional bool. Defaults to False. If set to true, this indicates that the node was added to the graph as a result of a client-side feed or fetch of Tensor data, in which case the corresponding send or recv is expected to be managed locally by the caller.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.SendTPUEmbeddingGradients Performs gradient updates of embedding tables.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SendTPUEmbeddingGradients  
tf.raw_ops.SendTPUEmbeddingGradients(
    inputs, learning_rates, config, name=None
)

 


 Args
  inputs   A list of at least 1 Tensor objects with type float32. A TensorList of gradients with which to update embedding tables. This argument has the same length and shapes as the return value of RecvTPUEmbeddingActivations, but contains gradients of the model's loss with respect to the embedding activations. The embedding tables are updated from these gradients via the optimizer specified in the TPU embedding configuration given to tpu.initialize_system.  
  learning_rates   A list of Tensor objects with type float32. A TensorList of float32 scalars, one for each dynamic learning rate tag: see the comments in //third_party/tensorflow/core/protobuf/tpu/optimization_parameters.proto. Multiple tables can share the same dynamic learning rate tag as specified in the configuration. If the learning rates for all tables are constant, this list should be empty.  
  config   A string. Serialized TPUEmbeddingConfiguration proto.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.SerializeIterator Converts the given resource_handle representing an iterator to a variant tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SerializeIterator  
tf.raw_ops.SerializeIterator(
    resource_handle, external_state_policy=0, name=None
)

 


 Args
  resource_handle   A Tensor of type resource. A handle to an iterator resource.  
  external_state_policy   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.SerializeManySparse Serialize an N-minibatch SparseTensor into an [N, 3] Tensor object.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SerializeManySparse  
tf.raw_ops.SerializeManySparse(
    sparse_indices, sparse_values, sparse_shape, out_type=tf.dtypes.string,
    name=None
)
 The SparseTensor must have rank R greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the SparseTensor must be sorted in increasing order of this first dimension. The serialized SparseTensor objects going into each row of serialized_sparse will have rank R-1. The minibatch size N is extracted from sparse_shape[0].
 


 Args
  sparse_indices   A Tensor of type int64. 2-D. The indices of the minibatch SparseTensor.  
  sparse_values   A Tensor. 1-D. The values of the minibatch SparseTensor.  
  sparse_shape   A Tensor of type int64. 1-D. The shape of the minibatch SparseTensor.  
  out_type   An optional tf.DType from: tf.string, tf.variant. Defaults to tf.string. The dtype to use for serialization; the supported types are string (default) and variant.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type out_type.
tf.raw_ops.SerializeSparse Serialize a SparseTensor into a [3] Tensor object.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SerializeSparse  
tf.raw_ops.SerializeSparse(
    sparse_indices, sparse_values, sparse_shape, out_type=tf.dtypes.string,
    name=None
)

 


 Args
  sparse_indices   A Tensor of type int64. 2-D. The indices of the SparseTensor.  
  sparse_values   A Tensor. 1-D. The values of the SparseTensor.  
  sparse_shape   A Tensor of type int64. 1-D. The shape of the SparseTensor.  
  out_type   An optional tf.DType from: tf.string, tf.variant. Defaults to tf.string. The dtype to use for serialization; the supported types are string (default) and variant.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type out_type.
tf.raw_ops.SerializeTensor Transforms a Tensor into a serialized TensorProto proto.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SerializeTensor  
tf.raw_ops.SerializeTensor(
    tensor, name=None
)

 


 Args
  tensor   A Tensor. A Tensor of type T.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.SetSize Number of unique elements along last dimension of input set.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SetSize  
tf.raw_ops.SetSize(
    set_indices, set_values, set_shape, validate_indices=True, name=None
)
 Input set is a SparseTensor represented by set_indices, set_values, and set_shape. The last dimension contains values in a set, duplicates are allowed but ignored. If validate_indices is True, this op validates the order and range of set indices.
 


 Args
  set_indices   A Tensor of type int64. 2D Tensor, indices of a SparseTensor.  
  set_values   A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, string. 1D Tensor, values of a SparseTensor.  
  set_shape   A Tensor of type int64. 1D Tensor, shape of a SparseTensor.  
  validate_indices   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.SetStatsAggregatorDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SetStatsAggregatorDataset  
tf.raw_ops.SetStatsAggregatorDataset(
    input_dataset, stats_aggregator, tag, counter_prefix, output_types,
    output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  stats_aggregator   A Tensor of type resource.  
  tag   A Tensor of type string.  
  counter_prefix   A Tensor of type string.  
  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.Shape Returns the shape of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Shape  
tf.raw_ops.Shape(
    input, out_type=tf.dtypes.int32, name=None
)
 This operation returns a 1-D integer tensor representing the shape of input. For example: # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]

 


 Args
  input   A Tensor.  
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type out_type.
tf.raw_ops.ShapeN Returns shape of tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShapeN  
tf.raw_ops.ShapeN(
    input, out_type=tf.dtypes.int32, name=None
)
 This operation returns N 1-D integer tensors representing shape of input[i]s.
 


 Args
  input   A list of at least 1 Tensor objects with the same type.  
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A list with the same length as input of Tensor objects with type out_type.
tf.raw_ops.ShardDataset Creates a Dataset that includes only 1/num_shards of this dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShardDataset  
tf.raw_ops.ShardDataset(
    input_dataset, num_shards, index, output_types, output_shapes,
    require_non_empty=False, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  num_shards   A Tensor of type int64. An integer representing the number of shards operating in parallel.  
  index   A Tensor of type int64. An integer representing the current worker index.  
  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.  
  require_non_empty   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.ShardedFilename Generate a sharded filename. The filename is printf formatted as  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShardedFilename  
tf.raw_ops.ShardedFilename(
    basename, shard, num_shards, name=None
)
 %s-%05d-of-%05d, basename, shard, num_shards.
 


 Args
  basename   A Tensor of type string.  
  shard   A Tensor of type int32.  
  num_shards   A Tensor of type int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.ShardedFilespec Generate a glob pattern matching all sharded file names.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShardedFilespec  
tf.raw_ops.ShardedFilespec(
    basename, num_shards, name=None
)

 


 Args
  basename   A Tensor of type string.  
  num_shards   A Tensor of type int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.