neulab/docprompting-conala huggingface dataset:
tf.raw_ops.LookupTableImport Replaces the contents of the table with the specified keys and values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableImport  
tf.raw_ops.LookupTableImport(
    table_handle, keys, values, name=None
)
 The tensor keys must be of the same type as the keys of the table. The tensor values must be of the type of the table values.
 


 Args
  table_handle   A Tensor of type mutable string. Handle to the table.  
  keys   A Tensor. Any shape. Keys to look up.  
  values   A Tensor. Values to associate with keys.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LookupTableImportV2 Replaces the contents of the table with the specified keys and values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableImportV2  
tf.raw_ops.LookupTableImportV2(
    table_handle, keys, values, name=None
)
 The tensor keys must be of the same type as the keys of the table. The tensor values must be of the type of the table values.
 


 Args
  table_handle   A Tensor of type resource. Handle to the table.  
  keys   A Tensor. Any shape. Keys to look up.  
  values   A Tensor. Values to associate with keys.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LookupTableInsert Updates the table to associates keys with values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableInsert  
tf.raw_ops.LookupTableInsert(
    table_handle, keys, values, name=None
)
 The tensor keys must be of the same type as the keys of the table. The tensor values must be of the type of the table values.
 


 Args
  table_handle   A Tensor of type mutable string. Handle to the table.  
  keys   A Tensor. Any shape. Keys to look up.  
  values   A Tensor. Values to associate with keys.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LookupTableInsertV2 Updates the table to associates keys with values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableInsertV2  
tf.raw_ops.LookupTableInsertV2(
    table_handle, keys, values, name=None
)
 The tensor keys must be of the same type as the keys of the table. The tensor values must be of the type of the table values.
 


 Args
  table_handle   A Tensor of type resource. Handle to the table.  
  keys   A Tensor. Any shape. Keys to look up.  
  values   A Tensor. Values to associate with keys.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LookupTableRemoveV2 Removes keys and its associated values from a table.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableRemoveV2  
tf.raw_ops.LookupTableRemoveV2(
    table_handle, keys, name=None
)
 The tensor keys must of the same type as the keys of the table. Keys not already in the table are silently ignored.
 


 Args
  table_handle   A Tensor of type resource. Handle to the table.  
  keys   A Tensor. Any shape. Keys of the elements to remove.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LookupTableSize Computes the number of elements in the given table.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableSize  
tf.raw_ops.LookupTableSize(
    table_handle, name=None
)

 


 Args
  table_handle   A Tensor of type mutable string. Handle to the table.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.LookupTableSizeV2 Computes the number of elements in the given table.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableSizeV2  
tf.raw_ops.LookupTableSizeV2(
    table_handle, name=None
)

 


 Args
  table_handle   A Tensor of type resource. Handle to the table.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.LoopCond Forwards the input to the output.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoopCond  
tf.raw_ops.LoopCond(
    input, name=None
)
 This operator represents the loop termination condition used by the "pivot" switches of a loop.
 


 Args
  input   A Tensor of type bool. A boolean scalar, representing the branch predicate of the Switch op.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.LowerBound Applies lower_bound(sorted_search_values, values) along each row.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LowerBound  
tf.raw_ops.LowerBound(
    sorted_inputs, values, out_type=tf.dtypes.int32, name=None
)
 Each set of rows with the same index in (sorted_inputs, values) is treated independently. The resulting row is the equivalent of calling np.searchsorted(sorted_inputs, values, side='left'). The result is not a global index to the entire Tensor, but rather just the index in the last dimension. A 2-D example: sorted_sequence = [[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]] values = [[2, 4, 9], [0, 2, 6]] result = LowerBound(sorted_sequence, values) result == [[1, 2, 2], [0, 1, 5]]
 


 Args
  sorted_inputs   A Tensor. 2-D Tensor where each row is ordered.  
  values   A Tensor. Must have the same type as sorted_inputs. 2-D Tensor with the same numbers of rows as sorted_search_values. Contains the values that will be searched for in sorted_search_values.  
  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.LRN Local Response Normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LRN  
tf.raw_ops.LRN(
    input, depth_radius=5, bias=1, alpha=1, beta=0.5, name=None
)
 The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within depth_radius. In detail, sqr_sum[a, b, c, d] =
    sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
output = input / (bias + alpha * sqr_sum) ** beta
 For details, see Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012).
 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32. 4-D.  
  depth_radius   An optional int. Defaults to 5. 0-D. Half-width of the 1-D normalization window.  
  bias   An optional float. Defaults to 1. An offset (usually positive to avoid dividing by 0).  
  alpha   An optional float. Defaults to 1. A scale factor, usually positive.  
  beta   An optional float. Defaults to 0.5. An exponent.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.LRNGrad Gradients for Local Response Normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LRNGrad  
tf.raw_ops.LRNGrad(
    input_grads, input_image, output_image, depth_radius=5, bias=1, alpha=1,
    beta=0.5, name=None
)

 


 Args
  input_grads   A Tensor. Must be one of the following types: half, bfloat16, float32. 4-D with shape [batch, height, width, channels].  
  input_image   A Tensor. Must have the same type as input_grads. 4-D with shape [batch, height, width, channels].  
  output_image   A Tensor. Must have the same type as input_grads. 4-D with shape [batch, height, width, channels].  
  depth_radius   An optional int. Defaults to 5. A depth radius.  
  bias   An optional float. Defaults to 1. An offset (usually > 0 to avoid dividing by 0).  
  alpha   An optional float. Defaults to 1. A scale factor, usually positive.  
  beta   An optional float. Defaults to 0.5. An exponent.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input_grads.
tf.raw_ops.LSTMBlockCell Computes the LSTM cell forward propagation for 1 time step.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LSTMBlockCell  
tf.raw_ops.LSTMBlockCell(
    x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3,
    use_peephole=False, name=None
)
 This implementation uses 1 weight matrix and 1 bias vector, and there's an optional peephole connection. This kernel op implements the following mathematical equations: xh = [x, h_prev]
[i, f, ci, o] = xh * w + b
f = f + forget_bias

if not use_peephole:
  wci = wcf = wco = 0

i = sigmoid(cs_prev * wci + i)
f = sigmoid(cs_prev * wcf + f)
ci = tanh(ci)

cs = ci .* i + cs_prev .* f
cs = clip(cs, cell_clip)

o = sigmoid(cs * wco + o)
co = tanh(cs)
h = co .* o

 


 Args
  x   A Tensor. Must be one of the following types: half, float32. The input to the LSTM cell, shape (batch_size, num_inputs).  
  cs_prev   A Tensor. Must have the same type as x. Value of the cell state at previous time step.  
  h_prev   A Tensor. Must have the same type as x. Output of the previous cell at previous time step.  
  w   A Tensor. Must have the same type as x. The weight matrix.  
  wci   A Tensor. Must have the same type as x. The weight matrix for input gate peephole connection.  
  wcf   A Tensor. Must have the same type as x. The weight matrix for forget gate peephole connection.  
  wco   A Tensor. Must have the same type as x. The weight matrix for output gate peephole connection.  
  b   A Tensor. Must have the same type as x. The bias vector.  
  forget_bias   An optional float. Defaults to 1. The forget gate bias.  
  cell_clip   An optional float. Defaults to 3. Value to clip the 'cs' value to.  
  use_peephole   An optional bool. Defaults to False. Whether to use peephole weights.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (i, cs, f, o, ci, co, h).     i   A Tensor. Has the same type as x.  
  cs   A Tensor. Has the same type as x.  
  f   A Tensor. Has the same type as x.  
  o   A Tensor. Has the same type as x.  
  ci   A Tensor. Has the same type as x.  
  co   A Tensor. Has the same type as x.  
  h   A Tensor. Has the same type as x.
tf.raw_ops.LSTMBlockCellGrad Computes the LSTM cell backward propagation for 1 timestep.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LSTMBlockCellGrad  
tf.raw_ops.LSTMBlockCellGrad(
    x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad,
    use_peephole, name=None
)
 This implementation is to be used in conjunction of LSTMBlockCell.
 


 Args
  x   A Tensor. Must be one of the following types: half, float32. The input to the LSTM cell, shape (batch_size, num_inputs).  
  cs_prev   A Tensor. Must have the same type as x. The previous cell state.  
  h_prev   A Tensor. Must have the same type as x. The previous h state.  
  w   A Tensor. Must have the same type as x. The weight matrix.  
  wci   A Tensor. Must have the same type as x. The weight matrix for input gate peephole connection.  
  wcf   A Tensor. Must have the same type as x. The weight matrix for forget gate peephole connection.  
  wco   A Tensor. Must have the same type as x. The weight matrix for output gate peephole connection.  
  b   A Tensor. Must have the same type as x. The bias vector.  
  i   A Tensor. Must have the same type as x. The input gate.  
  cs   A Tensor. Must have the same type as x. The cell state before the tanh.  
  f   A Tensor. Must have the same type as x. The forget gate.  
  o   A Tensor. Must have the same type as x. The output gate.  
  ci   A Tensor. Must have the same type as x. The cell input.  
  co   A Tensor. Must have the same type as x. The cell after the tanh.  
  cs_grad   A Tensor. Must have the same type as x. The current gradient of cs.  
  h_grad   A Tensor. Must have the same type as x. The gradient of h vector.  
  use_peephole   A bool. Whether the cell uses peephole connections.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (cs_prev_grad, dicfo, wci_grad, wcf_grad, wco_grad).     cs_prev_grad   A Tensor. Has the same type as x.  
  dicfo   A Tensor. Has the same type as x.  
  wci_grad   A Tensor. Has the same type as x.  
  wcf_grad   A Tensor. Has the same type as x.  
  wco_grad   A Tensor. Has the same type as x.
tf.raw_ops.Lu Computes the LU decomposition of one or more square matrices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Lu  
tf.raw_ops.Lu(
    input, output_idx_type=tf.dtypes.int32, name=None
)
 The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. The input has to be invertible. The output consists of two tensors LU and P containing the LU decomposition of all input submatrices [..., :, :]. LU encodes the lower triangular and upper triangular factors. For each input submatrix of shape [M, M], L is a lower triangular matrix of shape [M, M] with unit diagonal whose entries correspond to the strictly lower triangular part of LU. U is a upper triangular matrix of shape [M, M] whose entries correspond to the upper triangular part, including the diagonal, of LU. P represents a permutation matrix encoded as a list of indices each between 0 and M-1, inclusive. If P_mat denotes the permutation matrix corresponding to P, then the L, U and P satisfies P_mat * input = L * U.
 


 Args
  input   A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. A tensor of shape [..., M, M] whose inner-most 2 dimensions form matrices of size [M, M].  
  output_idx_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (lu, p).     lu   A Tensor. Has the same type as input.  
  p   A Tensor of type output_idx_type.
tf.raw_ops.MakeIterator Makes a new iterator from the given dataset and stores it in iterator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MakeIterator  
tf.raw_ops.MakeIterator(
    dataset, iterator, name=None
)
 This operation may be executed multiple times. Each execution will reset the iterator in iterator to the first element of dataset.
 


 Args
  dataset   A Tensor of type variant.  
  iterator   A Tensor of type resource.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.MapAndBatchDataset Creates a dataset that fuses mapping with batching.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MapAndBatchDataset  
tf.raw_ops.MapAndBatchDataset(
    input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder,
    f, output_types, output_shapes, preserve_cardinality=False, name=None
)
 Creates a dataset that applies f to the outputs of input_dataset and then batches batch_size of them. Unlike a "MapDataset", which applies f sequentially, this dataset invokes up to batch_size * num_parallel_batches copies of f in parallel.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.  
  other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for f.  
  batch_size   A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch. It determines the number of concurrent invocations of f that process elements from input_dataset in parallel.  
  num_parallel_calls   A Tensor of type int64. A scalar representing the maximum number of parallel invocations of the map_fn function. Applying the map_fn on consecutive input elements in parallel has the potential to improve input pipeline throughput.  
  drop_remainder   A Tensor of type bool. A scalar representing whether the last batch should be dropped in case its size is smaller than desired.  
  f   A function decorated with @Defun. A function to apply to the outputs of input_dataset.  
  output_types   A list of tf.DTypes that has length >= 1.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  preserve_cardinality   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.MapClear 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.MapClear  
tf.raw_ops.MapClear(
    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.MapDataset Creates a dataset that applies f to the outputs of input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MapDataset  
tf.raw_ops.MapDataset(
    input_dataset, other_arguments, f, output_types, output_shapes,
    use_inter_op_parallelism=True, preserve_cardinality=False, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  other_arguments   A list of Tensor objects.  
  f   A function decorated with @Defun.  
  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.  
  use_inter_op_parallelism   An optional bool. Defaults to True.  
  preserve_cardinality   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.MapDefun Maps a function on the list of tensors unpacked from arguments on dimension 0.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MapDefun  
tf.raw_ops.MapDefun(
    arguments, captured_inputs, output_types, output_shapes, f,
    max_intra_op_parallelism=1, name=None
)
 The function given by f is assumed to be stateless, and is executed concurrently on all the slices; up to batch_size (i.e. the size of the 0th dimension of each argument) functions will be scheduled at once. The max_intra_op_parallelism attr, which defaults to 1, can be used to limit the intra op parallelism. To limit inter-op parallelism, a user can set a private threadpool on the dataset using tf.data.Options's ThreadingOptions. Note that this op is not exposed to users directly, but is invoked in tf.data rewrites.
 


 Args
  arguments   A list of Tensor objects. A list of tensors whose types are Targuments, corresponding to the inputs the function should be mapped over.  
  captured_inputs   A list of Tensor objects. A list of tensors whose types are Tcaptured, corresponding to the captured inputs of the defun.  
  output_types   A list of tf.DTypes that has length >= 1. A list of types.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1. A list of shapes.  
  f   A function decorated with @Defun.  
  max_intra_op_parallelism   An optional int. Defaults to 1.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type output_types.
tf.raw_ops.MapIncompleteSize Op returns the number of incomplete elements in the underlying container.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MapIncompleteSize  
tf.raw_ops.MapIncompleteSize(
    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.MapPeek Op peeks at the values at the specified key. If the  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MapPeek  
tf.raw_ops.MapPeek(
    key, indices, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 underlying container does not contain this key this op will block until it does.
 


 Args
  key   A Tensor of type int64.  
  indices   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.MapSize 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.MapSize  
tf.raw_ops.MapSize(
    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.MapStage Stage (key, values) in the underlying container which behaves like a hashtable.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MapStage  
tf.raw_ops.MapStage(
    key, indices, values, dtypes, capacity=0, memory_limit=0,
    container='', shared_name='', name=None
)

 


 Args
  key   A Tensor of type int64. int64  
  indices   A Tensor of type int32.  
  values   A list of Tensor objects. a list of tensors dtypes A list of data types that inserted values should adhere to.  
  dtypes   A list of tf.DTypes.  
  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.  
  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.MapUnstage Op removes and returns the values associated with the key  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MapUnstage  
tf.raw_ops.MapUnstage(
    key, indices, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 from the underlying container. If the underlying container does not contain this key, the op will block until it does.
 


 Args
  key   A Tensor of type int64.  
  indices   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.