neulab/docprompting-conala huggingface dataset:
tf.raw_ops.TensorListPushBackBatch  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListPushBackBatch  
tf.raw_ops.TensorListPushBackBatch(
    input_handles, tensor, name=None
)

 


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


 Returns   A Tensor of type variant.
tf.raw_ops.TensorListReserve List of the given size with empty elements.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListReserve  
tf.raw_ops.TensorListReserve(
    element_shape, num_elements, element_dtype, name=None
)
 element_shape: the shape of the future elements of the list num_elements: the number of elements to reserve handle: the output list element_dtype: the desired type of elements in the list.
 


 Args
  element_shape   A Tensor. Must be one of the following types: int32, int64.  
  num_elements   A Tensor of type int32.  
  element_dtype   A tf.DType.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TensorListResize Resizes the list.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListResize  
tf.raw_ops.TensorListResize(
    input_handle, size, name=None
)
 input_handle: the input list size: size of the output list
 


 Args
  input_handle   A Tensor of type variant.  
  size   A Tensor of type int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TensorListScatter Creates a TensorList by indexing into a Tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListScatter  
tf.raw_ops.TensorListScatter(
    tensor, indices, element_shape, name=None
)
 Each member of the TensorList corresponds to one row of the input tensor, specified by the given index (see tf.gather). tensor: The input tensor. indices: The indices used to index into the list. element_shape: The shape of the elements in the list (can be less specified than the shape of the tensor). output_handle: The TensorList.
 


 Args
  tensor   A Tensor.  
  indices   A Tensor of type int32.  
  element_shape   A Tensor. Must be one of the following types: int32, int64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TensorListScatterIntoExistingList Scatters tensor at indices in an input list.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListScatterIntoExistingList  
tf.raw_ops.TensorListScatterIntoExistingList(
    input_handle, tensor, indices, name=None
)
 Each member of the TensorList corresponds to one row of the input tensor, specified by the given index (see tf.gather). input_handle: The list to scatter into. tensor: The input tensor. indices: The indices used to index into the list. output_handle: The TensorList.
 


 Args
  input_handle   A Tensor of type variant.  
  tensor   A Tensor.  
  indices   A Tensor of type int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TensorListScatterV2 Creates a TensorList by indexing into a Tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListScatterV2  
tf.raw_ops.TensorListScatterV2(
    tensor, indices, element_shape, num_elements, name=None
)
 Each member of the TensorList corresponds to one row of the input tensor, specified by the given index (see tf.gather). tensor: The input tensor. indices: The indices used to index into the list. element_shape: The shape of the elements in the list (can be less specified than the shape of the tensor). num_elements: The size of the output list. Must be large enough to accommodate the largest index in indices. If -1, the list is just large enough to include the largest index in indices. output_handle: The TensorList.
 


 Args
  tensor   A Tensor.  
  indices   A Tensor of type int32.  
  element_shape   A Tensor. Must be one of the following types: int32, int64.  
  num_elements   A Tensor of type int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TensorListSetItem Sets the index-th position of the list to contain the given tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListSetItem  
tf.raw_ops.TensorListSetItem(
    input_handle, index, item, name=None
)
 input_handle: the list index: the position in the list to which the tensor will be assigned item: the element to be assigned to that position output_handle: the new list, with the element in the proper position
 


 Args
  input_handle   A Tensor of type variant.  
  index   A Tensor of type int32.  
  item   A Tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TensorListSplit Splits a tensor into a list.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListSplit  
tf.raw_ops.TensorListSplit(
    tensor, element_shape, lengths, name=None
)
 list[i] corresponds to lengths[i] tensors from the input tensor. The tensor must have rank at least 1 and contain exactly sum(lengths) elements. tensor: The input tensor. element_shape: A shape compatible with that of elements in the tensor. lengths: Vector of sizes of the 0th dimension of tensors in the list. output_handle: The list.
 


 Args
  tensor   A Tensor.  
  element_shape   A Tensor. Must be one of the following types: int32, int64.  
  lengths   A Tensor of type int64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TensorListStack Stacks all tensors in the list.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorListStack  
tf.raw_ops.TensorListStack(
    input_handle, element_shape, element_dtype, num_elements=-1, name=None
)
 Requires that all tensors have the same shape. input_handle: the input list tensor: the gathered result num_elements: optional. If not -1, the number of elements in the list.
 


 Args
  input_handle   A Tensor of type variant.  
  element_shape   A Tensor of type int32.  
  element_dtype   A tf.DType.  
  num_elements   An optional int. Defaults to -1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type element_dtype.
tf.raw_ops.TensorScatterAdd Adds sparse updates to an existing tensor according to indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterAdd  
tf.raw_ops.TensorScatterAdd(
    tensor, indices, updates, name=None
)
 This operation creates a new tensor by adding sparse updates to the passed in tensor. This operation is very similar to tf.scatter_nd_add, except that the updates are added onto an existing tensor (as opposed to a variable). If the memory for the existing tensor cannot be re-used, a copy is made and updated. indices is an integer tensor containing indices into a new tensor of shape tensor.shape. The last dimension of indices can be at most the rank of tensor.shape: indices.shape[-1] <= tensor.shape.rank
 The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = tensor.shape.rank) or slices (if indices.shape[-1] < tensor.shape.rank) along dimension indices.shape[-1] of tensor.shape. updates is a tensor with shape indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
 The simplest form of tensor_scatter_add is to add individual elements to a tensor by index. For example, say we want to add 4 elements in a rank-1 tensor with 8 elements. In Python, this scatter add operation would look like this: indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones([8], dtype=tf.int32)
updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
print(updated)
 The resulting tensor would look like this: [1, 12, 1, 11, 10, 1, 1, 13]
 We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values. In Python, this scatter add operation would look like this: indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                        [7, 7, 7, 7], [8, 8, 8, 8]],
                       [[5, 5, 5, 5], [6, 6, 6, 6],
                        [7, 7, 7, 7], [8, 8, 8, 8]]])
tensor = tf.ones([4, 4, 4],dtype=tf.int32)
updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
print(updated)
 The resulting tensor would look like this: [[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
 [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]
 Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.
 


 Args
  tensor   A Tensor. Tensor to copy/update.  
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.  
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.TensorScatterMax  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterMax  
tf.raw_ops.TensorScatterMax(
    tensor, indices, updates, name=None
)

 


 Args
  tensor   A Tensor. Tensor to update.  
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.  
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.TensorScatterMin  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterMin  
tf.raw_ops.TensorScatterMin(
    tensor, indices, updates, name=None
)

 


 Args
  tensor   A Tensor. Tensor to update.  
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.  
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.TensorScatterSub Subtracts sparse updates from an existing tensor according to indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterSub  
tf.raw_ops.TensorScatterSub(
    tensor, indices, updates, name=None
)
 This operation creates a new tensor by subtracting sparse updates from the passed in tensor. This operation is very similar to tf.scatter_nd_sub, except that the updates are subtracted from an existing tensor (as opposed to a variable). If the memory for the existing tensor cannot be re-used, a copy is made and updated. indices is an integer tensor containing indices into a new tensor of shape shape. The last dimension of indices can be at most the rank of shape: indices.shape[-1] <= shape.rank
 The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = shape.rank) or slices (if indices.shape[-1] < shape.rank) along dimension indices.shape[-1] of shape. updates is a tensor with shape indices.shape[:-1] + shape[indices.shape[-1]:]
 The simplest form of tensor_scatter_sub is to subtract individual elements from a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements. In Python, this scatter subtract operation would look like this: indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones([8], dtype=tf.int32)
updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
print(updated)
 The resulting tensor would look like this: [1, -10, 1, -9, -8, 1, 1, -11]
 We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values. In Python, this scatter add operation would look like this: indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                        [7, 7, 7, 7], [8, 8, 8, 8]],
                       [[5, 5, 5, 5], [6, 6, 6, 6],
                        [7, 7, 7, 7], [8, 8, 8, 8]]])
tensor = tf.ones([4, 4, 4],dtype=tf.int32)
updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
print(updated)
 The resulting tensor would look like this: [[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
 [[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]
 Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.
 


 Args
  tensor   A Tensor. Tensor to copy/update.  
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.  
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.TensorScatterUpdate Scatter updates into an existing tensor according to indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterUpdate  
tf.raw_ops.TensorScatterUpdate(
    tensor, indices, updates, name=None
)
 This operation creates a new tensor by applying sparse updates to the passed in tensor. This operation is very similar to tf.scatter_nd, except that the updates are scattered onto an existing tensor (as opposed to a zero-tensor). If the memory for the existing tensor cannot be re-used, a copy is made and updated. If indices contains duplicates, then we pick the last update for the index. If an out of bound index is found on CPU, an error is returned. Warning: There are some GPU specific semantics for this operation.  If an out of bound index is found, the index is ignored. The order in which updates are applied is nondeterministic, so the output will be nondeterministic if indices contains duplicates.  indices is an integer tensor containing indices into a new tensor of shape shape.  
indices must have at least 2 axes: (num_updates, index_depth). The last axis of indices is how deep to index into tensor so this index depth must be less than the rank of tensor: indices.shape[-1] <= tensor.ndim
  if indices.shape[-1] = tensor.rank this Op indexes and updates scalar elements. if indices.shape[-1] < tensor.rank it indexes and updates slices of the input tensor. Each update has a rank of tensor.rank - indices.shape[-1]. The overall shape of updates is: indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
 For usage examples see the python tf.tensor_scatter_nd_update function
 


 Args
  tensor   A Tensor. Tensor to copy/update.  
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.  
  updates   A Tensor. Must have the same type as tensor. Updates to scatter into output.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.TensorSliceDataset Creates a dataset that emits each dim-0 slice of components once.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorSliceDataset  
tf.raw_ops.TensorSliceDataset(
    components, output_shapes, name=None
)

 


 Args
  components   A list of Tensor objects.  
  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.TensorStridedSliceUpdate Assign value to the sliced l-value reference of input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorStridedSliceUpdate  
tf.raw_ops.TensorStridedSliceUpdate(
    input, begin, end, strides, value, begin_mask=0, end_mask=0, ellipsis_mask=0,
    new_axis_mask=0, shrink_axis_mask=0, name=None
)
 The values of value are assigned to the positions in the tensor input that are selected by the slice parameters. The slice parameters begin end strides etc. work exactly as in StridedSlice. NOTE this op currently does not support broadcasting and so value's shape must be exactly the shape produced by the slice of input.
 


 Args
  input   A Tensor.  
  begin   A Tensor. Must be one of the following types: int32, int64.  
  end   A Tensor. Must have the same type as begin.  
  strides   A Tensor. Must have the same type as begin.  
  value   A Tensor. Must have the same type as input.  
  begin_mask   An optional int. Defaults to 0.  
  end_mask   An optional int. Defaults to 0.  
  ellipsis_mask   An optional int. Defaults to 0.  
  new_axis_mask   An optional int. Defaults to 0.  
  shrink_axis_mask   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.TensorSummary Outputs a Summary protocol buffer with a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorSummary  
tf.raw_ops.TensorSummary(
    tensor, description='', labels=[], display_name='',
    name=None
)
 This op is being phased out in favor of TensorSummaryV2, which lets callers pass a tag as well as a serialized SummaryMetadata proto string that contains plugin-specific data. We will keep this op to maintain backwards compatibility.
 


 Args
  tensor   A Tensor. A tensor to serialize.  
  description   An optional string. Defaults to "". A json-encoded SummaryDescription proto.  
  labels   An optional list of strings. Defaults to []. An unused list of strings.  
  display_name   An optional string. Defaults to "". An unused string.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.TensorSummaryV2 Outputs a Summary protocol buffer with a tensor and per-plugin data.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TensorSummaryV2  
tf.raw_ops.TensorSummaryV2(
    tag, tensor, serialized_summary_metadata, name=None
)

 


 Args
  tag   A Tensor of type string. A string attached to this summary. Used for organization in TensorBoard.  
  tensor   A Tensor. A tensor to serialize.  
  serialized_summary_metadata   A Tensor of type string. A serialized SummaryMetadata proto. Contains plugin data.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.TextLineDataset Creates a dataset that emits the lines of one or more text files.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TextLineDataset  
tf.raw_ops.TextLineDataset(
    filenames, compression_type, buffer_size, name=None
)

 


 Args
  filenames   A Tensor of type string. A scalar or a vector containing the name(s) of the file(s) to be read.  
  compression_type   A Tensor of type string. A scalar containing either (i) the empty string (no compression), (ii) "ZLIB", or (iii) "GZIP".  
  buffer_size   A Tensor of type int64. A scalar containing the number of bytes to buffer.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TextLineReader A Reader that outputs the lines of a file delimited by '\n'.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TextLineReader  
tf.raw_ops.TextLineReader(
    skip_header_lines=0, container='', shared_name='', name=None
)

 


 Args
  skip_header_lines   An optional int. Defaults to 0. Number of lines to skip from the beginning of every file.  
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.TextLineReaderV2 A Reader that outputs the lines of a file delimited by '\n'.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TextLineReaderV2  
tf.raw_ops.TextLineReaderV2(
    skip_header_lines=0, container='', shared_name='', name=None
)

 


 Args
  skip_header_lines   An optional int. Defaults to 0. Number of lines to skip from the beginning of every file.  
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.TFRecordDataset Creates a dataset that emits the records from one or more TFRecord files.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TFRecordDataset  
tf.raw_ops.TFRecordDataset(
    filenames, compression_type, buffer_size, name=None
)

 


 Args
  filenames   A Tensor of type string. A scalar or vector containing the name(s) of the file(s) to be read.  
  compression_type   A Tensor of type string. A scalar containing either (i) the empty string (no compression), (ii) "ZLIB", or (iii) "GZIP".  
  buffer_size   A Tensor of type int64. A scalar representing the number of bytes to buffer. A value of 0 means no buffering will be performed.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.TFRecordReader A Reader that outputs the records from a TensorFlow Records file.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TFRecordReader  
tf.raw_ops.TFRecordReader(
    container='', shared_name='', compression_type='',
    name=None
)

 


 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.  
  compression_type   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.TFRecordReaderV2 A Reader that outputs the records from a TensorFlow Records file.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.TFRecordReaderV2  
tf.raw_ops.TFRecordReaderV2(
    container='', shared_name='', compression_type='',
    name=None
)

 


 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.  
  compression_type   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.