neulab/docprompting-conala huggingface dataset:
tf.raw_ops.CudnnRNNCanonicalToParams Converts CudnnRNN params from canonical form to usable form.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNCanonicalToParams  
tf.raw_ops.CudnnRNNCanonicalToParams(
    num_layers, num_units, input_size, weights, biases, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, name=None
)
 Writes a set of weights into the opaque params buffer so they can be used in upcoming training or inferences. Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. num_params: number of parameter sets for all layers. Each layer may contain multiple parameter sets, with each set consisting of a weight matrix and a bias vector. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout.
 


 Args
  num_layers   A Tensor of type int32.  
  num_units   A Tensor of type int32.  
  input_size   A Tensor of type int32.  
  weights   A list of at least 1 Tensor objects with the same type in: half, float32, float64.  
  biases   A list with the same length as weights of Tensor objects with the same type as weights.  
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".  
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".  
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".  
  dropout   An optional float. Defaults to 0.  
  seed   An optional int. Defaults to 0.  
  seed2   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as weights.
tf.raw_ops.CudnnRNNCanonicalToParamsV2 Converts CudnnRNN params from canonical form to usable form. It supports the projection in LSTM.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNCanonicalToParamsV2  
tf.raw_ops.CudnnRNNCanonicalToParamsV2(
    num_layers, num_units, input_size, weights, biases, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, num_proj=0, name=None
)
 Writes a set of weights into the opaque params buffer so they can be used in upcoming training or inferences. Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. num_params_weights: number of weight parameter matrix for all layers. num_params_biases: number of bias parameter vector for all layers. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout. num_proj: The output dimensionality for the projection matrices. If None or 0, no projection is performed.
 


 Args
  num_layers   A Tensor of type int32.  
  num_units   A Tensor of type int32.  
  input_size   A Tensor of type int32.  
  weights   A list of at least 1 Tensor objects with the same type in: half, float32, float64.  
  biases   A list of at least 1 Tensor objects with the same type as weights.  
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".  
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".  
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".  
  dropout   An optional float. Defaults to 0.  
  seed   An optional int. Defaults to 0.  
  seed2   An optional int. Defaults to 0.  
  num_proj   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as weights.
tf.raw_ops.CudnnRNNParamsSize Computes size of weights that can be used by a Cudnn RNN model.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNParamsSize  
tf.raw_ops.CudnnRNNParamsSize(
    num_layers, num_units, input_size, T, S, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, num_proj=0, name=None
)
 Return the params size that can be used by the Cudnn RNN model. Subsequent weight allocation and initialization should use this size. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout. params_size: The size of the params buffer that should be allocated and initialized for this RNN model. Note that this params buffer may not be compatible across GPUs. Please use CudnnRNNParamsWeights and CudnnRNNParamsBiases to save and restore them in a way that is compatible across different runs.
 


 Args
  num_layers   A Tensor of type int32.  
  num_units   A Tensor of type int32.  
  input_size   A Tensor of type int32.  
  T   A tf.DType from: tf.half, tf.float32, tf.float64.  
  S   A tf.DType from: tf.int32, tf.int64.  
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".  
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".  
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".  
  dropout   An optional float. Defaults to 0.  
  seed   An optional int. Defaults to 0.  
  seed2   An optional int. Defaults to 0.  
  num_proj   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type S.
tf.raw_ops.CudnnRNNParamsToCanonical Retrieves CudnnRNN params in canonical form.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNParamsToCanonical  
tf.raw_ops.CudnnRNNParamsToCanonical(
    num_layers, num_units, input_size, params, num_params,
    rnn_mode='lstm', input_mode='linear_input',
    direction='unidirectional', dropout=0, seed=0, seed2=0, name=None
)
 Retrieves a set of weights from the opaque params buffer that can be saved and restored in a way compatible with future runs. Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. num_params: number of parameter sets for all layers. Each layer may contain multiple parameter sets, with each set consisting of a weight matrix and a bias vector. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout.
 


 Args
  num_layers   A Tensor of type int32.  
  num_units   A Tensor of type int32.  
  input_size   A Tensor of type int32.  
  params   A Tensor. Must be one of the following types: half, float32, float64.  
  num_params   An int that is >= 1.  
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".  
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".  
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".  
  dropout   An optional float. Defaults to 0.  
  seed   An optional int. Defaults to 0.  
  seed2   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (weights, biases).     weights   A list of num_params Tensor objects with the same type as params.  
  biases   A list of num_params Tensor objects with the same type as params.
tf.raw_ops.Cumprod Compute the cumulative product of the tensor x along axis.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Cumprod  
tf.raw_ops.Cumprod(
    x, axis, exclusive=False, reverse=False, name=None
)
 By default, this op performs an inclusive cumprod, which means that the first element of the input is identical to the first element of the output: tf.cumprod([a, b, c])  # => [a, a * b, a * b * c]
 By setting the exclusive kwarg to True, an exclusive cumprod is performed instead: tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
 By setting the reverse kwarg to True, the cumprod is performed in the opposite direction: tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
 This is more efficient than using separate tf.reverse ops. The reverse and exclusive kwargs can also be combined: tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]

 


 Args
  x   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. A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.  
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).  
  exclusive   An optional bool. Defaults to False. If True, perform exclusive cumprod.  
  reverse   An optional bool. Defaults to False. A bool (default: False).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Cumsum Compute the cumulative sum of the tensor x along axis.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Cumsum  
tf.raw_ops.Cumsum(
    x, axis, exclusive=False, reverse=False, name=None
)
 By default, this op performs an inclusive cumsum, which means that the first element of the input is identical to the first element of the output: tf.cumsum([a, b, c])  # => [a, a + b, a + b + c]
 By setting the exclusive kwarg to True, an exclusive cumsum is performed instead: tf.cumsum([a, b, c], exclusive=True)  # => [0, a, a + b]
 By setting the reverse kwarg to True, the cumsum is performed in the opposite direction: tf.cumsum([a, b, c], reverse=True)  # => [a + b + c, b + c, c]
 This is more efficient than using separate tf.reverse ops. The reverse and exclusive kwargs can also be combined: tf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]

 


 Args
  x   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. A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.  
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).  
  exclusive   An optional bool. Defaults to False. If True, perform exclusive cumsum.  
  reverse   An optional bool. Defaults to False. A bool (default: False).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.CumulativeLogsumexp Compute the cumulative product of the tensor x along axis.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CumulativeLogsumexp  
tf.raw_ops.CumulativeLogsumexp(
    x, axis, exclusive=False, reverse=False, name=None
)
 By default, this op performs an inclusive cumulative log-sum-exp, which means that the first element of the input is identical to the first element of the output: tf.math.cumulative_logsumexp([a, b, c])  # => [a, log(exp(a) + exp(b)), log(exp(a) + exp(b) + exp(c))]
 By setting the exclusive kwarg to True, an exclusive cumulative log-sum-exp is performed instead: tf.cumulative_logsumexp([a, b, c], exclusive=True)  # => [-inf, a, log(exp(a) * exp(b))]
 Note that the neutral element of the log-sum-exp operation is -inf, however, for performance reasons, the minimal value representable by the floating point type is used instead. By setting the reverse kwarg to True, the cumulative log-sum-exp is performed in the opposite direction.
 


 Args
  x   A Tensor. Must be one of the following types: half, float32, float64. A Tensor. Must be one of the following types: float16, float32, float64.  
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).  
  exclusive   An optional bool. Defaults to False. If True, perform exclusive cumulative log-sum-exp.  
  reverse   An optional bool. Defaults to False. A bool (default: False).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.DataFormatDimMap Returns the dimension index in the destination data format given the one in  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DataFormatDimMap  
tf.raw_ops.DataFormatDimMap(
    x, src_format='NHWC', dst_format='NCHW', name=None
)
 the source data format.
 


 Args
  x   A Tensor. Must be one of the following types: int32, int64. A Tensor with each element as a dimension index in source data format. Must be in the range [-4, 4).  
  src_format   An optional string. Defaults to "NHWC". source data format.  
  dst_format   An optional string. Defaults to "NCHW". destination data format.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.DataFormatVecPermute Permute input tensor from src_format to dst_format.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DataFormatVecPermute  
tf.raw_ops.DataFormatVecPermute(
    x, src_format='NHWC', dst_format='NCHW', name=None
)
 Input tensor must be a vector of size 4, or a 4x2 tensor. For example, with src_format of NHWC, dst_format of NCHW, and inputs: [1, 2, 3, 4]
 and [[1, 2, 3, 4],
 [5, 6, 7, 8]]
 , the outputs will be (respectively): [1, 4, 2, 3]
 and [[1, 4, 2, 3],
 [5, 8, 6, 7]]

 


 Args
  x   A Tensor. Must be one of the following types: int32, int64. Vector of size 4 or Tensor of shape (4, 2) in source data format.  
  src_format   An optional string. Defaults to "NHWC". source data format.  
  dst_format   An optional string. Defaults to "NCHW". destination data format.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.DataServiceDataset Creates a dataset that reads data from the tf.data service.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DataServiceDataset  
tf.raw_ops.DataServiceDataset(
    dataset_id, processing_mode, address, protocol, job_name,
    max_outstanding_requests, iteration_counter, output_types, output_shapes,
    task_refresh_interval_hint_ms=-1, name=None
)

 


 Args
  dataset_id   A Tensor of type int64.  
  processing_mode   A Tensor of type string.  
  address   A Tensor of type string.  
  protocol   A Tensor of type string.  
  job_name   A Tensor of type string.  
  max_outstanding_requests   A Tensor of type int64.  
  iteration_counter   A Tensor of type resource.  
  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.  
  task_refresh_interval_hint_ms   An optional int. Defaults to -1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.DatasetCardinality Returns the cardinality of input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetCardinality  
tf.raw_ops.DatasetCardinality(
    input_dataset, name=None
)
 Returns the cardinality of input_dataset.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the dataset to return cardinality for.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.DatasetFromGraph Creates a dataset from the given graph_def.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetFromGraph  
tf.raw_ops.DatasetFromGraph(
    graph_def, name=None
)
 Creates a dataset from the provided graph_def.
 


 Args
  graph_def   A Tensor of type string. The graph representation of the dataset (as serialized GraphDef).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.DatasetToGraph Returns a serialized GraphDef representing input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetToGraph  
tf.raw_ops.DatasetToGraph(
    input_dataset, stateful_whitelist=[], allow_stateful=False,
    strip_device_assignment=False, name=None
)
 Returns a graph representation for input_dataset.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the dataset to return the graph representation for.  
  stateful_whitelist   An optional list of strings. Defaults to [].  
  allow_stateful   An optional bool. Defaults to False.  
  strip_device_assignment   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.DatasetToGraphV2 Returns a serialized GraphDef representing input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetToGraphV2  
tf.raw_ops.DatasetToGraphV2(
    input_dataset, external_state_policy=0, strip_device_assignment=False, name=None
)
 Returns a graph representation for input_dataset.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the dataset to return the graph representation for.  
  external_state_policy   An optional int. Defaults to 0.  
  strip_device_assignment   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.DatasetToSingleElement Outputs the single element from the given dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetToSingleElement  
tf.raw_ops.DatasetToSingleElement(
    dataset, output_types, output_shapes, name=None
)

 


 Args
  dataset   A Tensor of type variant. A handle to a dataset that contains a single element.  
  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 list of Tensor objects of type output_types.
tf.raw_ops.DatasetToTFRecord Writes the given dataset to the given file using the TFRecord format.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetToTFRecord  
tf.raw_ops.DatasetToTFRecord(
    input_dataset, filename, compression_type, name=None
)

 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the dataset to write.  
  filename   A Tensor of type string. A scalar string tensor representing the filename to use.  
  compression_type   A Tensor of type string. A scalar string tensor containing either (i) the empty string (no compression), (ii) "ZLIB", or (iii) "GZIP".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.Dawsn  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Dawsn  
tf.raw_ops.Dawsn(
    x, name=None
)

 


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


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.DebugGradientIdentity Identity op for gradient debugging.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DebugGradientIdentity  
tf.raw_ops.DebugGradientIdentity(
    input, name=None
)
 This op is hidden from public in Python. It is used by TensorFlow Debugger to register gradient tensors for gradient debugging. This op operates on non-reference-type tensors.
 


 Args
  input   A Tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.