neulab/docprompting-conala huggingface dataset:
tf.raw_ops.LeakyRelu Computes rectified linear: max(features, features * alpha).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LeakyRelu  
tf.raw_ops.LeakyRelu(
    features, alpha=0.2, name=None
)

 


 Args
  features   A Tensor. Must be one of the following types: half, bfloat16, float32, float64.  
  alpha   An optional float. Defaults to 0.2.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as features.
tf.raw_ops.LeakyReluGrad Computes rectified linear gradients for a LeakyRelu operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LeakyReluGrad  
tf.raw_ops.LeakyReluGrad(
    gradients, features, alpha=0.2, name=None
)

 


 Args
  gradients   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding LeakyRelu operation.  
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding LeakyRelu operation, OR the outputs of that operation (both work equivalently).  
  alpha   An optional float. Defaults to 0.2.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as gradients.
tf.raw_ops.LearnedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LearnedUnigramCandidateSampler  
tf.raw_ops.LearnedUnigramCandidateSampler(
    true_classes, num_true, num_sampled, unique, range_max, seed=0, seed2=0,
    name=None
)
 See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels.
 


 Args
  true_classes   A Tensor of type int64. A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.  
  num_true   An int that is >= 1. Number of true labels per context.  
  num_sampled   An int that is >= 1. Number of candidates to randomly sample.  
  unique   A bool. If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.  
  range_max   An int that is >= 1. The sampler will sample integers from the interval [0, range_max).  
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.  
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count).     sampled_candidates   A Tensor of type int64.  
  true_expected_count   A Tensor of type float32.  
  sampled_expected_count   A Tensor of type float32.
tf.raw_ops.LeftShift Elementwise computes the bitwise left-shift of x and y.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LeftShift  
tf.raw_ops.LeftShift(
    x, y, name=None
)
 If y is negative, or greater than or equal to the width of x in bits the result is implementation defined. Example: import tensorflow as tf
from tensorflow.python.ops import bitwise_ops
import numpy as np
dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64]

for dtype in dtype_list:
  lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)

  left_shift_result = bitwise_ops.left_shift(lhs, rhs)

  print(left_shift_result)

# This will print:
# tf.Tensor([ -32   -5 -128    0], shape=(4,), dtype=int8)
# tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int16)
# tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int32)
# tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int64)

lhs = np.array([-2, 64, 101, 32], dtype=np.int8)
rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
bitwise_ops.left_shift(lhs, rhs)
# <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2,  64, 101,  32], dtype=int8)>

 


 Args
  x   A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, uint32, uint64.  
  y   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.LegacyParallelInterleaveDatasetV2 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.LegacyParallelInterleaveDatasetV2  
tf.raw_ops.LegacyParallelInterleaveDatasetV2(
    input_dataset, other_arguments, cycle_length, block_length,
    buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes,
    deterministic='default', name=None
)
 The resulting dataset is similar to the InterleaveDataset, with the exception that if retrieving the next value from a dataset would cause the requester to block, it will skip that input dataset. This dataset is especially useful when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it allows the training step to proceed so long as some data is available. !! WARNING !! This dataset is not deterministic!
 


 Args
  input_dataset   A Tensor of type variant.  
  other_arguments   A list of Tensor objects.  
  cycle_length   A Tensor of type int64.  
  block_length   A Tensor of type int64.  
  buffer_output_elements   A Tensor of type int64.  
  prefetch_input_elements   A Tensor of type int64.  
  f   A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.  
  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.  
  deterministic   An optional string. Defaults to "default".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.Less Returns the truth value of (x < y) element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Less  
tf.raw_ops.Less(
    x, y, name=None
)
 
Note: math.less supports broadcasting. More about broadcasting here
 Example: x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less(x, y) ==> [False, True, False]

x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 7])
tf.math.less(x, y) ==> [False, True, True]

 


 Args
  x   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  y   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.LessEqual Returns the truth value of (x <= y) element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LessEqual  
tf.raw_ops.LessEqual(
    x, y, name=None
)
 
Note: math.less_equal supports broadcasting. More about broadcasting here
 Example: x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less_equal(x, y) ==> [True, True, False]

x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 6])
tf.math.less_equal(x, y) ==> [True, True, True]

 


 Args
  x   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  y   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.Lgamma Computes the log of the absolute value of Gamma(x) element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Lgamma  
tf.raw_ops.Lgamma(
    x, name=None
)
 For positive numbers, this function computes log((input - 1)!) for every element in the tensor. lgamma(5) = log((5-1)!) = log(4!) = log(24) = 3.1780539 Example: x = tf.constant([0, 0.5, 1, 4.5, -4, -5.6])
tf.math.lgamma(x) ==> [inf, 0.5723649, 0., 2.4537368, inf, -4.6477685]

 


 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.LinSpace Generates values in an interval.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LinSpace  
tf.raw_ops.LinSpace(
    start, stop, num, name=None
)
 A sequence of num evenly-spaced values are generated beginning at start. If num > 1, the values in the sequence increase by stop - start / num - 1, so that the last one is exactly stop. For example: tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0  11.0  12.0]

 


 Args
  start   A Tensor. Must be one of the following types: bfloat16, half, float32, float64. 0-D tensor. First entry in the range.  
  stop   A Tensor. Must have the same type as start. 0-D tensor. Last entry in the range.  
  num   A Tensor. Must be one of the following types: int32, int64. 0-D tensor. Number of values to generate.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as start.
tf.raw_ops.ListDiff Computes the difference between two lists of numbers or strings.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ListDiff  
tf.raw_ops.ListDiff(
    x, y, out_idx=tf.dtypes.int32, name=None
)
 Given a list x and a list y, this operation returns a list out that represents all values that are in x but not in y. The returned list out is sorted in the same order that the numbers appear in x (duplicates are preserved). This operation also returns a list idx that represents the position of each out element in x. In other words: out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1] For example, given this input: x = [1, 2, 3, 4, 5, 6]
y = [1, 3, 5]
 This operation would return: out ==> [2, 4, 6]
idx ==> [1, 3, 5]

 


 Args
  x   A Tensor. 1-D. Values to keep.  
  y   A Tensor. Must have the same type as x. 1-D. Values to remove.  
  out_idx   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 (out, idx).     out   A Tensor. Has the same type as x.  
  idx   A Tensor of type out_idx.
tf.raw_ops.LMDBDataset Creates a dataset that emits the key-value pairs in one or more LMDB files.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LMDBDataset  
tf.raw_ops.LMDBDataset(
    filenames, output_types, output_shapes, name=None
)
 The Lightning Memory-Mapped Database Manager, or LMDB, is an embedded binary key-value database. This dataset can read the contents of LMDB database files, the names of which generally have the .mdb suffix. Each output element consists of a key-value pair represented as a pair of scalar string Tensors, where the first Tensor contains the key and the second Tensor contains the value. LMDB uses different file formats on big- and little-endian machines. LMDBDataset can only read files in the format of the host machine.
 


 Args
  filenames   A Tensor of type string. A scalar or a vector containing the name(s) of the binary file(s) to be read.  
  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.LMDBReader A Reader that outputs the records from a LMDB file.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LMDBReader  
tf.raw_ops.LMDBReader(
    container='', shared_name='', 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.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.LoadDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadDataset  
tf.raw_ops.LoadDataset(
    path, reader_func_other_args, output_types, output_shapes, reader_func,
    compression='', name=None
)

 


 Args
  path   A Tensor of type string.  
  reader_func_other_args   A list of Tensor objects.  
  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.  
  reader_func   A function decorated with @Defun.  
  compression   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.LoadTPUEmbeddingAdadeltaParameters Load Adadelta embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingAdadeltaParameters  
tf.raw_ops.LoadTPUEmbeddingAdadeltaParameters(
    parameters, accumulators, updates, num_shards, shard_id, table_id=-1,
    table_name='', config='', name=None
)
 An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
 


 Args
  parameters   A Tensor of type float32. Value of parameters used in the Adadelta optimization algorithm.  
  accumulators   A Tensor of type float32. Value of accumulators used in the Adadelta optimization algorithm.  
  updates   A Tensor of type float32. Value of updates used in the Adadelta optimization algorithm.  
  num_shards   An int.  
  shard_id   An int.  
  table_id   An optional int. Defaults to -1.  
  table_name   An optional string. Defaults to "".  
  config   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LoadTPUEmbeddingAdadeltaParametersGradAccumDebug Load Adadelta parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingAdadeltaParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingAdadeltaParametersGradAccumDebug(
    parameters, accumulators, updates, gradient_accumulators, num_shards, shard_id,
    table_id=-1, table_name='', config='', name=None
)
 An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
 


 Args
  parameters   A Tensor of type float32. Value of parameters used in the Adadelta optimization algorithm.  
  accumulators   A Tensor of type float32. Value of accumulators used in the Adadelta optimization algorithm.  
  updates   A Tensor of type float32. Value of updates used in the Adadelta optimization algorithm.  
  gradient_accumulators   A Tensor of type float32. Value of gradient_accumulators used in the Adadelta optimization algorithm.  
  num_shards   An int.  
  shard_id   An int.  
  table_id   An optional int. Defaults to -1.  
  table_name   An optional string. Defaults to "".  
  config   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LoadTPUEmbeddingAdagradParameters Load Adagrad embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingAdagradParameters  
tf.raw_ops.LoadTPUEmbeddingAdagradParameters(
    parameters, accumulators, num_shards, shard_id, table_id=-1,
    table_name='', config='', name=None
)
 An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
 


 Args
  parameters   A Tensor of type float32. Value of parameters used in the Adagrad optimization algorithm.  
  accumulators   A Tensor of type float32. Value of accumulators used in the Adagrad optimization algorithm.  
  num_shards   An int.  
  shard_id   An int.  
  table_id   An optional int. Defaults to -1.  
  table_name   An optional string. Defaults to "".  
  config   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LoadTPUEmbeddingAdagradParametersGradAccumDebug Load Adagrad embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingAdagradParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingAdagradParametersGradAccumDebug(
    parameters, accumulators, gradient_accumulators, num_shards, shard_id,
    table_id=-1, table_name='', config='', name=None
)
 An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
 


 Args
  parameters   A Tensor of type float32. Value of parameters used in the Adagrad optimization algorithm.  
  accumulators   A Tensor of type float32. Value of accumulators used in the Adagrad optimization algorithm.  
  gradient_accumulators   A Tensor of type float32. Value of gradient_accumulators used in the Adagrad optimization algorithm.  
  num_shards   An int.  
  shard_id   An int.  
  table_id   An optional int. Defaults to -1.  
  table_name   An optional string. Defaults to "".  
  config   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LoadTPUEmbeddingADAMParameters Load ADAM embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingADAMParameters  
tf.raw_ops.LoadTPUEmbeddingADAMParameters(
    parameters, momenta, velocities, num_shards, shard_id, table_id=-1,
    table_name='', config='', name=None
)
 An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
 


 Args
  parameters   A Tensor of type float32. Value of parameters used in the ADAM optimization algorithm.  
  momenta   A Tensor of type float32. Value of momenta used in the ADAM optimization algorithm.  
  velocities   A Tensor of type float32. Value of velocities used in the ADAM optimization algorithm.  
  num_shards   An int.  
  shard_id   An int.  
  table_id   An optional int. Defaults to -1.  
  table_name   An optional string. Defaults to "".  
  config   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LoadTPUEmbeddingADAMParametersGradAccumDebug Load ADAM embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingADAMParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingADAMParametersGradAccumDebug(
    parameters, momenta, velocities, gradient_accumulators, num_shards, shard_id,
    table_id=-1, table_name='', config='', name=None
)
 An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
 


 Args
  parameters   A Tensor of type float32. Value of parameters used in the ADAM optimization algorithm.  
  momenta   A Tensor of type float32. Value of momenta used in the ADAM optimization algorithm.  
  velocities   A Tensor of type float32. Value of velocities used in the ADAM optimization algorithm.  
  gradient_accumulators   A Tensor of type float32. Value of gradient_accumulators used in the ADAM optimization algorithm.  
  num_shards   An int.  
  shard_id   An int.  
  table_id   An optional int. Defaults to -1.  
  table_name   An optional string. Defaults to "".  
  config   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LoadTPUEmbeddingCenteredRMSPropParameters Load centered RMSProp embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingCenteredRMSPropParameters  
tf.raw_ops.LoadTPUEmbeddingCenteredRMSPropParameters(
    parameters, ms, mom, mg, num_shards, shard_id, table_id=-1,
    table_name='', config='', name=None
)
 An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
 


 Args
  parameters   A Tensor of type float32. Value of parameters used in the centered RMSProp optimization algorithm.  
  ms   A Tensor of type float32. Value of ms used in the centered RMSProp optimization algorithm.  
  mom   A Tensor of type float32. Value of mom used in the centered RMSProp optimization algorithm.  
  mg   A Tensor of type float32. Value of mg used in the centered RMSProp optimization algorithm.  
  num_shards   An int.  
  shard_id   An int.  
  table_id   An optional int. Defaults to -1.  
  table_name   An optional string. Defaults to "".  
  config   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.LoadTPUEmbeddingFTRLParameters Load FTRL embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingFTRLParameters  
tf.raw_ops.LoadTPUEmbeddingFTRLParameters(
    parameters, accumulators, linears, num_shards, shard_id, table_id=-1,
    table_name='', config='', name=None
)
 An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
 


 Args
  parameters   A Tensor of type float32. Value of parameters used in the FTRL optimization algorithm.  
  accumulators   A Tensor of type float32. Value of accumulators used in the FTRL optimization algorithm.  
  linears   A Tensor of type float32. Value of linears used in the FTRL optimization algorithm.  
  num_shards   An int.  
  shard_id   An int.  
  table_id   An optional int. Defaults to -1.  
  table_name   An optional string. Defaults to "".  
  config   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.