neulab/docprompting-conala huggingface dataset:
tf.raw_ops.LoadTPUEmbeddingFTRLParametersGradAccumDebug Load FTRL embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingFTRLParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingFTRLParametersGradAccumDebug(
    parameters, accumulators, linears, 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 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.  
  gradient_accumulators   A Tensor of type float32. Value of gradient_accumulators 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.
tf.raw_ops.LoadTPUEmbeddingMDLAdagradLightParameters Load MDL Adagrad Light embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingMDLAdagradLightParameters  
tf.raw_ops.LoadTPUEmbeddingMDLAdagradLightParameters(
    parameters, accumulators, weights, benefits, 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 MDL Adagrad Light optimization algorithm.  
  accumulators   A Tensor of type float32. Value of accumulators used in the MDL Adagrad Light optimization algorithm.  
  weights   A Tensor of type float32. Value of weights used in the MDL Adagrad Light optimization algorithm.  
  benefits   A Tensor of type float32. Value of benefits used in the MDL Adagrad Light 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.LoadTPUEmbeddingMomentumParameters Load Momentum embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingMomentumParameters  
tf.raw_ops.LoadTPUEmbeddingMomentumParameters(
    parameters, momenta, 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 Momentum optimization algorithm.  
  momenta   A Tensor of type float32. Value of momenta used in the Momentum 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.LoadTPUEmbeddingMomentumParametersGradAccumDebug Load Momentum embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingMomentumParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingMomentumParametersGradAccumDebug(
    parameters, momenta, 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 Momentum optimization algorithm.  
  momenta   A Tensor of type float32. Value of momenta used in the Momentum optimization algorithm.  
  gradient_accumulators   A Tensor of type float32. Value of gradient_accumulators used in the Momentum 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.LoadTPUEmbeddingProximalAdagradParameters Load proximal Adagrad embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingProximalAdagradParameters  
tf.raw_ops.LoadTPUEmbeddingProximalAdagradParameters(
    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 proximal Adagrad optimization algorithm.  
  accumulators   A Tensor of type float32. Value of accumulators used in the proximal 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.LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug Load proximal Adagrad embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug(
    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 proximal Adagrad optimization algorithm.  
  accumulators   A Tensor of type float32. Value of accumulators used in the proximal Adagrad optimization algorithm.  
  gradient_accumulators   A Tensor of type float32. Value of gradient_accumulators used in the proximal 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.LoadTPUEmbeddingProximalYogiParameters  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingProximalYogiParameters  
tf.raw_ops.LoadTPUEmbeddingProximalYogiParameters(
    parameters, v, m, num_shards, shard_id, table_id=-1, table_name='',
    config='', name=None
)

 


 Args
  parameters   A Tensor of type float32.  
  v   A Tensor of type float32.  
  m   A Tensor of type float32.  
  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.LoadTPUEmbeddingProximalYogiParametersGradAccumDebug  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingProximalYogiParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingProximalYogiParametersGradAccumDebug(
    parameters, v, m, gradient_accumulators, num_shards, shard_id, table_id=-1,
    table_name='', config='', name=None
)

 


 Args
  parameters   A Tensor of type float32.  
  v   A Tensor of type float32.  
  m   A Tensor of type float32.  
  gradient_accumulators   A Tensor of type float32.  
  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.LoadTPUEmbeddingRMSPropParameters Load RMSProp embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingRMSPropParameters  
tf.raw_ops.LoadTPUEmbeddingRMSPropParameters(
    parameters, ms, mom, 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 RMSProp optimization algorithm.  
  ms   A Tensor of type float32. Value of ms used in the RMSProp optimization algorithm.  
  mom   A Tensor of type float32. Value of mom used in the 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.LoadTPUEmbeddingRMSPropParametersGradAccumDebug Load RMSProp embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingRMSPropParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingRMSPropParametersGradAccumDebug(
    parameters, ms, mom, 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 RMSProp optimization algorithm.  
  ms   A Tensor of type float32. Value of ms used in the RMSProp optimization algorithm.  
  mom   A Tensor of type float32. Value of mom used in the RMSProp optimization algorithm.  
  gradient_accumulators   A Tensor of type float32. Value of gradient_accumulators used in the 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.LoadTPUEmbeddingStochasticGradientDescentParameters Load SGD embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingStochasticGradientDescentParameters  
tf.raw_ops.LoadTPUEmbeddingStochasticGradientDescentParameters(
    parameters, 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 stochastic gradient descent 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.LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug Load SGD embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug  
tf.raw_ops.LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug(
    parameters, 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 stochastic gradient descent 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.Log   Computes natural logarithm of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Log  
tf.raw_ops.Log(
    x, name=None
)
 I.e., \(y = \log_e x\). Example:  x = tf.constant([0, 0.5, 1, 5]) tf.math.log(x)   See: https://en.wikipedia.org/wiki/Logarithm
 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Log1p   Computes natural logarithm of (1 + x) element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Log1p  
tf.raw_ops.Log1p(
    x, name=None
)
 I.e., \(y = \log_e (1 + x)\). Example: 
x = tf.constant([0, 0.5, 1, 5])
tf.math.log1p(x)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([0.       , 0.4054651, 0.6931472, 1.7917595], dtype=float32)>

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.LogicalAnd Returns the truth value of x AND y element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LogicalAnd  
tf.raw_ops.LogicalAnd(
    x, y, name=None
)
 
Note: LogicalAnd supports broadcasting. More about broadcasting here

 


 Args
  x   A Tensor of type bool.  
  y   A Tensor of type bool.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.LogicalNot Returns the truth value of NOT x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LogicalNot  
tf.raw_ops.LogicalNot(
    x, name=None
)
 Example: 
tf.math.logical_not(tf.constant([True, False]))
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([False,  True])>

 


 Args
  x   A Tensor of type bool. A Tensor of type bool.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.LogicalOr Returns the truth value of x OR y element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LogicalOr  
tf.raw_ops.LogicalOr(
    x, y, name=None
)
 
Note: math.logical_or supports broadcasting. More about broadcasting here

 


 Args
  x   A Tensor of type bool.  
  y   A Tensor of type bool.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.LogMatrixDeterminant Computes the sign and the log of the absolute value of the determinant of  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LogMatrixDeterminant  
tf.raw_ops.LogMatrixDeterminant(
    input, name=None
)
 one or more square matrices. The input is a tensor of shape [N, M, M] whose inner-most 2 dimensions form square matrices. The outputs are two tensors containing the signs and absolute values of the log determinants for all N input submatrices [..., :, :] such that determinant = sign*exp(log_abs_determinant). The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU is the LU decomposition of the input and P is the corresponding permutation matrix.
 


 Args
  input   A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. Shape is [N, M, M].  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (sign, log_abs_determinant).     sign   A Tensor. Has the same type as input.  
  log_abs_determinant   A Tensor. Has the same type as input.
tf.raw_ops.LogSoftmax Computes log softmax activations.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LogSoftmax  
tf.raw_ops.LogSoftmax(
    logits, name=None
)
 For each batch i and class j we have logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))

 


 Args
  logits   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 2-D with shape [batch_size, num_classes].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as logits.
tf.raw_ops.LogUniformCandidateSampler Generates labels for candidate sampling with a log-uniform distribution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LogUniformCandidateSampler  
tf.raw_ops.LogUniformCandidateSampler(
    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.LookupTableExport Outputs all keys and values in the table.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableExport  
tf.raw_ops.LookupTableExport(
    table_handle, Tkeys, Tvalues, name=None
)

 


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


 Returns   A tuple of Tensor objects (keys, values).     keys   A Tensor of type Tkeys.  
  values   A Tensor of type Tvalues.
tf.raw_ops.LookupTableExportV2 Outputs all keys and values in the table.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableExportV2  
tf.raw_ops.LookupTableExportV2(
    table_handle, Tkeys, Tvalues, name=None
)

 


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


 Returns   A tuple of Tensor objects (keys, values).     keys   A Tensor of type Tkeys.  
  values   A Tensor of type Tvalues.
tf.raw_ops.LookupTableFind Looks up keys in a table, outputs the corresponding values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableFind  
tf.raw_ops.LookupTableFind(
    table_handle, keys, default_value, name=None
)
 The tensor keys must of the same type as the keys of the table. The output values is of the type of the table values. The scalar default_value is the value output for keys not present in the table. It must also be of the same type as 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.  
  default_value   A Tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as default_value.
tf.raw_ops.LookupTableFindV2 Looks up keys in a table, outputs the corresponding values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.LookupTableFindV2  
tf.raw_ops.LookupTableFindV2(
    table_handle, keys, default_value, name=None
)
 The tensor keys must of the same type as the keys of the table. The output values is of the type of the table values. The scalar default_value is the value output for keys not present in the table. It must also be of the same type as the table values.
 


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


 Returns   A Tensor. Has the same type as default_value.