neulab/docprompting-conala huggingface dataset:
tf.raw_ops.RestoreV2 Restores tensors from a V2 checkpoint.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RestoreV2  
tf.raw_ops.RestoreV2(
    prefix, tensor_names, shape_and_slices, dtypes, name=None
)
 For backward compatibility with the V1 format, this Op currently allows restoring from a V1 checkpoint as well:  This Op first attempts to find the V2 index file pointed to by "prefix", and if found proceed to read it as a V2 checkpoint; Otherwise the V1 read path is invoked. Relying on this behavior is not recommended, as the ability to fall back to read V1 might be deprecated and eventually removed.  By default, restores the named tensors in full. If the caller wishes to restore specific slices of stored tensors, "shape_and_slices" should be non-empty strings and correspondingly well-formed. Callers must ensure all the named tensors are indeed stored in the checkpoint.
 


 Args
  prefix   A Tensor of type string. Must have a single element. The prefix of a V2 checkpoint.  
  tensor_names   A Tensor of type string. shape {N}. The names of the tensors to be restored.  
  shape_and_slices   A Tensor of type string. shape {N}. The slice specs of the tensors to be restored. Empty strings indicate that they are non-partitioned tensors.  
  dtypes   A list of tf.DTypes that has length >= 1. shape {N}. The list of expected dtype for the tensors. Must match those stored in the checkpoint.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type dtypes.
tf.raw_ops.RetrieveTPUEmbeddingAdadeltaParameters Retrieve Adadelta embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingAdadeltaParameters  
tf.raw_ops.RetrieveTPUEmbeddingAdadeltaParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, accumulators, updates).     parameters   A Tensor of type float32.  
  accumulators   A Tensor of type float32.  
  updates   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug Retrieve Adadelta embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug  
tf.raw_ops.RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, accumulators, updates, gradient_accumulators).     parameters   A Tensor of type float32.  
  accumulators   A Tensor of type float32.  
  updates   A Tensor of type float32.  
  gradient_accumulators   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingAdagradParameters Retrieve Adagrad embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingAdagradParameters  
tf.raw_ops.RetrieveTPUEmbeddingAdagradParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, accumulators).     parameters   A Tensor of type float32.  
  accumulators   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingAdagradParametersGradAccumDebug Retrieve Adagrad embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingAdagradParametersGradAccumDebug  
tf.raw_ops.RetrieveTPUEmbeddingAdagradParametersGradAccumDebug(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, accumulators, gradient_accumulators).     parameters   A Tensor of type float32.  
  accumulators   A Tensor of type float32.  
  gradient_accumulators   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingADAMParameters Retrieve ADAM embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingADAMParameters  
tf.raw_ops.RetrieveTPUEmbeddingADAMParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, momenta, velocities).     parameters   A Tensor of type float32.  
  momenta   A Tensor of type float32.  
  velocities   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingADAMParametersGradAccumDebug Retrieve ADAM embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingADAMParametersGradAccumDebug  
tf.raw_ops.RetrieveTPUEmbeddingADAMParametersGradAccumDebug(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, momenta, velocities, gradient_accumulators).     parameters   A Tensor of type float32.  
  momenta   A Tensor of type float32.  
  velocities   A Tensor of type float32.  
  gradient_accumulators   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingCenteredRMSPropParameters Retrieve centered RMSProp embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingCenteredRMSPropParameters  
tf.raw_ops.RetrieveTPUEmbeddingCenteredRMSPropParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, ms, mom, mg).     parameters   A Tensor of type float32.  
  ms   A Tensor of type float32.  
  mom   A Tensor of type float32.  
  mg   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingFTRLParameters Retrieve FTRL embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingFTRLParameters  
tf.raw_ops.RetrieveTPUEmbeddingFTRLParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, accumulators, linears).     parameters   A Tensor of type float32.  
  accumulators   A Tensor of type float32.  
  linears   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingFTRLParametersGradAccumDebug Retrieve FTRL embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingFTRLParametersGradAccumDebug  
tf.raw_ops.RetrieveTPUEmbeddingFTRLParametersGradAccumDebug(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, accumulators, linears, gradient_accumulators).     parameters   A Tensor of type float32.  
  accumulators   A Tensor of type float32.  
  linears   A Tensor of type float32.  
  gradient_accumulators   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingMDLAdagradLightParameters Retrieve MDL Adagrad Light embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingMDLAdagradLightParameters  
tf.raw_ops.RetrieveTPUEmbeddingMDLAdagradLightParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, accumulators, weights, benefits).     parameters   A Tensor of type float32.  
  accumulators   A Tensor of type float32.  
  weights   A Tensor of type float32.  
  benefits   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingMomentumParameters Retrieve Momentum embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingMomentumParameters  
tf.raw_ops.RetrieveTPUEmbeddingMomentumParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, momenta).     parameters   A Tensor of type float32.  
  momenta   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingMomentumParametersGradAccumDebug Retrieve Momentum embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingMomentumParametersGradAccumDebug  
tf.raw_ops.RetrieveTPUEmbeddingMomentumParametersGradAccumDebug(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, momenta, gradient_accumulators).     parameters   A Tensor of type float32.  
  momenta   A Tensor of type float32.  
  gradient_accumulators   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingProximalAdagradParameters Retrieve proximal Adagrad embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingProximalAdagradParameters  
tf.raw_ops.RetrieveTPUEmbeddingProximalAdagradParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, accumulators).     parameters   A Tensor of type float32.  
  accumulators   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug Retrieve proximal Adagrad embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug  
tf.raw_ops.RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


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

 


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

 


 Args
  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   A tuple of Tensor objects (parameters, v, m, gradient_accumulators).     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.
tf.raw_ops.RetrieveTPUEmbeddingRMSPropParameters Retrieve RMSProp embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingRMSPropParameters  
tf.raw_ops.RetrieveTPUEmbeddingRMSPropParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, ms, mom).     parameters   A Tensor of type float32.  
  ms   A Tensor of type float32.  
  mom   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug Retrieve RMSProp embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug  
tf.raw_ops.RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, ms, mom, gradient_accumulators).     parameters   A Tensor of type float32.  
  ms   A Tensor of type float32.  
  mom   A Tensor of type float32.  
  gradient_accumulators   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingStochasticGradientDescentParameters Retrieve SGD embedding parameters.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingStochasticGradientDescentParameters  
tf.raw_ops.RetrieveTPUEmbeddingStochasticGradientDescentParameters(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A Tensor of type float32.
tf.raw_ops.RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug Retrieve SGD embedding parameters with debug support.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug  
tf.raw_ops.RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug(
    num_shards, shard_id, table_id=-1, table_name='', config='',
    name=None
)
 An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
 


 Args
  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   A tuple of Tensor objects (parameters, gradient_accumulators).     parameters   A Tensor of type float32.  
  gradient_accumulators   A Tensor of type float32.