parameters: (tpu_embedding, activations,
                                                dummy_table_variables)
return_statement: return new_activations
docstring: Have activations depend on dummy table variables for gradient intercept.
  
    Args:
      tpu_embedding: TPUEmbedding, activations and dummy_table_variables are from
        tpu_embedding.
      activations: An OrderedDict of feature name String to activation tensors.
      dummy_table_variables: An OrderedDict of table name String to dummy table
        variables.
  
    Returns:
      An OrderedDict of feature name String to activation tensors, which can be
        used just as the activations input.
docstring_summary: Have activations depend on dummy table variables for gradient intercept.
docstring_tokens: Have activations depend on dummy table variables for gradient intercept .
function: def hook_dummy_table_variables_to_activations(tpu_embedding, activations,
                                                dummy_table_variables):
    """Have activations depend on dummy table variables for gradient intercept.
  
    Args:
      tpu_embedding: TPUEmbedding, activations and dummy_table_variables are from
        tpu_embedding.
      activations: An OrderedDict of feature name String to activation tensors.
      dummy_table_variables: An OrderedDict of table name String to dummy table
        variables.
  
    Returns:
      An OrderedDict of feature name String to activation tensors, which can be
        used just as the activations input.
    """
    new_activations = collections.OrderedDict()
    for feature in activations:
      table = tpu_embedding.feature_to_config_dict[feature].table_id
      new_activations[feature] = tpu_ops.tpu_embedding_activations(
          dummy_table_variables[table],
          activations[feature],
          table_id=list(tpu_embedding.table_to_config_dict).index(table),
          lookup_id=tpu_embedding.table_to_features_dict[table].index(feature))
    return new_activations
function_tokens: def hook_dummy_table_variables_to_activations ( tpu_embedding , activations , dummy_table_variables ) : new_activations = collections . OrderedDict ( ) for feature in activations : table = tpu_embedding . feature_to_config_dict [ feature ] . table_id new_activations [ feature ] = tpu_ops . tpu_embedding_activations ( dummy_table_variables [ table ] , activations [ feature ] , table_id = list ( tpu_embedding . table_to_config_dict ) . index ( table ) , lookup_id = tpu_embedding . table_to_features_dict [ table ] . index ( feature ) ) return new_activations