parameters: (column,
                               columns_to_tensors,
                               embedding_lookup_arguments,
                               num_outputs,
                               trainable,
                               weight_collections)
docstring: Creates variables and returns predictions for linear weights in a model.
  
    Args:
     column: the column we're working on.
     columns_to_tensors: a map from column name to tensors.
     embedding_lookup_arguments: arguments for embedding lookup.
     num_outputs: how many outputs.
     trainable: whether the variable we create is trainable.
     weight_collections: weights will be placed here.
  
    Returns:
    variables: the created embeddings.
    predictions: the computed predictions.
docstring_summary: Creates variables and returns predictions for linear weights in a model.
docstring_tokens: Creates variables and returns predictions for linear weights in a model .
function: def _create_embedding_lookup(column,
                               columns_to_tensors,
                               embedding_lookup_arguments,
                               num_outputs,
                               trainable,
                               weight_collections):
    """Creates variables and returns predictions for linear weights in a model.
  
    Args:
     column: the column we're working on.
     columns_to_tensors: a map from column name to tensors.
     embedding_lookup_arguments: arguments for embedding lookup.
     num_outputs: how many outputs.
     trainable: whether the variable we create is trainable.
     weight_collections: weights will be placed here.
  
    Returns:
    variables: the created embeddings.
    predictions: the computed predictions.
    """
    with variable_scope.variable_scope(
        None, default_name=column.name, values=columns_to_tensors.values()):
      variable = contrib_variables.model_variable(
          name='weights',
          shape=[embedding_lookup_arguments.vocab_size, num_outputs],
          dtype=dtypes.float32,
          initializer=embedding_lookup_arguments.initializer,
          trainable=trainable,
          collections=weight_collections)
      if isinstance(variable, variables.Variable):
        variable = [variable]
      else:
        variable = variable._get_variable_list()  # pylint: disable=protected-access
      predictions = embedding_ops.safe_embedding_lookup_sparse(
          variable,
          embedding_lookup_arguments.input_tensor,
          sparse_weights=embedding_lookup_arguments.weight_tensor,
          default_id=0,
          combiner=embedding_lookup_arguments.combiner,
          name=column.name + '_weights')
      return variable, predictions
function_tokens: def _create_embedding_lookup ( column , columns_to_tensors , embedding_lookup_arguments , num_outputs , trainable , weight_collections ) : with variable_scope . variable_scope ( None , default_name = column . name , values = columns_to_tensors . values ( ) ) : variable = contrib_variables . model_variable ( name = 'weights' , shape = [ embedding_lookup_arguments . vocab_size , num_outputs ] , dtype = dtypes . float32 , initializer = embedding_lookup_arguments . initializer , trainable = trainable , collections = weight_collections ) if isinstance ( variable , variables . Variable ) : variable = [ variable ] else : variable = variable . _get_variable_list ( ) # pylint: disable=protected-access predictions = embedding_ops . safe_embedding_lookup_sparse ( variable , embedding_lookup_arguments . input_tensor , sparse_weights = embedding_lookup_arguments . weight_tensor , default_id = 0 , combiner = embedding_lookup_arguments . combiner , name = column . name + '_weights' ) return variable , predictions