parameters: (feature_columns)
return_statement: return sequence_columns, non_sequence_columns
docstring: Split a list of _TPUEmbeddingColumn into sequence and non-sequence columns.
  
    For use in a TPUEstimator model_fn function. E.g.
  
    def model_fn(features):
      sequence_columns, feature_columns = (
          tf.tpu.feature_column.split_sequence_columns(feature_columns))
      input = tf.feature_column.input_layer(
          features=features, feature_columns=feature_columns)
      sequence_features, sequence_lengths = (
          tf.contrib.feature_column.sequence_input_layer(
              features=features, feature_columns=sequence_columns))
  
    Args:
      feature_columns: A list of _TPUEmbeddingColumns to split.
  
    Returns:
      Two lists of _TPUEmbeddingColumns, the first is the sequence columns and the
      second is the non-sequence columns.
docstring_summary: Split a list of _TPUEmbeddingColumn into sequence and non-sequence columns.
docstring_tokens: Split a list of _TPUEmbeddingColumn into sequence and non - sequence columns .
function: def split_sequence_columns(feature_columns):
    """Split a list of _TPUEmbeddingColumn into sequence and non-sequence columns.
  
    For use in a TPUEstimator model_fn function. E.g.
  
    def model_fn(features):
      sequence_columns, feature_columns = (
          tf.tpu.feature_column.split_sequence_columns(feature_columns))
      input = tf.feature_column.input_layer(
          features=features, feature_columns=feature_columns)
      sequence_features, sequence_lengths = (
          tf.contrib.feature_column.sequence_input_layer(
              features=features, feature_columns=sequence_columns))
  
    Args:
      feature_columns: A list of _TPUEmbeddingColumns to split.
  
    Returns:
      Two lists of _TPUEmbeddingColumns, the first is the sequence columns and the
      second is the non-sequence columns.
    """
    sequence_columns = []
    non_sequence_columns = []
    for column in feature_columns:
      if not isinstance(column, (_TPUEmbeddingColumn, _TPUSharedEmbeddingColumn)):
        raise TypeError(
            'column must be a _TPUEmbeddingColumn or  _TPUSharedEmbeddingColumn '
            'but got %s instead.' % (type(column)))
      if column.is_sequence_column():
        sequence_columns.append(column)
      else:
        non_sequence_columns.append(column)
    return sequence_columns, non_sequence_columns
function_tokens: def split_sequence_columns ( feature_columns ) : sequence_columns = [ ] non_sequence_columns = [ ] for column in feature_columns : if not isinstance ( column , ( _TPUEmbeddingColumn , _TPUSharedEmbeddingColumn ) ) : raise TypeError ( 'column must be a _TPUEmbeddingColumn or  _TPUSharedEmbeddingColumn ' 'but got %s instead.' % ( type ( column ) ) ) if column . is_sequence_column ( ) : sequence_columns . append ( column ) else : non_sequence_columns . append ( column ) return sequence_columns , non_sequence_columns