code: 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 "
                  f"but got {type(column)} instead."
              )
          if column.is_sequence_column():
              sequence_columns.append(column)
          else:
              non_sequence_columns.append(column)
      return sequence_columns, non_sequence_columns
  
imports: import math
  from tensorflow.python.feature_column import feature_column
  from tensorflow.python.feature_column import feature_column_lib
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.tpu import tpu
  from tensorflow.python.tpu import tpu_function
  from tensorflow.python.tpu import tpu_replication
label_desc: Use image node_id 6 for calling a global function with example usage: split_sequence_columns(feature_columns) and returns: sequence_columns,  non_sequence_columns