class_docstr: A beam.DoFn to convert a parsed CSV line to a tf.Example.
code: def _make_column_handlers(
      self, column_infos: List[csv_decoder.ColumnInfo]
  ):
      result = []
      for column_info in column_infos:
          # pylint: disable=g-long-lambda
          if column_info.type == csv_decoder.ColumnType.INT:
              handler_fn = _int_handler
          elif column_info.type == csv_decoder.ColumnType.FLOAT:
              handler_fn = _float_handler
          elif column_info.type == csv_decoder.ColumnType.STRING:
              handler_fn = _bytes_handler
          else:
              handler_fn = None
          result.append((column_info.name, handler_fn))
      return result
  
imports: import os
  from typing import Any, Dict, Iterable, List
  from absl import logging
  import apache_beam
  import tensorflow
  from tfx.components.example_gen.base_example_gen_executor import BaseExampleGenExecutor
  from tfx.dsl.io import fileio
  from tfx.types import standard_component_specs
  from tfx.utils import io_utils
  from tfx_bsl.coders import csv_decoder
label_desc: Use image node_id 2 for calling the _ParsedCsvToTfExample obj's underlying member method code with example usage: obj._make_column_handlers(column_infos) and returns: result