class_docstr: Load configs from source files, after setting them up as modules.
  
  In order to support configs defined in Python files whose paths are passed
  on the command line, we need to first load those files as modules (using
  eval is for savages). If those files define classes or functions that need
  to be pickled to be sent to the workers (say, custom operators, comparators,
  ...) then their modules need to be "normally" importable: their filename
  must match their module name and they must reside in a standard location
  (i.e., a directory in the path).
  
  All the above is taken care of by this class, which creates a temporary
  directory in which to copy over the configs, with unique names, and then
  imports them from there.
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code: def __init__(self) -> None:
      self.config_dir = tempfile.TemporaryDirectory(  # noqa
          prefix="torchbiggraph_config_"
      )
      # Hold a reference because at destruction time it may not be available anymore.
      self.sys_path = sys.path
      self.sys_path.append(self.config_dir.name)
  
imports: import argparse
  import importlib
  import logging
  import os.path
  import shutil
  import sys
  import tempfile
  import uuid
  from enum import Enum
  from itertools import chain
  from typing import Any, ClassVar, Dict, List, Optional
  import attr
  from attr.validators import optional
  from torchbiggraph.schema import DeepTypeError, extract_nested_type, has_origin, inject_nested_value, non_empty, non_negative, positive, Schema, schema
label_desc: Use image node_id 1 to create a new ConfigFileLoader object  with example: obj = ConfigFileLoader()