code: def gen_config_impl(task_name, *args, **kwargs):
      # import the classes required by parameters
      requested_classes = [locate(opt) for opt in args] + [
          locate(task_name)
      ]
      register_tasks(requested_classes)
  
      task_class_set = find_config_class(task_name)
      if not task_class_set:
          raise Exception(
              f"Unknown task class: {task_name} "
              "(try fully qualified class name?)"
          )
      elif len(task_class_set) > 1:
          raise Exception(
              f"Multiple tasks named {task_name}: {task_class_set}"
          )
  
      task_class = next(iter(task_class_set))
      task_config = getattr(
          task_class, "example_config", task_class.Config
      )
      root = PyTextConfig(task=task_config(), version=LATEST_VERSION)
      eprint("INFO - Applying task option:", task_class.__name__)
  
      # Use components in args instead of defaults
      for opt in args:
          if "=" in opt:
              param_path, value = opt.split("=", 1)
              kwargs[param_path] = value
              continue
          replace_class_set = find_config_class(opt)
          if not replace_class_set:
              raise Exception(f"Not a component class: {opt}")
          elif len(replace_class_set) > 1:
              raise Exception(
                  f"Multiple component named {opt}: {replace_class_set}"
              )
          replace_class = next(iter(replace_class_set))
          found = replace_components(
              root, opt, get_subclasses(replace_class)
          )
          if found:
              eprint(
                  "INFO - Applying class option:",
                  ".".join(reversed(found)),
                  "=",
                  opt,
              )
              obj = root
              for k in reversed(found[1:]):
                  obj = getattr(obj, k)
              if hasattr(replace_class, "Config"):
                  setattr(obj, found[0], replace_class.Config())
              else:
                  setattr(obj, found[0], replace_class())
          else:
              raise Exception(f"Unknown class option: {opt}")
  
      # Use parameters in kwargs instead of defaults
      for param_path, value in kwargs.items():
          found = find_param(root, "." + param_path)
          if len(found) == 1:
              eprint(
                  "INFO - Applying parameter option to",
                  found[0],
                  "=",
                  value,
              )
              replace_param(root, found[0].split("."), value)
          elif not found:
              raise Exception(f"Unknown parameter option: {param_path}")
          else:
              raise Exception(
                  f"Multiple possibilities for {param_path}: {', '.join(found)}"
              )
  
      return root
  
imports: import json
  import pprint
  import sys
  import tempfile
  from importlib import import_module
  from pydoc import locate
  from typing import Dict, List, Optional, Union
  import click
  import torch
  from pytext import create_predictor
  from pytext.builtin_task import add_include
  from pytext.common.utils import eprint
  from pytext.config import ExportConfig, LATEST_VERSION, PyTextConfig
  from pytext.config.component import register_tasks
  from pytext.config.config_adapter import upgrade_to_latest
  from pytext.config.serialize import config_from_json, config_to_json, parse_config, pytext_config_from_json
  from pytext.config.utils import find_param, replace_param
  from pytext.data.data_handler import CommonMetadata
  from pytext.metric_reporters.channel import Channel, TensorBoardChannel
  from pytext.PreprocessingMap.ttypes import ModelType
  from pytext.task import load
  from pytext.utils.documentation import find_config_class, get_subclasses, pretty_print_config_class, replace_components, ROOT_CONFIG
  from pytext.utils.file_io import PathManager
  from pytext.workflow import export_saved_model_to_caffe2, export_saved_model_to_torchscript, get_logits, prepare_task_metadata, save_pytext_snapshot, test_model_from_snapshot_path, train_model
  from torch.multiprocessing.spawn import spawn
label_desc: Use image node_id 5 for calling a global function with example usage: gen_config_impl(task_name) and returns: root