code: def convert_gin_to_config(gin_file, num_experts):
      # Convert a google style config to the hugging face fromat
      import regex as re
  
      with open(gin_file, "r") as f:
          raw_gin = f.read()
  
      regex_match = re.findall(r"(.*) = ([0-9.]*)", raw_gin)
      args = {}
      for param, value in regex_match:
          if param in GIN_TO_CONFIG_MAPPING and value != "":
              args[GIN_TO_CONFIG_MAPPING[param]] = (
                  float(value) if "." in value else int(value)
              )
  
      activation = re.findall(
          r"(.*activations) = \(\'(.*)\',\)", raw_gin
      )[0]
      args[GIN_TO_CONFIG_MAPPING[activation[0]]] = str(activation[1])
  
      args["num_experts"] = num_experts
      config = SwitchTransformersConfig(**args)
      return config
  
file_path: transformers/src/transformers/models/switch_transformers/convert_switch_transformers_original_flax_checkpoint_to_pytorch.py
imports: import argparse
  import re
  from flax.traverse_util import flatten_dict, unflatten_dict
  from t5x import checkpoints
  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
  from transformers.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: convert_gin_to_config(gin_file, num_experts) and returns: config