code: def get_mobilenet_v1_config(model_name):
      config = MobileNetV1Config(layer_norm_eps=0.001)
  
      if "_quant" in model_name:
          raise ValueError("Quantized models are not supported.")
  
      matches = re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$", model_name)
      if matches:
          config.depth_multiplier = float(matches[1])
          config.image_size = int(matches[2])
  
      # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
      # the usual 1000. The first class (index 0) is "background".
      config.num_labels = 1001
      filename = "imagenet-1k-id2label.json"
      repo_id = "huggingface/label-files"
      id2label = json.load(
          open(
              hf_hub_download(repo_id, filename, repo_type="dataset"),
              "r",
          )
      )
      id2label = {int(k) + 1: v for k, v in id2label.items()}
      id2label[0] = "background"
      config.id2label = id2label
      config.label2id = {v: k for k, v in id2label.items()}
  
      return config
  
file_path: transformers/src/transformers/models/mobilenet_v1/convert_original_tf_checkpoint_to_pytorch.py
imports: import argparse
  import json
  import re
  from pathlib import Path
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from transformers import MobileNetV1Config, MobileNetV1ForImageClassification, MobileNetV1ImageProcessor, load_tf_weights_in_mobilenet_v1
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: get_mobilenet_v1_config(model_name) and returns: config