code: def get_mobilevit_config(mobilevit_name):
      config = MobileViTConfig()
  
      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
          config.hidden_sizes = [144, 192, 240]
          config.neck_hidden_sizes = [16, 32, 64, 96, 128, 160, 640]
      elif "mobilevit_xs" in mobilevit_name:
          config.hidden_sizes = [96, 120, 144]
          config.neck_hidden_sizes = [16, 32, 48, 64, 80, 96, 384]
      elif "mobilevit_xxs" in mobilevit_name:
          config.hidden_sizes = [64, 80, 96]
          config.neck_hidden_sizes = [16, 16, 24, 48, 64, 80, 320]
          config.hidden_dropout_prob = 0.05
          config.expand_ratio = 2.0
  
      if mobilevit_name.startswith("deeplabv3_"):
          config.image_size = 512
          config.output_stride = 16
          config.num_labels = 21
          filename = "pascal-voc-id2label.json"
      else:
          config.num_labels = 1000
          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): v for k, v in id2label.items()}
      config.id2label = id2label
      config.label2id = {v: k for k, v in id2label.items()}
  
      return config
  
file_path: transformers/src/transformers/models/mobilevit/convert_mlcvnets_to_pytorch.py
imports: import argparse
  import json
  from pathlib import Path
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from transformers import MobileViTConfig, MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTImageProcessor
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: get_mobilevit_config(mobilevit_name) and returns: config