code: def rename_key(name, base_model=False):
      for i in range(1, 6):
          if f"layer_{i}." in name:
              name = name.replace(
                  f"layer_{i}.", f"encoder.layer.{i - 1}."
              )
  
      if "conv_1." in name:
          name = name.replace("conv_1.", "conv_stem.")
      if ".block." in name:
          name = name.replace(".block.", ".")
  
      if "exp_1x1" in name:
          name = name.replace("exp_1x1", "expand_1x1")
      if "red_1x1" in name:
          name = name.replace("red_1x1", "reduce_1x1")
      if ".local_rep.conv_3x3." in name:
          name = name.replace(".local_rep.conv_3x3.", ".conv_kxk.")
      if ".local_rep.conv_1x1." in name:
          name = name.replace(".local_rep.conv_1x1.", ".conv_1x1.")
      if ".norm." in name:
          name = name.replace(".norm.", ".normalization.")
      if ".conv." in name:
          name = name.replace(".conv.", ".convolution.")
      if ".conv_proj." in name:
          name = name.replace(".conv_proj.", ".conv_projection.")
  
      for i in range(0, 2):
          for j in range(0, 4):
              if f".{i}.{j}." in name:
                  name = name.replace(f".{i}.{j}.", f".{i}.layer.{j}.")
  
      for i in range(2, 6):
          for j in range(0, 4):
              if f".{i}.{j}." in name:
                  name = name.replace(f".{i}.{j}.", f".{i}.")
                  if "expand_1x1" in name:
                      name = name.replace(
                          "expand_1x1", "downsampling_layer.expand_1x1"
                      )
                  if "conv_3x3" in name:
                      name = name.replace(
                          "conv_3x3", "downsampling_layer.conv_3x3"
                      )
                  if "reduce_1x1" in name:
                      name = name.replace(
                          "reduce_1x1", "downsampling_layer.reduce_1x1"
                      )
  
      for i in range(2, 5):
          if f".global_rep.{i}.weight" in name:
              name = name.replace(
                  f".global_rep.{i}.weight", ".layernorm.weight"
              )
          if f".global_rep.{i}.bias" in name:
              name = name.replace(
                  f".global_rep.{i}.bias", ".layernorm.bias"
              )
  
      if ".global_rep." in name:
          name = name.replace(".global_rep.", ".transformer.")
      if ".pre_norm_mha.0." in name:
          name = name.replace(".pre_norm_mha.0.", ".layernorm_before.")
      if ".pre_norm_mha.1.out_proj." in name:
          name = name.replace(
              ".pre_norm_mha.1.out_proj.", ".attention.output.dense."
          )
      if ".pre_norm_ffn.0." in name:
          name = name.replace(".pre_norm_ffn.0.", ".layernorm_after.")
      if ".pre_norm_ffn.1." in name:
          name = name.replace(
              ".pre_norm_ffn.1.", ".intermediate.dense."
          )
      if ".pre_norm_ffn.4." in name:
          name = name.replace(".pre_norm_ffn.4.", ".output.dense.")
      if ".transformer." in name:
          name = name.replace(".transformer.", ".transformer.layer.")
  
      if ".aspp_layer." in name:
          name = name.replace(".aspp_layer.", ".")
      if ".aspp_pool." in name:
          name = name.replace(".aspp_pool.", ".")
      if "seg_head." in name:
          name = name.replace("seg_head.", "segmentation_head.")
      if "segmentation_head.classifier.classifier." in name:
          name = name.replace(
              "segmentation_head.classifier.classifier.",
              "segmentation_head.classifier.",
          )
  
      if "classifier.fc." in name:
          name = name.replace("classifier.fc.", "classifier.")
      elif (not base_model) and ("segmentation_head." not in name):
          name = "mobilevit." + name
  
      return name
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/mobilevit/convert_mlcvnets_to_pytorch.py
imports: import argparse
  import json
  from pathlib import Path
  import torch
  from PIL import Image
  import requests
  from huggingface_hub import hf_hub_download
  from transformers import MobileViTConfig, MobileViTFeatureExtractor, MobileViTForImageClassification, MobileViTForSemanticSegmentation
  from transformers.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: rename_key(name, base_model) and returns: name