code: def embeddings(idx):
      """
      The function helps in renaming embedding layer weights.
  
      Args:
          idx: stage number in original model
      """
      embed = []
      embed.append(
          (
              f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
              f"stage{idx}.patch_embed.proj.weight",
          )
      )
      embed.append(
          (
              f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
              f"stage{idx}.patch_embed.proj.bias",
          )
      )
      embed.append(
          (
              f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
              f"stage{idx}.patch_embed.norm.weight",
          )
      )
      embed.append(
          (
              f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
              f"stage{idx}.patch_embed.norm.bias",
          )
      )
      return embed
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py
imports: import argparse
  import json
  from collections import OrderedDict
  import torch
  from huggingface_hub import cached_download, hf_hub_url
  from transformers import AutoFeatureExtractor, CvtConfig, CvtForImageClassification
label_desc: Use image node_id 1 for calling a global function with example usage: embeddings(idx) and returns: embed