code: def rename_key(name):
      if "encoder." in name:
          name = name.replace("encoder.", "")
      if "cls_token" in name:
          name = name.replace(
              "cls_token", "videomae.embeddings.cls_token"
          )
      if "decoder_pos_embed" in name:
          name = name.replace(
              "decoder_pos_embed", "decoder.decoder_pos_embed"
          )
      if "pos_embed" in name and "decoder" not in name:
          name = name.replace(
              "pos_embed", "videomae.embeddings.position_embeddings"
          )
      if "patch_embed.proj" in name:
          name = name.replace(
              "patch_embed.proj",
              "videomae.embeddings.patch_embeddings.projection",
          )
      if "patch_embed.norm" in name:
          name = name.replace(
              "patch_embed.norm", "videomae.embeddings.norm"
          )
      if "decoder.blocks" in name:
          name = name.replace(
              "decoder.blocks", "decoder.decoder_layers"
          )
      if "blocks" in name:
          name = name.replace("blocks", "videomae.encoder.layer")
      if "attn.proj" in name:
          name = name.replace("attn.proj", "attention.output.dense")
      if "attn" in name and "bias" not in name:
          name = name.replace("attn", "attention.self")
      if "attn" in name:
          name = name.replace("attn", "attention.attention")
      if "norm1" in name:
          name = name.replace("norm1", "layernorm_before")
      if "norm2" in name:
          name = name.replace("norm2", "layernorm_after")
      if "mlp.fc1" in name:
          name = name.replace("mlp.fc1", "intermediate.dense")
      if "mlp.fc2" in name:
          name = name.replace("mlp.fc2", "output.dense")
      if "decoder_embed" in name:
          name = name.replace("decoder_embed", "decoder.decoder_embed")
      if "decoder_norm" in name:
          name = name.replace("decoder_norm", "decoder.decoder_norm")
      if "decoder_pred" in name:
          name = name.replace("decoder_pred", "decoder.decoder_pred")
      if (
          "norm.weight" in name
          and "decoder" not in name
          and "fc" not in name
      ):
          name = name.replace(
              "norm.weight", "videomae.layernorm.weight"
          )
      if (
          "norm.bias" in name
          and "decoder" not in name
          and "fc" not in name
      ):
          name = name.replace("norm.bias", "videomae.layernorm.bias")
      if "head" in name and "decoder" not in name:
          name = name.replace("head", "classifier")
  
      return name
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/videomae/convert_videomae_to_pytorch.py
imports: import argparse
  import json
  import numpy
  import torch
  import gdown
  from huggingface_hub import hf_hub_download
  from transformers import VideoMAEConfig, VideoMAEFeatureExtractor, VideoMAEForPreTraining, VideoMAEForVideoClassification
label_desc: Use image node_id 2 for calling a global function with example usage: rename_key(name) and returns: name