code: def get_videomae_config(model_name):
      config = VideoMAEConfig()
  
      if "large" in model_name:
          config.hidden_size = 1024
          config.intermediate_size = 4096
          config.num_hidden_layers = 24
          config.num_attention_heads = 16
          config.decoder_num_hidden_layers = 12
          config.decoder_num_attention_heads = 8
          config.decoder_hidden_size = 512
          config.decoder_intermediate_size = 2048
  
      if "finetuned" not in model_name:
          config.use_mean_pooling = False
  
      if "finetuned" in model_name:
          repo_id = "huggingface/label-files"
          if "kinetics" in model_name:
              config.num_labels = 400
              filename = "kinetics400-id2label.json"
          elif "ssv2" in model_name:
              config.num_labels = 174
              filename = "something-something-v2-id2label.json"
          else:
              raise ValueError(
                  "Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."
              )
          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: 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 1 for calling a global function with example usage: get_videomae_config(model_name) and returns: config