code: def _load_hf_config(model_type="medium"):
      if model_type == "medium":
          kwargs = {
              "vocab_size": 256206,
              "t2u_vocab_size": 10082,
              "hidden_size": 1024,
              "max_position_embeddings": 4096,
              "encoder_layers": 12,
              "decoder_layers": 12,
              "encoder_ffn_dim": 4096,
              "decoder_ffn_dim": 4096,
              "t2u_encoder_layers": 4,
              "t2u_decoder_layers": 4,
              "speech_encoder_layers": 12,
          }
          return SeamlessM4TConfig(**kwargs)
      else:
          return SeamlessM4TConfig()
  
file_path: transformers/src/transformers/models/seamless_m4t/convert_fairseq2_to_hf.py
imports: import argparse
  import os
  from pathlib import Path
  import torch
  from accelerate.utils.modeling import find_tied_parameters
  from seamless_communication.models.inference.translator import Translator
  from transformers import SeamlessM4TConfig, SeamlessM4TFeatureExtractor, SeamlessM4TModel, SeamlessM4TProcessor, SeamlessM4TTokenizer
  from transformers.utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: _load_hf_config(model_type) and returns: SeamlessM4TConfig, SeamlessM4TConfig