code: def load_marian_model(self) -> MarianMTModel:
      state_dict, cfg = self.state_dict, self.hf_config
  
      if not cfg.static_position_embeddings:
          raise ValueError(
              "config.static_position_embeddings should be True"
          )
      model = MarianMTModel(cfg)
  
      if "hidden_size" in cfg.to_dict():
          raise ValueError("hidden_size is in config")
      load_layers_(
          model.model.encoder.layers,
          state_dict,
          BART_CONVERTER,
      )
      load_layers_(
          model.model.decoder.layers,
          state_dict,
          BART_CONVERTER,
          is_decoder=True,
      )
  
      # handle tensors not associated with layers
      if self.cfg["tied-embeddings-src"]:
          wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
          bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
          model.model.shared.weight = wemb_tensor
          model.model.encoder.embed_tokens = (
              model.model.decoder.embed_tokens
          ) = model.model.shared
      else:
          wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
          model.model.encoder.embed_tokens.weight = wemb_tensor
  
          decoder_wemb_tensor = nn.Parameter(
              torch.FloatTensor(self.dec_wemb)
          )
          bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
          model.model.decoder.embed_tokens.weight = decoder_wemb_tensor
  
      model.final_logits_bias = bias_tensor
  
      if "Wpos" in state_dict:
          print("Unexpected: got Wpos")
          wpos_tensor = torch.tensor(state_dict["Wpos"])
          model.model.encoder.embed_positions.weight = wpos_tensor
          model.model.decoder.embed_positions.weight = wpos_tensor
  
      if cfg.normalize_embedding:
          if not ("encoder_emb_ln_scale_pre" in state_dict):
              raise ValueError(
                  "encoder_emb_ln_scale_pre is not in state dictionary"
              )
          raise NotImplementedError(
              "Need to convert layernorm_embedding"
          )
  
      if self.extra_keys:
          raise ValueError(f"Failed to convert {self.extra_keys}")
  
      if model.get_input_embeddings().padding_idx != self.pad_token_id:
          raise ValueError(
              f"Padding tokens {model.get_input_embeddings().padding_idx} and {self.pad_token_id} mismatched"
          )
      return model
  
imports: import argparse
  import json
  import os
  import socket
  import time
  import warnings
  from pathlib import Path
  from typing import Dict, List, Union
  from zipfile import ZipFile
  import numpy
  import torch
  from torch import nn
  from tqdm import tqdm
  from huggingface_hub.hf_api import list_models
  from transformers import MarianConfig, MarianMTModel, MarianTokenizer
label_desc: Use image node_id 6 for calling the OpusState obj's underlying member method code with example usage: obj.load_marian_model() and returns: model