code: def create_model(model_name: str, model_class: Any) -> Any:
      """Creates and returns an instance of a specified test model.
      Args:
          model_name (str): Specifies which model to instantiate.
      Raises:
          NotImplementedError: If the specified model type is not implemented.
      """
  
      if model_name == "bart":
          bart_config = BartConfig(
              d_model=16,
              encoder_layers=2,
              decoder_layers=2,
              encoder_attention_heads=4,
              decoder_attention_heads=4,
              encoder_ffn_dim=4,
              decoder_ffn_dim=4,
          )
          model = model_class.from_config(bart_config)
  
      elif model_name == "albert":
          albert_config = AlbertConfig(
              embedding_size=16,
              hidden_size=64,
              num_hidden_layers=5,
              num_attention_heads=4,
              intermediate_size=37,
              num_hidden_groups=2,
          )
          model = model_class.from_config(albert_config)
  
      elif model_name == "beit":
          beit_config = BeitConfig(
              image_size=224,
              hidden_size=32,
              num_hidden_layers=4,
              num_attention_heads=4,
              intermediate_size=37,
          )
          model = model_class.from_config(beit_config)
  
      elif model_name == "bert":
          bert_config = BertConfig(
              hidden_size=32,
              num_hidden_layers=4,
              num_attention_heads=4,
              intermediate_size=37,
          )
          model = model_class.from_config(bert_config)
  
      elif model_name == "clip":
          clip_config = CLIPVisionConfig(
              image_size=30,
              hidden_size=32,
              num_hidden_layers=4,
              num_attention_heads=4,
              intermediate_size=37,
          )
          model = CLIPVisionModelWithProjection(clip_config)
  
      elif model_name == "deberta":
          deberta_config = DebertaConfig(
              hidden_size=32,
              num_hidden_layers=5,
              num_attention_heads=4,
              intermediate_size=37,
              hidden_act="gelu",
              relative_attention=True,
              pos_att_type="p2c|c2p",
          )
          model = model_class.from_config(deberta_config)
  
      elif model_name == "debertaV2":
          debertaV2_config = DebertaV2Config(
              hidden_size=32,
              num_hidden_layers=5,
              num_attention_heads=4,
              intermediate_size=37,
              hidden_act="gelu",
              relative_attention=True,
              pos_att_type="p2c|c2p",
          )
          model = model_class.from_config(debertaV2_config)
  
      elif model_name == "distilbert":
          distilbert_config = DistilBertConfig(
              dim=32,
              n_layers=4,
              n_heads=4,
              hidden_dim=37,
          )
          model = model_class.from_config(distilbert_config)
  
      elif model_name == "encoder_decoder":
          enc_dec_config = (
              EncoderDecoderConfig.from_encoder_decoder_configs(
                  BertConfig(
                      hidden_size=32,
                      num_hidden_layers=4,
                      num_attention_heads=4,
                      intermediate_size=37,
                  ),
                  BertConfig(
                      hidden_size=32,
                      num_hidden_layers=4,
                      num_attention_heads=4,
                      intermediate_size=37,
                      is_decoder=True,
                      add_cross_attention=True,
                  ),
              )
          )
          model = EncoderDecoderModel(enc_dec_config)
  
      elif model_name == "gpt2":
          gpt2_config = GPT2Config(
              n_embd=32,
              n_layer=4,
              n_head=4,
              # set pad token to eos token
              pad_token_id=50256,
          )
          model = model_class.from_config(gpt2_config)
  
      elif model_name == "gptj":
          gptj_config = GPTJConfig(
              n_embd=32,
              n_layer=4,
              n_head=4,
              rotary_dim=4,
              # set pad token to eos token
              pad_token_id=50256,
              resid_pdrop=0.1,
          )
          model = model_class.from_config(gptj_config)
  
      elif model_name == "mbart":
          mbart_config = MBartConfig(
              d_model=16,
              encoder_layers=2,
              decoder_layers=2,
              encoder_attention_heads=4,
              decoder_attention_heads=4,
              encoder_ffn_dim=4,
              decoder_ffn_dim=4,
              vocab_size=250027,
          )
          model = model_class.from_config(mbart_config)
  
      elif model_name == "roberta":
          roberta_config = RobertaConfig(
              hidden_size=32,
              num_hidden_layers=4,
              num_attention_heads=4,
              intermediate_size=37,
              vocab_size=50265,
          )
          model = model_class.from_config(roberta_config)
  
      elif model_name == "t5":
          t5_config = T5Config(
              d_model=16,
              num_layers=2,
              num_decoder_layers=2,
              num_heads=4,
              d_ff=4,
              d_kv=16 // 4,
              tie_word_embeddings=False,
              decoder_start_token_id=0,
          )
          model = model_class.from_config(t5_config)
  
      elif model_name == "vit":
          vit_config = ViTConfig(
              image_size=224,
              hidden_size=32,
              num_hidden_layers=4,
              num_attention_heads=4,
              intermediate_size=37,
          )
          model = model_class.from_config(vit_config)
  
      elif model_name == "xlm-r":
          xlm_config = XLMRobertaConfig(
              hidden_size=32,
              num_hidden_layers=4,
              num_attention_heads=4,
              intermediate_size=37,
              vocab_size=250002,
          )
          model = model_class.from_config(xlm_config)
  
      else:
          raise NotImplementedError(
              "The specified model type is not implemented."
          )
  
      return model
  
imports: import os
  import random
  from typing import Any, Union
  import numpy
  import torch
  from PIL import Image
  from torch import squeeze
  import jsonlines
  import requests
  import transformers
  from adapters import AutoAdapterModel, init
  from transformers import AlbertConfig, BartConfig, BatchEncoding, BeitConfig, BeitImageProcessor, BertConfig, CLIPProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection, DebertaConfig, DebertaV2Config, DistilBertConfig, EncoderDecoderConfig, EncoderDecoderModel, GPT2Config, GPTJConfig, MBartConfig, RobertaConfig, T5Config, ViTConfig, ViTImageProcessor, XLMRobertaConfig
label_desc: Use image node_id 6 for calling a global function with example usage: create_model(model_name, model_class) and returns: model