code: def transformers_model_dowloader(
      mode,
      pretrained_model_name,
      num_labels,
      do_lower_case,
      max_length,
      torchscript,
      hardware,
      batch_size,
  ):
      """This function, save the checkpoint, config file along with tokenizer config and vocab files
      of a transformer model of your choice.
      """
      print("Download model and tokenizer", pretrained_model_name)
      # loading pre-trained model and tokenizer
      if mode == "sequence_classification":
          config = AutoConfig.from_pretrained(
              pretrained_model_name,
              num_labels=num_labels,
              torchscript=torchscript,
          )
          model = AutoModelForSequenceClassification.from_pretrained(
              pretrained_model_name, config=config
          )
          tokenizer = AutoTokenizer.from_pretrained(
              pretrained_model_name, do_lower_case=do_lower_case
          )
      elif mode == "question_answering":
          config = AutoConfig.from_pretrained(
              pretrained_model_name, torchscript=torchscript
          )
          model = AutoModelForQuestionAnswering.from_pretrained(
              pretrained_model_name, config=config
          )
          tokenizer = AutoTokenizer.from_pretrained(
              pretrained_model_name, do_lower_case=do_lower_case
          )
      elif mode == "token_classification":
          config = AutoConfig.from_pretrained(
              pretrained_model_name,
              num_labels=num_labels,
              torchscript=torchscript,
          )
          model = AutoModelForTokenClassification.from_pretrained(
              pretrained_model_name, config=config
          )
          tokenizer = AutoTokenizer.from_pretrained(
              pretrained_model_name, do_lower_case=do_lower_case
          )
      elif mode == "text_generation":
          config = AutoConfig.from_pretrained(
              pretrained_model_name,
              num_labels=num_labels,
              torchscript=torchscript,
          )
          model = AutoModelForCausalLM.from_pretrained(
              pretrained_model_name, config=config
          )
          tokenizer = AutoTokenizer.from_pretrained(
              pretrained_model_name, do_lower_case=do_lower_case
          )
  
          # NOTE : for demonstration purposes, we do not go through the fine-tune processing here.
          # A Fine_tunining process based on your needs can be added.
          # An example of  Fine_tuned model has been provided in the README.
  
      NEW_DIR = "./Transformer_model"
      try:
          os.mkdir(NEW_DIR)
      except OSError:
          print("Creation of directory %s failed" % NEW_DIR)
      else:
          print("Successfully created directory %s " % NEW_DIR)
  
      print(
          "Save model and tokenizer/ Torchscript model based on the setting from setup_config",
          pretrained_model_name,
          "in directory",
          NEW_DIR,
      )
      if save_mode == "pretrained":
          model.save_pretrained(NEW_DIR)
          tokenizer.save_pretrained(NEW_DIR)
      elif save_mode == "torchscript":
          dummy_input = "This is a dummy input for torch jit trace"
          inputs = tokenizer.encode_plus(
              dummy_input,
              max_length=int(max_length),
              pad_to_max_length=True,
              add_special_tokens=True,
              return_tensors="pt",
          )
          model.to(device).eval()
          if hardware == "neuron":
              import torch_neuron
  
              input_ids = torch.cat(
                  [inputs["input_ids"]] * batch_size, 0
              ).to(device)
              attention_mask = torch.cat(
                  [inputs["attention_mask"]] * batch_size, 0
              ).to(device)
              traced_model = torch_neuron.trace(
                  model, (input_ids, attention_mask)
              )
              torch.jit.save(
                  traced_model,
                  os.path.join(
                      NEW_DIR,
                      "traced_{}_model_neuron_batch_{}.pt".format(
                          model_name, batch_size
                      ),
                  ),
              )
          elif hardware == "neuronx":
              import torch_neuronx
  
              input_ids = torch.cat(
                  [inputs["input_ids"]] * batch_size, 0
              ).to(device)
              attention_mask = torch.cat(
                  [inputs["attention_mask"]] * batch_size, 0
              ).to(device)
              traced_model = torch_neuronx.trace(
                  model, (input_ids, attention_mask)
              )
              torch.jit.save(
                  traced_model,
                  os.path.join(
                      NEW_DIR,
                      "traced_{}_model_neuronx_batch_{}.pt".format(
                          model_name, batch_size
                      ),
                  ),
              )
          else:
              input_ids = inputs["input_ids"].to(device)
              attention_mask = inputs["attention_mask"].to(device)
              traced_model = torch.jit.trace(
                  model, (input_ids, attention_mask)
              )
              torch.jit.save(
                  traced_model, os.path.join(NEW_DIR, "traced_model.pt")
              )
      return
  
imports: import json
  import os
  import sys
  import torch
  import transformers
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForQuestionAnswering, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoTokenizer, set_seed
label_desc: Use image node_id 1 for calling a global function with example usage: transformers_model_dowloader(mode, pretrained_model_name, num_labels, do_lower_case, max_length, torchscript, hardware, batch_size) without return types