code: def optimize_with_trace(
      model, data_type, per_gpu_eval_batch_size, max_seq_length
  ):
      model = optimize_no_trace(model, data_type)
      logger.info("Use TorchScript mode")
      fake_input_id = torch.LongTensor(
          per_gpu_eval_batch_size, max_seq_length
      )
      fake_input_id.fill_(1)
      fake_input_id = fake_input_id.to(device)
      fake_mask = torch.ones(
          per_gpu_eval_batch_size, max_seq_length
      ).to(device)
      fake_type_id = fake_input_id.clone().detach()
      if data_type == "fp16":
          fake_mask = fake_mask.half()
      elif data_type == "bf16":
          fake_mask = fake_mask.bfloat16()
      model.eval()
      with torch.no_grad():
          model_ = torch.jit.trace(
              model, (fake_input_id, fake_mask, fake_type_id)
          )
      return model_
  
file_path: nebuly/optimization/speedster/notebooks/huggingface/faster_transformer_bert.py
imports: import logging
  import random
  import time
  import speedster
  import torch
  from speedster import optimize_model
  from nebullvm.operations.optimizations.compilers.faster_transformer.bert import detect_and_swap_bert_model
  from nebullvm.operations.optimizations.compilers.utils import get_faster_transformer_repo_path
  from transformers import BertTokenizer
  from transformers.models.bert.modeling_bert import BertForSequenceClassification
label_desc: Use image node_id 3 for calling a global function with example usage: optimize_with_trace(model, data_type, per_gpu_eval_batch_size, max_seq_length) and returns: model_