code: def get_bert_model():
      model = BertModel.from_pretrained(_MODEL_NAME)
      model.eval()
      traced = symbolic_trace(
          model,
          input_names=["input_ids", "attention_mask", "token_type_ids"],
      )
      return model, traced
  
imports: import operator
  import unittest
  import torch
  from parameterized import parameterized
  from transformers import AutoModel, AutoModelForImageClassification, AutoTokenizer, BertModel
  from transformers.models.bert.modeling_bert import BertSelfAttention
  from transformers.utils.fx import symbolic_trace
  from optimum.fx.optimization import ChangeTrueDivToMulByInverse, FuseBatchNorm1dInLinear, FuseBatchNorm2dInConv2d, FuseBiasInLinear, MergeLinears, ReversibleTransformation, Transformation, compose
  from optimum.fx.utils import are_fx_features_available
label_desc: Use image node_id 1 for calling a global function with example usage: get_bert_model() and returns: model,  traced