code: def test_prepare_fx():
      model = DummyModel()
      model.eval()
      qconfig_dict = {
          "": torch.quantization.get_default_qconfig("fbgemm")
      }
      torch_fx_prepared_model = orig_prepare_fx(model, qconfig_dict)
      optimum_prepared_model = prepare_fx(
          model, qconfig_dict, input_names=["input_ids"], check=False
      )
      assert torch_fx_prepared_model.code == optimum_prepared_model.code
  
file_path: optimum/tests/fx/test_quantization.py
imports: import unittest
  import torch
  from torch.ao.quantization.quantize_fx import fuse_fx
  from torch.ao.quantization.quantize_fx import prepare_fx
  from torch.ao.quantization.quantize_fx import prepare_qat_fx
  from transformers import PretrainedConfig
  from optimum.fx.utils import are_fx_features_available
label_desc: Use image node_id 2 for calling a global function with example usage: test_prepare_fx() without return types