code: def test_hi():
      from turing.nvidia_modelingpreln import (
          BertConfig as BertConfigPreLN,
      )
      from turing.nvidia_modelingpreln import (
          BertForQuestionAnswering as BertForQuestionAnsweringPreLN,
      )
      from turing.nvidia_modelingpreln import BertLayer
  
      bert_model_config = {
          "vocab_size_or_config_json_file": 119547,
          "hidden_size": 1024,
          "num_hidden_layers": 1,
          "num_attention_heads": 16,
          "intermediate_size": 4096,
          "hidden_act": "gelu",
          "hidden_dropout_prob": 0.1,
          "attention_probs_dropout_prob": 0.1,
          "hidden_dropout_prob": 0.1,
          "attention_probs_dropout_prob": 0.1,
          "max_position_embeddings": 512,
          "type_vocab_size": 2,
          "initializer_range": 0.02,
      }
      bert_config = BertConfigPreLN(**bert_model_config)
      base_model = BertForQuestionAnsweringPreLN(bert_config, args=None)
  
      # base_model = LinearStack()
  
      test_model = copy.deepcopy(base_model)
      test_model = module_inject(
          BertLayer, test_model, bert_config, 4, 384, 1234
      )
  
      print("BASE", base_model)
      print("TEST", test_model)
  
imports: import copy
  import torch
  from deepspeed.ops.transformer import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
label_desc: Use image node_id 2 for calling a global function with example usage: test_hi() without return types