code: def test_gpt_layer():
      # text = "Replace me by any text you'd like."
      # tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
      # encoded_input = tokenizer(text, return_tensors="pt")
      torch.random.manual_seed(1234)
      test_input = torch.empty(4, 64, 768).normal_().cuda()
      training_args = TrainingArguments()
      model = GPT2Model.from_pretrained("gpt2")
      config = AutoConfig.from_pretrained("gpt2")
      layer = model.h[0].cuda().train(False)
      base_output = layer(test_input)[0]
      ls_layer = LSGptEncoderLayer.from_huggingface(
          layer, training_args, config
      ).train(False)
      ls_output = ls_layer(test_input)[0]
      np.testing.assert_allclose(
          base_output.detach().cpu().numpy(),
          ls_output.detach().cpu().numpy(),
          rtol=1e-2,
          atol=2e-1,
      )
  
imports: from dataclasses import dataclass
  import torch
  import numpy
  from transformers import GPT2Tokenizer, GPT2Model, AutoConfig
  from lightseq.training import LSGptEncoderLayer
label_desc: Use image node_id 1 for calling a global function with example usage: test_gpt_layer() without return types