code: def test_gpu_forward(self):
      parallel_transformer_block = self.parallel_transformer_block
      config: TransformerConfig = parallel_transformer_block.config
  
      sequence_length = 32
      micro_batch_size = 2
      parallel_transformer_block.cuda()
  
      # [sequence length, batch size, hidden size]
      hidden_states = torch.ones(
          (sequence_length, micro_batch_size, config.hidden_size)
      )
      hidden_states = hidden_states.cuda()
  
      attention_mask = torch.ones(
          (1, 1, sequence_length, sequence_length), dtype=bool
      ).cuda()
  
      hidden_states = parallel_transformer_block(
          hidden_states=hidden_states, attention_mask=attention_mask
      )
      assert hidden_states.shape[0] == sequence_length
      assert hidden_states.shape[1] == micro_batch_size
      assert hidden_states.shape[2] == config.hidden_size
  
file_path: megatron-lm/tests/unit_tests/transformer/test_transformer_block.py
imports: import os
  import pytest
  import torch
  from megatron.core import dist_checkpointing
  from megatron.core.transformer.transformer_config import TransformerConfig
  from megatron.core.transformer.transformer_layer import TransformerLayer
  from megatron.core.transformer.transformer_block import TransformerBlock
  from tests.unit_tests.test_utilities import Utils
  from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
  from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
label_desc: Use image node_id 4 for calling the TestParallelTransformerBlock obj's underlying member method code with example usage: obj.test_gpu_forward() without return types