code: def test_launch_dropout_relu_bias_bwd():
      batch_size, seq_len = kt.bs_sl()
      hidden_dim = kt.hidden_dim * 4
      print("test shape:", (batch_size, seq_len, hidden_dim))
  
      test_input = kt.rand((batch_size, seq_len, hidden_dim))
      test_bias = kt.rand((hidden_dim))
      test_out_grad = kt.rand((batch_size, seq_len, hidden_dim))
      test_in_grad_cus = kt.rand((batch_size, seq_len, hidden_dim))
      test_bias_grad_cus = kt.rand((hidden_dim))
      test_mask = torch.ones((batch_size, seq_len, hidden_dim)).to(
          dtype=torch.uint8,
          device="cuda:0",
      )
  
      if kt.dtype == torch.float:
          cus_func = (
              cuda_module.torch_launch_ls_dropout_relu_bias_bwd_fp32
          )
      else:
          cus_func = (
              cuda_module.torch_launch_ls_dropout_relu_bias_bwd_fp16
          )
  
      def custom():
          cus_func(
              test_in_grad_cus,
              test_bias_grad_cus,
              test_mask,
              test_input,
              test_bias,
              test_out_grad,
              batch_size * seq_len,
              hidden_dim,
              0.1,
          )
  
          return test_in_grad_cus, test_bias_grad_cus
  
      def baseline():
          temp = test_out_grad.float() * test_mask * (1 / (1 - 0.1))
          test_in_grad_base = temp * (
              (test_input.float() + test_bias.float()) > 0
          )
          test_bias_grad_base = torch.sum(
              test_in_grad_base, (0, 1), dtype=torch.float
          )
  
          return test_in_grad_base.to(kt.dtype), test_bias_grad_base.to(
              kt.dtype
          )
  
      return custom, baseline
  
imports: import random
  import torch
  from torch._C import ScriptModule, dtype
  from torch.nn import functional
  from lightseq.training.ops.pytorch.builder import KernelBuilder, AdamBuilder
  from tests.util import TestDecorator
  from torch_crf import CRF
label_desc: Use image node_id 19 for calling a global function with example usage: test_launch_dropout_relu_bias_bwd() and returns: custom,  baseline, test_in_grad_cus,  test_bias_grad_cus, test_in_grad_base,  test_bias_grad_base