code: def test_launch_dropout_relu_bias():
      batch_size, seq_len = kt.bs_sl()
      hidden_dim = kt.hidden_dim
      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_cus = kt.rand((batch_size, seq_len, hidden_dim))
      test_mask_cus = torch.rand((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_fp32
      else:
          cus_func = cuda_module.torch_launch_ls_dropout_relu_bias_fp16
  
      def custom():
          cus_func(
              test_out_cus,
              test_mask_cus,
              test_input,
              test_bias,
              batch_size * seq_len,
              hidden_dim,
              0,
          )
  
          return test_out_cus
  
      def baseline():
          test_out_base = torch.nn.functional.relu(
              test_input + test_bias
          )
          test_out_base = torch.nn.functional.dropout(
              test_out_base, p=0
          )
  
          return test_out_base
  
      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 17 for calling a global function with example usage: test_launch_dropout_relu_bias() and returns: custom,  baseline, test_out_cus, test_out_base