code: def test_launch_concat3_dim1():
      batch_size, seq_len = kt.bs_sl()
      hidden_dim = kt.hidden_dim
      nhead = kt.nhead
      head_dim = int(hidden_dim / nhead)
      assert seq_len > 1
      sl1 = random.randint(1, seq_len - 1)
      sl2 = seq_len - sl1
      beam_size = random.randint(1, 8)
      print(
          f"(batch_size, beam_size, nhead, sl1, sl2, head_dim): {batch_size},"
          f" {beam_size}, {nhead}, {sl1}, {sl2}, {head_dim}"
      )
  
      inp1 = kt.rand((batch_size, beam_size, nhead, sl1, head_dim))
      inp2 = kt.rand((batch_size, beam_size, nhead, sl2, head_dim))
      custom_res = kt.rand(
          (batch_size, beam_size, nhead, seq_len, head_dim)
      )
  
      if kt.dtype == torch.float:
          func = cuda_module.torch_launch_concat3_dim1_fp32
      else:
          func = cuda_module.torch_launch_concat3_dim1_fp16
  
      def custom():
          func(
              inp1,
              inp2,
              custom_res,
              batch_size * beam_size * nhead,
              head_dim,
              sl1,
              sl2,
          )
          return kt.norm_res_list(custom_res)
  
      def baseline():
          res = torch.cat((inp1, inp2), dim=3)
          return kt.norm_res_list(res)
  
      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 15 for calling a global function with example usage: test_launch_concat3_dim1() and returns: custom,  baseline, kt, kt