code: def test_launch_bias_add_transform_20314():
      batch_size, seq_len = kt.bs_sl()
      hidden_dim = kt.hidden_dim
      nhead = kt.nhead
      head_dim = int(hidden_dim / nhead)
      count = random.randint(1, 20)
      print(
          "(batch_size, seq_len, count, nhead, head_dim): "
          f"({batch_size}, {seq_len}, {count}, {nhead}, {head_dim})"
      )
  
      qkv = kt.rand((batch_size, seq_len, count, hidden_dim))
      bias = kt.zeros((1, 1, count, hidden_dim))
      custom_res = kt.rand(
          (count, batch_size, nhead, seq_len, head_dim)
      )
  
      if kt.dtype == torch.float:
          func = cuda_module.torch_launch_bias_add_transform_20314_fp32
      else:
          func = cuda_module.torch_launch_bias_add_transform_20314_fp16
  
      def custom():
          func(
              custom_res,
              qkv,
              bias,
              batch_size,
              seq_len,
              count,
              nhead,
              head_dim,
          )
          return [
              custom_res,
          ]
  
      def baseline():
          # [batch_size, seq_len, count, hidden_dim]
          base = qkv + bias
          # [count, batch_size, seq_len, hidden_dim]
          base = base.transpose(1, 2).transpose(0, 1)
          base = base.reshape(
              (count, batch_size, seq_len, nhead, head_dim)
          ).transpose(2, 3)
          return [
              base.contiguous(),
          ]
  
      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 1 for calling a global function with example usage: test_launch_bias_add_transform_20314() and returns: custom,  baseline, list, list