code: def test_launch_bias_add_transform_20314_new():
      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, 3)
      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_q = kt.rand((batch_size, nhead, seq_len, head_dim))
      custom_k = kt.rand((batch_size, nhead, seq_len, head_dim))
      custom_v = kt.rand((batch_size, nhead, seq_len, head_dim))
      base_res = kt.rand((count, batch_size, nhead, seq_len, head_dim))
  
      if kt.dtype == torch.float:
          cust_func = (
              cuda_module.torch_launch_bias_add_transform_20314_new_fp32
          )
      else:
          cust_func = (
              cuda_module.torch_launch_bias_add_transform_20314_new_fp16
          )
  
      if kt.dtype == torch.float:
          base_func = (
              cuda_module.torch_launch_bias_add_transform_20314_fp32
          )
      else:
          base_func = (
              cuda_module.torch_launch_bias_add_transform_20314_fp16
          )
  
      def custom():
          cust_func(
              custom_q,
              custom_k,
              custom_v,
              qkv,
              bias,
              batch_size,
              seq_len,
              count,
              nhead,
              head_dim,
          )
          if count == 1:
              return [custom_q]
          if count == 2:
              return [torch.cat((custom_q, custom_k), dim=0)]
          return [torch.cat((custom_q, custom_k, custom_v), dim=0)]
  
      def baseline():
          base_func(
              base_res,
              qkv,
              bias,
              batch_size,
              seq_len,
              count,
              nhead,
              head_dim,
          )
          return [
              base_res,
          ]
  
      return custom, baseline
  
imports: import sys
  from __init__ import lightseq_dir
  import random
  import torch
  from torch._C import ScriptModule, dtype
  from torch.nn import functional
  from csrc.pytorch.builder import CudaKernelBuilder
  from csrc.tests.util import TestDecorator
  from torch_crf import CRF
  from transformers import LlamaModel
  from transformers.activations import SiLUActivation
label_desc: Use image node_id 2 for calling a global function with example usage: test_launch_bias_add_transform_20314_new() and returns: custom,  baseline, list, list, list, list