code: def test_launch_layer_norm():
      batch_size, seq_len = kt.bs_sl()
      bsz_seq = batch_size * seq_len
      hidden_dim = kt.hidden_dim
      with_mean = random.choice([True, False])
      print(
          "(batch_token_num, hidden_dim, with_mean): "
          f"({bsz_seq}, {hidden_dim}, {with_mean})"
      )
  
      custom_res = kt.rand((bsz_seq, hidden_dim))
      inp = kt.rand((bsz_seq, hidden_dim))
      gamma = kt.rand((hidden_dim))
      beta = kt.rand((hidden_dim))
      vars = kt.rand((bsz_seq))
      means = kt.rand((bsz_seq))
  
      if kt.dtype == torch.float:
          func = cuda_module.torch_launch_layer_norm_fp32
      else:
          func = cuda_module.torch_launch_layer_norm_fp16
  
      def custom():
          func(
              custom_res,
              vars,
              means,
              inp,
              gamma,
              beta,
              bsz_seq,
              hidden_dim,
              with_mean,
          )
          return (
              [custom_res, vars, means]
              if with_mean
              else [custom_res, vars]
          )
  
      def baseline():
          base = torch.nn.functional.layer_norm(
              inp, [hidden_dim], gamma, beta, kt.epsilon
          )
          if with_mean:
              return [
                  base.contiguous(),
                  inp.var(dim=1).contiguous(),
                  inp.mean(dim=1).contiguous(),
              ]
          else:
              return [base.contiguous(), inp.var(dim=1).contiguous()]
  
      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 10 for calling a global function with example usage: test_launch_layer_norm() and returns: custom,  baseline, unknown, list, list