code: def test_rms_layer_norm():  # torch_rms_layer_norm
      batch_size, seq_len = 1, 1  # kt.bs_sl()
      hidden_size = 5120
      inp = kt.rand((batch_size, seq_len, hidden_size))
      scale = kt.rand((hidden_size))
      custom_out = torch.empty_like(inp)
      rms_out = kt.rand((batch_size, seq_len))
  
      func = (
          cuda_module.torch_rms_layer_norm_fp32
          if kt.dtype == torch.float
          else cuda_module.torch_rms_layer_norm_fp16
      )
  
      def custom():
          func(
              inp,
              scale,
              custom_out,
              rms_out,
              batch_size * seq_len,
              hidden_size,
              1e-6,
          )
          return [rms_out.contiguous(), custom_out.contiguous()]
  
      def baseline():
          # output = act_func(inpA) * inpB
          variance = inp.to(torch.float32).pow(2).mean(-1, keepdim=True)
          rms_var = torch.rsqrt(variance + 1e-6).to(dtype=kt.dtype)
          hidden_states = inp * rms_var
          output = (scale * hidden_states).to(dtype=kt.dtype)
          return [rms_var.contiguous(), output.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 33 for calling a global function with example usage: test_rms_layer_norm() and returns: custom,  baseline, list, list