code: def test_silu_elewise_product():
      batch_size, seq_len = 1, 256
      hidden_size = 13824
      inpA = kt.rand((batch_size, seq_len, hidden_size))
      inpB = kt.rand((batch_size, seq_len, hidden_size))
      custom_outC = torch.empty_like(inpA)
  
      act_func = SiLUActivation()
      func = (
          cuda_module.torch_silu_elewise_product_fp32
          if kt.dtype == torch.float
          else cuda_module.torch_silu_elewise_product_fp16
      )
  
      def custom():
          func(
              inpA, inpB, custom_outC, batch_size, seq_len, hidden_size
          )
          return [custom_outC.contiguous()]
  
      def baseline():
          output = act_func(inpA) * inpB
          return [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 32 for calling a global function with example usage: test_silu_elewise_product() and returns: custom,  baseline, list, list