code: def test_mobile_cpu_transmuter(self):
      # Input tensor
      input_blob_size = (1, 3, 2, 6, 6)
      input_tensor = torch.randn(input_blob_size)
  
      # Helper class to emulate user input model
      class _residual_block(nn.Module):
          def __init__(self):
              super().__init__()
              self.stem0 = nn.Conv3d(
                  3, 3, kernel_size=(3, 1, 1), padding=(1, 0, 0)
              )
              self.stem1 = nn.Conv3d(
                  3, 3, kernel_size=(5, 1, 1), padding=(2, 0, 0)
              )
              self.pw = nn.Conv3d(3, 6, kernel_size=1)
              self.relu = nn.ReLU()
              self.dw = nn.Conv3d(
                  6, 6, kernel_size=3, padding=1, groups=6
              )
              self.relu1 = nn.ReLU()
              self.pwl = nn.Conv3d(6, 3, kernel_size=1)
              self.relu2 = nn.ReLU()
  
          def forward(self, x):
              out = self.stem0(x)
              out = self.stem1(out)
              out = self.pw(out)
              out = self.relu(out)
              out = self.dw(out)
              out = self.relu1(out)
              out = self.pwl(out)
              return self.relu2(out + x)
  
      user_model_ref = _residual_block()
  
      user_model_ref.eval()
      out_ref = user_model_ref(input_tensor)
  
      user_model_efficient = deepcopy(user_model_ref)
      transmute_model(
          user_model_efficient,
          target_device="mobile_cpu",
      )
      logging.info(f"after convert_model {user_model_efficient}")
      # Check whether blocks has been replaced by efficientBlock
      assert isinstance(user_model_efficient.pw, EfficientBlockBase), (
          f"user_model_efficient.pw {user_model_efficient.pw.__class__.__name__} "
          "is not converted!"
      )
      assert isinstance(user_model_efficient.dw, EfficientBlockBase), (
          f"user_model_efficient.dw {user_model_efficient.dw.__class__.__name__} "
          "is not converted!"
      )
      assert isinstance(user_model_efficient.pwl, EfficientBlockBase), (
          f"user_model_efficient.pwl {user_model_efficient.pwl.__class__.__name__} "
          "is not converted!"
      )
      user_model_efficient_converted = convert_to_deployable_form(
          user_model_efficient, input_tensor
      )
      out = user_model_efficient_converted(input_tensor)
      # Check arithmetic equivalency
      max_err = float(torch.max(torch.abs(out_ref - out)))
      rel_err = torch.abs((out_ref - out) / out_ref)
      max_rel_err = float(torch.max(rel_err))
      logging.info(
          (
              "test_mobile_cpu_transmuter: "
              f"max_err {max_err}, max_rel_err {max_rel_err}"
          )
      )
      self.assertTrue(max_err < 1e-3)
  
file_path: pytorchvideo/tests/test_accelerator_deployment_model_transmuter.py
imports: import logging
  import unittest
  from copy import deepcopy
  import pytorchvideo.accelerator.deployment.mobile_cpu.transmuter
  import torch
  import torch.nn
  from pytorchvideo.accelerator.deployment.common.model_transmuter import transmute_model
  from pytorchvideo.accelerator.deployment.mobile_cpu.utils.model_conversion import convert_to_deployable_form
  from pytorchvideo.accelerator.efficient_blocks.efficient_block_base import EfficientBlockBase
label_desc: Use image node_id 1 for calling the TestModelTransmuter obj's underlying member method code with example usage: obj.test_mobile_cpu_transmuter() and returns: self