code: def test_trainer_channels_last_correctness_subprocess(self):
      model = ConvModel()
      model.conv1 = torch.nn.Conv2d(2, 1, (1, 2), bias=False)
      model.conv1.weight.data.fill_(1.0)
      optimizer = torch.optim.SGD(model.parameters(), lr=0.25)
      loss = torch.nn.MSELoss()
      pl_module = Trainer.compile(
          model=model, loss=loss, optimizer=optimizer
      )
      trainer = Trainer(
          max_epochs=1,
          channels_last=True,
          distributed_backend="subprocess",
          num_processes=2,
      )
      x = torch.Tensor(
          [
              [[[1, 0]], [[1, 0]]],
              [[[1, 0]], [[2, 0]]],
              [[[0, 3]], [[1, 0]]],
              [[[1, 1]], [[2, 1]]],
          ]
      )
      y = torch.Tensor([[0], [1], [0], [1]])
      dataset = torch.utils.data.TensorDataset(x, y)
      data_loader = torch.utils.data.DataLoader(
          dataset, batch_size=4, shuffle=False
      )
  
      trainer.fit(pl_module, data_loader)
      result = torch.tensor([[[[0.0, -1.0]], [[-1.25, 0.5]]]])
      assert pl_module.model.conv1.weight.equal(result)
  
imports: import os
  import pytest
  import torch
  from unittest import TestCase
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import TorchNano
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_12, TORCH_VERSION_LESS_2_0
  from torchvision.models.resnet import ResNet, BasicBlock
  from torchmetrics.functional import accuracy
  import pytorch_lightning
  import torch.nn.functional
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from test.pytorch.utils._train_torch_lightning import create_test_data_loader
label_desc: Use image node_id 5 for calling the ChannelsLast obj's underlying member method code with example usage: obj.test_trainer_channels_last_correctness_subprocess() without return types