code: def test_trainer_spawn_plugin_bf16(self):
      # IPEX BF16 weight prepack needs the cpu support avx512bw, avx512vl and avx512dq
      model = ResNet18(pretrained=False, include_top=False, freeze=True)
      loss = nn.CrossEntropyLoss()
      optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
      pl_model = LightningModule(
          model,
          loss,
          optimizer,
          metrics=[
              torchmetrics.F1Score(
                  "multiclass", num_classes=num_classes
              ),
              torchmetrics.Accuracy(
                  "multiclass", num_classes=num_classes
              ),
          ],
      )
      trainer = Trainer(
          num_processes=2,
          distributed_backend="spawn",
          max_epochs=4,
          use_ipex=True,
          precision="bf16",
          callbacks=[CheckIPEXCallback(), CheckIPEXFusedStepCallback()],
      )
      trainer.fit(pl_model, self.data_loader, self.test_data_loader)
      trainer.test(pl_model, self.test_data_loader)
  
imports: import os
  import pytest
  from unittest import TestCase
  import torch
  import torchmetrics
  from torch import nn
  from bigdl.nano.pytorch.lightning import LightningModule
  from bigdl.nano.pytorch import Trainer
  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
  from test.pytorch.utils._train_ipex_callback import CheckIPEXCallback, CheckIPEXFusedStepCallback
  from test.pytorch.tests.train.trainer.test_lightning import ResNet18
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_2_0
  from bigdl.nano.utils.common import _avx2_checker
label_desc: Use image node_id 3 for calling the Plugin obj's underlying member method code with example usage: obj.test_trainer_spawn_plugin_bf16() without return types