code: def test_trainer_ipex_bf16_unspport_optim(self):
      # IPEX BF16 weight prepack needs the cpu support avx512bw, avx512vl and avx512dq
      trainer = Trainer(
          max_epochs=max_epochs,
          use_ipex=True,
          precision="bf16",
          callbacks=[CheckIPEXFusedStepCallback()],
      )
  
      model = ResNet18(
          10, pretrained=False, include_top=False, freeze=True
      )
      optimizer = torch.optim.AdamW(
          model.parameters(), lr=0.01, weight_decay=5e-4
      )
      loss = nn.CrossEntropyLoss()
      scheduler_dict = {
          "scheduler": OneCycleLR(
              optimizer,
              0.1,
              epochs=max_epochs,
              steps_per_epoch=len(self.train_loader),
          ),
          "interval": "step",
      }
  
      pl_model = Trainer.compile(model, loss, optimizer, scheduler_dict)
      trainer.fit(pl_model, self.train_loader)
      trainer.test(pl_model, self.train_loader)
  
      if trainer.use_ipex and TORCH_VERSION_LESS_1_10:
          import intel_pytorch_extension as ipex
  
          # Diable IPEX AMP
          # Avoid affecting other tests
          ipex.enable_auto_mixed_precision(None)
  
imports: import os
  from unittest import TestCase
  import pytest
  import torch
  from torch.optim.lr_scheduler import OneCycleLR
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from test.pytorch.utils._train_ipex_callback import CheckIPEXFusedStepCallback
  from torch import nn
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_10, TORCH_VERSION_LESS_2_0
  from bigdl.nano.utils.common import _avx2_checker
label_desc: Use image node_id 3 for calling the PLTrainer obj's underlying member method code with example usage: obj.test_trainer_ipex_bf16_unspport_optim() without return types