code: def test_warmup_ray(self):
      model = ResNetWith2Optimizers()
      trainer = Trainer(
          num_processes=2,
          distributed_backend="ray",
          auto_lr=True,
          max_epochs=4,
          callbacks=[CheckWarmupCallback(2, [0.01, 0.05], 4, 4)],
      )
      trainer.fit(
          model,
          train_dataloaders=self.data_loader,
          val_dataloaders=self.test_data_loader,
      )
  
imports: import os
  import pytest
  from unittest import TestCase
  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 bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_10, TORCH_VERSION_LESS_2_0
  import pytorch_lightning
  import torch
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 7 for calling the ScaleLr obj's underlying member method code with example usage: obj.test_warmup_ray() without return types