code: def on_train_epoch_start(
      self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
  ) -> None:
      for lr, opt, sch in zip(
          self.lrs, pl_module.optimizers(), pl_module.lr_schedulers()
      ):
          assert sch.base_lrs[0] == lr * self.world_size
  
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 2 for calling the CheckLinearLRScaleCallback obj's underlying member method code with example usage: obj.on_train_epoch_start(trainer, pl_module) without return types