code: def __init__(
      self,
      start: float = 0.5,
      keep: float = 0.5,
      end: float = 0.9,
      interrupt: int = 2,
      batch_size: int = 256,
  ):
      self.start = start
      self.keep = keep
      self.end = end
      self.interrupt = interrupt
      self.batch_size = batch_size
  
imports: import os
  import torch
  import torchmetrics
  from torch import nn
  from torchvision.datasets import CIFAR10
  from torch.utils.data import DataLoader
  import torchvision.transforms
  from torch.utils.data.sampler import SequentialSampler
  import pytorch_lightning
  from pytorch_lightning.callbacks import Callback
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from typing import Any
  from bigdl.nano.pytorch.lightning import LightningModule
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.pytorch.algorithms.selective_backprop import SelectiveBackprop
label_desc: Use image node_id 1 to create a new CheckBatchSize object from inherited base classes: Callback with example: obj = CheckBatchSize(start, keep, end, interrupt, batch_size)