code: def on_train_batch_start(self, trainer, datamodule, batch, batch_idx):
      input_nodes, output_nodes, mfgs = batch
      features = mfgs[0].srcdata["features"]
      if hasattr(features, "__cache_miss__"):
          trainer.strategy.model.log(
              "cache_miss",
              features.__cache_miss__,
              prog_bar=True,
              on_step=True,
              on_epoch=False,
          )
  
imports: import argparse
  import glob
  import math
  import os
  import time
  import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from ladies_sampler import LadiesSampler, normalized_edata, PoissonLadiesSampler
  from load_graph import load_dataset
  from model import SAGE
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer
  from pytorch_lightning.callbacks import Callback, EarlyStopping, ModelCheckpoint
  from pytorch_lightning.loggers import TensorBoardLogger
  from torchmetrics.classification import MulticlassF1Score, MultilabelF1Score
label_desc: Use image node_id 6 for calling the BatchSizeCallback obj's underlying member method code with example usage: obj.on_train_batch_start(trainer, datamodule, batch, batch_idx) without return types