code: def on_train_batch_start(self, batch, batch_idx, dataloader_idx=0):
      if (
          self.cfg.scale_by_std
          and self.current_epoch == 0
          and self.global_step == 0
          and batch_idx == 0
      ):
          assert (
              self.cfg.scale_factor == 1.0
          ), "rather not use custom rescaling and std-rescaling simultaneously"
          batch[self.cfg.first_stage_key] = batch[
              self.cfg.first_stage_key
          ].cuda(non_blocking=True)
          self.model.on_train_batch_start(batch, batch_idx)
  
imports: from typing import Any
  import einops
  import torch
  import torch.nn
  from einops import rearrange, repeat
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from pytorch_lightning.utilities.rank_zero import rank_zero_only
  from torch._inductor import config
  from nemo.collections.multimodal.data.controlnet.controlnet_dataset import build_train_valid_datasets
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.ddim import DDIMSampler
  from nemo.collections.multimodal.modules.stable_diffusion.attention import SpatialTransformer
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel import AttentionBlock, Downsample, ResBlock, TimestepEmbedSequential, UNetModel
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import conv_nd, linear, timestep_embedding, zero_module
  from nemo.collections.multimodal.parts.stable_diffusion.utils import exists, log_txt_as_img
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the MegatronControlNet obj's underlying member method code with example usage: obj.on_train_batch_start(batch, batch_idx, dataloader_idx) without return types