code: def on_train_end(self, trainer, module):
      count = module.my_opt.param_groups[0]["count"]
      if (
          trainer.global_step != count
          or trainer.global_step != module.max_steps
      ):
          logging.debug(f"max_epochs: {trainer.max_epochs}")
          logging.debug(
              f"accumulate_grad_batches: {trainer.accumulate_grad_batches}"
          )
          logging.debug(
              f"limit_train_batches: {trainer.limit_train_batches}"
          )
          logging.debug(f"num_devices: {trainer.num_devices}")
          logging.debug(f"batch_size: {module.batch_size}")
          logging.debug(f"dataset_len: {module.dataset_len}")
          logging.debug(f"drop_last: {module.drop_last}")
          logging.debug(f"{len(trainer.train_dataloader)}")
          logging.debug(f"{trainer.num_training_batches }")
  
      self.assert_counts(trainer, module, count)
  
imports: import math
  import random
  import omegaconf
  import pytest
  import pytorch_lightning
  import torch
  import torch.optim
  from pytorch_lightning.utilities import rank_zero_only
  from nemo.core import config, optim
  from nemo.core.optim.lr_scheduler import AVAILABLE_SCHEDULERS
  from nemo.core.optim.optimizers import AVAILABLE_OPTIMIZERS
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the Callback obj's underlying member method code with example usage: obj.on_train_end(trainer, module) without return types