code: def get_batch_steps(batch, mini_batch_chunk_size):
      # if necessary, make batch_steps
      if (
          mini_batch_chunk_size is not None
          and len(batch) > mini_batch_chunk_size
      ):
          # break up the batch into slices of size
          # mini_batch_chunk_size
          return [
              batch[i : i + mini_batch_chunk_size]
              for i in range(0, len(batch), mini_batch_chunk_size)
          ]
      else:
          return [batch]
  
imports: import contextlib
  import inspect
  import logging
  import os
  import random
  import time
  import warnings
  from inspect import signature
  from pathlib import Path
  from typing import List, Optional, Tuple, Type, Union
  import torch
  from torch.optim.sgd import SGD
  from torch.utils.data.dataset import ConcatDataset
  import flair
  import flair.nn
  from flair.data import Corpus, Dictionary, _len_dataset
  from flair.datasets import DataLoader
  from flair.samplers import FlairSampler
  from flair.trainers.plugins import AnnealingPlugin, CheckpointPlugin, LinearSchedulerPlugin, LogFilePlugin, LossFilePlugin, MetricName, MetricRecord, Pluggable, ReduceTransformerVocabPlugin, TrainerPlugin, TrainingInterrupt, WeightExtractorPlugin
  from flair.training_utils import identify_dynamic_embeddings, log_line, store_embeddings
label_desc: Use image node_id 4 for calling the ModelTrainer obj's underlying member method code with example usage: obj.get_batch_steps(batch, mini_batch_chunk_size) and returns: unknown, list