code: def train_with_batch(
      self, simulated_bs, actual_bs, clip_grad_norm=None
  ):
      config = copy.deepcopy(get_fast_test_task_config())
      config["dataset"]["train"]["num_samples"] = 12
      config["dataset"]["train"]["batchsize_per_replica"] = actual_bs
      del config["dataset"]["test"]
  
      task = build_task(config)
      task.set_num_epochs(1)
      task.set_model(SimpleModel())
      task.set_loss(SimpleLoss())
      task.set_meters([])
      task.set_use_gpu(torch.cuda.is_available())
      if simulated_bs is not None:
          task.set_simulated_global_batchsize(simulated_bs)
      if clip_grad_norm is not None:
          task.set_clip_grad_norm(clip_grad_norm)
  
      task.set_optimizer(SGD(lr=1))
  
      trainer = LocalTrainer()
      trainer.train(task)
  
      return task.model.param
  
imports: import copy
  import itertools
  import shutil
  import tempfile
  import unittest
  import torch
  import torch.nn
  from classy_vision.dataset import build_dataset
  from classy_vision.generic.distributed_util import is_distributed_training_run
  from classy_vision.generic.util import get_checkpoint_dict, get_torch_version
  from classy_vision.hooks import CheckpointHook, LossLrMeterLoggingHook
  from classy_vision.losses import build_loss, ClassyLoss, register_loss
  from classy_vision.models import build_model, ClassyModel
  from classy_vision.optim import build_optimizer, SGD
  from classy_vision.tasks import build_task, ClassificationTask
  from classy_vision.trainer import LocalTrainer
  from test.generic.config_utils import get_fast_test_task_config, get_test_task_config
  from test.generic.utils import compare_model_state, compare_samples, compare_states, LimitedPhaseTrainer
label_desc: Use image node_id 20 for calling the TestClassificationTask obj's underlying member method code with example usage: obj.train_with_batch(simulated_bs, actual_bs, clip_grad_norm) and returns: task