code: def _manual_train_batch(trainer, model, batches, device, **kwargs):
      using_deepspeed = kwargs.get("strategy") == "deepspeed"
      out = []
      for i in range(batches):
          out.extend(
              [
                  {
                      "name": "on_before_batch_transfer",
                      "args": (ANY, 0),
                  },
                  {
                      "name": "transfer_batch_to_device",
                      "args": (ANY, device, 0),
                  },
                  {"name": "on_after_batch_transfer", "args": (ANY, 0)},
                  {
                      "name": "Callback.on_train_batch_start",
                      "args": (trainer, model, ANY, i),
                  },
                  {"name": "on_train_batch_start", "args": (ANY, i)},
                  {"name": "forward", "args": (ANY,)},
                  {
                      "name": "Callback.on_before_backward",
                      "args": (trainer, model, ANY),
                  },
                  {"name": "on_before_backward", "args": (ANY,)},
                  # DeepSpeed handles backward internally
                  *(
                      [{"name": "backward", "args": (ANY,)}]
                      if not using_deepspeed
                      else []
                  ),
                  {
                      "name": "Callback.on_after_backward",
                      "args": (trainer, model),
                  },
                  {"name": "on_after_backward"},
                  # `manual_backward` calls the previous 3
                  {"name": "manual_backward", "args": (ANY,)},
                  {"name": "closure"},
                  {
                      "name": "Callback.on_before_optimizer_step",
                      "args": (trainer, model, ANY),
                  },
                  {"name": "on_before_optimizer_step", "args": (ANY,)},
                  {"name": "training_step", "args": (ANY, i)},
                  {
                      "name": "Callback.on_train_batch_end",
                      "args": (trainer, model, {"loss": ANY}, ANY, i),
                  },
                  {
                      "name": "on_train_batch_end",
                      "args": ({"loss": ANY}, ANY, i),
                  },
              ]
          )
      return out
  
imports: from functools import partial, update_wrapper
  from inspect import getmembers, isfunction
  from unittest import mock
  from unittest.mock import ANY, PropertyMock
  import pytest
  import torch
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.pytorch import Callback, LightningDataModule, LightningModule, Trainer, __version__
  from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel, RandomDataset
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from torch import Tensor
  from torch.utils.data import DataLoader
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 4 for calling the HookedModel obj's underlying member method code with example usage: obj._manual_train_batch(trainer, model, batches, device) and returns: out