code: def _predict_batch(trainer, model, batches, device):
      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_predict_batch_start",
                      "args": (trainer, model, ANY, i),
                  },
                  {"name": "on_predict_batch_start", "args": (ANY, i)},
                  {"name": "forward", "args": (ANY,)},
                  {"name": "predict_step", "args": (ANY, i)},
                  {
                      "name": "Callback.on_predict_batch_end",
                      "args": (trainer, model, ANY, ANY, i),
                  },
                  {
                      "name": "on_predict_batch_end",
                      "args": (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 7 for calling the HookedModel obj's underlying member method code with example usage: obj._predict_batch(trainer, model, batches, device) and returns: out