code: def test_num_stepping_batches_iterable_dataset():
      """Test the stepping batches with iterable dataset configured with max steps."""
      max_steps = 1000
      trainer = Trainer(max_steps=max_steps)
      model = BoringModel()
      train_dl = DataLoader(
          RandomIterableDataset(size=7, count=int(1e10))
      )
      trainer._data_connector.attach_data(
          model, train_dataloaders=train_dl
      )
      trainer.strategy.connect(model)
      assert trainer.estimated_stepping_batches == max_steps
  
imports: import logging
  import os
  from unittest import mock
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomIterableDataset
  from lightning.pytorch.strategies import SingleDeviceXLAStrategy
  from torch.utils.data import DataLoader
  from tests_pytorch.conftest import mock_cuda_count
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 3 for calling a global function with example usage: test_num_stepping_batches_iterable_dataset() without return types