code: def test_rich_progress_bar_num_sanity_val_steps(
      tmp_path, limit_val_batches
  ):
      model = BoringModel()
  
      progress_bar = RichProgressBar()
      num_sanity_val_steps = 3
  
      trainer = Trainer(
          default_root_dir=tmp_path,
          num_sanity_val_steps=num_sanity_val_steps,
          limit_train_batches=1,
          limit_val_batches=limit_val_batches,
          max_epochs=1,
          callbacks=progress_bar,
      )
  
      trainer.fit(model)
      assert progress_bar.progress.tasks[0].completed == min(
          num_sanity_val_steps, limit_val_batches
      )
      assert progress_bar.progress.tasks[0].total == min(
          num_sanity_val_steps, limit_val_batches
      )
  
imports: import pickle
  from collections import defaultdict
  from unittest import mock
  from unittest.mock import DEFAULT, Mock
  import pytest
  from lightning.pytorch import Trainer
  from lightning.pytorch.callbacks import ProgressBar, RichProgressBar
  from lightning.pytorch.callbacks.progress.rich_progress import RichProgressBarTheme
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
  from lightning.pytorch.loggers import CSVLogger
  from lightning.pytorch.loggers.logger import DummyLogger
  from tests_pytorch.helpers.runif import RunIf
  from torch.utils.data import DataLoader
label_desc: Use image node_id 11 for calling a global function with example usage: test_rich_progress_bar_num_sanity_val_steps(tmp_path, limit_val_batches) without return types