code: def test_rich_progress_bar_counter_with_val_check_interval(tmp_path):
      """Test the completed and total counter for rich progress bar when using val_check_interval."""
      progress_bar = RichProgressBar()
      model = BoringModel()
      trainer = Trainer(
          default_root_dir=tmp_path,
          val_check_interval=2,
          max_epochs=1,
          limit_train_batches=7,
          limit_val_batches=4,
          callbacks=[progress_bar],
      )
      trainer.fit(model)
  
      fit_train_progress_bar = progress_bar.progress.tasks[1]
      assert fit_train_progress_bar.completed == 7
      assert fit_train_progress_bar.total == 7
  
      fit_val_bar = progress_bar.progress.tasks[2]
      assert fit_val_bar.completed == 4
      assert fit_val_bar.total == 4
  
      trainer.validate(model)
      val_bar = progress_bar.progress.tasks[0]
      assert val_bar.completed == 4
      assert val_bar.total == 4
  
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 12 for calling a global function with example usage: test_rich_progress_bar_counter_with_val_check_interval(tmp_path) without return types