code: def test_tqdm_progress_bar_totals(tmp_path, num_dl):
      """Test that the progress finishes with the correct total steps processed."""
  
      class CustomModel(BoringModel):
          def _get_dataloaders(self):
              dls = [
                  DataLoader(RandomDataset(32, 64)),
                  DataLoader(RandomDataset(32, 64)),
              ]
              return dls[0] if num_dl == 1 else dls
  
          def val_dataloader(self):
              return self._get_dataloaders()
  
          def test_dataloader(self):
              return self._get_dataloaders()
  
          def predict_dataloader(self):
              return self._get_dataloaders()
  
          def validation_step(self, batch, batch_idx, dataloader_idx=0):
              return
  
          def test_step(self, batch, batch_idx, dataloader_idx=0):
              return
  
          def predict_step(self, batch, batch_idx, dataloader_idx=0):
              return
  
      model = CustomModel()
  
      # check the sanity dataloaders
      num_sanity_val_steps = 4
      trainer = Trainer(
          default_root_dir=tmp_path,
          max_epochs=1,
          limit_train_batches=0,
          num_sanity_val_steps=num_sanity_val_steps,
      )
      pbar = trainer.progress_bar_callback
      with mock.patch(
          "lightning.pytorch.callbacks.progress.tqdm_progress.Tqdm",
          MockTqdm,
      ):
          trainer.fit(model)
  
      expected_sanity_steps = [num_sanity_val_steps] * num_dl
      assert not pbar.val_progress_bar.leave
      assert trainer.num_sanity_val_batches == expected_sanity_steps
      assert pbar.val_progress_bar.total_values == expected_sanity_steps
      assert (
          pbar.val_progress_bar.n_values
          == list(range(num_sanity_val_steps + 1)) * num_dl
      )
      assert pbar.val_progress_bar.descriptions == [
          f"Sanity Checking DataLoader {i}: " for i in range(num_dl)
      ]
  
      # fit
      trainer = Trainer(default_root_dir=tmp_path, max_epochs=1)
      pbar = trainer.progress_bar_callback
      with mock.patch(
          "lightning.pytorch.callbacks.progress.tqdm_progress.Tqdm",
          MockTqdm,
      ):
          trainer.fit(model)
  
      n = trainer.num_training_batches
      m = trainer.num_val_batches
      assert len(trainer.train_dataloader) == n
      # train progress bar should have reached the end
      assert pbar.train_progress_bar.total == n
      assert pbar.train_progress_bar.n == n
      assert pbar.train_progress_bar.leave
  
      # check val progress bar total
      assert pbar.val_progress_bar.total_values == m
      assert (
          pbar.val_progress_bar.n_values
          == list(range(m[0] + 1)) * num_dl
      )
      assert pbar.val_progress_bar.descriptions == [
          f"Validation DataLoader {i}: " for i in range(num_dl)
      ]
      assert not pbar.val_progress_bar.leave
  
      # validate
      with mock.patch(
          "lightning.pytorch.callbacks.progress.tqdm_progress.Tqdm",
          MockTqdm,
      ):
          trainer.validate(model)
      assert trainer.num_val_batches == m
      assert pbar.val_progress_bar.total_values == m
      assert (
          pbar.val_progress_bar.n_values
          == list(range(m[0] + 1)) * num_dl
      )
      assert pbar.val_progress_bar.descriptions == [
          f"Validation DataLoader {i}: " for i in range(num_dl)
      ]
  
      # test
      with mock.patch(
          "lightning.pytorch.callbacks.progress.tqdm_progress.Tqdm",
          MockTqdm,
      ):
          trainer.test(model)
      assert pbar.test_progress_bar.leave
      k = trainer.num_test_batches
      assert pbar.test_progress_bar.total_values == k
      assert (
          pbar.test_progress_bar.n_values
          == list(range(k[0] + 1)) * num_dl
      )
      assert pbar.test_progress_bar.descriptions == [
          f"Testing DataLoader {i}: " for i in range(num_dl)
      ]
      assert pbar.test_progress_bar.leave
  
      # predict
      with mock.patch(
          "lightning.pytorch.callbacks.progress.tqdm_progress.Tqdm",
          MockTqdm,
      ):
          trainer.predict(model)
      assert pbar.predict_progress_bar.leave
      k = trainer.num_predict_batches
      assert pbar.predict_progress_bar.total_values == k
      assert (
          pbar.predict_progress_bar.n_values
          == list(range(k[0] + 1)) * num_dl
      )
      assert pbar.predict_progress_bar.descriptions == [
          f"Predicting DataLoader {i}: " for i in range(num_dl)
      ]
      assert pbar.predict_progress_bar.leave
  
imports: import math
  import os
  import pickle
  import sys
  from collections import defaultdict
  from typing import Union
  from unittest import mock
  from unittest.mock import ANY, PropertyMock, call
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.callbacks import ModelCheckpoint, ProgressBar, TQDMProgressBar
  from lightning.pytorch.callbacks.progress.tqdm_progress import Tqdm
  from lightning.pytorch.core.module import LightningModule
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.loggers import CSVLogger
  from lightning.pytorch.loggers.logger import DummyLogger
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from tests_pytorch.helpers.runif import RunIf
  from torch.utils.data.dataloader import DataLoader
label_desc: Use image node_id 4 for calling a global function with example usage: test_tqdm_progress_bar_totals(tmp_path, num_dl) and returns: unknown, self, self, self