code: def test_rich_progress_bar_metric_display_task_id(tmp_path):
      class CustomModel(BoringModel):
          def training_step(self, *args, **kwargs):
              res = super().training_step(*args, **kwargs)
              self.log("train_loss", res["loss"], prog_bar=True)
              return res
  
      progress_bar = RichProgressBar()
      model = CustomModel()
      trainer = Trainer(
          default_root_dir=tmp_path,
          callbacks=progress_bar,
          limit_train_batches=1,
          limit_val_batches=1,
          max_epochs=1,
          enable_checkpointing=False,
          enable_model_summary=False,
      )
  
      trainer.fit(model)
      train_progress_bar_id = progress_bar.train_progress_bar_id
      val_progress_bar_id = progress_bar.val_progress_bar_id
      rendered = progress_bar.progress.columns[-1]._renderable_cache
  
      for key in ("loss", "v_num", "train_loss"):
          assert key in rendered[train_progress_bar_id][1]
          assert key not in rendered[val_progress_bar_id][1]
  
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 14 for calling a global function with example usage: test_rich_progress_bar_metric_display_task_id(tmp_path) and returns: res