code: def test_multiple_eval_dataloaders_seq(tmpdir, seq_type):
      class TestModel(BoringModel):
          def validation_step(self, batch, batch_idx, dataloader_idx):
              if dataloader_idx == 0:
                  assert batch.sum() == 0
              elif dataloader_idx == 1:
                  assert batch.sum() == 11
              else:
                  raise Exception("should only have two dataloaders")
  
          def val_dataloader(self):
              dl1 = torch.utils.data.DataLoader(
                  RandomDatasetA(32, 64), batch_size=11
              )
              dl2 = torch.utils.data.DataLoader(
                  RandomDatasetB(32, 64), batch_size=11
              )
              return seq_type((dl1, dl2))
  
      model = TestModel()
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=2,
          limit_val_batches=2,
          max_epochs=1,
          log_every_n_steps=1,
          enable_model_summary=False,
      )
  
      trainer.fit(model)
  
file_path: pytorch-lightning/tests/tests_pytorch/trainer/dynamic_args/test_multiple_eval_dataloaders.py
imports: import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel
  from torch.utils.data import Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_multiple_eval_dataloaders_seq(tmpdir, seq_type) and returns: seq_type