code: def test_dl_attr_with_mask(self, forward) -> None:
      # FeatureAblation does not support grouping across tensors for now
      # add such test cases after support grouping across tensors in FeatureAblation
      masks = (
          torch.tensor([[0, 0]]),
          torch.tensor([[[1, 2], [3, 2]]]),
          torch.tensor([[4]]),
      )
  
      fa = FeatureAblation(forward)
      dl_fa = DataLoaderAttribution(fa)
  
      dataloader = DataLoader(mock_dataset, batch_size=2)
  
      dl_attributions = dl_fa.attribute(dataloader, feature_mask=masks)
  
      # default reduce of DataLoaderAttribution works the same as concat all batches
      attr_list = []
      for batch in dataloader:
          batch_attr = fa.attribute(tuple(batch), feature_mask=masks)
          attr_list.append(batch_attr)
  
      expected_attr = tuple(
          torch.cat(feature_attrs, dim=0)
          for feature_attrs in zip(*attr_list)
      )
  
      assertAttributionComparision(self, dl_attributions, expected_attr)
  
imports: import math
  from typing import cast
  from unittest.mock import Mock, patch
  import torch
  from captum.attr._core.dataloader_attr import DataLoaderAttribution, InputRole
  from captum.attr._core.feature_ablation import FeatureAblation
  from parameterized import parameterized
  from tests.helpers.basic import assertAttributionComparision, assertTensorAlmostEqual, BaseTest
  from torch import Tensor
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 2 for calling the Test obj's underlying member method code with example usage: obj.test_dl_attr_with_mask(forward) without return types