code: def test_multi_input_lime_with_empty_input(self) -> None:
      net = BasicLinearModel()
      inp1 = torch.tensor([[23.0, 0.0, 0.0, 23.0, 0.0, 0.0, 23.0]])
      inp2 = torch.tensor([[]])  # empty input
      mask1 = torch.tensor([[0, 1, 2, 3, 4, 5, 6]])
      mask2 = torch.tensor([[]], dtype=torch.long)  # empty mask
      expected: Tuple[List[List[float]], ...] = (
          [[-4.0, 0, 0, 0, 0, 0, -4.0]],
          [[]],
      )
      # no mask
      self._lime_test_assert(
          net,
          (inp1, inp2),
          expected,
          n_samples=2000,
          expected_coefs_only=[[-4.0, 0, 0, 0, 0, 0, -4.0]],
      )
      # with mask
      self._lime_test_assert(
          net,
          (inp1, inp2),
          expected,
          n_samples=2000,
          expected_coefs_only=[[-4.0, 0, 0, 0, 0, 0, -4.0]],
          feature_mask=(mask1, mask2),
      )
  
imports: import io
  import unittest
  import unittest.mock
  from functools import partial
  from typing import Any, Callable, Generator, List, Optional, Tuple, Union
  import torch
  from captum._utils.models.linear_model import SGDLasso, SkLearnLasso
  from captum._utils.models.model import Model
  from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.lime import get_exp_kernel_similarity_function, Lime, LimeBase
  from captum.attr._utils.batching import _batch_example_iterator
  from captum.attr._utils.common import _construct_default_feature_mask, _format_input_baseline, _format_tensor_into_tuples
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicLinearModel, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput, BasicModelBoolInput
  from torch import Tensor
label_desc: Use image node_id 13 for calling the Test obj's underlying member method code with example usage: obj.test_multi_input_lime_with_empty_input() without return types