class_docstr: The following conversion tests are underlying assumptions
  made by the rest of tests in this file.
  
  We are testing them explicitly just in case they break behaviour
  in the future. As in this case it will be easier to update the tests.
class_docstr_tok: ["The","following","conversion","tests","are","underlying","assumptions","made","by","the","rest","of","tests","in","this","file",".","We","are","testing","them","explicitly","just","in","case","they","break","behaviour","in","the","future",".","As","in","this","case","it","will","be","easier","to","update","the","tests","."]
code: def test_single_ablation_batch_scalar_tensor_0d(self) -> None:
      net = BasicModel_MultiLayer()
      ablation_algo = FeatureAblation(lambda inp: torch.sum(net(inp)))
      self._single_input_one_sample_batch_scalar_ablation_assert(
          ablation_algo
      )
  
imports: import io
  import unittest
  import unittest.mock
  from typing import Any, cast, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.feature_ablation import FeatureAblation
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._utils.attribution import Attribution
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel, BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput, BasicModelBoolInput, BasicModelWithSparseInputs
  from torch import Tensor
label_desc: Use image node_id 24 for calling the Test obj's underlying member method code with example usage: obj.test_single_ablation_batch_scalar_tensor_0d() without return types