code: def test_correct_influences_batch_single(self) -> None:
      with tempfile.TemporaryDirectory() as tmpdir:
          num_features = 4
          low, high = 0, 16
          batch_size = 1
          mymodel = BasicLinearNet(num_features)
          mydata = RangeDataset(low, high, num_features)
          layers = []
          for name, _module in mymodel.named_modules():
              layers.append(name)
          layers: List[str] = list(filter(None, layers))
          testlayers = layers[1:]
  
          sim = SimilarityInfluence(
              mymodel,
              testlayers,
              mydata,
              tmpdir,
              "linear",
              batch_size=batch_size,
              similarity_metric=euclidean_distance,
              similarity_direction="min",
          )
          inputs = torch.stack((mydata[1], mydata[8], mydata[14]))
          influences = sim.influence(inputs, top_k=3)
  
          self.assertEqual(len(influences), len(testlayers))
          assertTensorAlmostEqual(
              self,
              torch.sum(influences[layers[1]][0], 1),
              torch.sum(
                  torch.Tensor([[1, 0, 2], [8, 7, 9], [14, 15, 13]]), 1
              ),
          )
          assertTensorAlmostEqual(
              self,
              torch.sum(influences[layers[2]][0], 1),
              torch.sum(
                  torch.Tensor([[1, 0, 2], [8, 7, 9], [14, 15, 13]]), 1
              ),
          )
  
imports: import tempfile
  from typing import List
  import torch
  import torch.nn
  from captum.influence._core.similarity_influence import cosine_similarity, euclidean_distance, SimilarityInfluence
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from torch.utils.data import Dataset
label_desc: Use image node_id 3 for calling the Test obj's underlying member method code with example usage: obj.test_correct_influences_batch_single() without return types