code: def test_correct_grad_dot_sim_result_torch(seed, normed_ds):
      """
      `grad_dot` method orders data points distributed on the unit circle by their angular separation. Test is applied to
      `torch` backend.
      """
      model = nn.Linear(2, 1, bias=False)
      explainer = GradientSimilarity(
          model,
          task="regression",
          loss_fn=loss_torch,
          sim_fn="grad_dot",
          backend="pytorch",
      )
      explainer = explainer.fit(normed_ds, normed_ds)
      explanation = explainer.explain(normed_ds[0], Y=normed_ds[0])
      last = np.dot(normed_ds[0], normed_ds[0])
      for ind in explanation["ordered_indices"][0][1:]:
          current = np.dot(normed_ds[0], normed_ds[ind])
          assert current <= last
          last = current
  
imports: import pytest
  import numpy
  import torch.nn
  import torch
  import tensorflow
  from tensorflow import keras
  from alibi.explainers.similarity.grad import GradientSimilarity
label_desc: Use image node_id 5 for calling a global function with example usage: test_correct_grad_dot_sim_result_torch(seed, normed_ds) without return types