code: def test_correct_grad_cos_sim_result_torch(seed, ds):
      """
      `grad_cos` method orders normally distributed data points 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_cos",
          backend="pytorch",
      )
      explainer = explainer.fit(ds, ds)
      explanation = explainer.explain(ds[0], Y=ds[0])
      last = 0
      for ind in explanation["ordered_indices"][0][1:]:
          current = compute_angle(ds[0], 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 6 for calling a global function with example usage: test_correct_grad_cos_sim_result_torch(seed, ds) without return types