code: def test_test_step(unimodal_model, dataset):
      """Test if the test step return the expected statistics."""
      x = dataset["input"]
      y_true = dataset["output"]["unimodal"]["y_true"]
  
      # reset gradients
      unimodal_model.optimizer.zero_grad()
  
      # perform test step
      results = unimodal_model.test_step(x, y_true)
  
      # check if gradients are not computed
      for param in unimodal_model.parameters():
          assert (param.grad is None) or (
              torch.allclose(param, torch.zeros_like(param))
          )
  
      # compute prediction
      unimodal_model.eval()
      with torch.no_grad():
          y_pred = unimodal_model(x)
  
      expected_loss_val = loss_fn(y_pred, y_true).item()
      expected_accuracy_val = torch.mean(
          (torch.argmax(y_pred, dim=-1) == y_true).float()
      ).item()
      assert np.allclose(results["loss"], expected_loss_val)
      assert np.allclose(results["accuracy"], expected_accuracy_val)
  
imports: from typing import List
  import numpy
  import pytest
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader, TensorDataset
  from alibi.models.pytorch.metrics import AccuracyMetric, LossContainer
  from alibi.models.pytorch.model import Model
label_desc: Use image node_id 16 for calling a global function with example usage: test_test_step(unimodal_model, dataset) without return types