code: def test_evaluate(unimodal_model, dataset, batch_size, mocker):
      """Test if the fit function returns the expected statistics."""
      x = dataset["input"]
      y = dataset["output"]["unimodal"]["y_true"]
      dataset = TensorDataset(x, y)
      dataloader = DataLoader(dataset, batch_size=batch_size)
  
      m = mocker.patch(
          "alibi.models.pytorch.model.Model.test_step",
          return_value={"loss": 0, "accuracy": 1.0},
      )
      # no need to reset the loss and the metrics since the `test_step` is mocked
      metrics_val = unimodal_model.evaluate(testloader=dataloader)
      num_calls = (n_instances // batch_size) + (
          (n_instances % batch_size) > 0
      )
  
      assert m.call_count == num_calls
      assert np.allclose(metrics_val["loss"], 0)
      assert np.allclose(metrics_val["accuracy"], 1)
  
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 18 for calling a global function with example usage: test_evaluate(unimodal_model, dataset, batch_size, mocker) without return types