code: def test_train_step(unimodal_model, dataset):
      """Test if the train step return the appropriate statistics."""
      x = dataset["input"]
      y_true = dataset["output"]["unimodal"]["y_true"]
  
      # compute the loss manually
      w = unimodal_model.fc1.weight.clone()
      diff = y_true - x @ w.T
      expected_loss = (
          torch.trace(diff @ diff.T) / (n_instances * output_dim)
      ).item()
  
      # compute the gradients manually
      grad = -2 * (diff.T @ x) / (n_instances * output_dim)
      new_w = w - lr * grad
  
      # perform training step
      results = unimodal_model.train_step(x, y_true)
  
      # check if the loss and the updated weights match the manually computed ones
      assert np.isclose(results["loss"], expected_loss)
      assert torch.allclose(unimodal_model.fc1.weight, new_w)
  
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 15 for calling a global function with example usage: test_train_step(unimodal_model, dataset) without return types