code: def test_loss_batch_online_equivalence(loss):
      y_true = np.random.randint(2, size=30)
      y_pred = np.random.uniform(-10, 10, size=30)
  
      for yt, yp, g in zip(
          y_true, y_pred, loss.gradient(y_true, y_pred)
      ):
          assert math.isclose(loss.gradient(yt, yp), g, abs_tol=1e-9)
  
file_path: river/river/optim/test_.py
imports: from __future__ import annotations
  import importlib
  import inspect
  import math
  import random
  import typing
  import numpy
  import pytest
  from river import optim, utils
label_desc: Use image node_id 2 for calling a global function with example usage: test_loss_batch_online_equivalence(loss) without return types