code: def test_gpu_loss(n_users, n_items, n_samples, regularization):
      # we used to have some errors in the gpu loss function
      # if  n_items * n_users >2**31. Test out that the loss on the gpu
      # matches that on the cpu
      # https://github.com/benfred/implicit/issues/441
      # https://github.com/benfred/implicit/issues/367
      liked_items = np.random.randint(0, n_items, n_samples)
      liked_users = np.random.randint(0, n_users, n_samples)
      ratings = coo_matrix(
          (np.ones(n_samples), (liked_users, liked_items)),
          shape=(n_users, n_items),
      ).tocsr()
  
      factors = 32
      item_factors = np.random.random((n_items, factors)).astype(
          "float32"
      )
      user_factors = np.random.random((n_users, factors)).astype(
          "float32"
      )
  
      gpu_loss = implicit.gpu.als.calculate_loss(
          ratings, user_factors, item_factors, regularization
      )
      cpu_loss = implicit.cpu.als.calculate_loss(
          ratings, user_factors, item_factors, regularization
      )
  
      assert gpu_loss == pytest.approx(cpu_loss, rel=1e-5)
  
imports: import pickle
  import unittest
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from recommender_base_test import RecommenderBaseTestMixin, get_checker_board
  from scipy.sparse import coo_matrix, csr_matrix, random
  import implicit
  from implicit.als import AlternatingLeastSquares
  from implicit.gpu import HAS_CUDA
label_desc: Use image node_id 11 for calling a global function with example usage: test_gpu_loss(n_users, n_items, n_samples, regularization) without return types