code: def test_recalculate_after_pickle(use_gpu):
      user_items = get_checker_board(10)
      model = AlternatingLeastSquares(
          factors=2, use_gpu=use_gpu, regularization=0.1
      )
      model.fit(user_items, show_progress=False)
      model._XtX = model._YtY = None
  
      original_ids, _ = model.recommend(
          0, user_items=user_items[0], recalculate_user=True
      )
  
      model = pickle.loads(pickle.dumps(model))
      ids, _ = model.recommend(
          0, user_items=user_items[0], recalculate_user=True
      )
  
      assert_array_equal(ids, original_ids)
  
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 3 for calling a global function with example usage: test_recalculate_after_pickle(use_gpu) without return types