code: def test_predict_all_items(train_test_dummy_timestamp, header):
      model = SAR(**header)
      trainset, _ = train_test_dummy_timestamp
      model.fit(trainset)
  
      user_items = itertools.product(
          trainset[header["col_user"]].unique(),
          trainset[header["col_item"]].unique(),
      )
      testset = pd.DataFrame(
          user_items, columns=[header["col_user"], header["col_item"]]
      )
      preds = model.predict(testset)
  
      assert len(preds) == len(testset)
      assert isinstance(preds, pd.DataFrame)
      assert (
          preds[header["col_user"]].dtype
          == trainset[header["col_user"]].dtype
      )
      assert (
          preds[header["col_item"]].dtype
          == trainset[header["col_item"]].dtype
      )
      assert (
          preds[DEFAULT_PREDICTION_COL].dtype
          == trainset[header["col_rating"]].dtype
      )
  
imports: import json
  import pytest
  import itertools
  import numpy
  import pandas
  from pandas.testing import assert_frame_equal
  from recommenders.utils.constants import DEFAULT_PREDICTION_COL
  from recommenders.models.sar import SAR
label_desc: Use image node_id 4 for calling a global function with example usage: test_predict_all_items(train_test_dummy_timestamp, header) without return types