code: def test_predict(
      similarity_type,
      timedecay_formula,
      train_test_dummy_timestamp,
      header,
  ):
      model = SAR(
          similarity_type=similarity_type,
          timedecay_formula=timedecay_formula,
          **header,
      )
      trainset, testset = train_test_dummy_timestamp
      model.fit(trainset)
      preds = model.predict(testset)
  
      assert len(preds) == 2
      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 3 for calling a global function with example usage: test_predict(similarity_type, timedecay_formula, train_test_dummy_timestamp, header) without return types