code: def test_recommend_k_items(
      threshold,
      similarity_type,
      file,
      header,
      sar_settings,
      demo_usage_data,
  ):
      time_now = demo_usage_data[header["col_timestamp"]].max()
      model = SAR(
          similarity_type=similarity_type,
          timedecay_formula=True,
          time_decay_coefficient=30,
          time_now=time_now,
          threshold=threshold,
          **header,
      )
      model.fit(demo_usage_data)
  
      true_userpred = pd.read_csv(
          sar_settings["FILE_DIR"]
          + "userpred_"
          + file
          + str(threshold)
          + ".csv",
          index_col=0,
      )
      test_results = model.recommend_k_items(
          demo_usage_data[
              demo_usage_data[header["col_user"]]
              == sar_settings["TEST_USER_ID"]
          ],
          top_k=10,
          sort_top_k=True,
          remove_seen=True,
      )
  
      if true_userpred.shape[0] == 0:
          assert test_results.shape[0] == 0
      else:
          pd.testing.assert_frame_equal(
              test_results, true_userpred, atol=sar_settings["ATOL"]
          )
  
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 7 for calling a global function with example usage: test_recommend_k_items(threshold, similarity_type, file, header, sar_settings, demo_usage_data) without return types