code: def test_ranking_preprocessing_filter_strategies(tenrec_data_path):
      with tempfile.TemporaryDirectory() as tmp_output_folder:
          args = kwargs_to_cli_ags(
              data_path=os.path.join(
                  tenrec_data_path, "raw/QK-video-10M.csv"
              ),
              input_data_format="csv",
              csv_na_values="\\N",
              output_path=tmp_output_folder,
              categorical_features="user_id,item_id,video_category,gender,age",
              binary_classif_targets="click,follow,like,share",
              regression_targets="watching_times",
              to_int32="user_id,item_id",
              to_int16="watching_times",
              to_int8="gender,age,video_category,click,follow,like,share",
              user_id_feature="user_id",
              item_id_feature="item_id",
              filter_query="click==1 or (click==0 and follow==0 and like==0 and share==0)",
              min_item_freq=5,
              min_user_freq=5,
              max_user_freq=200,
              num_max_rounds_filtering=5,
          )
          runner = PreprocessingRunner(args)
          runner.run()
  
          total_rows = 9102904
  
          train_df = cudf.read_parquet(
              os.path.join(tmp_output_folder, "train/*.parquet")
          )
          assert len(train_df) == total_rows
  
          assert train_df.groupby("item_id").size().min() >= 5
          assert train_df.groupby("user_id").size().min() >= 5
  
file_path: Merlin/tests/integration/examples/quick_start/test_preproc.py
imports: import os
  import tempfile
  import cudf
  import numpy
  import pytest
  from examples.quick_start.scripts.preproc.preprocessing import PreprocessingRunner
  from merlin.schema import Tags
  from merlin.schema.io.tensorflow_metadata import TensorflowMetadata
label_desc: Use image node_id 7 for calling a global function with example usage: test_ranking_preprocessing_filter_strategies(tenrec_data_path) without return types