code: def demo_usage_data(header, sar_settings):
      # load the data
      data = pd.read_csv(sar_settings["FILE_DIR"] + "demoUsage.csv")
      data["rating"] = pd.Series([1] * data.shape[0])
      data = data.rename(
          columns={
              "userId": header["col_user"],
              "productId": header["col_item"],
              "rating": header["col_rating"],
              "timestamp": header["col_timestamp"],
          }
      )
  
      # convert timestamp
      data[header["col_timestamp"]] = data[
          header["col_timestamp"]
      ].apply(
          lambda s: float(
              calendar.timegm(
                  datetime.datetime.strptime(
                      s, "%Y/%m/%dT%H:%M:%S"
                  ).timetuple()
              )
          )
      )
  
      return data
  
file_path: recommenders/contrib/sarplus/python/tests/conftest.py
imports: import calendar
  import datetime
  import pandas
  import pytest
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 for calling a global function with example usage: demo_usage_data(header, sar_settings) and returns: data