code: def demo_usage_data(header, sar_settings):
      # load the data
      data = pd.read_csv(
          sar_settings["FILE_DIR"] + "demoUsageNoDups.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
  
imports: import calendar
  import datetime
  import os
  from pathlib import Path
  from tempfile import TemporaryDirectory
  import numpy
  import pandas
  import pytest
  from sklearn.model_selection import train_test_split
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_TIMESTAMP_COL
  from recommenders.datasets.python_splitters import numpy_stratified_split
  from recommenders.datasets.python_splitters import python_chrono_split
  from recommenders.utils.spark_utils import start_or_get_spark
label_desc: Use image node_id 11 for calling a global function with example usage: demo_usage_data(header, sar_settings) and returns: data