code: def dataset_ncf(test_specs_ncf):
      """Get Python labels"""
  
      def random_date_generator(start_date, range_in_days):
          """Helper function to generate random timestamps.
  
          Reference: https://stackoverflow.com/questions/41006182/generate-random-dates-within-a-range-in-numpy
          """
          days_to_add = np.arange(0, range_in_days)
          random_dates = []
          for i in range(range_in_days):
              random_date = np.datetime64(
                  start_date
              ) + np.random.choice(days_to_add)
              random_dates.append(random_date)
  
          return random_dates
  
      np.random.seed(test_specs_ncf["seed"])
  
      rating = pd.DataFrame(
          {
              DEFAULT_USER_COL: np.random.randint(
                  1, 100, test_specs_ncf["number_of_rows"]
              ),
              DEFAULT_ITEM_COL: np.random.randint(
                  1, 100, test_specs_ncf["number_of_rows"]
              ),
              DEFAULT_RATING_COL: np.random.randint(
                  1, 5, test_specs_ncf["number_of_rows"]
              ),
              DEFAULT_TIMESTAMP_COL: random_date_generator(
                  "2018-01-01", test_specs_ncf["number_of_rows"]
              ),
          }
      )
  
      train, test = python_chrono_split(
          rating, ratio=test_specs_ncf["ratio"]
      )
  
      return train, test
  
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 16 for calling a global function with example usage: dataset_ncf(test_specs_ncf) and returns: train,  test, random_dates