code: def python_float_dataset(test_specs):
      np.random.seed(test_specs["seed"])
  
      # generates the user/item affinity matrix. Ratings are in the interval [0, 5), with 0s denoting unrated items.
      return (
          np.random.random(
              size=(
                  test_specs["number_of_users"],
                  test_specs["number_of_items"],
              )
          )
          * 5
      )
  
file_path: recommenders/tests/unit/recommenders/datasets/test_python_splitter.py
imports: import pytest
  import pandas
  import numpy
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_TIMESTAMP_COL
  from recommenders.datasets.split_utils import min_rating_filter_pandas, split_pandas_data_with_ratios
  from recommenders.datasets.python_splitters import python_chrono_split, python_random_split, python_stratified_split, numpy_stratified_split
label_desc: Use image node_id 4 for calling a global function with example usage: python_float_dataset(test_specs) and returns: unknown