code: def read_movielens_df_item() -> pd.DataFrame:
      """
      Read ``u.item`` from the MovieLens 100K dataset.
  
      If there is not a directory at ``$DATA_PATH/ml-100k``, this function creates that directory and
      downloads the entire dataset there.
  
      See the MovieLens 100K README for additional information on the dataset:
      https://files.grouplens.org/datasets/movielens/ml-100k-README.txt
  
      Returns
      -------
      df_item: pd.DataFrame
          MovieLens 100K ``u.item`` containing columns:
  
              * item_id
  
              * movie_title
  
              * release_date
  
              * video_release_date
  
              * IMDb_URL
  
              * unknown
  
              * Action
  
              * Adventure
  
              * Animation
  
              * Children
  
              * Comedy
  
              * Crime
  
              * Documentary
  
              * Drama
  
              * Fantasy
  
              * Film_Noir
  
              * Horror
  
              * Musical
  
              * Mystery
  
              * Romance', 'Sci_Fi
  
              * Thriller
  
              * War
  
              * Wester
  
      Side Effects
      ------------
      Creates directory at ``$DATA_PATH/ml-100k`` and downloads data files if data does not exist.
  
      """
      _make_data_path_dirs_if_not_exist()
  
      df_item_path = os.path.join(DATA_PATH, "ml-100k", "u.item")
      if not Path(df_item_path).exists():
          _download_movielens_100k()
  
      column_names = [
          "item_id",
          "movie_title",
          "release_date",
          "video_release_date",
          "IMDb_URL",
          "unknown",
          "Action",
          "Adventure",
          "Animation",
          "Children",
          "Comedy",
          "Crime",
          "Documentary",
          "Drama",
          "Fantasy",
          "Film_Noir",
          "Horror",
          "Musical",
          "Mystery",
          "Romance",
          "Sci_Fi",
          "Thriller",
          "War",
          "Western",
      ]
      df_item = pd.read_csv(
          df_item_path, sep="|", encoding="latin-1", names=column_names
      )
  
      df_item["release_date"] = pd.to_datetime(df_item["release_date"])
      df_item = df_item.drop(columns=["video_release_date"])
  
      return df_item
  
file_path: collie/collie/movielens/get_data.py
imports: import os
  from pathlib import Path
  import re
  import zipfile
  import pandas
  import requests
  from collie.config import DATA_PATH
label_desc: Use image node_id 2 for calling a global function with example usage: read_movielens_df_item() and returns: df_item