code: def load_item_df(
      size="100k",
      local_cache_path=None,
      movie_col=DEFAULT_ITEM_COL,
      title_col=None,
      genres_col=None,
      year_col=None,
  ):
      """Loads Movie info.
  
      Args:
          size (str): Size of the data to load. One of ("100k", "1m", "10m", "20m").
          local_cache_path (str): Path (directory or a zip file) to cache the downloaded zip file.
              If None, all the intermediate files will be stored in a temporary directory and removed after use.
          movie_col (str): Movie id column name.
          title_col (str): Movie title column name. If None, the column will not be loaded.
          genres_col (str): Genres column name. Genres are '|' separated string.
              If None, the column will not be loaded.
          year_col (str): Movie release year column name. If None, the column will not be loaded.
  
      Returns:
          pandas.DataFrame: Movie information data, such as title, genres, and release year.
      """
      size = size.lower()
      if size not in DATA_FORMAT:
          raise ValueError(f"Size: {size}. " + ERROR_MOVIE_LENS_SIZE)
  
      with download_path(local_cache_path) as path:
          filepath = os.path.join(path, "ml-{}.zip".format(size))
          _, item_datapath = _maybe_download_and_extract(size, filepath)
          item_df = _load_item_df(
              size,
              item_datapath,
              movie_col,
              title_col,
              genres_col,
              year_col,
          )
  
      return item_df
  
imports: import os
  import re
  import random
  import shutil
  import warnings
  import pandas
  from typing import Optional
  from zipfile import ZipFile
  from recommenders.datasets.download_utils import maybe_download, download_path
  from recommenders.utils.notebook_utils import is_databricks
  from recommenders.utils.constants import DEFAULT_HEADER, DEFAULT_ITEM_COL, DEFAULT_USER_COL, DEFAULT_RATING_COL, DEFAULT_TIMESTAMP_COL, DEFAULT_TITLE_COL, DEFAULT_GENRE_COL
  import pandera
  import pandera.extensions
  from pandera import Field
  from pandera.typing import Series
label_desc: Use image node_id 2 for calling a global function with example usage: load_item_df(size, local_cache_path, movie_col, title_col, genres_col, year_col) and returns: item_df