code: def convert_to_implicit(
      explicit_df: pd.DataFrame,
      min_rating_to_keep: Optional[float] = 4,
      user_col: str = "user_id",
      item_col: str = "item_id",
      ratings_col: str = "rating",
  ) -> pd.DataFrame:
      """
      Convert explicit interactions data to implicit data.
  
      Duplicate user ID and item ID pairs will be dropped, as well as all scores that are
      ``< min_rating_to_keep``. All remaining interactions will have a rating of ``1``.
  
      Parameters
      ----------
      explicit_df: pd.DataFrame
          Dataframe with explicit ratings in the rating column
      min_rating_to_keep: int
          Minimum rating to be considered a valid interaction
      ratings_col: str
          Column name for the ratings column
  
      Returns
      -------
      implicit_df: pd.DataFrame
          Dataframe that converts all ``ratings >= min_rating_to_keep`` to 1 and drops the rest with a
          reset index. Note that the order of ``implicit_df`` will not be equal to ``explicit_df``
  
      """
      implicit_df = explicit_df.copy()
  
      implicit_df = implicit_df.sort_values(
          by=ratings_col
      ).drop_duplicates(
          subset=[user_col, item_col], keep="last", ignore_index=True
      )
      implicit_df = implicit_df.drop(
          implicit_df[
              implicit_df[ratings_col] < min_rating_to_keep
          ].index
      )
      implicit_df[ratings_col] = 1
  
      return implicit_df.reset_index(drop=True)
  
imports: from datetime import datetime
  import inspect
  from pathlib import Path
  import re
  import time
  from typing import Any, Dict, Iterable, List, Optional, Union
  import docstring_parser
  import numpy
  import pandas
  import pytorch_lightning
  from scipy.sparse import coo_matrix
  import torch
  from collie.interactions import Interactions
label_desc: Use image node_id 6 for calling a global function with example usage: convert_to_implicit(explicit_df, min_rating_to_keep, user_col, item_col, ratings_col) and returns: implicit_df