class_docstr: Integration with polar based popularity recommender.
code: def create(
      self,
      target: str,
      df: pd.DataFrame = None,
      meta_data: Dict = None,
      args: Optional[Dict] = None,
  ) -> None:
      args = args["using"]
  
      interaction_data = pl.from_pandas(df)
  
      args["ave_per_item_user"] = (
          interaction_data.get_column(args["user_id"])
          .value_counts()
          .mean()["counts"][0]
      )
  
      popularity = (
          interaction_data.get_column(args["item_id"])
          .value_counts()
          .sort("counts", descending=True)
          .get_column(args["item_id"])
          .head(
              args["n_recommendations"] * args["ave_per_item_user"]
          )  # to ensure there are enough to predict
          .to_pandas()
          .reset_index()
          .to_dict(orient="list")
      )
  
      self.model_storage.file_set("interaction", dill.dumps(df))
      self.model_storage.json_set("popularity", popularity)
      self.model_storage.json_set("args", args)
  
file_path: mindsdb/mindsdb/integrations/handlers/popularity_recommender_handler/popularity_recommender_handler.py
imports: from typing import Dict, Optional
  import dill
  import pandas
  import polars
  from mindsdb.integrations.libs.base import BaseMLEngine
label_desc: Use image node_id 1 for calling the PopularityRecommenderHandler obj's underlying member method code with example usage: obj.create(target, df, meta_data, args) without return types