code: def get_outliers(
      embedding: Union[np.ndarray, pd.DataFrame],
      df: pd.DataFrame,
      outlier_fraction: float = 0.2,
  ):
      """get outliers using sklearn's LocalOutlierFactor
  
      Args:
          embedding (Union[np.ndarray, pd.DataFrame]): umap embedding
          df (pd.DataFrame): dataframe for enrichment
          outlier_fraction (float, optional): fraction of outliers parameter. Defaults to 0.2.
  
      Returns:
          Tuple: tuple of outlying dataframe, outlier scores, and outlier classifier
      """
      outlier_clf = neighbors.LocalOutlierFactor(
          contamination=outlier_fraction
      )
      outlier_scores = outlier_clf.fit_predict(embedding)
      df["outlier"] = outlier_scores
      outlying = df[outlier_scores == -1]
  
      return outlying, outlier_scores, outlier_clf
  
imports: from typing import Union, Tuple
  import pandas
  import logging
label_desc: Use image node_id 1 for calling a global function with example usage: get_outliers(embedding, df, outlier_fraction) and returns: outlying,  outlier_scores,  outlier_clf