code: def get_quantiles(neighbors_df, quantile_levels):
      """Compute predicted quantiles for the given sample.
  
      Parameters
      ----------
      neighbors_df : pd.DataFrame
          DataFrame with columns y (target values for each sample) and weight (weight assigned to each sample)
      quantile_levels : List[float]
          List of quantiles to predict between 0.0 and 1.0
  
      Returns
      -------
      quantiles : array, shape [len(quantile_levels)]
          Predicted quantiles.
      """
      result = []
      for q in quantile_levels:
          result.append(
              weighted_percentile(
                  neighbors_df.y,
                  int(q * 100),
                  neighbors_df.weight,
                  sorter=slice(
                      None
                  ),  # targets are already sorted, so no sorting required
                  is_filtered=True,
              )
          )
      return result
  
imports: import logging
  from functools import partial
  import numpy
  import pandas
  from sklearn.ensemble._forest import ForestRegressor
  from sklearn.tree import BaseDecisionTree, DecisionTreeRegressor, ExtraTreeRegressor
  from sklearn.utils import check_array, check_random_state, check_X_y
label_desc: Use image node_id 4 for calling a global function with example usage: get_quantiles(neighbors_df, quantile_levels) and returns: result