code: def set_quantiles(self):
      # convert quantiles to empty list [] if None
      if self.quantiles is None:
          self.quantiles = []
      # assert quantiles is a list type
      assert isinstance(
          self.quantiles, list
      ), "Quantiles must be in a list format, not None or scalar."
      # check if quantiles contain 0.5 or close to 0.5, remove if so as 0.5 will be inserted again as first index
      self.quantiles = [
          quantile
          for quantile in self.quantiles
          if not math.isclose(0.5, quantile)
      ]
      # check if quantiles are float values in (0, 1)
      assert all(
          0 < quantile < 1 for quantile in self.quantiles
      ), "The quantiles specified need to be floats in-between (0, 1)."
      # sort the quantiles
      self.quantiles.sort()
      # 0 is the median quantile index
      self.quantiles.insert(0, 0.5)
  
imports: from __future__ import annotations
  import logging
  import math
  import types
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from typing import Callable, List, Optional
  from typing import OrderedDict
  from typing import Type, Union
  import numpy
  import pandas
  import torch
  from neuralprophet import df_utils, np_types, utils, utils_torch
  from neuralprophet.custom_loss_metrics import PinballLoss
label_desc: Use image node_id 3 for calling the Train obj's underlying member method code with example usage: obj.set_quantiles() without return types