class_docstr: A class containing functions supporting quantile, poisson and gaussian regression, to be used as a mixin for some
  `RegressionModel` subclasses.
class_docstr_tok: ["A","class","containing","functions","supporting","quantile",",","poisson","and","gaussian","regression",",","to","be","used","as","a","mixin","for","some","`","RegressionModel","`","subclasses","."]
code: def _prepare_quantiles(quantiles):
      if quantiles is None:
          quantiles = [
              0.01,
              0.05,
              0.1,
              0.25,
              0.5,
              0.75,
              0.9,
              0.95,
              0.99,
          ]
      else:
          quantiles = sorted(quantiles)
          _check_quantiles(quantiles)
      median_idx = quantiles.index(0.5)
  
      return quantiles, median_idx
  
imports: from collections import OrderedDict
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from sklearn.linear_model import LinearRegression
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
  from darts.models.forecasting.forecasting_model import GlobalForecastingModel
  from darts.timeseries import TimeSeries
  from darts.utils.data.tabularization import add_static_covariates_to_lagged_data, create_lagged_component_names, create_lagged_training_data
  from darts.utils.historical_forecasts import _check_optimizable_historical_forecasts_global_models, _optimized_historical_forecasts_all_points, _optimized_historical_forecasts_last_points_only, _process_historical_forecast_input
  from darts.utils.multioutput import MultiOutputRegressor
  from darts.utils.utils import _check_quantiles, get_single_series, seq2series, series2seq
label_desc: Use image node_id 3 for calling the _LikelihoodMixin obj's underlying member method code with example usage: obj._prepare_quantiles(quantiles) and returns: quantiles,  median_idx