class_docstr: Base probability distribution.
docstr: Return entry-wise quantiles, in Proba/pred_quantiles mtype format.
  
  This method broadcasts as follows:
  for a scalar `alpha`, computes the `alpha`-quantile entry-wise,
  and returns as a `pd.DataFrame` with same index, and columns as in return.
  If `alpha` is iterable, multiple quantiles will be calculated,
  and the result will be concatenated column-wise (axis=1).
  
  The `ppf` method also computes quantiles, but broadcasts differently, in
  `numpy` style closer to `tensorflow`.
  In contrast, this `quantile` method broadcasts
  as ``sktime`` forecaster `predict_quantiles`, i.e., columns first.
  
  Parameters
  ----------
  alpha : float or list of float of unique values
      A probability or list of, at which quantiles are computed.
  
  Returns
  -------
  quantiles : pd.DataFrame
      Column has multi-index: first level is variable name from `self.columns`,
      second level being the values of `alpha` passed to the function.
      Row index is `self.index`.
      Entries in the i-th row, (j, p)-the column is
      the p-th quantile of the marginal of `self` at index (i, j).
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code: def quantile(self, alpha):
      """Return entry-wise quantiles, in Proba/pred_quantiles mtype format.
  
      This method broadcasts as follows:
      for a scalar `alpha`, computes the `alpha`-quantile entry-wise,
      and returns as a `pd.DataFrame` with same index, and columns as in return.
      If `alpha` is iterable, multiple quantiles will be calculated,
      and the result will be concatenated column-wise (axis=1).
  
      The `ppf` method also computes quantiles, but broadcasts differently, in
      `numpy` style closer to `tensorflow`.
      In contrast, this `quantile` method broadcasts
      as ``sktime`` forecaster `predict_quantiles`, i.e., columns first.
  
      Parameters
      ----------
      alpha : float or list of float of unique values
          A probability or list of, at which quantiles are computed.
  
      Returns
      -------
      quantiles : pd.DataFrame
          Column has multi-index: first level is variable name from `self.columns`,
          second level being the values of `alpha` passed to the function.
          Row index is `self.index`.
          Entries in the i-th row, (j, p)-the column is
          the p-th quantile of the marginal of `self` at index (i, j).
      """
      if not isinstance(alpha, list):
          alpha = [alpha]
  
      qdfs = []
      for p in alpha:
          p = self._coerce_to_self_index_df(p)
          qdf = self.ppf(p)
          qdfs += [qdf]
  
      qres = pd.concat(qdfs, axis=1, keys=alpha)
      qres = qres.reorder_levels([1, 0], axis=1)
  
      cols = pd.MultiIndex.from_product([self.columns, alpha])
      quantiles = qres.loc[:, cols]
      return quantiles
  
imports: from warnings import warn
  import numpy
  import pandas
  from sktime.base import BaseObject
  from sktime.utils.pandas import df_map
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 21 for calling the BaseDistribution obj's underlying member method code with example usage: obj.quantile(alpha) and returns: quantiles