class_docstr: Laplace distribution.
  
  Parameters
  ----------
  mean : float or array of float (1D or 2D)
      mean of the distribution
  scale : float or array of float (1D or 2D), must be positive
      scale parameter of the distribution, same as standard deviation / sqrt(2)
  index : pd.Index, optional, default = RangeIndex
  columns : pd.Index, optional, default = RangeIndex
  
  Example
  -------
  >>> from sktime.proba.laplace import Laplace
  
  >>> n = Laplace(mu=[[0, 1], [2, 3], [4, 5]], scale=1)
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code: def __init__(self, mu, scale, index=None, columns=None):
      self.mu = mu
      self.scale = scale
      self.index = index
      self.columns = columns
  
      # todo: untangle index handling
      # and broadcast of parameters.
      # move this functionality to the base class
      self._mu, self._scale = self._get_bc_params(self.mu, self.scale)
      shape = self._mu.shape
  
      if index is None:
          index = pd.RangeIndex(shape[0])
  
      if columns is None:
          columns = pd.RangeIndex(shape[1])
  
      super().__init__(index=index, columns=columns)
  
file_path: sktime/sktime/proba/laplace.py
imports: import numpy
  import pandas
  from sktime.proba.base import BaseDistribution
label_desc: Use image node_id 1 to create a new Laplace object from inherited base classes: BaseDistribution with example: obj = Laplace(mu, scale, index, columns)