class_docstr: Base probability distribution.
docstr: Return element/entry-wise variance of the distribution.
  
  Let :math:`X` be a random variable with the distribution of `self`.
  Returns :math:`\mathbb{V}[X] = \mathbb{E}\left(X - \mathbb{E}[X]\right)^2`
  
  Returns
  -------
  pd.DataFrame with same rows, columns as `self`
  variance of distribution (entry-wise)
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code: def var(self):
      r"""Return element/entry-wise variance of the distribution.
  
      Let :math:`X` be a random variable with the distribution of `self`.
      Returns :math:`\mathbb{V}[X] = \mathbb{E}\left(X - \mathbb{E}[X]\right)^2`
  
      Returns
      -------
      pd.DataFrame with same rows, columns as `self`
      variance of distribution (entry-wise)
      """
      approx_spl_size = self.get_tag("approx_var_spl")
      approx_method = (
          "by approximating the variance by the arithmetic mean of "
          f"{approx_spl_size} samples of squared differences"
      )
      warn(self._method_error_msg("var", fill_in=approx_method))
  
      spl1 = self.sample(approx_spl_size)
      spl2 = self.sample(approx_spl_size)
      spl = (spl1 - spl2) ** 2
      return spl.groupby(level=1, sort=False).mean()
  
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 18 for calling the BaseDistribution obj's underlying member method code with example usage: obj.var() and returns: spl