class_docstr: Normal distribution with tensorflow-probability back-end.
  
  Parameters
  ----------
  mean : float or array of float (1D or 2D)
      mean of the normal distribution
  sd : float or array of float (1D or 2D), must be positive
      standard deviation of the normal distribution
  index : pd.Index, optional, default = RangeIndex
  columns : pd.Index, optional, default = RangeIndex
  
  Example
  -------
  >>> from sktime.proba.tfp import TFNormal  # doctest: +SKIP
  
  >>> n = TFNormal(mu=[[0, 1], [2, 3], [4, 5]], sigma=1)  # doctest: +SKIP
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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)
docstr_tok: ["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",")"]
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)
      """
      sd_arr = self._sigma
      return (
          pd.DataFrame(sd_arr, index=self.index, columns=self.columns)
          ** 2
      )
  
file_path: sktime/sktime/proba/tfp.py
imports: import numpy
  import pandas
  from sktime.proba.base import _BaseTFDistribution
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 4 for calling the TFNormal obj's underlying member method code with example usage: obj.var() and returns: unknown