docstr: initialize instance
  
  Parameters
  ----------
  mean : array_like
      parameter mu (might be renamed), for symmetric distributions this
      is the mean
  sigma : array_like, 2d
      dispersion matrix, covariance matrix in normal distribution, but
      only proportional to covariance matrix in t distribution
  args : list
      distribution specific arguments, e.g. df for t distribution
  kwds : dict
      currently not used
code: def __init__(self, mean, sigma, df):
      """initialize instance
  
      Parameters
      ----------
      mean : array_like
          parameter mu (might be renamed), for symmetric distributions this
          is the mean
      sigma : array_like, 2d
          dispersion matrix, covariance matrix in normal distribution, but
          only proportional to covariance matrix in t distribution
      args : list
          distribution specific arguments, e.g. df for t distribution
      kwds : dict
          currently not used
  
      """
      super().__init__(mean, sigma)
      self.extra_args = ["df"]  # overwrites extra_args of super
      self.df = df
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 1 to create a new MVT object from inherited base classes: MVElliptical with example: obj = MVT(mean, sigma, df)