docstr: cumulative distribution function
  
  Parameters
  ----------
  x : array_like
      can be 1d or 2d, if 2d, then each row is taken as independent
      multivariate random vector
  kwds : dict
      contains options for the numerical calculation of the cdf
  
  Returns
  -------
  cdf : float or array
      probability density value of each random vector
code: def cdf(self, x, **kwds):
      """cumulative distribution function
  
      Parameters
      ----------
      x : array_like
          can be 1d or 2d, if 2d, then each row is taken as independent
          multivariate random vector
      kwds : dict
          contains options for the numerical calculation of the cdf
  
      Returns
      -------
      cdf : float or array
          probability density value of each random vector
  
      """
      lower = -np.inf * np.ones_like(x)
      # std_sigma = np.sqrt(np.diag(self.sigma))
      upper = (x - self.mean) / self.std_sigma
      return mvstdtprob(lower, upper, self.corr, self.df, **kwds)
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 4 for calling the MVT obj's underlying member method code with example usage: obj.cdf(x) and returns: mvstdtprob