code: def rand(self, n=1):
          
          if n == 1:
              return self._rand1()
          else:
              out = np.empty((n, self._p, self._p))
              for i in range(n):
                  out[i] = self._rand1()
  
              return out
docs: Generate random samples from the distribution
  
          Parameters
          ----------
          n : int, optional(default=1)
              The number of samples to generate
  
          Returns
          -------
          out : array_like
              The generated samples