code: def elementwise_cdf(self, p):
          r
          p = scipy.atleast_1d(p)
          if len(p) != len(self.a):
              raise ValueError("length of p must equal the number of parameters!")
          if p.ndim != 1:
              raise ValueError("p must be one-dimensional!")
          return scipy.asarray([scipy.stats.gamma.cdf(v, a, loc=0, scale=1.0 / b) for v, a, b in zip(p, self.a, self.b)])
docs: r"""Convert a sample to random variates uniform on :math:`[0, 1]`.
          
          For a univariate distribution, this is simply evaluating the CDF. To
          facilitate efficient sampling, this function returns a *vector* of CDF
          values, one value for each variable. Basically, the idea is that, given
          a vector :math:`q` of `num_params` values each of which is distributed
          according to the prior, this function will return variables uniform on
          :math:`[0, 1]` corresponding to each variable. This is the inverse
          operation to :py:meth:`sample_u`.
          
          Parameters
          ----------
          p : array-like, (`num_params`,)
              Values to evaluate CDF at.