code: def _make_phi(self, F):
          
          scaled_bins_left = tf.concat([self.bin_edges / self.sigma, np.array([np.inf])], 0)
          scaled_bins_right = tf.concat([np.array([-np.inf]), self.bin_edges / self.sigma], 0)
          return inv_probit(scaled_bins_left - tf.reshape(F, (-1, 1)) / self.sigma) \
                 - inv_probit(scaled_bins_right - tf.reshape(F, (-1, 1)) / self.sigma)
docs: A helper function for making predictions. Constructs a probability
          matrix where each row output the probability of the corresponding
          label, and the rows match the entries of F.
  
          Note that a matrix of F values is flattened.