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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _make_phi(self, F):
 """ 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. """

        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)