def get_mu_sigma(self, X_noisy, t):
        Z = self.mlp.apply(X_noisy)
        mu_coeff, beta_coeff = self.temporal_readout(Z, t)
        # reverse variance is perturbation around forward variance
        beta_forward = self.get_beta_forward(t)
        # make impact of beta_coeff scaled appropriately with mu_coeff
        beta_coeff_scaled = beta_coeff / np.sqrt(self.trajectory_length).astype(theano.config.floatX)
        beta_reverse = T.nnet.sigmoid(beta_coeff_scaled + util.logit(beta_forward))
        # # reverse mean is decay towards mu_coeff
        # mu = (X_noisy - mu_coeff)*T.sqrt(1. - beta_reverse) + mu_coeff
        # reverse mean is a perturbation around the mean under forward
        # process


        # # DEBUG -- use these lines to test objective is 0 for isotropic Gaussian model
        # beta_reverse = beta_forward
        # mu_coeff = mu_coeff*0


        mu = X_noisy*T.sqrt(1. - beta_forward) + mu_coeff*T.sqrt(beta_forward)
        sigma = T.sqrt(beta_reverse)
        mu.name = 'mu p'
        sigma.name = 'sigma p'
        return mu, sigma
Generate mu and sigma for one step in the reverse trajectory,
dataset/ETHPy150Open Sohl-Dickstein/Diffusion-Probabilistic-Models/model.py/DiffusionModel.get_mu_sigma