@application
    def apply(self, X):
        """
        Take in noisy input image and output temporal coefficients for mu and sigma.
        """
        Y = self.mlp_conv.apply(X)
        Y = Y.dimshuffle(0,2,3,1)
        if self.n_hidden_dense_lower > 0:
            n_images = X.shape[0].astype('int16')
            X = X.reshape((n_images, self.n_colors*self.spatial_width**2))
            Y_dense = self.mlp_dense_lower.apply(X)
            Y_dense = Y_dense.reshape((n_images, self.spatial_width, self.spatial_width,
                self.n_hidden_dense_lower_output))
            Y = T.concatenate([Y+T.sqrt(self.n_hidden_conv),
                Y_dense/T.sqrt(self.n_hidden_dense_lower_output)], axis=3)
        Z = self.mlp_dense_upper.apply(Y)
        return Z
dataset/ETHPy150Open Sohl-Dickstein/Diffusion-Probabilistic-Models/regression.py/MLP_conv_dense.apply/operator `/` -> `+`