import tensorflow as tf

            self.paddings = [[size,size],[size,size],[0,0]]
        elif type=='no_op':
            self.augment = self.no_op


    def gaussian_kernel(self,size,mean,std):
        """Makes 2D gaussian Kernel for convolution."""
        d = tfp.distributions.Normal(mean, std)
        vals = d.prob(tf.range(start = -size, limit = size + 1, dtype = tf.float32))
        gauss_kernel = tf.einsum('i,j->ij',vals,vals)
        return gauss_kernel / tf.reduce_sum(gauss_kernel)

    def get_random_patch_size(self):
        return np.random.choice([1,2,4,8])

    def scramble(self,x):
