Rewrite the following code in a shorter waay:
import numpy as np
import scipy
from abc import ABC
import os
import copy
import logging
import inspect
import datetime
import pickle
from sklearn.decomposition import TruncatedSVD
import tensorflow as tf
from .SindyLayer import SindyLayer
import matplotlib.pyplot as plt

logging.basicConfig()
logging.getLogger().setLevel(logging.INFO)


class AutoencoderSindy(tf.keras.Model, ABC):

    def __init__(self, reduced_order, pca_order=None, x=None, mu=None, layer_sizes=[10, 10, 10], activation='selu',
                 pca_scaling=False, build_autoencoder=True, use_pca=True, second_order=True,
                 l1=1, l2=0, l_rec: float = 1, l_dz: float = 1, l_dx: float = 1, l_int: float = 0, dt=0,
                 feature_libraries: list = [], param_feature_libraries: list = [], x_mu_interaction=False,
                 mask=None, fixed_coeffs=None, decoupled=True, **kwargs):
        """
        Model to discover low-dimensional dynamics of a high-dimensional system using autoencoders and SINDy
        :param reduced_order: Order of the reduced model
        :param pca_order: Order of the PCA model
        :param x: Input data
        :param y: Output data
        :param scaler: Scaler for the data
        :param l_rec: Weight of the reconstruction loss
        :param l_dz: Weight of the derivative loss
        :param l_dx: Weight of the derivative loss
        :param kwargs:
        """

        super(AutoencoderSindy, self).__init__(**kwargs)

        if not hasattr(self, "config"):
            self._init_to_config(locals())

        if pca_order is None:
            pca_order = 10 * reduced_order
        self.layer_sizes = layer_sizes
        self.activation = tf.keras.activations.get(activation)
        self.reduced_order = reduced_order
        self.pca_order = pca_order
        self.pca_scaling = pca_scaling
        self.build_autoencoder = build_autoencoder
        self.use_pca = use_pca
        self.second_order = second_order
        # weighting of the different losses
        self.l_rec, self.l_dz, self.l_dx, self.l_int = l_rec, l_dz, l_dx, l_int
        self.dt = dt

        # kernel regularization weights
        self.l1, self.l2 = l1, l2

        # sindy params
        self.x_mu_interaction = x_mu_interaction
        self.feature_libraries, self.param_feature_libraries = feature_libraries, param_feature_libraries
        self.mask = mask
        self.fixed_coeffs = fixed_coeffs
        self.decoupled = decoupled

        # create the model
        self.x_shape = x.