code: import numpy as np
  import pandas as pd
  import tensorflow as tf
  import os
  import warnings
  import time
  
  warnings.filterwarnings('ignore') 
  
  from tensorflow import keras
  from sklearn.preprocessing import RobustScaler, Normalizer, StandardScaler
  from sklearn.model_selection import train_test_split
  from datasets import load_data, random_benchmark, list_datasets
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.svm import SVC
  from sklearn.neural_network import MLPClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.metrics import f1_score, accuracy_score
  
  np.random.seed(7)
  tf.random.set_seed(7)
  
  from tensorflow.keras.layers import Conv1D, LSTM, GRU, Bidirectional, MaxPool1D, RepeatVector, Dense, Attention, Flatten, Dot
  from tensorflow.keras.layers import BatchNormalization, Input, Activation, Dropout, Lambda, Multiply, Add, Concatenate, Conv2DTranspose
  from tensorflow.keras.models import Model
  
  def get_output_dim(original_dim):
      if original_dim // 1.3 >= 512:
          return 512
      elif original_dim // 1.3 <= 128:
          return 128
      else:
          return int(original_dim // 1.3)
  
  def TRepNet(n_steps, n_features, activation='elu'):
      codings_size = get_output_dim(n_steps * n_features)
      dilation_rates = [2**i for i in range(10)] * 1
      
      skips = []
  
      encoder_input = Input(shape=[n_steps, n_features])
      # Convolution
      conv = encoder_input
      for dilation_rate in dilation_rates:
          conv = keras.layers.GaussianNoise(0.01)(conv)
          conv = Conv1D(16, 1, activation=activation, padding='same')(conv)
          
          conv_filter = Conv1D(filters=128, kernel_size=3, padding='causal', activation=activation, dilation_rate=dilation_rate)(conv)
          conv_filter = Dropout(0.1)(conv_filter)
          
          conv_gate = Conv1D(filters=128, kernel_size=3, padding='causal', activation=activation, dilation_rate=dilation_rate)(conv)
          conv_gate = Dropout(0.1)(conv_gate)
          
          mul = Multiply()([Activation('tanh')(conv_filter), Activation('sigmoid')(conv_gate)])
          skip = Conv1D(16, 1, padding='same', activation=activation)(mul)
          
          conv = Add()([conv, skip])
          
          skips.append(skip)
      
      conv = Activation(activation)(Add()(skips))
      conv = Conv1D(16, 1, activation=activation, padding='same')(conv)
      conv = MaxPool1D(pool_size=2)(conv)
      conv = Flatten()(conv)
  
      
      z = Dense(codings_size, kernel_initializer='lecun_normal',  activation='selu')(conv)
      
      encoder_output = Dense(codings_size, activation='sigmoid')(z)
      encoder = Model(inputs=[encoder_input], outputs=[encoder_output])
  
      # Decoder
      decoder_input = Input(shape=[codings_size])
      noise_input = keras.layers.GaussianNoise(0.01)(decoder_input)
      dconv = keras.layers.Reshape([codings_size, 1, 1])(noise_input)
      dconv = Conv2DTranspose(filters=32, kernel_size=3, activation=activation)(dconv)
      dconv = Conv2DTranspose(filters=16, kernel_size=1, activation=activation)(dconv)
      dconv = Flatten()(dconv)
      x = Dense(n_steps * n_features)(dconv)
      decoder_output = keras.layers.Reshape([n_steps, n_features])(x)
      decoder = Model(inputs=[decoder_input], outputs=[decoder_output])
  
      return encoder, decoder
apis: tensorflow.keras.layers.Flatten tensorflow.keras.layers.Dropout tensorflow.keras.layers.Conv2DTranspose tensorflow.keras.layers.Reshape tensorflow.keras.layers.Conv1D numpy.random.seed tensorflow.keras.layers.Activation tensorflow.keras.models.Model tensorflow.keras.layers.Multiply tensorflow.keras.layers.Add tensorflow.keras.layers.Dense tensorflow.keras.layers.GaussianNoise tensorflow.random.set_seed tensorflow.keras.layers.MaxPool1D tensorflow.keras.layers.Input