code: def __init__(self, d, encoded_size):
      super(AutoEncoder, self).__init__()
  
      self.encoded_size = encoded_size
      self.data_size = len(d.encoded_feature_names)
      self.encoded_categorical_feature_indexes = d.get_data_params()[2]
  
      self.encoded_continuous_feature_indexes = []
      for i in range(self.data_size):
          valid = 1
          for v in self.encoded_categorical_feature_indexes:
              if i in v:
                  valid = 0
          if valid:
              self.encoded_continuous_feature_indexes.append(i)
  
      self.encoded_start_cat = len(
          self.encoded_continuous_feature_indexes
      )
  
      self.encoder_mean = nn.Sequential(
          nn.Linear(self.data_size, 20),
          nn.BatchNorm1d(20),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(20, 16),
          nn.BatchNorm1d(16),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(16, 14),
          nn.BatchNorm1d(14),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(14, 12),
          nn.BatchNorm1d(12),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(12, self.encoded_size),
      )
  
      self.encoder_var = nn.Sequential(
          nn.Linear(self.data_size, 20),
          nn.BatchNorm1d(20),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(20, 16),
          nn.BatchNorm1d(16),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(16, 14),
          nn.BatchNorm1d(14),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(14, 12),
          nn.BatchNorm1d(12),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(12, self.encoded_size),
          nn.Sigmoid(),
      )
  
      self.decoder_mean = nn.Sequential(
          nn.Linear(self.encoded_size, 12),
          nn.BatchNorm1d(12),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(12, 14),
          nn.BatchNorm1d(14),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(14, 16),
          nn.BatchNorm1d(16),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(16, 20),
          nn.BatchNorm1d(20),
          nn.Dropout(0.1),
          nn.ReLU(),
          nn.Linear(20, self.data_size),
          nn.Sigmoid(),
      )
  
imports: import torch
  import torch.utils.data
  from torch import nn
label_desc: Use image node_id 1 to create a new AutoEncoder object from inherited base classes: nn with example: obj = AutoEncoder(d, encoded_size)