code: def __init__(self, args: dict):
      BaseEncoder.__init__(self)
      self.input_dim = args.input_dim
      self.latent_dim = args.latent_dim
  
      self.fc1 = nn.Linear(np.prod(args.input_dim), 200)
      self.fc2 = nn.Linear(200, 200)
  
      self.embedding = nn.Linear(200, self.latent_dim)
      self.log_var = nn.Linear(200, self.latent_dim)
  
      for m in self.modules():
          if isinstance(m, nn.Linear):
              unif_init(m, m.in_features, m.out_features)
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import IWAE, AutoModel, IWAEConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseEncoder
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 1 to create a new Encoder object from inherited base classes: BaseEncoder with example: obj = Encoder(args)