code: def __init__(self, args: dict):
      BaseDecoder.__init__(self)
  
      self.input_dim = args.input_dim
      self.latent_dim = args.latent_dim
  
      self.fc1 = nn.Linear(self.latent_dim, 200)
      self.fc2 = nn.Linear(200, 200)
      self.fc3 = nn.Linear(200, np.prod(args.input_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 torch.utils.data import Dataset
  from pythae.data.datasets import DatasetOutput
  from pythae.models import CIWAE, AutoModel, CIWAEConfig
  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 Decoder object from inherited base classes: BaseDecoder with example: obj = Decoder(args)