code: def forward(
      self, z: torch.Tensor, output_layer_levels: List[int] = None
  ):
      output = ModelOutput()
  
      out = torch.tanh(self.fc1(z))
      out = torch.tanh(self.fc2(out))
      out = torch.sigmoid(self.fc3(out))
  
      output["reconstruction"] = out.reshape(
          (z.shape[0],) + self.input_dim
      )
  
      return output
  
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 MIWAE, AutoModel, MIWAEConfig
  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 2 for calling the Decoder obj's underlying member method code with example usage: obj.forward(z, output_layer_levels) and returns: output