code: def forward(
      self, z: torch.Tensor, output_layer_levels: List[int] = None
  ):
      output = ModelOutput()
  
      max_depth = self.depth
  
      if output_layer_levels is not None:
          assert all(
              self.depth >= levels > 0 or levels == -1
              for levels in output_layer_levels
          ), (
              f"Cannot output layer deeper than depth ({self.depth}). "
              f"Got ({output_layer_levels})."
          )
  
          if -1 in output_layer_levels:
              max_depth = self.depth
          else:
              max_depth = max(output_layer_levels)
  
      out = z
  
      for i in range(max_depth):
          out = self.layers[i](out)
  
          if i == 0:
              out = out.reshape(z.shape[0], 1024, 8, 8)
  
          if output_layer_levels is not None:
              if i + 1 in output_layer_levels:
                  output[f"reconstruction_layer_{i+1}"] = out
  
          if i + 1 == self.depth:
              output["reconstruction"] = out
  
      return output
  
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 Adversarial_AE, Adversarial_AE_Config
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseDiscriminator, BaseEncoder
  from pythae.trainers import AdversarialTrainer, AdversarialTrainerConfig
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