code: def test_discriminator_default(
      self, ae_mnist_config, mnist_like_data, recon_layers_default
  ):
      ae_mnist_config.discriminator_input_dim = (1, 28, 28)
  
      discriminator = Discriminator_MLP(ae_mnist_config).to(device)
  
      scores = discriminator(
          mnist_like_data, output_layer_levels=recon_layers_default
      )
  
      if recon_layers_default is None:
          assert "embedding" in scores.keys()
  
      else:
          for lev in recon_layers_default:
              if lev != -1:
                  assert f"embedding_layer_{lev}" in scores.keys()
              else:
                  assert "embedding" in scores.keys()
  
      if recon_layers_default is None:
          assert scores["embedding"].shape[1] == 1
  
      else:
          if 1 in recon_layers_default:
              assert scores[f"embedding_layer_1"].shape[1] == 256
  
          if 2 in recon_layers_default:
              assert scores[f"embedding_layer_2"].shape[1] == 1
  
          if -1 in recon_layers_default:
              assert scores["embedding"].shape[1] == 1
  
imports: import numpy
  import pytest
  import torch
  from pythae.models import AEConfig, VAEConfig
  from pythae.models.nn.benchmarks.celeba import *
  from pythae.models.nn.benchmarks.cifar import *
  from pythae.models.nn.benchmarks.mnist import *
  from pythae.models.nn.default_architectures import *
label_desc: Use image node_id 5 for calling the Test_MNIST_Default obj's underlying member method code with example usage: obj.test_discriminator_default(ae_mnist_config, mnist_like_data, recon_layers_default) without return types