code: def test_model_output(self, rhvae, demo_data):
      # model_configs.input_dim = demo_data['data'][0].shape[-1]
  
      rhvae.eval()
  
      out = rhvae(demo_data)
      assert set(
          [
              "loss",
              "recon_x",
              "z",
              "z0",
              "rho",
              "eps0",
              "gamma",
              "mu",
              "log_var",
              "G_inv",
              "G_log_det",
          ]
      ) == set(out.keys())
  
      assert out.z.shape[0] == demo_data["data"].shape[0]
      assert out.recon_x.shape == demo_data["data"].shape
  
imports: import os
  from copy import deepcopy
  import pytest
  import torch
  from pythae.customexception import BadInheritanceError
  from pythae.models import RHVAE, AutoModel, RHVAEConfig
  from pythae.models.rhvae.rhvae_config import RHVAEConfig
  from pythae.pipelines import GenerationPipeline, TrainingPipeline
  from pythae.samplers import GaussianMixtureSamplerConfig, IAFSamplerConfig, MAFSamplerConfig, NormalSamplerConfig, TwoStageVAESamplerConfig
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
  from tests.data.custom_architectures import Decoder_AE_Conv, Encoder_VAE_Conv, Metric_MLP_Custom, NetBadInheritance
label_desc: Use image node_id 4 for calling the Test_Model_forward obj's underlying member method code with example usage: obj.test_model_output(rhvae, demo_data) without return types