code: def test_vae_predict_step(self, trainer, train_dataset):
      start_model_state_dict = deepcopy(trainer.model.state_dict())
  
      inputs, recon, generated = trainer.predict(trainer.model)
  
      step_1_model_state_dict = deepcopy(trainer.model.state_dict())
  
      # check that weights were not updated
      assert all(
          [
              torch.equal(
                  start_model_state_dict[key],
                  step_1_model_state_dict[key],
              )
              for key in start_model_state_dict.keys()
          ]
      )
  
      assert inputs.cpu() in train_dataset.data
      assert recon.shape == inputs.shape
      assert generated.shape == inputs.shape
  
imports: import os
  from copy import deepcopy
  import pytest
  import torch
  from pythae.customexception import BadInheritanceError
  from pythae.models import VAE_IAF, AutoModel, VAE_IAF_Config
  from pythae.models.base.base_utils import ModelOutput
  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, NetBadInheritance
label_desc: Use image node_id 7 for calling the Test_VAE_Training obj's underlying member method code with example usage: obj.test_vae_predict_step(trainer, train_dataset) without return types