code: def test_piwae_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 PIWAE, AutoModel, PIWAEConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.pipelines import GenerationPipeline, TrainingPipeline
  from pythae.samplers import GaussianMixtureSamplerConfig, IAFSamplerConfig, MAFSamplerConfig, NormalSamplerConfig
  from pythae.trainers import BaseTrainerConfig, CoupledOptimizerTrainer, CoupledOptimizerTrainerConfig
  from tests.data.custom_architectures import Decoder_AE_Conv, Encoder_VAE_Conv, NetBadInheritance
label_desc: Use image node_id 7 for calling the Test_PIWAE_Training obj's underlying member method code with example usage: obj.test_piwae_predict_step(trainer, train_dataset) without return types