code: def test_model_train_output(self, maf, demo_data):
      maf.train()
      out = maf(demo_data["data"])
  
      assert isinstance(out, ModelOutput)
  
      assert set(["out", "log_abs_det_jac"]) == set(out.keys())
  
      assert out.out.shape[0] == demo_data["data"].shape[0]
      assert out.log_abs_det_jac.shape == (demo_data["data"].shape[0],)
      assert out.out.shape[1:] == np.prod(
          maf.model_config.input_dim
      )  # input_dim = output_dim
  
      assert torch.equal(
          out.log_abs_det_jac, out.log_abs_det_jac
      )  # check no NaN
  
      out = maf.inverse(out.out)
  
      assert isinstance(out, ModelOutput)
  
      assert set(["out", "log_abs_det_jac"]) == set(out.keys())
  
      assert out.out.shape[0] == demo_data["data"].shape[0]
      assert out.log_abs_det_jac.shape == (demo_data["data"].shape[0],)
      assert out.out.shape[1:] == np.prod(maf.model_config.input_dim)
  
      assert torch.equal(out.log_abs_det_jac, out.log_abs_det_jac)
  
imports: import os
  from copy import deepcopy
  import numpy
  import pytest
  import torch
  from pythae.models import AutoModel
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.normalizing_flows import MAF, MAFConfig, NFModel
  from pythae.pipelines import TrainingPipeline
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 3 for calling the Test_Model_forward obj's underlying member method code with example usage: obj.test_model_train_output(maf, demo_data) without return types