code: def test_model_train_output(self, pixelcnn, demo_data):
      pixelcnn.train()
      out = pixelcnn(demo_data)
  
      assert isinstance(out, ModelOutput)
  
      assert set(["out", "loss"]) == set(out.keys())
  
      assert (
          out.out.shape
          == (
              demo_data["data"].shape[0],
              pixelcnn.model_config.n_embeddings,
              demo_data["data"].shape[1],
          )
          + demo_data["data"].shape[2:]
      )
  
imports: import os
  from copy import deepcopy
  import numpy
  import pytest
  import torch
  from pydantic import ValidationError
  from pythae.models import AutoModel
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.normalizing_flows import PixelCNN, PixelCNNConfig
  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(pixelcnn, demo_data) without return types