code: def test_planar_flow_eval_step(self, trainer):
      start_model_state_dict = deepcopy(trainer.model.state_dict())
  
      step_1_loss = trainer.eval_step(epoch=1)
  
      step_1_model_state_dict = deepcopy(trainer.model.state_dict())
  
      # check that weights were updated
      assert all(
          [
              torch.equal(
                  start_model_state_dict[key],
                  step_1_model_state_dict[key],
              )
              for key in start_model_state_dict.keys()
          ]
      )
  
imports: import os
  from copy import deepcopy
  import numpy
  import pytest
  import torch
  from pydantic import ValidationError
  from pythae.data.datasets import BaseDataset
  from pythae.models import AutoModel
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.normalizing_flows import NFModel, PlanarFlow, PlanarFlowConfig
  from pythae.pipelines import TrainingPipeline
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 8 for calling the Test_PlanarFlow_Training obj's underlying member method code with example usage: obj.test_planar_flow_eval_step(trainer) without return types