code: def test_training_step(self):
      trainer = self._construct_trainer()
      inp = defaultdict(lambda: torch.ones(1, 5))
      trainer.update_model = mock.Mock()
      trainer.training_step(inp, batch_idx=1)
      trainer.update_model.assert_called_with()
      trainer.update_freq = 10
      trainer.update_model = mock.Mock()
      trainer.training_step(inp, batch_idx=1)
      trainer.update_model.assert_not_called()
  
imports: import unittest
  from collections import defaultdict
  from unittest import mock
  import torch
  from reagent.core.types import PolicyGradientInput
  from reagent.evaluation.evaluator import get_metrics_to_score
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler
  from reagent.models.dueling_q_network import DuelingQNetwork
  from reagent.models.fully_connected_network import FloatFeatureFullyConnected
  from reagent.training.parameters import PPOTrainerParameters
  from reagent.training.ppo_trainer import PPOTrainer
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 7 for calling the TestPPO obj's underlying member method code with example usage: obj.test_training_step() without return types