code: def test_ppo_v_head_loss_batch():
      loss_fn = PPOVHeadLoss(clip_likelihood_ratio_using_epsilon=0.1)
      total_return = mx.nd.array((5, -3, 0))
      old_policy_values = mx.nd.array((3, -1, -1))
      new_policy_values_worse = mx.nd.array((2, 0, -1))
      new_policy_values_better = mx.nd.array((4, -2, -1))
  
      loss_worse = loss_fn(
          new_policy_values_worse, old_policy_values, total_return
      )
      loss_better = loss_fn(
          new_policy_values_better, old_policy_values, total_return
      )
  
      assert len(loss_worse) == 1  # (LOSS)
      loss_worse_val = loss_worse[0]
      assert loss_worse_val.ndim == 1
      assert loss_worse_val.shape[0] == 1
      assert len(loss_better) == 1  # (LOSS)
      loss_better_val = loss_better[0]
      assert loss_better_val.ndim == 1
      assert loss_better_val.shape[0] == 1
      assert loss_worse_val > loss_better_val
  
file_path: coach/rl_coach/tests/architectures/mxnet_components/heads/test_ppo_v_head.py
imports: import mxnet
  import os
  import pytest
  import sys
  from rl_coach.architectures.mxnet_components.heads.ppo_v_head import PPOVHead, PPOVHeadLoss
  from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters
  from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace
label_desc: Use image node_id 1 for calling a global function with example usage: test_ppo_v_head_loss_batch() without return types