def test_ppo_value_clip() -> None:
    """
    **Overview**:
        Test function for ppo_value_clip function. The test case generates random data and uses it to calculate the value loss.
        Then it checks whether the shape of the returned value loss is a scalar, as expected.
    """
    # Generate random data for testing. The batch size is 6.
    B = 6
    value_old = torch.randn(B)
    value_new = torch.randn(B)
    return_ = torch.randn(B)
    # Calculate the value loss using the ppo_value_clip function.
    value_loss = ppo_value_clip(value_old, value_new, return_)
    # Assert that the returned value loss is a scalar (i.e., its shape is an empty tuple).
    assert value_loss.shape == torch.Size([])

    **Overview**:
        Test function for ppo_value_clip function. The test case generates random data and uses it to calculate the value loss.
        Then it checks whether the shape of the returned value loss is a scalar, as expected.
    
chapter7_tricks/value_clip.py