def METHOD_NAME(self):
    """Test if output is correct for the LCWA training loop use case."""
    # Create dummy dense labels
    labels = torch.zeros(self.batch_size, self.num_entities)
    for i in range(self.batch_size):
        labels[i, self.random.randint(self.num_entities)] = 1.0
    # Check if labels form a probability distribution
    np.testing.assert_allclose(torch.sum(labels, dim=1).numpy(), 1.0)
    # Apply label smoothing
    smooth_labels = apply_label_smoothing(labels=labels, epsilon=self.epsilon, num_classes=self.num_entities)
    # Check if smooth labels form probability distribution
    np.testing.assert_allclose(torch.sum(smooth_labels, dim=1).numpy(), 1.0, rtol=self.relative_tolerance)