code: def test_forward(self):
      pred = torch.rand((10, 4))
      target = torch.rand((10, 4))
      gaussian_focal_loss = GaussianFocalLoss()
      loss1 = gaussian_focal_loss(pred, target)
      self.assertIsInstance(loss1, torch.Tensor)
  
      loss2 = gaussian_focal_loss(pred, target, avg_factor=0.5)
      self.assertIsInstance(loss2, torch.Tensor)
  
      # test reduction
      gaussian_focal_loss = GaussianFocalLoss(reduction="none")
      loss = gaussian_focal_loss(pred, target)
      self.assertTrue(loss.shape == (10, 4))
  
      # test reduction_override
      loss = gaussian_focal_loss(
          pred, target, reduction_override="mean"
      )
      self.assertTrue(loss.ndim == 0)
  
      # Only supports None, 'none', 'mean', 'sum'
      with self.assertRaises(AssertionError):
          gaussian_focal_loss(pred, target, reduction_override="max")
  
      # test pos_inds
      pos_inds = (torch.rand(5) * 8).long()
      pos_labels = (torch.rand(5) * 2).long()
      gaussian_focal_loss = GaussianFocalLoss()
      loss = gaussian_focal_loss(pred, target, pos_inds, pos_labels)
      self.assertIsInstance(loss, torch.Tensor)
  
imports: import unittest
  import torch
  from mmdet.models.losses import GaussianFocalLoss
label_desc: Use image node_id 1 for calling the TestGaussianFocalLoss obj's underlying member method code with example usage: obj.test_forward() without return types