code: def test_forward_loss(self, device):
      if device == "cuda":
          if not torch.cuda.is_available():
              return unittest.skip("test requires GPU and torch+cuda")
  
      semantic_head = FusedSemanticHead(
          num_ins=5,
          fusion_level=1,
          in_channels=4,
          conv_out_channels=4,
          num_classes=6,
      )
      feats = [
          torch.rand((1, 4, 32 // 2 ** (i + 1), 32 // 2 ** (i + 1)))
          for i in range(5)
      ]
      mask_pred, x = semantic_head(feats)
      labels = torch.randint(0, 6, (1, 1, 64, 64))
      loss = semantic_head.loss(mask_pred, labels)
      self.assertIsInstance(loss, Tensor)
  
file_path: mmdetection/tests/test_models/test_roi_heads/test_mask_heads/test_fused_semantic_head.py
imports: import unittest
  from unittest import TestCase
  import torch
  from parameterized import parameterized
  from torch import Tensor
  from mmdet.models.roi_heads.mask_heads import FusedSemanticHead
label_desc: Use image node_id 1 for calling the TestFusedSemanticHead obj's underlying member method code with example usage: obj.test_forward_loss(device) and returns: unittest