code: def forward(self, *args, **kwargs):
      self.output = self.model(self.input)
      if self._weight_classes is not None:
          self._weight_classes = self._weight_classes.to(self.device)
      if self.labels is not None:
          self.loss_seg = F.cross_entropy(
              self.output,
              self.labels,
              weight=self._weight_classes,
              ignore_index=IGNORE_LABEL,
          )
  
file_path: torch-points3d/torch_points3d/models/segmentation/pvcnn.py
imports: import logging
  import torch.nn.functional
  import torch
  from torch_points3d.modules.PVCNN import pvcnn
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
  from torchsparse import SparseTensor
label_desc: Use image node_id 3 for calling the PVCNN obj's underlying member method code with example usage: obj.forward() without return types