code: def forward(self, inputs):
      image, label = inputs
      pred = self.model(image.to(self.device))
      if self.training:
          label = label.to(self.device)
          return F.cross_entropy(pred, label)
      else:
          return pred
  
imports: import torch
  from torch import nn
  from torch.nn import functional
  from omegaconf import OmegaConf
  import torchvision
  from torchvision.transforms import transforms
  from torchvision.models.resnet import ResNet, Bottleneck
  from fvcore.common.param_scheduler import MultiStepParamScheduler
  from detectron2.solver import WarmupParamScheduler
  from detectron2.solver.build import get_default_optimizer_params
  from detectron2.config import LazyCall
  from detectron2.model_zoo import get_config
  from detectron2.data.samplers import TrainingSampler, InferenceSampler
  from detectron2.evaluation import DatasetEvaluator
  from detectron2.utils import comm
label_desc: Use image node_id 3 for calling the ClassificationNet obj's underlying member method code with example usage: obj.forward(inputs) and returns: F, pred