code: def d_hinge_loss(real: Tensor, fake: Tensor) -> Tensor:
      l_real = torch.mean(F.relu(1.0 - real))
      l_fake = torch.mean(F.relu(1.0 + fake))
      loss = 0.5 * (l_real + l_fake)
      return loss
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from abc import abstractmethod
  from abc import ABCMeta
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Tuple
  from typing import Union
  from typing import Callable
  from typing import Optional
  from torch.autograd import grad
  from cftool.misc import shallow_copy_dict
  from cftool.types import tensor_dict_type
  from ..general import PureEncoderDecoder
  from ..gan.discriminators import NLayerDiscriminator
  from ...schemas import CustomTrainStep
  from ...schemas import CustomTrainStepLoss
  from ...schemas import ModelWithCustomSteps
  from ...schemas import GaussianGeneratorMixin
  from ....schema import TrainerState
  from ....schema import MetricsOutputs
  from ....constants import INPUT_KEY
  from ....constants import PREDICTIONS_KEY
  from ....misc.toolkit import get_device
  from ....losses.lpips import LPIPS
  from ....modules.blocks import HijackConv2d
label_desc: Use image node_id 1 for calling a global function with example usage: d_hinge_loss(real, fake) and returns: loss