code: def forward(self, net_input: Tensor, output: Tensor) -> Tensor:
      gradients = get_gradient(output, net_input, True, True)
      gradients = gradients.view(net_input.shape[0], -1)  # type: ignore
      gradients_norm = gradients.norm(2, dim=1)
      return torch.mean((gradients_norm - self.k) ** 2)
  
file_path: carefree-learn/cflearn/losses/gan.py
imports: import torch
  from torch import nn
  from torch import Tensor
  from typing import Optional
  from typing import NamedTuple
  from ..misc.toolkit import get_gradient
label_desc: Use image node_id 2 for calling the GradientNormLoss obj's underlying member method code with example usage: obj.forward(net_input, output) and returns: torch