class_docstr: GradientLoss.
  
  Adapted from https://www.cs.cornell.edu/projects/megadepth/
  
  Args:
      valid_mask (bool): Whether filter invalid gt (gt > 0). Default: True.
      loss_weight (float): Weight of the loss. Default: 1.0.
      max_depth (int): When filtering invalid gt, set a max threshold. Default: None.
docstr: Forward function.
docstr_tok: ["Forward","function","."]
code: def forward(self, depth_pred, depth_gt):
      """Forward function."""
  
      gradient_loss = self.loss_weight * self.gradientloss(
          depth_pred, depth_gt
      )
      return gradient_loss
  
file_path: dinov2/dinov2/eval/depth/models/losses/gradientloss.py
imports: import torch
  import torch.nn
  from ...models.builder import LOSSES
label_desc: Use image node_id 3 for calling the GradientLoss obj's underlying member method code with example usage: obj.forward(depth_pred, depth_gt) and returns: gradient_loss