class_docstr: Criterion that computes image-aware inverse depth smoothness loss.
  
  .. math::
  
      \text{loss} = \left | \partial_x d_{ij} \right | e^{-\left \|
      \partial_x I_{ij} \right \|} + \left |
      \partial_y d_{ij} \right | e^{-\left \| \partial_y I_{ij} \right \|}
  
  Shape:
      - Inverse Depth: :math:`(N, 1, H, W)`
      - Image: :math:`(N, 3, H, W)`
      - Output: scalar
  
  Examples:
      >>> idepth = torch.rand(1, 1, 4, 5)
      >>> image = torch.rand(1, 3, 4, 5)
      >>> smooth = InverseDepthSmoothnessLoss()
      >>> loss = smooth(idepth, image)
code: def forward(
      self, idepth: torch.Tensor, image: torch.Tensor
  ) -> torch.Tensor:
      return inverse_depth_smoothness_loss(idepth, image)
  
file_path: kornia/kornia/losses/depth_smooth.py
imports: from __future__ import annotations
  import torch
  from torch import nn
label_desc: Use image node_id 1 for calling the InverseDepthSmoothnessLoss obj's underlying member method code with example usage: obj.forward(idepth, image) and returns: inverse_depth_smoothness_loss