code: def _y_gradient(self, x: torch.Tensor) -> torch.Tensor:
      if x.ndim > 4:
          original_shape = x.shape
          is_reshaped = True
          x = x.reshape(-1, *original_shape[-3:])
      else:
          is_reshaped = False
  
      padded = F.pad(x, (0, 0, 0, 1), mode="replicate")
      grad = padded[..., :-1, :] - padded[..., 1:, :]
      if is_reshaped:
          grad = grad.reshape(original_shape)
      return grad
  
imports: from typing import List, Optional
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from torchvision.prototype.models.depth.stereo.raft_stereo import grid_sample, make_coords_grid
label_desc: Use image node_id 3 for calling the SmoothnessLoss obj's underlying member method code with example usage: obj._y_gradient(x) and returns: grad