code: def _x_gradient(self, img: Tensor) -> Tensor:
      if img.ndim > 4:
          original_shape = img.shape
          is_reshaped = True
          img = img.reshape(-1, *original_shape[-3:])
      else:
          is_reshaped = False
  
      padded = F.pad(img, (0, 1, 0, 0), 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 2 for calling the SmoothnessLoss obj's underlying member method code with example usage: obj._x_gradient(img) and returns: grad