Hi @Felipe_bivort_haiek, I managed to find a workaround before. You can achieve symmetric padding by manually making the index arrays. For example, this function will do 2d padding for you:
import torch
import numpy as np
from typing import Tuple

def symm_pad(im: torch.Tensor, padding: Tuple[int, int, int, int]):
     h, w = im.shape[-2:]
     left, right, top, bottom = padding
 
     x_idx = np.arange(-left, w+right)
     y_idx = np.arange(-top, h+bottom)
 
     def reflect(x, minx, maxx):
         """ Reflects an array around two points making a triangular waveform that ramps up
         and down,  allowing for pad lengths greater than the input length """
         rng = maxx - minx
         double_rng = 2*rng
         mod = np.fmod(x - minx, double_rng)
         normed_mod = np.where(mod < 0, mod+double_rng, mod)
         out = np.where(normed_mod >= rng, double_rng - normed_mod, normed_mod) + minx
         return np.array(out, dtype=x.dtype)

     x_pad = reflect(x_idx, -0.5, w-0.5)
     y_pad = reflect(y_idx, -0.5, h-0.5)
     xx, yy = np.meshgrid(x_pad, y_pad)
     return im[..., yy, xx]