code: def make_deconv_layers(
      self, in_features: int, up_scale: int
  ) -> list[Module]:
      layers: list[Module] = []
      all_pads = [0, 0, 1, 3, 7]
      for i in range(up_scale):
          kernel_size = 2**up_scale
          pad = all_pads[up_scale]  # kernel_size-1
          out_features = self.compute_out_features(i, up_scale)
          layers.append(nn.Conv2d(in_features, out_features, 1))
          layers.append(nn.ReLU(inplace=True))
          layers.append(
              nn.ConvTranspose2d(
                  out_features,
                  out_features,
                  kernel_size,
                  stride=2,
                  padding=pad,
              )
          )
          in_features = out_features
      return layers
  
imports: from __future__ import annotations
  from collections import OrderedDict
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor, concatenate
  from kornia.core.check import KORNIA_CHECK
  from kornia.utils import map_location_to_cpu
label_desc: Use image node_id 2 for calling the UpConvBlock obj's underlying member method code with example usage: obj.make_deconv_layers(in_features, up_scale) and returns: layers