class_docstr: Class that implements an adapted UNet. This class also serve as the parent class of LocalNet and GlobalNet
  
  Reference:
      O. Ronneberger, P. Fischer, and T. Brox,
      “U-net: Convolutional networks for biomedical image segmentation,”,
      Lecture Notes in Computer Science, 2015, vol. 9351, pp. 234–241.
      https://arxiv.org/abs/1505.04597
  
  Adapted from:
      DeepReg (https://github.com/DeepRegNet/DeepReg)
code: def build_decode_layers(self):
      self.decode_deconvs = nn.ModuleList(
          [
              self.build_up_sampling_block(
                  in_channels=self.num_channels[d + 1],
                  out_channels=self.num_channels[d],
              )
              for d in range(
                  self.depth - 1, self.min_extract_level - 1, -1
              )
          ]
      )
      self.decode_convs = nn.ModuleList(
          [
              self.build_conv_block(
                  in_channels=(
                      2 * self.num_channels[d]
                      if self.concat_skip
                      else self.num_channels[d]
                  ),
                  out_channels=self.num_channels[d],
                  kernel_size=3,
              )
              for d in range(
                  self.depth - 1, self.min_extract_level - 1, -1
              )
          ]
      )
  
      # extraction
      self.output_block = self.build_output_block()
  
imports: from __future__ import annotations
  import torch
  from torch import nn
  from torch.nn import functional
  from monai.networks.blocks.regunet_block import RegistrationDownSampleBlock, RegistrationExtractionBlock, RegistrationResidualConvBlock, get_conv_block, get_deconv_block
  from monai.networks.utils import meshgrid_ij
label_desc: Use image node_id 7 for calling the RegUNet obj's underlying member method code with example usage: obj.build_decode_layers() without return types