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_up_sampling_block(
      self, in_channels: int, out_channels: int
  ) -> nn.Module:
      return get_deconv_block(
          spatial_dims=self.spatial_dims,
          in_channels=in_channels,
          out_channels=out_channels,
      )
  
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 8 for calling the RegUNet obj's underlying member method code with example usage: obj.build_up_sampling_block(in_channels, out_channels) and returns: get_deconv_block