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_down_sampling_block(self, channels: int):
      return RegistrationDownSampleBlock(
          spatial_dims=self.spatial_dims,
          channels=channels,
          pooling=self.pooling,
      )
  
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 5 for calling the RegUNet obj's underlying member method code with example usage: obj.build_down_sampling_block(channels) and returns: RegistrationDownSampleBlock