class_docstr: Enhanced version of UNet which has residual units implemented with the ResidualUnit class.
  The residual part uses a convolution to change the input dimensions to match the output dimensions
  if this is necessary but will use nn.Identity if not.
  Refer to: https://link.springer.com/chapter/10.1007/978-3-030-12029-0_40.
  
  Each layer of the network has a encode and decode path with a skip connection between them. Data in the encode path
  is downsampled using strided convolutions (if `strides` is given values greater than 1) and in the decode path
  upsampled using strided transpose convolutions. These down or up sampling operations occur at the beginning of each
  block rather than afterwards as is typical in UNet implementations.
  
  To further explain this consider the first example network given below. This network has 3 layers with strides
  of 2 for each of the middle layers (the last layer is the bottom connection which does not down/up sample). Input
  data to this network is immediately reduced in the spatial dimensions by a factor of 2 by the first convolution of
  the residual unit defining the first layer of the encode part. The last layer of the decode part will upsample its
  input (data from the previous layer concatenated with data from the skip connection) in the first convolution. this
  ensures the final output of the network has the same shape as the input.
  
  Padding values for the convolutions are chosen to ensure output sizes are even divisors/multiples of the input
  sizes if the `strides` value for a layer is a factor of the input sizes. A typical case is to use `strides` values
  of 2 and inputs that are multiples of powers of 2. An input can thus be downsampled evenly however many times its
  dimensions can be divided by 2, so for the example network inputs would have to have dimensions that are multiples
  of 4. In the second example network given below the input to the bottom layer will have shape (1, 64, 15, 15) for
  an input of shape (1, 1, 240, 240) demonstrating the input being reduced in size spatially by 2**4.
  
  Args:
      spatial_dims: number of spatial dimensions.
      in_channels: number of input channels.
      out_channels: number of output channels.
      channels: sequence of channels. Top block first. The length of `channels` should be no less than 2.
      strides: sequence of convolution strides. The length of `stride` should equal to `len(channels) - 1`.
      kernel_size: convolution kernel size, the value(s) should be odd. If sequence,
          its length should equal to dimensions. Defaults to 3.
      up_kernel_size: upsampling convolution kernel size, the value(s) should be odd. If sequence,
          its length should equal to dimensions. Defaults to 3.
      num_res_units: number of residual units. Defaults to 0.
      act: activation type and arguments. Defaults to PReLU.
      norm: feature normalization type and arguments. Defaults to instance norm.
      dropout: dropout ratio. Defaults to no dropout.
      bias: whether to have a bias term in convolution blocks. Defaults to True.
          According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,
          if a conv layer is directly followed by a batch norm layer, bias should be False.
      adn_ordering: a string representing the ordering of activation (A), normalization (N), and dropout (D).
          Defaults to "NDA". See also: :py:class:`monai.networks.blocks.ADN`.
  
  Examples::
  
      from monai.networks.nets import UNet
  
      # 3 layer network with down/upsampling by a factor of 2 at each layer with 2-convolution residual units
      net = UNet(
          spatial_dims=2,
          in_channels=1,
          out_channels=1,
          channels=(4, 8, 16),
          strides=(2, 2),
          num_res_units=2
      )
  
      # 5 layer network with simple convolution/normalization/dropout/activation blocks defining the layers
      net=UNet(
          spatial_dims=2,
          in_channels=1,
          out_channels=1,
          channels=(4, 8, 16, 32, 64),
          strides=(2, 2, 2, 2),
      )
  
  Note: The acceptable spatial size of input data depends on the parameters of the network,
      to set appropriate spatial size, please check the tutorial for more details:
      https://github.com/Project-MONAI/tutorials/blob/master/modules/UNet_input_size_constrains.ipynb.
      Typically, when using a stride of 2 in down / up sampling, the output dimensions are either half of the
      input when downsampling, or twice when upsampling. In this case with N numbers of layers in the network,
      the inputs must have spatial dimensions that are all multiples of 2^N.
      Usually, applying `resize`, `pad` or `crop` transforms can help adjust the spatial size of input data.
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docstr: Returns the block object defining a layer of the UNet structure including the implementation of the skip
  between encoding (down) and decoding (up) sides of the network.
  
  Args:
      down_path: encoding half of the layer
      up_path: decoding half of the layer
      subblock: block defining the next layer in the network.
  Returns: block for this layer: `nn.Sequential(down_path, SkipConnection(subblock), up_path)`
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code: def _get_connection_block(
      self,
      down_path: nn.Module,
      up_path: nn.Module,
      subblock: nn.Module,
  ) -> nn.Module:
      """
      Returns the block object defining a layer of the UNet structure including the implementation of the skip
      between encoding (down) and decoding (up) sides of the network.
  
      Args:
          down_path: encoding half of the layer
          up_path: decoding half of the layer
          subblock: block defining the next layer in the network.
      Returns: block for this layer: `nn.Sequential(down_path, SkipConnection(subblock), up_path)`
      """
      return nn.Sequential(down_path, SkipConnection(subblock), up_path)
  
file_path: MONAI/monai/networks/nets/unet.py
imports: from __future__ import annotations
  import warnings
  from collections.abc import Sequence
  import torch
  import torch.nn
  from monai.networks.blocks.convolutions import Convolution, ResidualUnit
  from monai.networks.layers.factories import Act, Norm
  from monai.networks.layers.simplelayers import SkipConnection
  from monai.utils import alias, export
label_desc: Use image node_id 2 for calling the UNet obj's underlying member method code with example usage: obj._get_connection_block(down_path, up_path, subblock) and returns: nn