class_docstr: A skip-connection based module that can be used for DynUNet, based on:
  `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.
  `nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation <https://arxiv.org/abs/1809.10486>`_.
  
  Args:
      spatial_dims: number of spatial dimensions.
      in_channels: number of input channels.
      out_channels: number of output channels.
      kernel_size: convolution kernel size.
      stride: convolution stride.
      norm_name: feature normalization type and arguments.
      act_name: activation layer type and arguments.
      dropout: dropout probability.
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code: def forward(self, inp):
      residual = inp
      out = self.conv1(inp)
      out = self.norm1(out)
      out = self.lrelu(out)
      out = self.conv2(out)
      out = self.norm2(out)
      if hasattr(self, "conv3"):
          residual = self.conv3(residual)
      if hasattr(self, "norm3"):
          residual = self.norm3(residual)
      out += residual
      out = self.lrelu(out)
      return out
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import numpy
  import torch
  import torch.nn
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.layers.factories import Act, Norm
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
label_desc: Use image node_id 2 for calling the UnetResBlock obj's underlying member method code with example usage: obj.forward(inp) and returns: out