class_docstr: A block with skip links and layer - norm - activation.
  Only changes the number of channels, the spatial size is kept same.
class_docstr_tok: ["A","block","with","skip","links","and","layer","-","norm","-","activation",".","Only","changes","the","number","of","channels",",","the","spatial","size","is","kept","same","."]
docstr: Args:
      spatial_dims: number of spatial dimensions
      in_channels: number of input channels
      out_channels: number of output channels
      num_layers: number of layers inside the block
      kernel_size: kernel_size
docstr_tok: ["Args",":","spatial_dims",":","number","of","spatial","dimensions","in_channels",":","number","of","input","channels","out_channels",":","number","of","output","channels","num_layers",":","number","of","layers","inside","the","block","kernel_size",":","kernel_size"]
code: def __init__(
      self,
      spatial_dims: int,
      in_channels: int,
      out_channels: int,
      num_layers: int = 2,
      kernel_size: int = 3,
  ):
      """
  
      Args:
          spatial_dims: number of spatial dimensions
          in_channels: number of input channels
          out_channels: number of output channels
          num_layers: number of layers inside the block
          kernel_size: kernel_size
      """
      super().__init__()
      self.num_layers = num_layers
      self.layers = nn.ModuleList(
          [
              get_conv_layer(
                  spatial_dims=spatial_dims,
                  in_channels=in_channels if i == 0 else out_channels,
                  out_channels=out_channels,
                  kernel_size=kernel_size,
              )
              for i in range(num_layers)
          ]
      )
      self.norms = nn.ModuleList(
          [
              Norm[Norm.BATCH, spatial_dims](out_channels)
              for _ in range(num_layers)
          ]
      )
      self.acts = nn.ModuleList([nn.ReLU() for _ in range(num_layers)])
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  from torch import nn
  from torch.nn import functional
  from monai.networks.blocks import Convolution
  from monai.networks.layers import Conv, Norm, Pool, same_padding
label_desc: Use image node_id 1 to create a new RegistrationResidualConvBlock object from inherited base classes: nn with example: obj = RegistrationResidualConvBlock(spatial_dims, in_channels, out_channels, num_layers, kernel_size)