class_docstr: Simple residual block to refine the details of the activation maps.
class_docstr_tok: ["Simple","residual","block","to","refine","the","details","of","the","activation","maps","."]
docstr: Args:
      planes: number of input channels.
docstr_tok: ["Args",":","planes",":","number","of","input","channels","."]
code: def __init__(self, planes: int):
      """
      Args:
          planes: number of input channels.
      """
      super().__init__()
  
      relu_type: type[nn.ReLU] = Act[Act.RELU]
      conv2d_type: type[nn.Conv2d] = Conv[Conv.CONV, 2]
      norm2d_type: type[nn.BatchNorm2d] = Norm[Norm.BATCH, 2]
  
      self.bn = norm2d_type(num_features=planes)
      self.relu = relu_type(inplace=True)
      self.conv1 = conv2d_type(
          in_channels=planes,
          out_channels=planes,
          kernel_size=3,
          padding=1,
      )
      self.conv2 = conv2d_type(
          in_channels=planes,
          out_channels=planes,
          kernel_size=3,
          padding=1,
      )
  
imports: from __future__ import annotations
  import torch
  import torch.nn
  import torch.nn.functional
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.blocks.upsample import UpSample
  from monai.networks.layers.factories import Act, Conv, Norm
  from monai.utils import optional_import
label_desc: Use image node_id 1 to create a new Refine object from inherited base classes: nn with example: obj = Refine(planes)