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:
      x: in shape (batch, planes, spatial_1, spatial_2).
docstr_tok: ["Args",":","x",":","in","shape","(","batch",",","planes",",","spatial_1",",","spatial_2",")","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Args:
          x: in shape (batch, planes, spatial_1, spatial_2).
      """
      residual = x
      x = self.bn(x)
      x = self.relu(x)
      x = self.conv1(x)
      x = self.bn(x)
      x = self.relu(x)
      x = self.conv2(x)
  
      return residual + x
  
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 2 for calling the Refine obj's underlying member method code with example usage: obj.forward(x) and returns: unknown