class_docstr: The Global Convolutional Network module using large 1D
  Kx1 and 1xK kernels to represent 2D kernels.
class_docstr_tok: ["The","Global","Convolutional","Network","module","using","large","1D","Kx1","and","1xK","kernels","to","represent","2D","kernels","."]
docstr: Args:
      x: in shape (batch, inplanes, spatial_1, spatial_2).
docstr_tok: ["Args",":","x",":","in","shape","(","batch",",","inplanes",",","spatial_1",",","spatial_2",")","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Args:
          x: in shape (batch, inplanes, spatial_1, spatial_2).
      """
      x_l = self.conv_l1(x)
      x_l = self.conv_l2(x_l)
      x_r = self.conv_r1(x)
      x_r = self.conv_r2(x_r)
      x = x_l + x_r
      return 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 GCN obj's underlying member method code with example usage: obj.forward(x) and returns: x