code: def __init__(self, in_channels, out_channels, k):
      super(GlobalConvolutionBlock, self).__init__()
      self.left = nn.Sequential(
          nn.Conv2d(
              in_channels,
              out_channels,
              kernel_size=(k[0], 1),
              padding=(k[0] // 2, 0),
          ),
          nn.Conv2d(
              out_channels,
              out_channels,
              kernel_size=(1, k[1]),
              padding=(0, k[1] // 2),
          ),
      )
  
      self.right = nn.Sequential(
          nn.Conv2d(
              in_channels,
              out_channels,
              kernel_size=(1, k[1]),
              padding=(0, k[1] // 2),
          ),
          nn.Conv2d(
              out_channels,
              out_channels,
              kernel_size=(k[0], 1),
              padding=(k[0] // 2, 0),
          ),
      )
  
file_path: pywick/pywick/models/segmentation/resnet_gcn.py
imports: import torch.nn
  import torch.nn.init
  import torch.nn.functional
  import torchvision.models
  import numpy
label_desc: Use image node_id 1 to create a new GlobalConvolutionBlock object from inherited base classes: nn with example: obj = GlobalConvolutionBlock(in_channels, out_channels, k)