text: import torch.nn as nn
  from mmcv.cnn import ConvModule
  
  from mmedit.models.registry import COMPONENTS
  
  
  @COMPONENTS.register_module()
  class GLEncoder(nn.Module):
      """Encoder used in Global&Local model.
  
      This implementation follows:
      Globally and locally Consistent Image Completion
  
      Args:
          norm_cfg (dict): Config dict to build norm layer.
          act_cfg (dict): Config dict for activation layer, "relu" by default.
      """
  
      def __init__(self, norm_cfg=None, act_cfg=dict(type='ReLU')):
          super(GLEncoder, self).__init__()
  
          channel_list = [64, 128, 128, 256, 256, 256]
          kernel_size_list = [5, 3, 3, 3, 3, 3]
          stride_list = [1, 2, 1, 2, 1, 1]
          in_channels = 4
          for i in range(6):
              ks = kernel_size_list[i]
              padding = (ks - 1) // 2
              self.add_module(
                  f'enc{i + 1}',
                  ConvModule(
                      in_channels,
                      channel_list[i],
                      kernel_size=ks,
                      stride=stride_list[i],
                      padding=padding,
                      norm_cfg=norm_cfg,
                      act_cfg=act_cfg))
              in_channels = channel_list[i]
  
      def forward(self, x):
          """Forward Function.
  
          Args:
              x (torch.Tensor): Input tensor with shape of (n, c, h, w).
  
          Returns:
              torch.Tensor: Output tensor with shape of (n, c, h', w').
          """
          for i in range(6):
              x = getattr(self, f'enc{i + 1}')(x)
          return x
  