class_docstr: Spatial Squeeze and Channel Excitation module
code: def __init__(self, channels, reduction=4):
      super().__init__()
      squeeze_channels = max(1, channels // reduction)
      self.squeeze = nn.Conv2d(
          channels, squeeze_channels, kernel_size=1, padding=0
      )
      self.conv = nn.Conv2d(
          squeeze_channels,
          squeeze_channels,
          kernel_size=7,
          dilation=3,
          padding=3 * 3,
      )
      self.expand = nn.Conv2d(
          squeeze_channels, channels, kernel_size=1, padding=0
      )
  
imports: from torch import nn, Tensor
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new SpatialGate2dV2 object from inherited base classes: nn with example: obj = SpatialGate2dV2(channels, reduction)