code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=1,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      bias_attr=False,
  ):
      super().__init__()
      self._gate_conv = nn.Sequential(
          layers.SyncBatchNorm(in_channels + 1),
          nn.Conv2D(in_channels + 1, in_channels + 1, kernel_size=1),
          nn.ReLU(),
          nn.Conv2D(in_channels + 1, 1, kernel_size=1),
          layers.SyncBatchNorm(1),
          nn.Sigmoid(),
      )
      self.conv = nn.Conv2D(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=groups,
          bias_attr=bias_attr,
      )
  
imports: import cv2
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.models.backbones import resnet_vd
  from paddleseg.models import deeplab
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new GatedSpatailConv2d object from inherited base classes: nn with example: obj = GatedSpatailConv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias_attr)