code: def __init__(
      self,
      in_channels,
      key_channels,
      out_channels,
      scale=1,
      dropout=0.1,
  ):
      super().__init__()
      self.scale = scale
      self.in_channels = in_channels
      self.key_channels = key_channels
      self.pool = nn.MaxPool2D(kernel_size=(scale, scale))
      self.f_pixel = SpatialConvBNReLU(
          self.in_channels,
          self.key_channels,
          kernel_size=1,
          padding=0,
          bias_attr=False,
      )
      self.f_object = SpatialConvBNReLU(
          self.in_channels,
          self.key_channels,
          kernel_size=1,
          padding=0,
          bias_attr=False,
      )
      self.f_down = layers.ConvBNReLU(
          self.in_channels,
          self.key_channels,
          kernel_size=1,
          padding=0,
          bias_attr=False,
      )
      self.f_up = layers.ConvBNReLU(
          self.key_channels,
          self.in_channels,
          kernel_size=1,
          padding=0,
          bias_attr=False,
      )
  
      _in_channels = 2 * in_channels
      self.conv_bn_dropout = nn.Sequential(
          layers.ConvBNReLU(
              _in_channels,
              out_channels,
              kernel_size=1,
              padding=0,
              bias_attr=False,
          ),
          nn.Dropout2D(dropout),
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
label_desc: Use image node_id 1 to create a new SpatialOCRModule object from inherited base classes: nn with example: obj = SpatialOCRModule(in_channels, key_channels, out_channels, scale, dropout)