code: def forward(self, feats, proxy):
      batch_size, _, h, w = paddle.shape(feats)
      if self.scale > 1:
          feats = self.pool(feats)
  
      query = self.f_pixel(feats).reshape(
          (batch_size, self.key_channels, -1)
      )
      query = query.transpose((0, 2, 1))
      key = self.f_object(proxy).reshape(
          (batch_size, self.key_channels, -1)
      )
      value = self.f_down(proxy).reshape(
          (batch_size, self.key_channels, -1)
      )
      value = value.transpose((0, 2, 1))
      sim_map = paddle.matmul(query, key)
      sim_map = (self.key_channels**-0.5) * sim_map
      sim_map = F.softmax(sim_map, axis=-1)
      context = paddle.matmul(sim_map, value)
      context = context.transpose((0, 2, 1))
      context = context.reshape(
          (batch_size, self.key_channels, *paddle.shape(feats)[2:])
      )
      context = self.f_up(context)
      if self.scale > 1:
          context = F.interpolate(context, size=(h, w), mode="bilinear")
      output = paddle.concat([context, feats], 1)
      output = self.conv_bn_dropout(output)
      return output
  
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 2 for calling the SpatialOCRModule obj's underlying member method code with example usage: obj.forward(feats, proxy) and returns: output