code: def forward(self, x, proposal_feat, mask_preds, mask_shape=None):
      N, num_proposals = paddle.shape(proposal_feat)[:2]
      if self.feat_transform is not None:
          x = self.feat_transform(x)
  
      C, H, W = x.shape[-3:]
  
      mask_h, mask_w = paddle.shape(mask_preds)[-2:]
      if mask_h != H or mask_w != W:
          gather_mask = F.interpolate(
              mask_preds, (H, W), align_corners=False, mode="bilinear"
          )
      else:
          gather_mask = mask_preds
  
      sigmoid_masks = F.softmax(gather_mask, 1)
  
      # Group Feature Assembling. Eq.(3) in original paper.
      x_feat = paddle.einsum("bnhw,bchw->bnc", sigmoid_masks, x)
  
      # obj_feat in shape [B, N, C, K, K] -> [B, N, C, K*K] -> [B, N, K*K, C]
      proposal_feat = proposal_feat.reshape(
          [N, num_proposals, self.in_channels, -1]
      ).transpose([0, 1, 3, 2])
      obj_feat = self.kernel_update_conv(x_feat, proposal_feat)
  
      # [B, N, K*K, C] -> [B, N, K*K*C] -> [N, B, K*K*C]
      obj_feat = obj_feat.reshape([N, num_proposals, -1]).transpose(
          [1, 0, 2]
      )
      obj_feat = self.attention_norm(self.attention(obj_feat))
      # [N, B, K*K*C] -> [B, N, K*K*C]
      obj_feat = obj_feat.transpose([1, 0, 2])
  
      # obj_feat in shape [B, N, K*K*C] -> [B, N, K*K, C]
      obj_feat = obj_feat.reshape(
          [N, num_proposals, -1, self.in_channels]
      )
  
      # FFN
      if self.with_ffn:
          obj_feat = self.ffn_norm(self.ffn(obj_feat))
  
      mask_feat = obj_feat
  
      for reg_layer in self.mask_fcs:
          mask_feat = reg_layer(mask_feat)
  
      # [B, N, K*K, C] -> [B, N, C, K*K]
      mask_feat = self.fc_mask(mask_feat).transpose([0, 1, 3, 2])
  
      if (
          self.mask_transform_stride == 2
          and self.feat_gather_stride == 1
      ):
          mask_x = F.interpolate(
              x, scale_factor=0.5, mode="bilinear", align_corners=False
          )
          H, W = paddle.shape(mask_x)[-2:]
      else:
          mask_x = x
      mask_feat = mask_feat.reshape(
          [
              N,
              num_proposals,
              C,
              self.conv_kernel_size,
              self.conv_kernel_size,
          ]
      )
      # [B, C, H, W] -> [1, B*C, H, W]
      new_mask_preds = []
      for i in range(N):
          new_mask_preds.append(
              F.conv2d(
                  mask_x[i : i + 1],
                  mask_feat[i],
                  padding=int(self.conv_kernel_size // 2),
              )
          )
  
      new_mask_preds = paddle.concat(new_mask_preds, axis=0)
      new_mask_preds = new_mask_preds.reshape([N, num_proposals, H, W])
      if self.mask_transform_stride == 2:
          new_mask_preds = F.interpolate(
              new_mask_preds,
              scale_factor=2,
              mode="bilinear",
              align_corners=False,
          )
  
      if mask_shape is not None and mask_shape[0] != H:
          new_mask_preds = F.interpolate(
              new_mask_preds,
              mask_shape,
              align_corners=False,
              mode="bilinear",
          )
  
      return new_mask_preds, obj_feat.transpose([0, 1, 3, 2]).reshape(
          [
              N,
              num_proposals,
              self.in_channels,
              self.conv_kernel_size,
              self.conv_kernel_size,
          ]
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from .upernet import UPerNetHead
label_desc: Use image node_id 2 for calling the KernelUpdateHead obj's underlying member method code with example usage: obj.forward(x, proposal_feat, mask_preds, mask_shape) and returns: new_mask_preds,  obj_feat