code: def forward(self, update_feature, input_feature):
      update_feature = update_feature.reshape([-1, self.in_channels])
      num_proposals = paddle.shape(update_feature)[0]
      # dynamic_layer works for
      # phi_1 and psi_3 in Eq.(4) and (5) of K-Net paper
      parameters = self.dynamic_layer(update_feature)
      param_in = parameters[:, : self.num_params_in].reshape(
          [-1, self.feat_channels]
      )
      param_out = parameters[:, -self.num_params_out :].reshape(
          [-1, self.feat_channels]
      )
  
      # input_layer works for
      # phi_2 and psi_4 in Eq.(4) and (5) of K-Net paper
      input_feats = self.input_layer(
          input_feature.reshape([num_proposals, -1, self.feat_channels])
      )
      input_in = input_feats[..., : self.num_params_in]
      input_out = input_feats[..., -self.num_params_out :]
  
      # `gate_feats` is F^G in K-Net paper
      gate_feats = input_in * param_in.unsqueeze(-2)
      if self.gate_norm_act:
          gate_feats = self.activation(self.gate_norm(gate_feats))
  
      input_gate = self.input_norm_in(self.input_gate(gate_feats))
      update_gate = self.norm_in(self.update_gate(gate_feats))
      if self.gate_sigmoid:
          input_gate = F.sigmoid(input_gate)
          update_gate = F.sigmoid(update_gate)
      param_out = self.norm_out(param_out)
      input_out = self.input_norm_out(input_out)
  
      if self.activate_out:
          param_out = self.activation(param_out)
          input_out = self.activation(input_out)
  
      # Gate mechanism. Eq.(5) in original paper.
      # param_out has shape (batch_size, feat_channels, out_channels)
      features = (
          update_gate * param_out.unsqueeze(-2) + input_gate * input_out
      )
  
      features = self.fc_layer(features)
      features = self.fc_norm(features)
      features = self.activation(features)
  
      return features
  
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 KernelUpdator obj's underlying member method code with example usage: obj.forward(update_feature, input_feature) and returns: features