code: def __init__(
      self,
      in_channels=256,
      feat_channels=64,
      out_channels=None,
      input_feat_shape=3,
      gate_sigmoid=True,
      gate_norm_act=False,
      activate_out=False,
      norm_fn=nn.LayerNorm,
      act_fn=nn.ReLU,
  ):
      super(KernelUpdator, self).__init__()
      self.in_channels = in_channels
      self.feat_channels = feat_channels
      self.out_channels_raw = out_channels
      self.gate_sigmoid = gate_sigmoid
      self.gate_norm_act = gate_norm_act
      self.activate_out = activate_out
      if isinstance(input_feat_shape, int):
          input_feat_shape = [input_feat_shape] * 2
      self.input_feat_shape = input_feat_shape
      self.act_fn = act_fn
      self.norm_fn = norm_fn
      self.out_channels = out_channels if out_channels else in_channels
  
      self.num_params_in = self.feat_channels
      self.num_params_out = self.feat_channels
      self.dynamic_layer = nn.Linear(
          self.in_channels, self.num_params_in + self.num_params_out
      )
      self.input_layer = nn.Linear(
          self.in_channels, self.num_params_in + self.num_params_out
      )
      self.input_gate = nn.Linear(self.in_channels, self.feat_channels)
      self.update_gate = nn.Linear(self.in_channels, self.feat_channels)
      if self.gate_norm_act:
          self.gate_norm = self.norm_fn(self.feat_channels)
  
      self.norm_in = self.norm_fn(self.feat_channels)
      self.norm_out = self.norm_fn(self.feat_channels)
      self.input_norm_in = self.norm_fn(self.feat_channels)
      self.input_norm_out = self.norm_fn(self.feat_channels)
  
      self.activation = self.act_fn()
  
      self.fc_layer = nn.Linear(self.feat_channels, self.out_channels)
      self.fc_norm = self.norm_fn(self.out_channels)
  
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 1 to create a new KernelUpdator object from inherited base classes: nn with example: obj = KernelUpdator(in_channels, feat_channels, out_channels, input_feat_shape, gate_sigmoid, gate_norm_act, activate_out, norm_fn, act_fn)