code: def __init__(
      self,
      num_classes=150,
      num_ffn_fcs=2,
      num_heads=8,
      num_mask_fcs=3,
      feedforward_channels=2048,
      in_channels=256,
      out_channels=256,
      dropout=0.0,
      act_fn=nn.ReLU,
      ffn_act_fn=nn.ReLU,
      conv_kernel_size=3,
      feat_transform=False,
      kernel_init=False,
      with_ffn=True,
      feat_gather_stride=1,
      mask_transform_stride=1,
      kernel_updator_cfg=None,
  ):
      super(KernelUpdateHead, self).__init__()
      if kernel_updator_cfg is None:
          kernel_updator_cfg = dict(
              in_channels=256,
              feat_channels=256,
              out_channels=256,
              input_feat_shape=3,
          )
      self.num_classes = num_classes
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.dropout = dropout
      self.num_heads = num_heads
      self.kernel_init = kernel_init
      self.with_ffn = with_ffn
      self.conv_kernel_size = conv_kernel_size
      self.feat_gather_stride = feat_gather_stride
      self.mask_transform_stride = mask_transform_stride
  
      self.attention = nn.MultiHeadAttention(
          in_channels * conv_kernel_size**2,
          num_heads,
          dropout,
          bias_attr=True,
      )
      self.attention_norm = nn.LayerNorm(
          in_channels * conv_kernel_size**2
      )
  
      self.kernel_update_conv = KernelUpdator(**kernel_updator_cfg)
  
      if feat_transform is not None:
          kernel_size = 1
          transform_channels = in_channels
          self.feat_transform = nn.Conv2D(
              transform_channels,
              in_channels,
              kernel_size,
              stride=feat_gather_stride,
              padding=int(feat_gather_stride // 2),
          )
      else:
          self.feat_transform = None
  
      if self.with_ffn:
          self.ffn = FFN(
              in_channels,
              feedforward_channels,
              act_fn=ffn_act_fn,
              ffn_drop=dropout,
          )
          self.ffn_norm = nn.LayerNorm(in_channels)
  
      self.mask_fcs = nn.LayerList()
      for _ in range(num_mask_fcs):
          self.mask_fcs.append(
              nn.Linear(in_channels, in_channels, bias_attr=False)
          )
          self.mask_fcs.append(nn.LayerNorm(in_channels))
          self.mask_fcs.append(act_fn())
  
      self.fc_mask = nn.Linear(in_channels, 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 KernelUpdateHead object from inherited base classes: nn with example: obj = KernelUpdateHead(num_classes, num_ffn_fcs, num_heads, num_mask_fcs, feedforward_channels, in_channels, out_channels, dropout, act_fn, ffn_act_fn, conv_kernel_size, feat_transform, kernel_init, with_ffn, feat_gather_stride, mask_transform_stride, kernel_updator_cfg)