code: def __init__(
      self, in_channel, out_channel, factor, require_grad=False
  ):
      super(Refine_block2_1, self).__init__()
      self.pre_conv1 = adap_conv(in_channel[0], out_channel)
      self.pre_conv2 = adap_conv(in_channel[1], out_channel)
      self.deconv_weight = nn.Parameter(
          bilinear_upsample_weights(factor, out_channel),
          requires_grad=require_grad,
      )
      self.factor = factor
  
imports: import torch.nn.functional
  from .utils import *
  from .backbone import *
label_desc: Use image node_id 1 to create a new Refine_block2_1 object from inherited base classes: nn with example: obj = Refine_block2_1(in_channel, out_channel, factor, require_grad)