code: def __init__(self, in_channels, out_channels):
      super(adap_conv, self).__init__()
      self.conv = nn.Sequential(
          *[
              nn.Conv2d(
                  in_channels, out_channels, kernel_size=3, padding=1
              ),
              # nn.BatchNorm2d(out_channels),
              nn.ReLU(inplace=True),
          ]
      )
      self.weight = nn.Parameter(torch.Tensor([0.0]))
  
imports: import torch.nn.functional
  from .utils import *
  from .backbone import *
label_desc: Use image node_id 1 to create a new adap_conv object from inherited base classes: nn with example: obj = adap_conv(in_channels, out_channels)