code: def __init__(
      self,
      in_channels: int = 3,
      out_channels: int = 3,
      dilation_rate: int = 1,
      stride: int = 1,
  ):
      super().__init__()
      self.conv_s1 = nn.Conv2d(
          in_channels,
          out_channels,
          3,
          padding=dilation_rate,
          dilation=dilation_rate,
          stride=stride,
      )
      self.bn_s1 = nn.BatchNorm2d(out_channels)
      self.relu_s1 = nn.ReLU(inplace=True)
  
imports: import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from PIL import Image
  from torch import Tensor
  from cftool.cv import to_rgb
  from torchvision.transforms.functional import normalize
  from ....misc.toolkit import download_model
label_desc: Use image node_id 1 to create a new REBNCONV object from inherited base classes: nn with example: obj = REBNCONV(in_channels, out_channels, dilation_rate, stride)