code: def __init__(
      self, in_channels, out_channels, norm_layer, activation, padding
  ):
      super(UNetDoubleConv, self).__init__()
      self.block = nn.Sequential(
          ConvBlock(
              in_channels,
              out_channels,
              kernel_size=3,
              stride=1,
              padding=padding,
              norm_layer=norm_layer,
              activation=activation,
          ),
          ConvBlock(
              out_channels,
              out_channels,
              kernel_size=3,
              stride=1,
              padding=padding,
              norm_layer=norm_layer,
              activation=activation,
          ),
      )
  
imports: import torch
  from torch import nn
  from functools import partial
  from .basic_blocks import ConvBlock
  from ..ops import FeaturesConnector
label_desc: Use image node_id 1 to create a new UNetDoubleConv object from inherited base classes: nn with example: obj = UNetDoubleConv(in_channels, out_channels, norm_layer, activation, padding)