code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=4,
      stride=2,
      padding=1,
      norm_layer=nn.BatchNorm2d,
      activation=nn.ELU,
      with_se=False,
  ):
      super(SEDeconvBlock, self).__init__()
      self.with_se = with_se
      self.block = nn.Sequential(
          nn.ConvTranspose2d(
              in_channels,
              out_channels,
              kernel_size,
              stride=stride,
              padding=padding,
          ),
          norm_layer(out_channels)
          if norm_layer is not None
          else nn.Identity(),
          activation(),
      )
      if self.with_se:
          self.se = MaskedChannelAttention(out_channels)
  
file_path: carefree-learn/cflearn/api/cv/third_party/iharm/model/modeling/conv_autoencoder.py
imports: import torch
  import torch.nn
  from .basic_blocks import ConvBlock
  from ..ops import FeaturesConnector
  from ..ops import MaskedChannelAttention
label_desc: Use image node_id 1 to create a new SEDeconvBlock object from inherited base classes: nn with example: obj = SEDeconvBlock(in_channels, out_channels, kernel_size, stride, padding, norm_layer, activation, with_se)