code: def __init__(
      self,
      start_filts,
      planes,
      conv,
      stride=1,
      downsample=None,
      norm=None,
      relu="relu",
  ):
      super(ResBlock, self).__init__()
      self.conv1 = conv(
          start_filts, planes, ks=1, stride=stride, norm=norm, relu=relu
      )
      self.conv2 = conv(
          planes, planes, ks=3, pad=1, norm=norm, relu=relu
      )
      self.conv3 = conv(planes, planes * 4, ks=1, norm=norm, relu=None)
      self.relu = (
          nn.ReLU(inplace=True)
          if relu == "relu"
          else nn.LeakyReLU(inplace=True)
      )
      if downsample is not None:
          self.downsample = conv(
              downsample[0],
              downsample[0] * downsample[1],
              ks=1,
              stride=downsample[2],
              norm=norm,
              relu=None,
          )
      else:
          self.downsample = None
      self.stride = stride
  
file_path: medicaldetectiontoolkit/models/backbone.py
imports: import torch.nn
  import torch.nn.functional
  import torch
label_desc: Use image node_id 1 to create a new ResBlock object from inherited base classes: nn with example: obj = ResBlock(start_filts, planes, conv, stride, downsample, norm, relu)