code: def __init__(
      self, inplanes, planes, stride=1, downsample=None, dcn=None
  ):
      super(BasicBlock, self).__init__()
      self.with_dcn = dcn is not None
      self.conv1 = conv3x3(inplanes, planes, stride)
      self.bn1 = BatchNorm2d(planes)
      self.relu = nn.ReLU(inplace=True)
      self.with_modulated_dcn = False
      if self.with_dcn:
          fallback_on_stride = dcn.get("fallback_on_stride", False)
          self.with_modulated_dcn = dcn.get("modulated", False)
      # self.conv2 = conv3x3(planes, planes)
      if not self.with_dcn or fallback_on_stride:
          self.conv2 = nn.Conv2d(
              planes, planes, kernel_size=3, padding=1, bias=False
          )
      else:
          deformable_groups = dcn.get("deformable_groups", 1)
          if not self.with_modulated_dcn:
              # from assets.ops.dcn import DeformConv
              from ..assets.ops.dcn import DeformConv
  
              conv_op = DeformConv
              offset_channels = 18
          else:
              # from assets.ops.dcn import ModulatedDeformConv
              from ..assets.ops.dcn import ModulatedDeformConv
  
              conv_op = ModulatedDeformConv
              offset_channels = 27
          self.conv2_offset = nn.Conv2d(
              planes,
              deformable_groups * offset_channels,
              kernel_size=3,
              padding=1,
          )
          self.conv2 = conv_op(
              planes,
              planes,
              kernel_size=3,
              padding=1,
              deformable_groups=deformable_groups,
              bias=False,
          )
      self.bn2 = BatchNorm2d(planes)
      self.downsample = downsample
      self.stride = stride
  
imports: import torch.nn
  import math
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(inplanes, planes, stride, downsample, dcn)