code: def __init__(
      self, inplanes1, outplanes1, outplanes2, kernel=3, downsample=None
  ):
      super(BasicBlock, self).__init__()
      self.conv1 = nn.Conv3d(
          inplanes1, outplanes1, kernel_size=kernel, dilation=2
      )
      self.bn1 = nn.BatchNorm3d(outplanes1)
      self.conv2 = nn.Conv3d(
          outplanes1, outplanes2, kernel_size=kernel, dilation=2
      )
      self.bn2 = nn.BatchNorm3d(outplanes2)
      self.relu = nn.ReLU(inplace=True)
      if inplanes1 == outplanes2:
          self.downsample = downsample
      else:
          self.downsample = nn.Sequential(
              nn.Conv3d(inplanes1, outplanes2, kernel_size=1),
              nn.BatchNorm3d(outplanes2),
          )
  
      for m in self.modules():
          if isinstance(m, nn.Conv3d):
              m.weight.data.normal_(0, 0.01)
          elif isinstance(m, nn.BatchNorm3d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(inplanes1, outplanes1, outplanes2, kernel, downsample)