code: def __init__(
      self, in_size, out_size, is_batchnorm=True, n=3, ks=3, stride=1
  ):
      super(unetConv2_dilation2, self).__init__()
      self.n = n
      self.ks = ks
      self.stride = stride
      s = stride
      if is_batchnorm:
          for i in range(1, n + 1):
              conv = nn.Sequential(
                  nn.Conv2d(
                      in_size,
                      out_size,
                      ks,
                      s,
                      2 ** (i - 1),
                      2 ** (i - 1),
                  ),
                  nn.BatchNorm2d(out_size),
                  nn.ReLU(inplace=True),
              )
              setattr(self, "conv%d" % i, conv)
              in_size = out_size
  
      else:
          for i in range(1, n + 1):
              conv = nn.Sequential(
                  nn.Conv2d(in_size, out_size, ks, s),
                  nn.ReLU(inplace=True),
              )
              setattr(self, "conv%d" % i, conv)
              in_size = out_size
  
      # initialise the blocks
      for m in self.children():
          init_weights(m, init_type="kaiming")
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import init
label_desc: Use image node_id 1 to create a new unetConv2_dilation2 object from inherited base classes: nn with example: obj = unetConv2_dilation2(in_size, out_size, is_batchnorm, n, ks, stride)