class_docstr: block for unwrapped Resnet
class_docstr_tok: ["block","for","unwrapped","Resnet"]
code: def __init__(
      self,
      up_conv_nn,
      kernel_size,
      stride,
      dilation,
      dimension=3,
      bn_momentum=0.01,
      norm_type=NormType.BATCH_NORM,
      block_norm_type=NormType.BATCH_NORM,
      **kwargs,
  ):
      ME.MinkowskiNetwork.__init__(self, dimension)
      self.conv = ME.MinkowskiConvolutionTranspose(
          in_channels=up_conv_nn[0],
          out_channels=up_conv_nn[1],
          kernel_size=kernel_size,
          stride=stride,
          dilation=dilation,
          bias=False,
          dimension=dimension,
      )
      if len(up_conv_nn) == 3:
          self.final = ME.MinkowskiConvolution(
              in_channels=up_conv_nn[1],
              out_channels=up_conv_nn[2],
              kernel_size=kernel_size,
              stride=stride,
              dilation=dilation,
              bias=True,
              dimension=dimension,
          )
      else:
          self.norm = get_norm(
              norm_type,
              up_conv_nn[1],
              bn_momentum=bn_momentum,
              D=dimension,
          )
          self.block = get_block(
              block_norm_type,
              up_conv_nn[1],
              up_conv_nn[1],
              bn_momentum=bn_momentum,
              D=dimension,
          )
          self.final = None
  
imports: import torch
  import MinkowskiEngine
  import MinkowskiEngine.MinkowskiFunctional
  from .common import get_norm
  from .res16unet import get_block
  from .common import NormType
label_desc: Use image node_id 1 to create a new Res2BlockUp object from inherited base classes: ME with example: obj = Res2BlockUp(up_conv_nn, kernel_size, stride, dilation, dimension, bn_momentum, norm_type, block_norm_type)