code: def build_conv_block(self, dim, padding_type, use_dropout):
      conv_block = nn.HybridSequential()
      p = 0
      with self.name_scope():
          if padding_type == "reflect":
              conv_block.add(nn.ReflectionPad2D(1))
          elif padding_type == "zero":
              p = 1
          else:
              raise NotImplementedError(
                  "padding [%s] is not implemented" % padding_type
              )
  
          conv_block.add(
              nn.Conv2D(dim, kernel_size=3, padding=p),
              nn.InstanceNorm(),
              nn.Activation("relu"),
          )
          if use_dropout:
              conv_block.add(nn.Dropout(0.5))
  
          p = 0
          if padding_type == "reflect":
              conv_block.add(nn.ReflectionPad2D(1))
          elif padding_type == "zero":
              p = 1
          else:
              raise NotImplementedError(
                  "padding [%s] is not implemented" % padding_type
              )
          conv_block.add(
              nn.Conv2D(dim, kernel_size=3, padding=p),
              nn.InstanceNorm(),
          )
  
      return conv_block
  
imports: import argparse
  import random
  from mxnet import gluon, image, autograd
  from mxnet.gluon.data.vision import transforms
  from mxnet.base import numeric_types
  import gluoncv
  from gluoncv import utils
  from mxnet.gluon.data import DataLoader
  from mxnet.gluon import nn
  import mxnet.ndarray
  import mxnet
  import os
  import numpy
  from mxboard import SummaryWriter
label_desc: Use image node_id 2 for calling the ResnetBlock obj's underlying member method code with example usage: obj.build_conv_block(dim, padding_type, use_dropout) and returns: conv_block