code: def build_conv_block(
      self, dim, padding_type, norm_layer, activation, use_dropout
  ):
      conv_block = []
      p = 0
      if padding_type == "reflect":
          conv_block += [nn.ReflectionPad2d(1)]
      elif padding_type == "replicate":
          conv_block += [nn.ReplicationPad2d(1)]
      elif padding_type == "zero":
          p = 1
      else:
          raise NotImplementedError(
              "padding [%s] is not implemented" % padding_type
          )
  
      conv_block += [
          nn.Conv2d(dim, dim, kernel_size=3, padding=p),
          norm_layer(dim),
          activation,
      ]
      if use_dropout:
          conv_block += [nn.Dropout(0.5)]
  
      p = 0
      if padding_type == "reflect":
          conv_block += [nn.ReflectionPad2d(1)]
      elif padding_type == "replicate":
          conv_block += [nn.ReplicationPad2d(1)]
      elif padding_type == "zero":
          p = 1
      else:
          raise NotImplementedError(
              "padding [%s] is not implemented" % padding_type
          )
      conv_block += [
          nn.Conv2d(dim, dim, kernel_size=3, padding=p),
          norm_layer(dim),
      ]
  
      return nn.Sequential(*conv_block)
  
imports: import torch
  import torch.nn
  import functools
  from torch.autograd import Variable
  import numpy
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, norm_layer, activation, use_dropout) and returns: nn