code: def __init__(
      self,
      dim,
      latent_size,
      padding_type="reflect",
      activation=nn.ReLU(True),
  ):
      super(ResnetBlock_Adain, self).__init__()
  
      p = 0
      conv1 = []
      if padding_type == "reflect":
          conv1 += [nn.ReflectionPad2d(1)]
      elif padding_type == "replicate":
          conv1 += [nn.ReplicationPad2d(1)]
      elif padding_type == "zero":
          p = 1
      else:
          raise NotImplementedError(
              "padding [%s] is not implemented" % padding_type
          )
      conv1 += [
          nn.Conv2d(dim, dim, kernel_size=3, padding=p),
          InstanceNorm(),
      ]
      self.conv1 = nn.Sequential(*conv1)
      self.style1 = ApplyStyle(latent_size, dim)
      self.act1 = activation
  
      p = 0
      conv2 = []
      if padding_type == "reflect":
          conv2 += [nn.ReflectionPad2d(1)]
      elif padding_type == "replicate":
          conv2 += [nn.ReplicationPad2d(1)]
      elif padding_type == "zero":
          p = 1
      else:
          raise NotImplementedError(
              "padding [%s] is not implemented" % padding_type
          )
      conv2 += [
          nn.Conv2d(dim, dim, kernel_size=3, padding=p),
          InstanceNorm(),
      ]
      self.conv2 = nn.Sequential(*conv2)
      self.style2 = ApplyStyle(latent_size, dim)
  
imports: import os
  import time
  import timm
  import glob
  import numpy
  import os.path
  import torch
  import torch.distributed
  from torch import nn
  import torch.nn.functional
  from .iresnet import get_model
label_desc: Use image node_id 1 to create a new ResnetBlock_Adain object from inherited base classes: nn with example: obj = ResnetBlock_Adain(dim, latent_size, padding_type, activation)