code: def scale(self, wc, dc, dis, divisor=8) -> "EffNetArch":
      sw = EffNetArch._scale_width(wc, divisor=divisor)
      sd = EffNetArch._scale_depth(dc)
  
      return EffNetArch(
          block=self.block,
          stem_channels=sw(self.stem_channels),
          feature_channels=sw(self.feature_channels),
          kernel=self.kernel,
          stride=self.stride,
          num_repeat=list(map(sd, self.num_repeat)),
          expansion=self.expansion,
          channels=list(map(sw, self.channels)),
          default_image_size=dis,
          squeeze_excitation_ratio=self.squeeze_excitation_ratio,
      )
  
imports: import argparse
  import random
  import math
  import warnings
  from typing import List, Any, Optional
  from collections import namedtuple, OrderedDict
  from dataclasses import dataclass, replace
  import torch
  from torch import nn
  from functools import partial
  from .common import SequentialSqueezeAndExcitation, SequentialSqueezeAndExcitationTRT, LayerBuilder, StochasticDepthResidual, Flatten
  from .model import Model, ModelParams, ModelArch, OptimizerParams, create_entrypoint, EntryPoint
label_desc: Use image node_id 5 for calling the EffNetArch obj's underlying member method code with example usage: obj.scale(wc, dc, dis, divisor) and returns: EffNetArch