code: def efficientnetv2_s(num_classes: int = 1000):
      """
      EfficientNetV2
      https://arxiv.org/abs/2104.00298
      """
      # train_size: 300, eval_size: 384
  
      # repeat, kernel, stride, expansion, in_c, out_c, operator, se_ratio
      model_config = [
          [2, 3, 1, 1, 24, 24, 0, 0],
          [4, 3, 2, 4, 24, 48, 0, 0],
          [4, 3, 2, 4, 48, 64, 0, 0],
          [6, 3, 2, 4, 64, 128, 1, 0.25],
          [9, 3, 1, 6, 128, 160, 1, 0.25],
          [15, 3, 2, 6, 160, 256, 1, 0.25],
      ]
  
      model = EfficientNetV2(
          model_cnf=model_config,
          num_classes=num_classes,
          dropout_rate=0.2,
      )
      return model
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Callable, Optional
  import torch.nn
  import torch
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: efficientnetv2_s(num_classes) and returns: model