code: def efficientnetv2_l(num_classes: int = 1000):
      """
      EfficientNetV2
      https://arxiv.org/abs/2104.00298
      """
      # train_size: 384, eval_size: 480
  
      # repeat, kernel, stride, expansion, in_c, out_c, operator, se_ratio
      model_config = [
          [4, 3, 1, 1, 32, 32, 0, 0],
          [7, 3, 2, 4, 32, 64, 0, 0],
          [7, 3, 2, 4, 64, 96, 0, 0],
          [10, 3, 2, 4, 96, 192, 1, 0.25],
          [19, 3, 1, 6, 192, 224, 1, 0.25],
          [25, 3, 2, 6, 224, 384, 1, 0.25],
          [7, 3, 1, 6, 384, 640, 1, 0.25],
      ]
  
      model = EfficientNetV2(
          model_cnf=model_config,
          num_classes=num_classes,
          dropout_rate=0.4,
      )
      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
  from utils import *
label_desc: Use image node_id 4 for calling a global function with example usage: efficientnetv2_l(num_classes) and returns: model