code: def efficientnetv2_m(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 = [
          [3, 3, 1, 1, 24, 24, 0, 0],
          [5, 3, 2, 4, 24, 48, 0, 0],
          [5, 3, 2, 4, 48, 80, 0, 0],
          [7, 3, 2, 4, 80, 160, 1, 0.25],
          [14, 3, 1, 6, 160, 176, 1, 0.25],
          [18, 3, 2, 6, 176, 304, 1, 0.25],
          [5, 3, 1, 6, 304, 512, 1, 0.25],
      ]
  
      model = EfficientNetV2(
          model_cnf=model_config,
          num_classes=num_classes,
          dropout_rate=0.3,
          name="efficientnetv2-m",
      )
      return model
  
imports: import itertools
  import tensorflow
  from tensorflow.keras import layers, Model, Input
label_desc: Use image node_id 2 for calling a global function with example usage: efficientnetv2_m(num_classes) and returns: model