code: def EfficientNetV2M(
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
      include_preprocessing=True,
  ):
      return EfficientNetV2(
          width_coefficient=1.0,
          depth_coefficient=1.0,
          default_size=480,
          model_name="efficientnetv2-m",
          include_top=include_top,
          weights=weights,
          input_tensor=input_tensor,
          input_shape=input_shape,
          pooling=pooling,
          classes=classes,
          classifier_activation=classifier_activation,
          include_preprocessing=include_preprocessing,
      )
  
imports: import copy
  import math
  from keras import backend
  from keras import initializers
  from keras import layers
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.models import Functional
  from keras.ops import operation_utils
  from keras.utils import file_utils
label_desc: Use image node_id 11 for calling a global function with example usage: EfficientNetV2M(include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation, include_preprocessing) and returns: EfficientNetV2