code: def load_model():
      model_class = MODEL_MAP[FLAGS.model]
      # Load the EfficientNetV2B0 model and add a classification head.
      model = model_class(include_top=False, weights="imagenet")
      classifier = keras.models.Sequential(
          [
              keras.Input([IMAGE_SIZE[0], IMAGE_SIZE[1], CHANNELS]),
              model,
              keras.layers.GlobalAveragePooling2D(),
              keras.layers.Dense(2),
          ]
      )
      return classifier
  
file_path: keras/benchmarks/model_benchmark/image_classification_benchmark.py
imports: import time
  import numpy
  import tensorflow
  import tensorflow_datasets
  from absl import app
  from absl import flags
  from absl import logging
  from model_benchmark.benchmark_utils import BenchmarkMetricsCallback
  import keras
label_desc: Use image node_id 2 for calling a global function with example usage: load_model() and returns: classifier