text: import tensorflow as tf
  import numpy as np
  
  def representative_dataset_gen():
      raw_test_data = np.load('janken_dataset.npy')
      for image in raw_test_data:
          image = tf.image.resize(image, (96, 96))
          image = image / 255
          calibration_data = image[np.newaxis, :, :, :]
          yield [calibration_data]
  
  # Integer Quantization - Input/Output=float32
  # INPUT  = input_1 (float32, 1 x 96 x 96 x 3)
  # OUTPUT = block_16_expand_relu, global_average_pooling2d_1
  model = tf.keras.models.model_from_json(open('model_for_quantization.json').read())
  model.load_weights('weights.h5')
  converter = tf.lite.TFLiteConverter.from_keras_model(model)
  converter.optimizations = [tf.lite.Optimize.DEFAULT]
  converter.representative_dataset = representative_dataset_gen
  tflite_quant_model = converter.convert()
  with open('./weights_integer_quant.tflite', 'wb') as w:
      w.write(tflite_quant_model)
  print("Integer Quantization complete! - weights_integer_quant.tflite")
  