text: import tensorflow as tf
  import tensorflow_datasets as tfds
  import numpy as np
  
  #def representative_dataset_gen():
  #  for data in raw_test_data.take(100):
  #    image = data['image'].numpy()
  #    image = tf.image.resize(image, (320, 320))
  #    image = image[np.newaxis,:,:,:]
  #    yield [image]
  
  tf.compat.v1.enable_eager_execution()
  
  ## Generating a calibration data set
  ##raw_test_data, info = tfds.load(name="coco/2017", with_info=True, split="test", data_dir="./TFDS")
  #raw_test_data, info = tfds.load(name="coco/2017", with_info=True, split="test", data_dir="./TFDS", download=False)
  #print(info)
  
  # Weight Quantization - Input/Output=float32
  converter = tf.lite.TFLiteConverter.from_saved_model('./0')
  converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  tflite_quant_model = converter.convert()
  with open('./model-mobilenet_v1_101_225_weight_quant.tflite', 'wb') as w:
      w.write(tflite_quant_model)
  print("Weight Quantization complete! - model-mobilenet_v1_101_225_weight_quant.tflite")
  
  # Weight Quantization - Input/Output=float32
  converter = tf.lite.TFLiteConverter.from_saved_model('./0')
  converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  tflite_quant_model = converter.convert()
  with open('./model-mobilenet_v1_101_257_weight_quant.tflite', 'wb') as w:
      w.write(tflite_quant_model)
  print("Weight Quantization complete! - model-mobilenet_v1_101_257_weight_quant.tflite")
  
  # Weight Quantization - Input/Output=float32
  converter = tf.lite.TFLiteConverter.from_saved_model('./0')
  converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  tflite_quant_model = converter.convert()
  with open('./model-mobilenet_v1_101_321_weight_quant.tflite', 'wb') as w:
      w.write(tflite_quant_model)
  print("Weight Quantization complete! - model-mobilenet_v1_101_321_weight_quant.tflite")
  
  # Weight Quantization - Input/Output=float32
  converter = tf.lite.TFLiteConverter.from_saved_model('./0')
  converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  tflite_quant_model = converter.convert()
  with open('./model-mobilenet_v1_101_385_weight_quant.tflite', 'wb') as w:
      w.write(tflite_quant_model)
  print("Weight Quantization complete! - model-mobilenet_v1_101_385_weight_quant.tflite")
  
  # Weight Quantization - Input/Output=float32
  converter = tf.lite.TFLiteConverter.from_saved_model('./0')
  converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  tflite_quant_model = converter.convert()
  with open('./model-mobilenet_v1_101_513_weight_quant.tflite', 'wb') as w:
      w.write(tflite_quant_model)
  print("Weight Quantization complete! - model-mobilenet_v1_101_513_weight_quant.tflite")
  