text: ### tensorflow==2.3.0-rc1
  
  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, (368, 432))
      image = image[np.newaxis,:,:,:]
      image = image - 127.5
      image = image * 0.007843
      yield [image]
  
  raw_test_data, info = tfds.load(name="coco/2017", with_info=True, split="test", data_dir="~/TFDS", download=False)
  
  # Full Integer Quantization - Input/Output=int8
  converter = tf.lite.TFLiteConverter.from_saved_model('saved_model')
  converter.optimizations = [tf.lite.Optimize.DEFAULT]
  converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
  converter.inference_input_type = tf.int8
  converter.inference_output_type = tf.int8
  converter.representative_dataset = representative_dataset_gen
  tflite_quant_model = converter.convert()
  with open('mobilenet_v2_pose_368x432_dm075_full_integer_quant.tflite', 'wb') as w:
      w.write(tflite_quant_model)
  print("Integer Quantization complete! - mobilenet_v2_pose_368x432_dm075_full_integer_quant.tflite")
  