text: ### tensorflow==2.3.0
  
  import tensorflow as tf
  
  # Weight Quantization - Input/Output=float32
  height = 256
  width  = 256
  converter = tf.lite.TFLiteConverter.from_saved_model('saved_model_{}x{}'.format(height, width))
  converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  tflite_model = converter.convert()
  with open('bisenetv2_cityscapes_{}x{}_weight_quant.tflite'.format(height, width), 'wb') as w:
      w.write(tflite_model)
  print('Weight Quantization complete! - bisenetv2_cityscapes_{}x{}_weight_quant.tflite'.format(height, width))
  
  height = 480
  width  = 640
  converter = tf.lite.TFLiteConverter.from_saved_model('saved_model_{}x{}'.format(height, width))
  converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  tflite_model = converter.convert()
  with open('bisenetv2_cityscapes_{}x{}_weight_quant.tflite'.format(height, width), 'wb') as w:
      w.write(tflite_model)
  print('Weight Quantization complete! - bisenetv2_cityscapes_{}x{}_weight_quant.tflite'.format(height, width))
  
  height = 512
  width  = 1024
  converter = tf.lite.TFLiteConverter.from_saved_model('saved_model_{}x{}'.format(height, width))
  converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  tflite_model = converter.convert()
  with open('bisenetv2_cityscapes_{}x{}_weight_quant.tflite'.format(height, width), 'wb') as w:
      w.write(tflite_model)
  print('Weight Quantization complete! - bisenetv2_cityscapes_{}x{}_weight_quant.tflite'.format(height, width))