Post quantization does not utilize GPU

System information

• TensorFlow version (you are using): 2.2.0
• Are you willing to contribute it (Yes/No): No

Motivation
During post quantization , the GPU is idle (confirmed via nvidia-smi ), i.e. the post quantization is not using GPU to speed things up. It is very slow. It takes > 60 min to run on a server grade xeon (for test set of 2336 on our model):

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
def representative_dataset_gen():
  with tf.io.gfile.GFile(test_set, 'r') as f:
    test_list = f.readlines()
  for i in test_list:
    # Get sample input data as a numpy array
    with Image.open(os.path.join(datasetdir,  i).split()[0]) as img:
        yield [np.array(img).reshape(1,120,160,1).astype(np.float32)/255.0]
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8  # or tf.int8
converter.inference_output_type = tf.uint8  # or tf.int8
tflite_quant_model = converter.convert()

Describe the feature
post quantization should utilize GPU to speed things up.

Start with the TFLiteConverter flow shown in the issue, especially converter.convert(), representative_dataset, and the INT8 target settings. Determine where post-quantization work is performed and how GPU execution could be supported; done would mean the conversion uses the GPU and is measurably faster for the reported test set.