message: Clarify conversion used for Quantization
  The converter uses the Standard score to standardize the input stats. The standardization is interchangeably known as Quantization.
diff: @@ -56,16 +56,20 @@ Convert the graph:
  converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
  input_arrays = converter.get_input_arrays()
  -converter.quantized_input_stats = {input_arrays[0] : (0., 1.)} # mean, std_dev
  +converter.quantized_input_stats = {input_arrays[0] : (0., 1.)} # mean_value, std_dev
  tflite_model = converter.convert()
  ```
  -For fully integer models, the inputs are uint8. The `mean` and `std_dev values`
  +For fully integer models, the inputs are uint8. When the `inference_type` is set to `QUANTIZED_UINT8` as above, the real_input_value is standardised using the [standard-score](https://en.wikipedia.org/wiki/Standard_score) as follows:
  +
  +real_input_value = (quantized_input_value - mean_value) / std_dev_value
  +
  +The `mean_value` and `std_dev values`
  specify how those uint8 values map to the float input values used while training
  -the model.
  +the model. For more details, please see the [TFLiteConverter](https://www.tensorflow.org/api_docs/python/tf/compat/v1/lite/TFLiteConverter)
  `mean` is the integer value from 0 to 255 that maps to floating point 0.0f.
  -`std_dev` is 255 / (float_max - float_min)
  +`std_dev` is 255 / (float_max - float_min).
  For most users, we recommend using post-training quantization. We are working on
  new tools for post-training and during training quantization that we hope will
  