message: Update document regarding the update of custom op defs
diff: @@ -161,17 +161,26 @@ with open('model.tflite', 'wb') as f:
  and then [create the TensorFlow Lite operator](../guide/ops_custom.md).
  If you were unsuccessful at creating the TensorFlow operator or don't
  wish to create one (**not recommended, proceed with caution**), you can
  - still convert using the `custom_opdefs` attribute and then directly
  - [create the TensorFlow Lite operator](../guide/ops_custom.md). The
  - `custom_opdefs` attribute is a string containing an (or a list of)
  + still convert using the `register_custom_opdefs` method and then
  + directly [create the TensorFlow Lite operator](../guide/ops_custom.md).
  + The `register_custom_opdefs` method takes a list of a string containing
  + an
  [OpDef](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto)
  - (s) or operator definition proto(s). Below is an example of a
  - `TFLiteAwesomeCustomOp` with 1 input, 1 output, and 2 attributes:
  + (s). Below is an example of a `TFLiteAwesomeCustomOp` with 1 input, 1
  + output, and 2 attributes:
  ```python
  - converter.custom_opdefs="""name: 'TFLiteAwesomeCustomOp' input_arg:
  + import tensorflow as tf
  +
  + custom_opdef = """name: 'TFLiteAwesomeCustomOp' input_arg:
  { name: 'In' type: DT_FLOAT } output_arg: { name: 'Out' type: DT_FLOAT }
  attr : { name: 'a1' type: 'float'} attr : { name: 'a2' type: 'list(float)'}"""
  +
  + # Register custom opdefs before the invocation of converter API.
  + tf.lite.python.convert.register_custom_opdefs([custom_opdef])
  +
  + converter = tf.lite.TFLiteConverter.from_saved_model(...)
  + converter.allow_custom_ops = True
  ```
  ## Command Line Tool <a name="cmdline"></a>
  