..

   1.1 Declare NNI API
       Include `import nni` in your trial code to use NNI APIs.

   1.2 Get predefined parameters
       Use the following code snippet:

           RECEIVED_PARAMS = nni.get_next_parameter()

       to get hyper-parameters' values assigned by tuner. `RECEIVED_PARAMS` is an object, for example:

           {"conv_size": 2, "hidden_size": 124, "learning_rate": 0.0307, "dropout_rate": 0.2029}

   1.3 Report NNI results
       Use the API:

           `nni.report_intermediate_result(accuracy)`

       to send `accuracy` to assessor.

       Use the API:

           `nni.report_final_result(accuracy)`

       to send `accuracy` to tuner.
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* Declare NNI API: include ``import nni`` in your trial code to use NNI APIs.
* Get predefined parameters

Use the following code snippet:

.. code-block:: python

   RECEIVED_PARAMS = nni.get_next_parameter()

to get hyper-parameters' values assigned by tuner. ``RECEIVED_PARAMS`` is an object, for example:

.. code-block:: json

   {"conv_size": 2, "hidden_size": 124, "learning_rate": 0.0307, "dropout_rate": 0.2029}

* Report NNI results: Use the API: ``nni.report_intermediate_result(accuracy)`` to send ``accuracy`` to assessor.
  Use the API: ``nni.report_final_result(accuracy)`` to send `accuracy` to tuner.