I am building a neural network with my own custom loss function (pretty long and complicated). My network is unsupervised so my input and expected output are identical and also at the moment I am using one single input (just trying to optimize the loss for a single input). 

I am trying to use tensorboard.plugins.hparams api for hyperparameter tuning and don't know how to incorporate my custom loss function there. I'm trying to follow the code suggested on the Tensorflow 2.0 [website](https://www.tensorflow.org/tensorboard/r2/hyperparameter_tuning_with_hparams).

This is what the website suggests:

```py
    HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32]))
    HP_DROPOUT = hp.HParam('dropout', hp.RealInterval(0.1, 0.2))
    HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd']))

    METRIC_ACCURACY = 'accuracy'

    with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
      hp.hparams_config(
        hparams=[HP_NUM_UNITS, HP_DROPOUT, HP_OPTIMIZER],
        metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],
          )

```

I need to change that as I don't want to use the dropout layer, so I can just delete that. In terms of the METRIC\_ACCURACY, I don't want to use accuracy as that has no use in my model but rather use my custom loss function. If I were to do the regular fit model it would look like this:

```py
    model.compile(optimizer=adam,loss=dl_tf_loss, metrics=[dl_tf_loss])

```

So I tried to change the suggested code into the following code but I get an error and am wondering how I should change it so that it suits my needs. Here is what I tried:

```py
    HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32]))
    HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd']))

    #METRIC_LOSS = dl_tf_loss

    with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
      hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics= 
       [hp.Metric(dl_tf_loss, display_name='Loss')])

```

It gives me the following error:

```py
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-26-27d079c6be49> in <module>()
      5 
      6 with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
----> 7   hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics=[hp.Metric(dl_tf_loss, display_name='Loss')])
      8 

3 frames
/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in hparams_config(hparams, metrics, time_created_secs)
    127       hparams=hparams,
    128       metrics=metrics,
--> 129       time_created_secs=time_created_secs,
    130   )
    131   return _write_summary("hparams_config", pb)

/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in hparams_config_pb(hparams, metrics, time_created_secs)
    161       domain.update_hparam_info(info)
    162     hparam_infos.append(info)
--> 163   metric_infos = [metric.as_proto() for metric in metrics]
    164   experiment = api_pb2.Experiment(
    165       hparam_infos=hparam_infos,

/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in <listcomp>(.0)
    161       domain.update_hparam_info(info)
    162     hparam_infos.append(info)
--> 163   metric_infos = [metric.as_proto() for metric in metrics]
    164   experiment = api_pb2.Experiment(
    165       hparam_infos=hparam_infos,

/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in as_proto(self)
    532         name=api_pb2.MetricName(
    533             group=self._group,
--> 534             tag=self._tag,
    535         ),
    536         display_name=self._display_name,

TypeError: <tensorflow.python.eager.def_function.Function object at 0x7f9f3a78e5c0> has type Function, but expected one of: bytes, unicode

```

I also tried running the following code:

```py
    with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
      hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics= 
      [dl_tf_loss])

```

but got the following error:

```py
AttributeError                            Traceback (most recent call last)
<ipython-input-28-6778bdf7f1b1> in <module>()
      8 
      9 with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
---> 10   hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics=[dl_tf_loss])

2 frames
/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in <listcomp>(.0)
    161       domain.update_hparam_info(info)
    162     hparam_infos.append(info)
--> 163   metric_infos = [metric.as_proto() for metric in metrics]
    164   experiment = api_pb2.Experiment(
    165       hparam_infos=hparam_infos,

AttributeError: 'Function' object has no attribute 'as_proto'

```

Would greatly appreciate any help.
Thanks in advance!