Calling Predictions from Models

For all the co-lab examples (except for basic_retrieval), there isn't a clear way to get predictions from the models. For basic_retrieval, the use of the FacorizedTopK metric tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(songs.batch(64).map(song_model)) , and use of brute force to get the index tfrs.layers.factorized_top_k.BruteForce(model.user_model) is presented and works. However, for basic_ranking, context_features, deep_recommenders, multitask, and dcn, the code example ends at evaluation of test/dev sets.

So, the question is how does one do predictions? How does one infer from the model?

An option I have tried is calling model.predict() or model(), but that gives an error. The error codes below are from code I have run after running through the entire basic_ranking model co-lab notebook.

for i in train.take(1):
  print(i)
  model.predict(i)

{'movie_title': <tf.Tensor: shape=(), dtype=string, numpy=b'Postman, The (1997)'>, 'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'681'>, 'user_rating': <tf.Tensor: shape=(), dtype=float32, numpy=4.0>}
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-68-be1677278cba> in <module>()
      1 for i in train.take(1):
      2   print(i)
----> 3   model.predict(i)
      4   model(i)

4 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py in __getitem__(self, key)
    887       else:
    888         if self._v2_behavior:
--> 889           return self._dims[key].value
    890         else:
    891           return self._dims[key]

IndexError: list index out of range

And,

for i in train.take(1):
  print(i)
  model(i)

{'movie_title': <tf.Tensor: shape=(), dtype=string, numpy=b'Postman, The (1997)'>, 'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'681'>, 'user_rating': <tf.Tensor: shape=(), dtype=float32, numpy=4.0>}
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-69-fb4f109a1076> in <module>()
      1 for i in train.take(1):
      2   print(i)
----> 3   model(i)

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py in call(self, inputs, training, mask)
    444         a list of tensors if there are more than one outputs.
    445     """
--> 446     raise NotImplementedError('When subclassing the `Model` class, you should '
    447                               'implement a `call` method.')
    448 

NotImplementedError: When subclassing the `Model` class, you should implement a `call` method.

Thanks for your help!