Combine Time-series with time-invariant data in RNN/LSTM using Keras Functional API - tensorflow

Question

Update: As rvinas pointed out, I had forgotten to add inputs_aux as second input in Model. Fixed now, and it works. So ConditionalRNN can readily be used to do what I want.
I'd like to treat time-series together with non-time-series characteristics in extended LSTM cells (a requirement also discussed here). ConditionalRNN (cond-rnn) for Tensorflow in Python seems to allow this.
Can it be used in Keras Functional API (without eager execution)?
That is, does anyone have a clue how to fix my failed approach below, or a different example where ConditionalRNN (or alternatives) are used to readily combine TS and non-TS data in LSTM-style cells or any equivalent?
I've seen the eager execution-bare tf example on Pilippe Remy's ConditionalRNN github page, but I did not manage to extend it to a readily fittable version in Keras Functional API.
My code looks as follows; it works if, instead of the ConditionalRNN, I use a standard LSTM cell (and adjust the model 'x' input correspondingly). With ConditionalRNN, I did not get it to execute; I receive either the must feed a value for placeholder tensor 'in_aux' error (cf. below), or instead some different type of input size complaints when I change the code, despite trying to be careful about data dimensions compatibility.
(Using Python 3.6, Tensorflow 2.1, cond-rnn 2.1, on Ubuntu 16.04)
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import LSTM, Dense, Input
from cond_rnn import ConditionalRNN
inputs = Input(name='in',shape=(5,5)) # Each observation has 5 dimensions à 5 time-steps each
x = Dense(64)(inputs)
inputs_aux = Input(name='in_aux', shape=[5]) # For each of the 5 dimensions, a non-time-series observation too
x = ConditionalRNN(7, cell='LSTM')([x,inputs_aux]) # Updated Syntax for cond_rnn v2.1
# x = ConditionalRNN(7, cell='LSTM', cond=inputs_aux)(x) # Syntax for cond_rnn in some version before v2.1
predictions = Dense(1)(x)
model = Model(inputs=[inputs, inputs_aux], outputs=predictions) # With this fix, [inputs, inputs_aux], it now works, solving the issue
#model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer='rmsprop', loss='mean_squared_error', metrics=['mse'])
data = np.random.standard_normal([100,5,5]) # Sample of 100 observations with 5 dimensions à 5 time-steps each
data_aux = np.random.standard_normal([100,5]) # Sample of 100 observations with 5 dimensions à only 1 non-time-series value each
labels = np.random.standard_normal(size=[100]) # For each of the 100 obs., a corresponding (single) outcome variable
model.fit([data,data_aux], labels)
The error I get is
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'in_aux' with dtype float and shape [?,5]
[[{{node in_aux}}]]
and the traceback is
Traceback (most recent call last):
File "/home/florian/temp_nonclear/playground/test/est1ls_bare.py", line 20, in <module>
model.fit({'in': data, 'in_aux': data_aux}, labels) #model.fit([data,data_aux], labels) # Also crashes when using model.fit({'in': data, 'in_aux': data_aux}, labels)
File "/home/florian/BB/tsgenerator/ts_wgan/venv/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py", line 643, in fit
use_multiprocessing=use_multiprocessing)
File "/home/florian/BB/tsgenerator/ts_wgan/venv/lib/python3.5/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 664, in fit
steps_name='steps_per_epoch')
File "/home/florian/BB/tsgenerator/ts_wgan/venv/lib/python3.5/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 383, in model_iteration
batch_outs = f(ins_batch)
File "/home/florian/BB/tsgenerator/ts_wgan/venv/lib/python3.5/site-packages/tensorflow/python/keras/backend.py", line 3353, in __call__
run_metadata=self.run_metadata)
File "/home/florian/BB/tsgenerator/ts_wgan/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1458, in __call__
run_metadata_ptr)

Answer 1

I noticed that you are not passing inputs_aux as input to your model. TF is complaining because this tensor is required to compute your output predictions and it is not being fed with any value. Defining your model as follows should solve the problem:
model = Model(inputs=[inputs, inputs_aux], outputs=predictions)