Multinomial logistic regression for stacked ensemble learning with 2d input into regression
So the ultimate workaround ended up being very simple: flattening the 3x12 vector outputs for each base model into a 1x36 feature vector that was then fed into the ensemble model. 
The other option was to build a tensor out of the base model predictions and then use a different type of ensemble classifier i.e. a CNN, but that approach was rejected.
I am trying to implement a stacked ensemble model for multiclass prediction. 
Each training example is a vector with 14 features.
There are a total of 12 possible classes.
The three base models (described below) each ingest a 14-dimensional feature vector training example and output a 12-dimensional vector of probabilities (1 probability / class).
The three base classification models were built from scratch (i.e. not built using sklearn so I can't simply use the sklearn ensemble methods) and are as follows:

Bayes Net: outputs a 12-dimensional vector of probabilities (1 probability / class) for each 14-dimensional training example
Other graphical model: outputs a 12-dimensional vector of probabilities (1 probability / class) for each 14-dimensional training example
Metric learning model: outputs a 12-dimensional vector of probabilities (1 probability / class) for each 14-dimensional training example

I would like to stack the outputs from the base models for each training example (i.e. stack 3 x 12-d probability vectors) for each training example and feed this 3x12 array as an input to the multinomial logistic regression ensemble model to output a 12-dimensional vector of probabilities for the final multi-class predictions for each training example.
I'm not sure, however, if this is possible using the sklearn multinomial regression model.  From my understanding, the sklearn model can only ingest a 1-d array of features rather than a 2-d array of features.
Any advice would be welcome. Thank you.