advanced deep-learning best practices runtimeerrorgraph disconnectedcannot obtain value for tensor tensor("input_ : "shape=(? )dtype=float at layer "input_ this error tells youin essencethat keras couldn' reach input_ from the provided output tensor when it comes to compilingtrainingor evaluating such an instance of modelthe api is the same as that of sequentialmodel compile(optimizer='rmsprop'loss='categorical_crossentropy'import numpy as np x_train np random random(( )y_train np random random(( )generates dummy numpy data to train on model fit(x_trainy_trainepochs= batch_size= score model evaluate(x_trainy_trainevaluates the model multi-input models compiles the model trains the model for epochs the functional api can be used to build models that have multiple inputs typicallysuch models at some point merge their different input branches using layer that can combine several tensorsby adding themconcatenating themand so on this is usually done via keras merge operation such as keras layers addkeras layers concatenateand so on let' look at very simple example of multi-input modela question-answering model typical question-answering model has two inputsa natural-language question and text snippet (such as news articleproviding information to be used for answering the question the model must then produce an answerin the simplest possible setupthis is one-word answer obtained via softmax over some predefined vocabulary (see figure answer dense concatenate lstm lstm embedding embedding reference text question figure quest ion-answering model licensed to