advanced deep-learning best practices answers are onequestion np random randint( question_vocabulary_sizehot encodedsize=(num_samplesmax_length)not integers answers np random randint( size=(num_samplesanswer_vocabulary_size)model fit([textquestion]answersepochs= batch_size= model fit({'text'text'question'question}answersepochs= batch_size= fitting using dictionary of inputs (only if inputs are namedfitting using list of inputs multi-output models in the same wayyou can use the functional api to build models with multiple outputs (or multiple headsa simple example is network that attempts to simultaneously predict different properties of the datasuch as network that takes as input series of social media posts from single anonymous person and tries to predict attributes of that personsuch as agegenderand income level (see figure listing funct ional api implement at ion of hree-out put model from keras import layers from keras import input from keras models import model vocabulary_size num_income_groups posts_input input(shape=(none,)dtype='int 'name='posts'embedded_posts layers embedding( vocabulary_size)(posts_inputx layers conv ( activation='relu')(embedded_postsx layers maxpooling ( )(xx layers conv ( activation='relu')(xx layers conv ( activation='relu')(xx layers maxpooling ( )(xx layers conv ( activation='relu')(xx layers conv ( activation='relu')(xx layers globalmaxpooling ()(xx layers dense( activation='relu')(xnote that the output layers are given names age_prediction layers dense( name='age')(xincome_prediction layers dense(num_income_groupsactivation='softmax'name='income')(xgender_prediction layers dense( activation='sigmoid'name='gender')(xmodel model(posts_input[age_predictionincome_predictiongender_prediction]licensed to