A neural network has two different kinds of inputs that meet later in the model. What specific Keras tool must be used to build this network?

The specific Keras tool that must be used to build a neural network with two different kinds of inputs that meet later in the model is the Keras Functional API. This API is essential because it allows for the creation of complex, non-linear network topologies, unlike the `Sequential` model which is limited to a single input and a linear stack of layers. To implement a multi-input network, one begins by defining separate `Input` layers for each distinct type of input data. An `Input` layer specifies the shape of the data entering a particular branch of the network. After defining these individual input branches, various Keras layers can be applied independently to process each type of input. When these distinct input paths need to combine, Keras provides specific merging layers, such as `keras.layers.Concatenate` or `keras.layers.Add`. For instance, `Concatenate` combines tensors from multiple upstream layers along a designated axis, effectively merging the information streams from the different input types. Once all the branches are defined and merged, the entire network structure, from its multiple inputs to its final output layers, is encapsulated by creating an instance of the `keras.Model` class, which takes a list of the input layers and the final output layer(s) as arguments. This `Model` object then represents the complete multi-input network, capable of processing diverse data streams simultaneously.