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/**
 * Implementation of prediction routines.
 *
 * @param modelPathPrefix     Path prefix from where to load the model artifacts.
 *                            These include the symbol, parameters, and synset.txt
 *                            Example: file://model-dir/resnet-152 (containing
 *                            resnet-152-symbol.json, resnet-152-0000.params, and synset.txt).
 * @param inputDescriptors    Descriptors defining the input node names, shape,
 *                            layout and type parameters
 *                            <p>Note: If the input Descriptors is missing batchSize
 *                            ('N' in layout), a batchSize of 1 is assumed for the model.
 * @param contexts            Device contexts on which you want to run inference; defaults to CPU
 * @param epoch               Model epoch to load; defaults to 0

 */
Predictor(String modelPathPrefix, Array<DataDesc>List<DataDesc> inputDescriptors,
                ArrayList[Context] Contexts, int epoch)

/**
 * Takes input as IndexedSeq one dimensional arrays and creates the NDArray needed for inference
 * The array will be reshaped based on the input descriptors.
 *
 * @param input:            An Array of a one-dimensional array.
                            An Array is needed when the model has more than one input.
 * @return                  Indexed sequence array of outputs
 */
ArrayList <Array<List <Float>> predict(ArrayList <Array<List <Float>> input)

/**
 * Predict using NDArray as input
 * This method is useful when the input is a batch of data
 * Note: User is responsible for managing allocation/deallocation of input/output NDArrays.
 *
 * @param inputBatch        Array of NDArrays
 * @return                  Output of predictions as NDArrays
 */
ArrayList <NDArray> predictWithNDArray(ArrayList <NDArray> inputBatch)