Introduce new general-purpose prediction methods for InferenceModel

[juliabeliaeva]

This PR removes classification methods from the InferenceModel interface in favor of the more general api. It introduces two prediction methods: for single input and for multiple inputs and adds api to extract prediction results from the model. Instance methods are replaced with the following options for classification:

1. If a model outputs a vector of probabilities, there are PredictionKt#predictLabel/PredictionKt#predictProbabilities extensions or the model could be wrapped into ImageRecognitionModelBase.
2. For models with different outputs, function InferenceModel#predict(kotlin.Pair<float[], TensorShape>, Function1<? super R,? extends T>) could be used combined with the InferenceResultConverter for result extraction.

Additional comments:

1. TensorFlow models still have some methods for classification left, for convenience.
2. TensorResult right now only has a list of tensors, but ideally it could also contain a map "output name" -> "tensor" and allow access by String.
3. InferenceResultConverter only has 2 methods returning LongArray and FloatArray, this list should be expanded for other data types.
4. InferenceModel does not provide enough input-output information. Currently it only has inputDimensions property, instead we should replace it with something similar to OnnxInferenceModel#inputInfo/outputInfo to get access to all inputs and outputs.

[ermolenkodev]

I like the design. It's great that we don't have hardcode classification-related methods in interfaces anymore. Thanks!