ML cost model integration for Wayang

[mspruc]

Wayang currently is based on manually tuned cost models, this can be time consuming to manually tune therefore we implement

• DefaultPointwiseCost which is called using Wayangs native EstimatableCostFactory, you just need to provide it to Wayangs Configuration
• cost_model.onnx a pretrained sample model, based off a tree convolutional neural network for cost prediction, we also allow you to manually set a model with config.setProperty("wayang.ml.model.file", modelPath);
• Introduces TreeNode, TreeEncoder, TreeDecoder, OneHotEncoder, OneHotMappings encoding schemes which allows capturing deep relationships within a plan
• Integrates cost_model.onnx calls via OnnxRuntime

and various other features. We include WordCountIntegrationTest as an example of how to call it on a standard wayang plan. We also provide setComplexityClass() on FunctionDescriptors that likewise will be used by the new cost model.