Initial support for Catboost

We would like to add support for [Catboost models](https://github.com/catboost/catboost). Users of Treelite should be able to load Catboost models and run prediction.

## Overview
Catboost has a custom target encoding method to encode categorical data, and produces special kinds of decision trees called [oblivious trees](https://stats.stackexchange.com/questions/353172/what-is-oblivious-decision-tree-and-why). See [the Catboost paper](https://arxiv.org/abs/1706.09516) for more details.

In general, target encoder is a function that takes a categorical input and puts out a numeric output. The function is an "encoding," in the sense that the categorical input is encoded as a real number. The advantage of target encoding is that we can exclusively use the simple test of form `[feature] < [threshold]` in all of our decision trees.

The challenge is that Catboost uses a custom flavor of target encoding. The goal, therefore, is to abstract away as much complexity as possible.

## Proposed Design
The treelite model spec
https://github.com/dmlc/treelite/blob/4cc4f7e61abb624c65bdb7ff83972ca4b11ff10d/include/treelite/tree.h#L792-L796
should be updated to include an optional field to store the target encoding function. The target encoding component should be a lookup table of form
```
(categorical_feature_id, categorical_value) -> [ numerical vector ]
(categorical_feature_id, categorical_value) -> [ numerical vector ]
(categorical_feature_id, categorical_value) -> [ numerical vector ]
...
```
where each possible categorical value is mapped to a vector of length 1 or greater.

Catboost uses CityHash to convert string categories into int64, so the target encoding field must allow both int64 and float32 types for the categorical input.

## Scope
Catboost allows users to save models in two formats: FlatBuffer and JSON. For the initial version, we'll only support the JSON format.
Initially, we'll convert oblivious trees into regular decision trees. We may add `ObliviousTree` class to the Treelite model spec in the future.
In addition, we'll only support the `simple_ctr` configuration, where the target encoding function takes in only one single categorical feature at a time. We won't support the `combination_ctr` configuration where multiple categorical features are fed into the target encoder.

## TODOs
- [ ] Add the target encoder to the Treelite model spec
- [ ] Implement the deserializer for the Catboost JSON model. The deserializer will be placed in `src/frontend`.
- [ ] Update GTIL to support inferencing with Catboost.
- [ ] Update the C codegen to support text inputs and target encoding. I expect this step to be challenging, given the complexity in the C codegen.