Doubt

Fixed
- When `return_all` is specified in `Boot.predict` and multiple samples have been
inputted, then it now returns an array of shape `(num_samples, num_boots)` rather
than the previous `(num_boots, num_samples)`.

Added
- Added a `return_all` argument to the `Boot.predict` method, which will override the
`uncertainty` and `quantiles` arguments and return the raw bootstrap distribution
over which the quantiles would normally be calculated. This allows other uses of the
bootstrap distribution than for computing prediction intervals.

Fixed
- Previously, all the trees in `QuantileRegressionForest` were the same. This has now
been fixed. Thanks to gugerlir for noticing this!
- The `random_seed` argument in `QuantileRegressionTree` and `QuantileRegressionForest`
has been changed to `random_state` to be consistent with `DecisionTreeRegressor`, and
to avoid an `AttributeError` when accessing the estimators of a
`QuantileRegressionForest`.

Added
- The `QuantileRegressionForest` now has a `feature_importances_` attribute.

Changed
- `Boot.fit` and `Boot.predict` methods are now parallelised, speeding up both training
and prediction time a bit.
- Updated `README` to include generalised linear models, rather than only
mentioning linear regression.

Fixed
- Removed mention of `PyTorch` model support, as that has not been implemented
yet

Changed
- The `verbose` argument to `QuantileRegressionForest` also displays a progress
bar during inference now.

Fixed
- Fixed `QuantileRegressionForest.__repr__`.