Labelbox

Updates:
* Geometry.raster now has a consistent interface and improved functionality
* renamed schema_id to feature_schema_id in the `FeatureSchema` class
* `Mask` objects now use `MaskData` to represent segmentation masks instead of `ImageData`

Updates
* Rename
* `RasterData` to `ImageData`
* `data` property of `TextData`, `ImageData`, and `VideoData` types to `value`.
* Decrease wait time between updates for `BulkImportRequest.wait_until_done()`
* Organization param is no longer used to create the LFO in `Project.setup()`

Added
* More flexible drawing features for geometry annotations

Fixes
* Fixed annotation type conversion bugs that appeared when projects had mixed data types or empty labels.

Added
* Annotation types
- A set of python objects for working with labelbox data
- Creates a standard interface for both exports and imports
- See example notebooks on how to use under examples/annotation_types
- Note that these types are not yet supported for tiled imagery
* MEA Support
- Beta MEA users can now just use the latest SDK release
* Metadata support
- New metadata features are now fully supported by the SDK
* Easier export
- `project.export_labels()` accepts a boolean indicating whether or not to download the result
- Create annotation objects directly from exports with `project.label_generator()` or `project.video_label_generator()`
- `project.video_label_generator()` asynchronously fetches video annotations
* Retry logic on data uploads
- Bulk creation of data rows will be more reliable
* Datasets
- Determine the number of data rows just by calling `dataset.row_count`.
- Updated threading logic in create_data_rows() to make it compatible with aws lambdas
* Ontology
- `OntologyBuilder`, `Classification`, `Option`, and `Tool` can now be imported from `labelbox` instead of `labelbox.schema.ontology`

Removed
* Deprecated:
- `project.reviews()`
- `project.create_prediction()`
- `project.create_prediction_model()`
- `project.create_label()`
- `Project.predictions()`
- `Project.active_prediction_model`
- `data_row.predictions`
- `PredictionModel`
- `Prediction`
* Replaced:
- `data_row.metadata()` use `data_row.attachments()` instead
- `data_row.create_metadata()` use `data_row.create_attachments()` instead
- `AssetMetadata` use `AssetAttachment` instead

Fixes
* Support derived classes of ontology objects when using `from_dict`
* Notebooks:
- Video export bug where the code would fail if the exported projects had tools other than bounding boxes
- MAL demos were broken due to an image download failing.

Installation
* Data processing dependencies are not installed by default to for users that only want client functionality.
* To install all dependencies required for the data modules (annotation types and mea metric calculation) use `pip install labelbox[data]`. For a specific version (such as the rc release) use `pip install "labelbox[data]==3.0.0rc0"`

Added
* Added `dataset.export_data_rows()` which returns all `DataRows` for a `Dataset`.

Fix
* Upated `create_mask_ndjson` helper function in `image_mal.ipynb` to use the color arguement
instead of a hardcoded color.

Added
* asset_metadata is now deprecated and has been replaced with asset_attachments
* `AssetAttachment` replaces `AssetMetadata` ( see definition for updated attribute names )
* Use `DataRow.attachments()` instead of `DataRow.metadata()`
* Use `DataRow.create_attachment()` instead of `DataRow.create_metadata()`
* Updated pydantic version