Onnx-coreml

- Miscellaneous Bug fixes

This release includes
- Generic `remove unused layer` pass to eliminate layers whose output is not being used
- Transforming `Conv` -> `Crop` -> `BN` to `Conv` -> `BN` -> `Crop` to allow Conv-BN fusion
- Includes bug fixes related to `expand_dims` being generated internally and error messages

Breaking change: converter argument `target_ios` has been renamed to `minimum_ios_deployment_target`, as that is more accurate description of what it represents (details [here](https://github.com/apple/coremltools/blob/master/docs/NeuralNetworkGuide.mdneural-network-guide))

This release introduces support for more layers and operators which can be found [here](https://github.com/onnx/onnx-coreml/blob/842d0b1213bea7928cd3f2d54cfa82f38f306f4a/onnx_coreml/_operators_nd.pyL2233). This release adds support for new layers introduces in [Core ML 3](https://github.com/apple/coremltools/blob/c6e7d15e3aef676a60247fea235da58aedbfcfd7/mlmodel/format/NeuralNetwork.protoL535).

- Added the argument `target_ios` to choose the Core ML spec version that is produced by the converter. `target_ios='13'` will enable the converter to use all the new layers added in Core ML 3.
- Added custom_conversion_function option where users can provide their own custom conversion function. Please check example [here](https://github.com/onnx/onnx-coreml/blob/842d0b1213bea7928cd3f2d54cfa82f38f306f4a/tests/custom_layers_test.pyL58).
- Supports new model specification version 4 with target iOS 13 or later.
- Find examples for converting PyTorch models into Core ML format [here](https://github.com/onnx/onnx-coreml/tree/master/examples).

Any questions or concerns related to this release can be submitted as an issue and will be review by the team. All comments are welcomed and will be used to improve the existing documentation.

- Added the argument `target_ios` to choose the Core ML spec version that is produced by the converter. `target_ios` = '13' will enable the converter to use all the new layers added in Core ML 3.
- (diff with 1.0b2) use rank inference implemented in neural network builder API of coremltools 3.0b6. This helps in converting more models, which previously produced errors.
- (diff with 1.0b2) added support for ops GRU, ROIAlign, TopK.

**Known Issues**
- Model with `Upsample` may require using `custom_conversion_function`, through which value of the `scale` parameter can be provided.
This is due to the fact that CoreML upsample layer supports only statically known scale factors, and in certain cases the ONNX graph has dynamic scale inputs, even though the source program (e.g. pytorch code) uses static scales.