Batchflow

Fix small bug relating padding in `ResBlock`

This release fixes `crop` behavior of `TorchModel`, as well as adds new blocks and methods:
- `InternBlock` with deformable convolutions
- separate `BottleneckBlock` that extends the functionality of `ResBlock`
- method for getting a reference to the current `TorchModel` instance inside `train/predict` contexts
- `mode` parameter for `train` and `predict` methods to control `nn.Module` behavior.

Also, this is the first version after `numpy` deprecation of autocast to `dtype=object` of mishaped arrays, so this is fixed in some places.

This release fixes one small `TorchModel` bug.

This release changes the way `Batch.apply_parallel` works: now it accepts both `init` and `post` functions, and should be the preferrable way to decorate batch methods (by marking them with `decorators.apply_parallel`).

Other than that, there are a few new building blocks for `TorchModel`, parameter to `pad` the last microbatches to full `microbatch_size`, and small bug fixes.

Models
- added gradient clipping and new layers


Plot
- refactored existing plots across the library to rely on `plot`, introduced in the previous release


Research improvements
- modified stored configs to use `aliases` instead of actual values: that fixes some pickling problems

Models
- refactored model building procedure: split modules into separate entities like `EncoderModule` and `DecoderModule`
- introduced new modules that import ready-to-use networks from other libraries: currently, we support `TIMM` and `HuggingFace` libraries
- better module `repr`
- check 645 for other changes


Plot
- introduced `plot` module with utilities for displaying images and curves
- `plot` has a few tutorials with lots of examples: refer to [them ](https://github.com/analysiscenter/batchflow/tree/master/examples/plot) to get a more in-depth understanding of `plot` usages


Research improvements
- added separate `Storage` class, that manages output streams of research results.
- various fixes and QoL changes