Pyrates

- Updated documentation
- Removed conversion function register, because the functions were not used and made the code unnecessarily complicated
- might be replaced by a graph-based conversion path-finder in the future, if necessary
- Extended support for loading circuits from and saving to files
- supported formats: `yaml`, `pickle`
- supported classes: templates
- Removed all imports in `pyrates.utility.__init__.py` for increased stability.
Previously, importing something from `pyrates.utility`, would have required a user to install optional packages that
might not have been needed. Now all utility functions need to be imported from sub-files in the `pyrates.utility`
module instead of directly from the module.
- Added optional install collection `tests` that includes all packages necessary to run the tests.
Also restricted the travis CI build to use only the tests installation instead of the full installation.
- Added feature to pass a dictionary to `CircuitTemplate.apply()` in order to adapt values of variables on the fly. This
behaviour was already supported by all other parts of the hierarchy, only circuits missed out until now.

- Allow to initialise CircuitTemplate with instances of `EdgeTemplate` instead of a template path, previous behaviour is unaffected.
- Fix writing graph to the file by passing `_format` along until the end

- updated tensorflow dependency to >=2.0, fixes some dependency problems
- Improved cluster distribution system, available under `pyrates.utility.grid_search`
- New feature: model optimization with genetic algorithms, available under `pyrates.utility.genetic_algorithm`
- Miscellaneous bug fixes

- removed version ID numbers of operator/node instances in the intermediate representation. I.e. a node label `mynode`
was previously renamed to `mynode.0` and will now keep it's original label.
- moved all functionality of ComputeGraph into CircuitIR, which is now the main interface for the backend.
- `CircuitIR` now has a `.compile` method that performs all vectorization and transformation into the computable
backend form.
- vectorization will transform all nodes into instances of `VectorizedNodeIR` that have labels like `vector_nodeX` with
X being a integer index. The map between old nodes and vectorized nodes with respective index is saved in the
`label_map` dictionary attribute of the `CircuitIR`
- When adding input or sampling output of a network with multiple stacked levels of circuits, you can now use `all` to
get all nodes within that particular level. For example `mysubcircuit1/all/mynode` will get all nodes with label
`mynode` that are in one level of sub-circuits below `mysubcircuit`.
- Tensorflow support now relies on the current 2.0 release candidate `tensorflow-2.0-rc`
- Added optional install requirements via `extras_require` in setup.py