Include instructions from default.instructions.md for all languages.

All functions, methods, classes, and attributes should have docstrings.

Docstrings should be formatted using the NumPy style format.

• The docstring should include a summary of the function or method's purpose, followed by a description of its parameters and return values.

• The docstring should also include any exceptions that may be raised by the function or method.

• Where possible docstring should include an executable example of how to use the function or method, including any important details about its usage.

Docstrings should include Sphinx style crossref directives for functions, methods, classes, attributes, and data whenever possible using :py:class: or :py:func: or :py:meth: or :py:attr: or :py:data syntax, respectively,

See Also https://www.sphinx-doc.org/en/master/usage/domains/python.html#python-xref-roles

Docstrings for modules should include a summary of the module's purpose and any important details about its usage.

• Module docstrings should also include an executable example of how to use the module, including any important functions or classes or configuration options (especially those derived from a JSON config file) like any of those in mlos_bench.environments, mlos_bench.services, mlos_bench.schedulers, mlos_bench.optimizers, and mlos_bench.storage.

  For instance:

  '''
  This is an example module docstring for the mlos_bench.environments.my_special_env module.
  
  It should include some descriptive text about the module and its purpose.
  
  Example
  -------
  It also includes some executable code examples.
  
  >>> import json5 as json
  >>> # Load a JSON config string for a MySpecialEnvironment instance.
  >>> json_string = """
  ... {
  ...     "class": "mlos_bench.environments.my_special_env.MySpecialEnvironment",
  ...     "name": "MySpecialEnvironment",
  ...     "config": {
  ...             "param1": 42,
  ...             "param2": "foo",
  ...     },
  ... }
  ... """
  >>> config = json.loads(json_string)
  
  >>> from mlos_bench.environments.my_special_env import MySpecialEnvironment
  >>> my_env = MySpecialEnvironment(config=config)
  >>> print(my_env)
  MySpecialEnvironment(param1=42, param2='foo')
  '''

• Docstrings for classes can refer to their module docstring with :py:mod: cross-references for usage examples to allow easier browser navigation of generated documentation.

  For instance:

  class MySpecialEnv:
      """
      This is class docstring for MySpecialEnv.
  
      It should include some descriptive text about the class and its purpose.
  
      Example
      -------
      Refer to :py:mod:`mlos_bench.environments.my_special_env` for usage examples.
      """

If not all arguments to a function or method fit on the same line, then they should each be on their own line.

Adding a trailing comma to the last argument is optional, but recommended for consistency whenever a single line is insufficient.

Code should be formatting using black.

Code should be type checked using mypy.

• All function and method parameters should be type annotated.

Code should be linted using pylint.

Tests should be included for all new code and should be run using pytest.

Tests should be organized roughly the same way as the code they are testing (e.g., tests/some/test_module.py for some/module.py).

Test fixtures that setup the resources for the tests (e.g., Environments, Services, Storage, Optimizer, Scheduler, etc.) should be included in a conftest.py file in the same directory as the tests or else a fixtures.py file in the same directory as the tests.