* Refactor logging setup to support distributed attrs

* `cleanup_logging()` is replaced with stdlib's `logging.shutdown()`
* Remove `TeeLogger` and use standard log handlers
* Remove `replace_cr_with_newline` and use the standard logging practice of using
`logging.Filter`
* Introduce `rank` and `world_size` optional attributes to support
distributed workers

* Support for distributed training in `get_metrics`

* Remove bad import

* Fix duplicate log messages in stdout

* Remove preemptive `logging.shutdown`

`logging.shutdown` is called by the logging module
by default during exit which makes it unnecessary to
be called from `train_model`

* Fix black formatting issues

* Remove `tee_logger` references in API doc

* Set log level from `ALLENNLP_DEBUG` env

* High level API changes to support distributed training

* Fix flake8 error

* Fix mypy error

* Add docstring and misc fixes

* Fix flake tests

Followup PR to #3390 and #3372 to bring in distributed training support. Following are the major changes done:

* Workers are spawned using `mp.spawn` and each worker creates its own `Trainer` instance
* `Trainer.__init__` wraps up `self.model` with `DistributedDataParallel`
*  Logging and metric aggregation are already done in the previous PRs
* `Vocabulary` creation in case of distributed training is done before spawning the workers and creating `Trainer` class

To run distributed training, the trainer needs to have the following flag to be enabled:

```jsonnet
{
    "trainer": {
        "distributed": true,
        // ...
    }
}
```

TODO:
* Try to reproduce comparable results and share extensive results for existing/selected models
* Check if other commands like `evaluate`, `predict`, `fine-tune` works well with the new changes
* Should all the callbacks need to be called from every worker in case callback based training?
* Should the current dataset readers be changed to support distributed training as well?(to selectively yield data based on their rank)
* Write tests - _would be happy to get some suggestions on how to write tests for this_

* add some tests

* another test, fix incorrect type annotations

* torch mp uses it's own context, no need to set default

* lint

* strip out old DP stuff, ensure multiple cuda devices raises errors

* lint

* remove unused attribute

* remove _cuda_devices everywhere

* fixes

* move distributed config up to top level

* lint

* clean up

* rename occurences of batch_group

* remove hack from find_learning_rate

* fix last tests

* black

* use a top level distributed config

* correct error for int

* change up parse_cuda_devices to raise good error and be strongly typed