Device parsing improvements

Nobody has claimed this yet.

## Proposed refactoring or deprecation

Introduce a device selection dataclass that holds the device selection in a standardized format. Idea by @ananthsub

### Motivation

We have a `_parse_devices` function used in the Trainer and Lite that returns a tuple of parsed device indices.

https://github.com/PyTorchLightning/pytorch-lightning/blob/9237106451f97393b17009a0ca571b6ff5ba5484/pytorch_lightning/trainer/trainer.py#L1459-L1470

From @ananthsub in https://github.com/PyTorchLightning/pytorch-lightning/pull/10230#discussion_r738806860

> returning a tuple isn't going to scale well with more device types. it's not easy to tell which positional index maps to which device id type. it could be better to introduce a dataclass to represent the schema concretely. that would also naturally allow for extensions like IPUs

### Pitch
```py

@dataclass
class DeviceSelection

devices: List[int] = []
type: DeviceType = CPU

def parse_input(gpus, tpu_cores, ipus, ...)
# validate user inputs
# map various input formats to standardized one in this dataclass
...
return DeviceSelection(devices=..., type=...)

```
The AcceleratorConnector new gets as input the DeviceSelection instance instead of a growing list of arguments. It currently takes: devices, gpus, gpu_ids, tpu_cores, ipus, num_processes

### Additional context

Alternative to #10231

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cc @justusschock @awaelchli @akihironitta @rohitgr7 @tchaton @borda

Start with the _parse_devices function in pytorch_lightning/trainer/trainer.py and trace how Trainer and Lite pass device arguments into AcceleratorConnector. Review the proposed DeviceSelection dataclass and the current devices, gpus, gpu_ids, tpu_cores, ipus, and num_processes inputs. Done means device selection is represented consistently and the affected integration points and validations work across the supported device types.

Written by the indexing model from the issue text.