Log `TensorBoard` histograms

Nobody has claimed this yet.

### Description & Motivation

I'd like to be able to log histograms using `TensorBoard`. Looking at how multiple/single scalars are currently logged by `lightning.fabric.loggers.TensorBoardLogger` [1], I think `SummaryWriter.add_histogram` [2] could be called (non-empty `torch.Tensor` instances) in a similar way

Unfortunately, values passed into `LightningModule.log` are first type-checked by `LightningModule.__to_tensor` [3] - this enforces values to be either `dict` or single-value (0Dim) `torch.Tensor` instances. So in order to support histograms, this check would need to be loosen slightly...

- [1] https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/fabric/loggers/tensorboard.py#L211
- [2] https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_histogram
- [3] https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/pytorch/core/module.py#L642

### Pitch

Firstly, I'm proposing [4] becomes:
```
if not (torch.numel(value) == 1 or (torch.numel(value) > 0 and value.ndim == 1)):
raise ValueError(
f"`self.log({name}, {value})` was called, but the tensor must have a single element,"
f"or a single non-empty dimension. You can try doing `self.log({name}, {value}.mean())`"
)
```

and [1] becomes (say):
```
try:
if isinstance(v, dict):
self.experiment.add_scalars(k, v, step)
elif isinstance(v, Tensor):
self.experiment.add_histogram(k, v, step)
else:
self.experiment.add_scalar(k, v, step)

# TODO(fabric): specify the possible exception
except Exception as ex:
raise ValueError(
f"\n you tried to log {v} which is currently not supported. Try a dict or a scalar/tensor."
) from ex
```

I'm sure you're keen not to modify `LightnginModule` too much, but I couldn't figure out a way to pass 1+Dim `Tensor` instances through to the `TensorBoardLogger` without loosing the type checking.

- [4] https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/pytorch/core/module.py#L648

### Alternatives

As a workaround, I'm currently using a subclass of `LightningModule` that overloads the `.log` method [5] with:
```
def log(self, name: str, value: _METRIC, *args, **kwargs):
if isinstance(value, Tensor) and value.ndim > 0:
for logger in self.loggers:
if isinstance(logger, TensorBoardLogger):
logger.experiment.add_histogram(name, value, self.global_step)

else:
super().log(name, value, *args, **kwargs)
```

Introducing `TensorBoard` specifics into the `LightningModule` class, is a horrible hack (hence wanting to incorporate the "nicer" fix above!)

- [5] https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/pytorch/core/module.py#L371

### Additional context

I'm very happy to open a PR (I have one written up from exploring potential implementations) is the above sounds reasonable?

Many thanks!

cc @lantiga @borda

Start with lightning/pytorch/core/module.py at __to_tensor and lightning/fabric/loggers/tensorboard.py at the existing scalar logging path. Read SummaryWriter.add_histogram and compare the proposed handling of non-empty one-dimensional tensors with current scalar and dict behavior. Done means supported histogram values pass validation and are emitted through TensorBoard without breaking existing logging.

Written by the indexing model from the issue text.