Replace lambdas with partials to allow multi-gpu training

[BramVanroy]

Currently, the CometModel has lambda functions defined. These cannot be pickled and therefore multiprocessing (~multi-GPU training) is not possible.

This PR replaces the lambda functions with partials, that should be picklable.

closes #159

[BramVanroy]

This PR is currently not ready for integration. I have done some work on making the metric RegressionMetrics(Metric) (not the model) compatible with distributed computing and this seems to work for training. However, I can't get this to work with prediction.
As far as I can tell, the issue is that the metrics are not gathered correctly over the different processes. So in this piece of code, RegressionMetrics should get dist_sync_on_step=True but only in distributed settings.

COMET/comet/models/regression/regression_metric.py

Lines 130 to 135 in a324526

	def init_metrics(self):
	"""Initializes train/validation metrics."""
	self.train_metrics = RegressionMetrics(prefix="train")
	self.val_metrics = nn.ModuleList(
	[RegressionMetrics(prefix=d) for d in self.hparams.validation_data]
	)

PyTorch Lightning has so many hoops (subclasses, properties) to jump through that I lost my patience to figure out how we can do something if multi_gpu or if distributed. For someone who knows PyTorch Lightning well, this is perhaps an easy fix so feel free to chime in.

A test case for distributed scenario has also been added. Note that for dev'ing this, I updated torchmetrics to the newest version to be sure to avoid underlying issues.