Tracker module for ML based TC tracking algortihms

[surbhigoel77]

Linked issue: #190
The PR aims to build the core infrastructure for running ML models within the TCTrack framework. It uses exisiting base classes and defines new ml-specific classes.

It includes the following files (based on #204):

• ml_tracker.py : contains a base dataclass TCMLParameters to fetch ML Parameters and a base abstract class TCMLTracker that extends the existing Tracker by adding a load_model function (for ml models). These classes remain common for all ML models (basically it is a way to standardise processes for any incoming new model).

• cyclone_track_ml.py : inherits the base classes defined in ml_tracker.py and adds new model specific functions. Reads the model from hugging face hub repository and connects it to the run_tracker pipeline. For classes that are common for both ML and non-ML models like TCTrackerMetadata and Trajectory are inherited from the original core/tracker.py

• test_ml_tracker.py : unit, integration

To be added:

• add preprocessing to cyclone_track_ml.py (regrid the inference input to match the resolution of the training data). Do we have a standard resolution of data for TCTrack
• find the standard way to store trained ml models (gitutb or hugging face) .pt or torchscript
• find a way to deal with large dataset:
  batching (proprocess -detect-stitch-read_trajectories),
  lazy reading with cf.read() - field = cf.read("file.nc")[0].subspace(T=cf.wi(t_start, t_end))

Current progress: Able to load the trained model from hugging face, yet to run detect function (inference)