Feature/ml flow integration

[ArshaanNazir]

Description

This PR aims to add a new feature of enabling tracking the run metrics (logs) on mlflow. We just need to define a flag: mlflow_tracking = True in the report method and our metrics will be logged on local mlflow tracking server.
This is done as follows:

-> Load and run the model as usual. Define mlfow_tracling = True

-> You will get the normal report. To check the mlflow logs. Run !mlflow ui

-> This guides us to the local mlflow tracking server. It has the model-name as experiment name and task and date as a run-name.

-> To check the logs, select the run-name and check the metrics section.

-> It helps us to maintain and compare different configurations of same or different models. Let us run the same model again with different configuration.

-> In the mlflow tracking server, we can see a different run for the same model ( with different configuration )

-> We can select the compare button and compare the different runs.

Here we can compare the different run metrics.

Type of change

Please delete options that are not relevant.

• New feature (non-breaking change which adds functionality)
• This change requires a documentation update

Usage

Checklist:

• I've added Google style docstrings to my code.
• I've used pydantic for typing when/where necessary.
• I have linted my code
• I have added tests to cover my changes.