Implementing code changes for improving scalability as per the requirements in this issue# 950 in e-mission-docs.

Initial approach involves utilizing Singleton design pattern concept on instance variables to check whether model has already been loaded before attempting to load the model.

Thus this should prevent model from being loaded if it is already present in the instance variable of the current class instance of BuiltinModelStorage.

Added import for logging.
Added self parameter to pass reference to current instance object.

Changes after refactoring label inference pipeline to load model only once. Will be merging with previous PR branch.

Starting from scratch to implement refactored label pipeline approach

Using a dictionary of lists of predictions returned for each trip instead of a simple list.
This will help keep predictions of each trip separate but keep all the algorithms_wise predictions of each trip together in a list.

Updated function call to predict_labels_with_n() as per latest changes to include trip_list and user_id

Fixed typo in eacilp for trip.get_id()

eamur.predict_labels_with_n() now takes a trip instead of a trip_list.
Passed a trip_list in previous commit but forgot to update parameter name.

Receive a prediction list of (prediction, n) for each trip in trip_list.

Then for each of the predictions in the prediction list, run the assertion tests.

Also, cleaned up some debug print statements from earlier commits / PRs.

Cleaning up previously used debug statements

A couple of pipeline tests were failing - TestLableInferencePipeline, TestExpectationPipeline.
Failed since they were calling placeholder_predict_n functions in eacili inferrers.
Had to update these to now receive trip_list instead of single trip.

Fetching from trip_list itself.
Added assertion to ensure identical and only one unique user_id present for all trips.
Assertion fails if multiple user_ids found for the trip_list.

Removed from Test files and inferrers.py which contained placeholder_predictor functions.

Refactored code to pass in the trip model directly to predict_labels_with_n() in eamur.

Moved the load model step to eacil.inferrers by using load_model() of eamur.

Modified TestRunGreedyModel to use this refactored function.

Failing test due to missed user_id parameter.
Passed user_id to rectify.