Added docstrings, type hints to following functions:

tinyfl/scorer.py

@@ -1,18 +1,54 @@
 from tinyfl.model import create_model, test_model
 import numpy as np
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from typing import List
 
 
-def _compute_accuracy(weight, testloader):
+def _compute_accuracy(weight: dict, testloader: Dataloader)-> float:
+    """
+    Computes accuracy of model.
+
+    Compares output of model with current set of weights to calculate percentage of correct answers.
+
+    Args:
+        weight: Weights of the model stored in a dictionary
+        testloader: The loaded dataset
+
+    Returns:
+        A float value of the accuracy of the model (% of correct answers)
+    """
     model = create_model()
     model.load_state_dict(weight)
     return test_model(model, testloader)[0]
 
 
-def accuracy_scorer(weights, testloader):
+def accuracy_scorer(weights: list[dict], testloader: Dataloader)-> List(float):
+    """Computes accuracy of models.
+
+    Args:
+        weights: A list of weights of each model which are stored in dictionaries
+        testloader: The loaded dataset
+
+    Returns:
+        A list with float values of the accuracies of the models (% of correct answers)
+    """
     return [_compute_accuracy(weight, testloader) for weight in weights]
 
 
-def marginal_gain_scorer(weights, prev_scores, testloader):
+def marginal_gain_scorer(weights: List[dict], prev_scores: List[float], testloader: Dataloader)-> List[float]:
+    """Calculates marginal gain in accuracy of model
+
+    Calculates increase in accuracy of model after pulling wieghts
+
+    Args:
+        weights: A list of weights of each model which are stored in dictionaries
+        prev_scores: List storing accuracies of models prior to most recent updation of weights
+
+    Returns:
+        List of floats which represent the marginal increases in accuracies(if any) of each party
+    """
     assert len(weights) == len(prev_scores)
     return [
         max(a - b, 0)
@@ -23,7 +59,7 @@ def marginal_gain_scorer(weights, prev_scores, testloader):
     ]
 
 
-def multikrum_scorer(weights):
+def multikrum_scorer(weights: List[dict]):
     R = len(weights)
     f = R // 3 - 1
     closest_updates = R - f - 2