add better documantaiton to the fedprox optimizer

Signed-off-by: Buchnik, Yehonatan <[email protected]>

openfl/utilities/optimizers/keras/fedprox.py

@@ -41,7 +41,7 @@ def build(self, variables):
         """Initialize optimizer variables.
 
         Args:
-          var_list: list of model variables to build FedProx variables on.
+            variables (list): List of model variables to build FedProx variables on.
         """
         if self.built:
             return
@@ -55,15 +55,33 @@ def build(self, variables):
             )
 
     def update_step(self, gradient, variable, learning_rate):
-        """Update step given gradient and the associated model variable."""
+        """ Update step given gradient and the associated model variable.
+            In the update_step method, variable is updated using the gradient and the proximal term (mu). 
+            The proximal term helps to regularize the update by considering the difference between
+            the current value of variable and its initial value (vstar), which was stored during the build method.
+        Args:
+            gradient (tf.Tensor): The gradient tensor for the variable.
+            variable (tf.Variable): The model variable to be updated.
+            learning_rate (float): The learning rate for the update step.
+        """
         lr_t = tf.cast(learning_rate, variable.dtype)
         mu_t = tf.cast(self.mu, variable.dtype)
         gradient_t = tf.cast(gradient, variable.dtype)
+        # Get the corresponding vstar for the current variable
         vstar = self.vstars[self._get_variable_index(variable)]
 
+        # Update the variable using the gradient and the proximal term
         self.assign_sub(variable, lr_t * (gradient_t + mu_t * (variable - vstar)))
 
     def get_config(self):
+        """Return the config of the optimizer.
+        An optimizer config is a Python dictionary (serializable)
+        containing the configuration of an optimizer.
+        The same optimizer can be reinstantiated later
+        (without any saved state) from this configuration.
+        Returns:
+            dict: The optimizer configuration.
+        """
         config = super().get_config()
         config.update(
             {