Fix sphinx/build_docs warnings for searches

Author: MaximSmolskiy

searches/double_linear_search.py

@@ -7,6 +7,7 @@ def double_linear_search(array: list[int], search_item: int) -> int:
 
     :param array: the array to be searched
     :param search_item: the item to be searched
+
     :return the index of search_item, if search_item is in array, else -1
 
     Examples:

searches/hill_climbing.py

@@ -99,12 +99,14 @@ def hill_climbing(
     move towards the neighbor which provides the maximum (or minimum) change.
     We keep doing this until we are at a state where we do not have any
     neighbors which can improve the solution.
+
         Args:
             search_prob: The search state at the start.
             find_max: If True, the algorithm should find the maximum else the minimum.
             max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
             visualization: If True, a matplotlib graph is displayed.
             max_iter: number of times to run the iteration.
+
         Returns a search state having the maximum (or minimum) score.
     """
     current_state = search_prob

searches/interpolation_search.py

@@ -13,6 +13,7 @@ def interpolation_search(sorted_collection: list[int], item: int) -> int | None:
 
     Returns:
         int: The index of the found item, or None if the item is not found.
+
     Examples:
     >>> interpolation_search([1, 2, 3, 4, 5], 2)
     1

searches/simulated_annealing.py

@@ -34,6 +34,7 @@ def simulated_annealing(
         start_temperate: the initial temperate of the system when the program starts.
         rate_of_decrease: the rate at which the temperate decreases in each iteration.
         threshold_temp: the threshold temperature below which we end the search
+
     Returns a search state having the maximum (or minimum) score.
     """
     search_end = False

searches/tabu_search.py

@@ -139,6 +139,7 @@ def find_neighborhood(solution, dict_of_neighbours):
         from the solution that the method took as an input
 
     Example:
+
     >>> find_neighborhood(['a', 'c', 'b', 'd', 'e', 'a'],
     ...                   {'a': [['b', '20'], ['c', '18'], ['d', '22'], ['e', '26']],
     ...                    'c': [['a', '18'], ['b', '10'], ['d', '23'], ['e', '24']],