c

jayraj175coder · jayraj175coder · commit 69a51b2de4eb · 2024-06-03T09:36:37.000+05:30
diff --git a/contrib/Multithreading/Multithreading.md b/contrib/Multithreading/Multithreading.md
@@ -112,12 +112,12 @@
   for thread in threads:
     thread.join()
 
-     >> Common Pitfalls
+      >> Common Pitfalls
         1.Global Interpreter Lock (GIL): Python's GIL can limit the performance benefits of threading for CPU-bound tasks. Consider using 
          multiprocessing for such tasks.
         2.Race conditions: Ensure proper synchronization to avoid race conditions when accessing shared resources.
          Deadlocks: Be cautious of deadlocks when using multiple locks.
 
-  >> Conclusion
-       Multithreading in Python is a powerful tool for concurrent execution, especially for I/O-bound tasks. By understanding and correctly 
-      implementing threading, you can significantly improve the performance and responsiveness of your applications.
+     >> Conclusion
+          Multithreading in Python is a powerful tool for concurrent execution, especially for I/O-bound tasks. By understanding and correctly 
+          implementing threading, you can significantly improve the performance and responsiveness of your applications.
