⚡️ Speed up method AlexNet._extract_features by 938%

Here is an optimized version of your code. The original for loop is a no-op (does nothing: the body is just `pass`). In Python, looping with an empty body over a range is inefficient and serves no useful purpose. 
- The fastest equivalent code is to simply skip the loop entirely if you're not modifying `result` or computing something.
- If you want to return an empty list every time (matching the original program), you can just do that directly.
- No need for `range`, `len`, or the loop at all.

Here is the rewritten program.



**Explanation of optimizations:**
- **Removed the for loop** which did nothing and was expensive in terms of runtime.
- **Directly returns an empty list** which matches the behavior and output of the original function.
- **Kept all comments intact** as required.

This version is as fast and memory-efficient as possible for the originally-defined semantics!

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -35,11 +35,8 @@ def forward(self, x):
         return output
 
     def _extract_features(self, x):
-        result = []
-        for i in range(len(x)):
-            pass
-
-        return result
+        # Directly return the empty result list
+        return []
 
     def _classify(self, features):
         total_mod = sum(features) % self.num_classes