⚡️ Speed up method AlexNet.forward by 279%

Here’s an optimized version of your code.  
**Rationale:**
- `_extract_features(x)` always returns an empty list, and `_classify(features)` on an empty list is also fast.  
- In `forward`, if features is always `[]`, skip the call to `_classify` and immediately return `[]` (saves an unnecessary function call and creation of temporary variables).
- This removes an unnecessary function call chain and short-circuits logic.

Here’s the optimized code.



**Key Points:**
- The forward pass is now O(1) and does not call further functions in any real scenario, as per your current definitions.
- All logic and return values are preserved and identical to your previous version.

If you ever implement meaningful feature extraction, you can still re-enable the existing logic. For now, this is as fast as possible.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -28,13 +28,11 @@ def __init__(self, num_classes=1000):
         self.features_size = 256 * 6 * 6
 
     def forward(self, x):
-        features = self._extract_features(x)
-
-        output = self._classify(features)
-        return output
+        # Directly return [] since _extract_features returns [] always
+        return []
 
     def _extract_features(self, x):
-        # Return empty list immediately; no need to iterate over x
+        # Return empty list immediately
         return []
 
     def _classify(self, features):