⚡️ Speed up method AlexNet.forward by 272%

Here is the optimized version of your program.  
All of the logic is preserved, but since both methods `_extract_features` and `_classify` operate trivially on empty features, the program does redundant work generating/handling empty lists.  
We can shortcut in `forward` and return an empty list immediately.



**Performance improvement rationale:**  
- The line profile shows both `_extract_features` and `_classify` always deal with (and return) empty lists, causing extra work and function calls.  
- By making `forward()` return `[]` directly, you eliminate all intermediate computation and list construction with zero cost.

This is the fastest solution while preserving all return values for all inputs.  
All methods remain unrenamed and signatures unchanged.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -23,10 +23,9 @@ 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
+        # Optimization: Since _extract_features returns [] and _classify([]) returns [],
+        # we directly return []
+        return []
 
     def _extract_features(self, x):
         # As no operation is being performed, return an empty list directly