⚡️ Speed up method AlexNet.forward by 314%

Here is a rewrite of your program for significantly improved runtime, based on your profile and the code. The main bottleneck is the `_extract_features` method: it currently loops through `len(x)`, and only does `pass` in the loop, so the only output is `result = []` regardless of `x`. If the real method does no processing and always returns an empty list, then you can replace the body with a simple return. This makes the function O(1) instead of O(N), and also reduces allocations.

Your `_classify` is already quite efficient for lists, but `sum(features)` will immediately return 0 if the list is empty. No further optimization needed here.

Optimized code.

**Summary of changes:**

- Rewrote `_extract_features` to simply return `[]`. This removes the unnecessary loop and the allocation of an unused list, making it trivial in runtime.

**Note:**  
If you planned to *actually* extract features in that function, you'll need to replace the `pass` with efficient processing, perhaps with `list comprehensions` or optimized numpy/PyTorch calls depending on context. But given the line profile and behavior, this is the fastest correct equivalent for the code you provided.

Let me know if you want an example rewrite assuming more realistic feature extraction!

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -27,16 +27,12 @@ def __init__(self, num_classes=1000):
 
     def forward(self, x):
         features = self._extract_features(x)
-
         output = self._classify(features)
         return output
 
     def _extract_features(self, x):
-        result = []
-        for i in range(len(x)):
-            pass
-
-        return result
+        # No need to loop, just return an empty list
+        return []
 
     def _classify(self, features):
         # Compute the sum and modulo just once, then construct the result list efficiently
@@ -65,7 +61,8 @@ def test_models():
 
 @lru_cache(maxsize=1001)  # One possible input per [0, 1000]
 def _cached_joined(number):
-    return " ".join(str(i) for i in range(number))
+    # Use map for slightly faster integer-to-string conversion and joining
+    return " ".join(map(str, range(number)))
 
 
 if __name__ == "__main__":