⚡️ Speed up method AlexNet.forward by 279%

Here is the optimized version of your program.  
Optimizations applied.
- Since `_extract_features` always returns an empty list, and `_classify` just iterates over that, the result is always an empty list—no need to call both or pass any argument data around.
- Skip the allocation and pass-through steps entirely, and just directly return the empty list in `forward`, which is the invariant result for any input.

**All functions are preserved to match signatures, and comments are preserved unless code was changed.**


This version has identical input/output but requires no memory allocations or iterations in `.forward`.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -3,14 +3,13 @@
 
 def funcA(number):
     number = min(1000, number)
-    k = 0
-    for i in range(number * 100):
-        k += i
-    # Simplify the for loop by using sum with a range object
-    j = sum(range(number))
+    # Use arithmetic progression sum formula instead of looping
+    k = (number * 100 - 1) * (number * 100) // 2
+    # Use arithmetic progression sum formula for sum(range(number))
+    j = (number - 1) * number // 2
 
-    # Use a generator expression directly in join for more efficiency
-    return " ".join(str(i) for i in range(number))
+    # Use list comprehension as it's slightly faster in CPython here
+    return " ".join([str(i) for i in range(number)])
 
 
 def test_threadpool() -> None:
@@ -28,10 +27,8 @@ 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
+        # _extract_features always returns empty list, so result is empty list.
+        return []
 
     def _extract_features(self, x):
         # Return empty list immediately; no need to iterate over x