⚡️ Speed up function generate__notify by 38%

To optimize the given Python program, let's focus on the processing inside the loop and avoid redundant operations.

1. The function `flag_env()` is called multiple times within the nested loop, which introduces unnecessary redundancy.
2. Since `flag_env()` computes the same result every time it's called, we can compute it once and reuse the result.
3. Let's apply these optimizations.

Here's the optimized version of your program.



In this optimized version.
- The `flag_env()` is called once and its result is stored in `flag_value` within the `add_flag_env` function, thereby avoiding multiple redundant calls.
- This reduces the overall time complexity and improves the program’s runtime performance. 

This refactoring maintains the functionality while optimizing for performance.

Author: codeflash-ai[bot]

bench_runner/scripts/install.py

@@ -99,13 +99,14 @@ def flag_env():
 
 
 def add_flag_env(jobs: dict[str, Any]):
+    flag_value = flag_env()  # Compute flag_env once and reuse the result
     for job in jobs.values():
         if "steps" in job:
             job.setdefault("env", {})
-            job["env"]["flags"] = flag_env()
+            job["env"]["flags"] = flag_value
             for step in job["steps"]:
                 if "run" in step:
-                    step["run"] = step["run"].replace("${{ env.flags }}", flag_env())
+                    step["run"] = step["run"].replace("${{ env.flags }}", flag_value)
 
 
 def generate__benchmark(src: Any) -> Any: