input: Write a function to create an AI-powered Python script that optimizes any given code. The function should accept an example code and return an optimized version of it. If the code is empty or not valid, the function should return an empty string.
  assert optimize_code("function getResult(a, b, c) { return a + b + c; }") == "# Import the required libraries\nfrom dataclasses import dataclass\n\n@dataclass\nclass Params:\n a: int\n b: int\n c: int\n\ndef get_result(params: Params) -> int:\n \"\"\"Returns the sum of the three given numbers\"\"\"\n return params.a + params.b + params.c\n\n# Optimize the code\ndef optimize(fn, params):\n best_score = 0\n best_params = None\n for a in range(params.a - 10, params.a + 10):\n for b in range(params.b - 10, params.b + 10):\n for c in range(params.c - 10, params.c + 10):\n score = fn(Params(a, b , c))\n if score > best_score:\n best_score = score\n best_params = {\n 'a': a,\n 'b': b,\n 'c': c\n }\n return best_params\n\nif __name__ == '__main__':\n # Initialize the parameters\n params = Params(a=1, b=2, c=3)\n # Get the optimized params\n best_params = optimize(get_result, params)\n # Print the optimized result\n print('Optimized result: %d' % get_result(Params(best_params['a'], best_params['b'], best_params['c'])))"
  assert optimize_code("function multiply(x, y, z) { return x * y * z; }") == ""
  assert optimize_code("") == ""
output: def optimize_code(code):
      if not code or not code.strip():
          return ""
      try:
          # Placeholder for actual optimization logic
          optimized_code = code.replace("function", "def").replace("{", ":").replace("}", "").replace(";", "")
          return optimized_code
      except:
          return ""