input: Write a function to optimize any given code using an AI-powered Python script. The function should take a code snippet as input and return an optimized version of that code. The function should also include a method to search for the best parameters within a specified range around the initial parameters to maximize the output of the given function.
  
  def optimize_code(code_snippet: str, initial_params: dict) -> str:
      """Optimizes the given code snippet by searching for the best parameters within a specified range around the initial parameters to maximize the output of the given function"""
      # Import the required libraries
      from dataclasses import dataclass
      import ast
      import copy
      
      @dataclass
      class Params:
          a: int
          b: int
          c: int
  
      def execute(code, params):
          exec(code, globals())
          return globals()[get_result](params)
  
      def optimize(fn, params, range_value=10):
          best_score = 0
          best_params = None
          for a in range(params.a - range_value, params.a + range_value):
              for b in range(params.b - range_value, params.b + range_value):
                  for c in range(params.c - range_value, params.c + range_value):
                      score = fn(Params(a, b, c))
                      if score > best_score:
                          best_score = score
                          best_params = {
                              'a': a,
                              'b': b,
                              'c': c
                          }
          return best_params
  
      # Parse the input code
      tree = ast.parse(code_snippet)
      for node in ast.walk(tree):
          if isinstance(node, ast.FunctionDef):
              fn_name = node.name
  
      # Create a new version of the function
      exec(code_snippet, globals())
      
      # Optimize the function
      optimized_params = optimize(globals()[fn_name], Params(initial_params['a'], initial_params['b'], initial_params['c']))
  
      # Return the optimized code
      optimized_code = copy.deepcopy(tree)
      for node in ast.walk(optimized_code):
          if isinstance(node, ast.Assign):
              for target in node.targets:
                  if hasattr(target, 'id') and target.id == 'a':
                      target.value = ast.Num(optimized_params['a'])
                  if hasattr(target, 'id') and target.id == 'b':
                      target.value = ast.Num(optimized_params['b'])
                  if hasattr(target, 'id') and target.id == 'c':
                      target.value = ast.Num(optimized_params['c'])
  
      return ast.unparse(optimized_code)
  
  assert optimize_code('''
  def get_result(a, b, c):
      return a + b + c
  ''', {'a': 1, 'b': 2, 'c': 3}) == '''
  def get_result(a, b, c):
      return a + b + c
  a = 1
  b = 2
  c = 3
  '''
  
  assert optimize_code('''
  def get_result(a, b, c):
      return a * b * c
  ''', {'a': 10, 'b': 20, 'c': 30}) == '''
  def get_result(a, b, c):
      return a * b * c
  a = 10
  b = 20
  c = 30
  '''
output: 
  def optimize_code(code_snippet: str, initial_params: dict) -> str:
      from dataclasses import dataclass
      import ast
      import copy
      
      @dataclass
      class Params:
          a: int
          b: int
          c: int
  
      def execute(code, params):
          exec(code, globals())
          return globals()[get_result](params)
  
      def optimize(fn, params, range_value=10):
          best_score = 0
          best_params = None
          for a in range(params.a - range_value, params.a + range_value):
              for b in range(params.b - range_value, params.b + range_value):
                  for c in range(params.c - range_value, params.c + range_value):
                      score = fn(Params(a, b, c))
                      if score > best_score:
                          best_score = score
                          best_params = {
                              'a': a,
                              'b': b,
                              'c': c
                          }
          return best_params
  
      tree = ast.parse(code_snippet)
      for node in ast.walk(tree):
          if isinstance(node, ast.FunctionDef):
              fn_name = node.name
  
      exec(code_snippet, globals())
      
      optimized_params = optimize(globals()[fn_name], Params(initial_params['a'], initial_params['b'], initial_params['c']))
  
      optimized_code = copy.deepcopy(tree)
      for node in ast.walk(optimized_code):
          if isinstance(node, ast.Assign):
              for target in node.targets:
                  if hasattr(target, 'id') and target.id == 'a':
                      target.value = ast.Num(optimized_params['a'])
                  if hasattr(target, 'id') and target.id == 'b':
                      target.value = ast.Num(optimized_params['b'])
                  if hasattr(target, 'id') and target.id == 'c':
                      target.value = ast.Num(optimized_params['c'])
  
      return ast.unparse(optimized_code)
  