def combinationSum(self, nums, target):           def backtrack(start, combination, current_sum):             if current_sum == target:                 result.append(list(combination))                 return             elif current_sum > target:                 return               for i in range(start, len(nums)):                 combination.append(nums[i])                 backtrack(i, combination, current_sum + nums[i])                 combination.pop()           result = []         backtrack(0, [], 0)         return result What is the time and space complexity of this algorithm? Give in terms of Big O notation

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    def combinationSum(self, nums, target):

 

        def backtrack(start, combination, current_sum):

            if current_sum == target:

                result.append(list(combination))

                return

            elif current_sum > target:

                return

 

            for i in range(start, len(nums)):

                combination.append(nums[i])

                backtrack(i, combination, current_sum + nums[i])

                combination.pop()

 

        result = []

        backtrack(0, [], 0)

        return result

What is the time and space complexity of this algorithm? Give in terms of Big O notation

 

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