Bugfix

The model receives the prompt (and optional system message). The run uses scorer contains_all with the JSON configuration below. Pass/fail and partial credit are determined entirely by that scorer against the model output; no human grading.

Fix the bug and return corrected Python code only.

def average_01(nums):
    if len(nums) == 0:
        return 0
    total = 0
    for i in range(len(nums) - 1):
        total += nums[i]
    return total / len(nums)

{
  "expected_contains": [
    "range(len(nums))",
    "total += nums[i]",
    "return total / len(nums)"
  ]
}