Filter None

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

Refactor without changing behavior. Return only Python code.

def non_empty(values):
    out = []
    for v in values:
        if v is not None and v != "":
            out.append(v)
    return out

{
  "expected_contains": [
    "v not in (None, \"\")",
    "out.append(v)"
  ]
}