docstr: Take a single document and the LM results and evaluates, returning a
  dict where keys are the names of submetrics and values are the values of
  the metric for that one document
  
  :param doc:
      The document as returned from training_docs, validation_docs, or test_docs.
  :param results:
      The results of the requests created in construct_requests.
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code: def process_results(self, doc, results):
      """Take a single document and the LM results and evaluates, returning a
      dict where keys are the names of submetrics and values are the values of
      the metric for that one document
  
      :param doc:
          The document as returned from training_docs, validation_docs, or test_docs.
      :param results:
          The results of the requests created in construct_requests.
      """
      continuation, (logprob_unanswerable, _) = results
  
      no_answer_probability = exp(logprob_unanswerable)
  
      predictions = {
          "id": doc["id"],
          "prediction_text": continuation,
          "no_answer_probability": no_answer_probability,
      }
  
      references = {
          "id": doc["id"],
          "answers": doc["answers"],
      }
  
      return {
          "exact": (
              predictions,
              references,
          ),  # Exact match (the normalized answer exactly match the gold answer)
          "f1": (
              predictions,
              references,
          ),  # The F-score of predicted tokens versus the gold answer
          "HasAns_exact": (
              predictions,
              references,
          ),  # Exact match (the normalized answer exactly match the gold answer)
          "HasAns_f1": (
              predictions,
              references,
          ),  # The F-score of predicted tokens versus the gold answer
          "NoAns_exact": (
              predictions,
              references,
          ),  # Exact match (the normalized answer exactly match the gold answer)
          "NoAns_f1": (
              predictions,
              references,
          ),  # The F-score of predicted tokens versus the gold answer
          "best_exact": (
              predictions,
              references,
          ),  # Best exact match (with varying threshold)
          "best_f1": (
              predictions,
              references,
          ),  # Best F1 (with varying threshold)
      }
  
imports: import datasets
  from math import exp
  from lm_eval.base import rf, Task
  from functools import partial
  from packaging import version
label_desc: Use image node_id 11 for calling the SQuAD2 obj's underlying member method code with example usage: obj.process_results(doc, results) and returns: dict