docstr: :returns: {str: [float] -> float}
      A dictionary where keys are the names of submetrics and values are
      functions that aggregate a list of metrics
docstr_tok: [":","returns",":","{","str",":","[","float","]","-",">","float","}","A","dictionary","where","keys","are","the","names","of","submetrics","and","values","are","functions","that","aggregate","a","list","of","metrics"]
code: def aggregation(self):
      """
      :returns: {str: [float] -> float}
          A dictionary where keys are the names of submetrics and values are
          functions that aggregate a list of metrics
      """
      return {
          "exact": partial(
              _squad_agg, "exact"
          ),  # Exact match (the normalized answer exactly match the gold answer)
          "f1": partial(
              _squad_agg, "f1"
          ),  # The F-score of predicted tokens versus the gold answer
          "HasAns_exact": partial(
              _squad_agg, "HasAns_exact"
          ),  # Exact match (the normalized answer exactly match the gold answer)
          "HasAns_f1": partial(
              _squad_agg, "HasAns_f1"
          ),  # The F-score of predicted tokens versus the gold answer
          "NoAns_exact": partial(
              _squad_agg, "NoAns_exact"
          ),  # Exact match (the normalized answer exactly match the gold answer)
          "NoAns_f1": partial(
              _squad_agg, "NoAns_f1"
          ),  # The F-score of predicted tokens versus the gold answer
          "best_exact": partial(
              _squad_agg, "best_exact"
          ),  # Best exact match (with varying threshold)
          "best_f1": partial(
              _squad_agg, "best_f1"
          ),  # 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 12 for calling the SQuAD2 obj's underlying member method code with example usage: obj.aggregation() and returns: dict