class_docstr: Analyzes the human evaluation data of on summarization datasets
  
  1. loads human evaluation data from CodaLab
  2. averages and report {faithfulness, relevance, coherence} scores
  3. compute paired bootstrap test for all pairwise model comparison
docstr: Computes average for each entry in a {model_name: score_list} dict
code: def _compute_average(self, scores: dict):
      """
      Computes average for each entry in a {model_name: score_list} dict
      """
      return [(x, np.mean(y)) for x, y in scores.items()]
  
imports: import json
  import pandas
  import numpy
  import os
  import pickle
  import spacy
  from typing import List, Dict, Optional
  from collections import defaultdict
  from helm.benchmark.adaptation.scenario_state import ScenarioState
  from helm.benchmark.adaptation.request_state import RequestState
  from helm.benchmark.adaptation.adapter_spec import AdapterSpec
  from helm.common.hierarchical_logger import hlog
  from helm.common.general import ensure_file_downloaded
  from .metric import Metric, MetricResult
  from .metric_name import MetricName
  from .metric_service import MetricService
  from .basic_metrics import get_rouge_function
  from .statistic import Stat
  from .summac.model_summac import SummaCZS
  from bert_score import BERTScorer
label_desc: Use image node_id 3 for calling the SummarizationHumanEvalAnalyzer obj's underlying member method code with example usage: obj._compute_average(scores) and returns: unknown