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
code: def print_summary(self):
      assert self.faithfulness
      assert self.coherence
      assert self.relevance
  
      print("FAITHFULNESS")
      for model, score in self._compute_average(self.faithfulness):
          print(f"{model:40}: {score:.4f}")
      print("=" * 40)
  
      print("RELEVANCE")
      for model, score in self._compute_average(self.relevance):
          print(f"{model:40}: {score:.4f}")
      print("=" * 40)
  
      print("COHERENCE")
      for model, score in self._compute_average(self.relevance):
          print(f"{model:40}: {score:.4f}")
      print("=" * 40)
  
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 4 for calling the SummarizationHumanEvalAnalyzer obj's underlying member method code with example usage: obj.print_summary() without return types