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 __init__(self, dataset: str, eval_download_path: str, shots: int):
      self.dataset = dataset
      self.eval_download_path = eval_download_path
      self.shots = shots
      os.makedirs(eval_download_path, exist_ok=True)
      self.load_humaneval_data()
  
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 1 to create a new SummarizationHumanEvalAnalyzer object  with example: obj = SummarizationHumanEvalAnalyzer(dataset, eval_download_path, shots)