code: def _paired_bootstrap_test(
      treatment_list: list, control_list: list, nboot: int = 10000
  ):
      """
      Computes paired bootstrap test for the Hypothesis: treament > control
  
      Args:
          treatment: list of float, representing results of treament (better model results)
          control: list of float, representing results of control (worse model results)
          nboot: int, number of bootstraps to perform
      """
      treatment = np.array(treatment_list)
      control = np.array(control_list)
      delta = treatment.mean() - control.mean()
      sample_idx = np.random.choice(
          np.arange(len(treatment)), size=(nboot, len(treatment))
      )
      boot_treatment = treatment[sample_idx]
      boot_control = control[sample_idx]
      diff = boot_treatment.mean(axis=1) - boot_control.mean(axis=1)
      return (diff > 2 * delta).mean()
  
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 for calling a global function with example usage: _paired_bootstrap_test(treatment_list, control_list, nboot) and returns: unknown