code: def _monte_carlo_entropy(
      completions: List[Sequence], **unused_kwargs
  ) -> float:
      """Monte Carlo estimate of model entropy in nats."""
      #  This estimator is biased with non-unit temperature, since OpenAI API doesn't adjust logprob
      #  computation based on temperature.
      #  The actual entropy under non-unit temperatures cannot be evaluated, since the Mercury API doesn't give logprobs
      #  over the full vocabulary.
      completions = [
          completion for completion in completions if completion.tokens
      ]
  
      # bnewm0609: If there are no completions with tokens, there is no support for calculating entropy, so return nan
      if not completions:
          return float("nan")
  
      mlogps = [
          -sum(token.logprob for token in completion.tokens)
          for completion in completions
      ]
      return sum(mlogps) / len(mlogps)
  
imports: import json
  import os
  from typing import Dict, List, Optional
  import numpy
  from sacrebleu.metrics import BLEU
  from helm.common.general import ensure_file_downloaded
  from helm.common.request import RequestResult, Sequence
  from helm.benchmark.adaptation.request_state import RequestState
  from helm.benchmark.adaptation.adapter_spec import AdapterSpec
  from .metric import Metric
  from .metric_name import MetricName
  from .metric_service import MetricService
  from .statistic import Stat
label_desc: Use image node_id 2 for calling a global function with example usage: _monte_carlo_entropy(completions) and returns: unknown, float