class_docstr: Confidence Mixin class.
  
  It is responsible for confidence estimation method initialization and high-level confidence score calculation.
class_docstr_tok: ["Confidence","Mixin","class",".","It","is","responsible","for","confidence","estimation","method","initialization","and","high-level","confidence","score","calculation","."]
docstr: Computes high-level (per-token and/or per-word) confidence scores for a list of hypotheses.
  Assumes that `frame_confidence` is present in the hypotheses.
  
  Args:
      hypotheses_list: List of Hypothesis.
  
  Returns:
      A list of hypotheses with high-level confidence scores.
docstr_tok: ["Computes","high-level","(","per-token","and\/or","per-word",")","confidence","scores","for","a","list","of","hypotheses",".","Assumes","that","`","frame_confidence","`","is","present","in","the","hypotheses",".","Args",":","hypotheses_list",":","List","of","Hypothesis",".","Returns",":","A","list","of","hypotheses","with","high-level","confidence","scores","."]
code: def compute_confidence(
      self, hypotheses_list: List[Hypothesis]
  ) -> List[Hypothesis]:
      """Computes high-level (per-token and/or per-word) confidence scores for a list of hypotheses.
      Assumes that `frame_confidence` is present in the hypotheses.
  
      Args:
          hypotheses_list: List of Hypothesis.
  
      Returns:
          A list of hypotheses with high-level confidence scores.
      """
      raise NotImplementedError()
  
imports: import math
  from abc import ABC, abstractmethod
  from dataclasses import dataclass, field
  from functools import partial
  from typing import List, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf
  from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the ConfidenceMixin obj's underlying member method code with example usage: obj.compute_confidence(hypotheses_list) without return types