class_docstr: Options:
      - "majority": the majority value of the rankings
      - "max": the max value of the rankings
      - "min": the min value of the rankings
docstr: The function `_mean` takes a list of `FeedbackRecord` objects and a question, and
  aggregates the responses for that question based on the average of the ranks.
  
  Args:
  - records The `records` parameter is a list of `FeedbackRecord` objects. Each
  `FeedbackRecord` object represents a feedback record and contains information about the
  responses given for a particular feedback.
  - question The `question` parameter in the `_aggregate` method is a string that represents
  the question for which the responses are being aggregated.
  
  Returns:
  the updated list of FeedbackRecord objects after aggregating the responses for the
  specified question.
docstr_tok: ["The","function","`","_mean","`","takes","a","list","of","`","FeedbackRecord","`","objects","and","a","question",",","and","aggregates","the","responses","for","that","question","based","on","the","average","of","the","ranks",".","Args",":","-","records","The","`","records","`","parameter","is","a","list","of","`","FeedbackRecord","`","objects",".","Each","`","FeedbackRecord","`","object","represents","a","feedback","record","and","contains","information","about","the","responses","given","for","a","particular","feedback",".","-","question","The","`","question","`","parameter","in","the","`","_aggregate","`","method","is","a","string","that","represents","the","question","for","which","the","responses","are","being","aggregated",".","Returns",":","the","updated","list","of","FeedbackRecord","objects","after","aggregating","the","responses","for","the","specified","question","."]
code: def _mean(self, records: List[FeedbackRecord], question: str):
      """
      The function `_mean` takes a list of `FeedbackRecord` objects and a question, and
      aggregates the responses for that question based on the average of the ranks.
  
      Args:
      - records The `records` parameter is a list of `FeedbackRecord` objects. Each
      `FeedbackRecord` object represents a feedback record and contains information about the
      responses given for a particular feedback.
      - question The `question` parameter in the `_aggregate` method is a string that represents
      the question for which the responses are being aggregated.
  
      Returns:
      the updated list of FeedbackRecord objects after aggregating the responses for the
      specified question.
      """
      from collections import defaultdict
  
      UnifiedValueSchema.update_forward_refs()
  
      for rec in records:
          if not rec.responses:
              continue
          # only allow for submitted responses
          responses = [
              resp
              for resp in rec.responses
              if resp.status == "submitted"
          ]
          # Step 1: Create an empty dictionary to store cumulative ranks and counts
          cumulative_ranks = defaultdict(lambda: {"sum": 0, "count": 0})
  
          # Step 2: Iterate through each ranking and update cumulative ranks and counts
          for resp in responses:
              if question in resp.values:
                  for item in resp.values[question].value:
                      value = item.value
                      rank = item.rank
                      cumulative_ranks[value]["sum"] += rank
                      cumulative_ranks[value]["count"] += 1
  
          # Step 3: Calculate the average rank for each response
          average_ranking = [
              {
                  "rank": round(
                      cumulative_ranks[value]["sum"]
                      / cumulative_ranks[value]["count"]
                  ),
                  "value": value,
              }
              for value in cumulative_ranks
          ]
  
          # Step 4: Create a new list representing the average ranking
          average_ranking = sorted(
              average_ranking, key=lambda x: x["rank"]
          )
          rec._unified_responses[question] = [
              UnifiedValueSchema(
                  value=average_ranking, strategy=self.value
              )
          ]
  
      return records
  
imports: import random
  from abc import abstractmethod
  from collections import Counter
  from enum import Enum
  from typing import Any, Dict, List, Union
  import pandas
  from argilla.client.feedback.schemas import FeedbackRecord, LabelQuestion, MultiLabelQuestion, RankingQuestion, RatingQuestion, ValueSchema
  from argilla.pydantic_v1 import BaseModel, root_validator, validator
label_desc: Use image node_id 3 for calling the RankingQuestionStrategy obj's underlying member method code with example usage: obj._mean(records, question) and returns: records