class_docstr: Krippendorf's alpha agreement metric.
  
  Is a statistical measure of the inter-annotator agreement achieved when coding a set
  of units of analysis.
  
  To interpret the results from this metric, we refer the reader to the wikipedia entry.
  The common consensus dictates that a value of alpha >= 0.8 indicates a reliable annotation,
  a value >= 0.667 can only guarantee tentative conclusions, while lower values suggest an
  unreliable annotation.
  
  See Also:
      - Take a look at this metric definition:
      https://en.wikipedia.org/wiki/Krippendorff%27s_alpha
  
      - We use the implementation from nltk:
      https://www.nltk.org/api/nltk.metrics.agreement.html#nltk.metrics.agreement.AnnotationTask.alpha
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code: def _compute(self, dataset) -> float:
      return self.alpha()
  
file_path: argilla/src/argilla/client/feedback/metrics/agreement_metrics.py
imports: from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
  from nltk.metrics.agreement import AnnotationTask
  from nltk.metrics.distance import binary_distance, interval_distance, masi_distance
  from argilla.client.feedback.dataset import FeedbackDataset
  from argilla.client.feedback.metrics.base import AgreementMetricResult, AnnotationTaskMetricBase, MetricBase
  from argilla.client.feedback.schemas import LabelQuestion, MultiLabelQuestion, RankingQuestion, RatingQuestion
  from argilla.client.feedback.schemas.remote.shared import RemoteSchema
label_desc: Use image node_id 1 for calling the KrippendorfAlpha obj's underlying member method code with example usage: obj._compute(dataset) and returns: self