class_docstr: The label model by `FlyingSquid <https://github.com/HazyResearch/flyingsquid>`__.
  
  .. note:: It is not suited for multi-label classification and does not support it!
  
  Args:
      weak_labels: A `WeakLabels` object containing the weak labels and records.
      **kwargs: Passed on to the init of the FlyingSquid's
          `LabelModel <https://github.com/HazyResearch/flyingsquid/blob/master/flyingsquid/label_model.py#L18>`__.
  
  Examples:
      >>> from argilla.labeling.text_classification import WeakLabels
      >>> weak_labels = WeakLabels(dataset="my_dataset")
      >>> label_model = FlyingSquid(weak_labels)
      >>> label_model.fit()
      >>> records = label_model.predict()
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code: def __init__(self, weak_labels: WeakLabels, **kwargs):
      from flyingsquid.label_model import (
          LabelModel as FlyingSquidLabelModel,
      )
  
      self._FlyingSquidLabelModel = FlyingSquidLabelModel
  
      super().__init__(weak_labels)
  
      if len(self._weak_labels.rules) < 3:
          raise TooFewRulesError(
              "The FlyingSquid label model needs at least three (independent) rules!"
          )
  
      if "m" in kwargs:
          raise ValueError(
              "Your kwargs must not contain 'm', it is provided automatically."
          )
  
      self._init_kwargs = kwargs
      self._models: List[FlyingSquidLabelModel] = []
  
imports: import hashlib
  import logging
  from enum import Enum
  from typing import Dict, List, Tuple, Union
  import numpy
  from argilla import DatasetForTextClassification, TextClassificationRecord
  from argilla.labeling.text_classification.weak_labels import WeakLabels, WeakMultiLabels
  from argilla.utils.dependency import requires_dependencies
label_desc: Use image node_id 1 to create a new FlyingSquid object from inherited base classes: LabelModel with example: obj = FlyingSquid(weak_labels)