class_docstr: Computes F-Beta score.
  
  Formula:
  
  ```python
  b2 = beta ** 2
  f_beta_score = (1 + b2) * (precision * recall) / (precision * b2 + recall)
  ```
  This is the weighted harmonic mean of precision and recall.
  Its output range is `[0, 1]`. It works for both multi-class
  and multi-label classification.
  
  Args:
      average: Type of averaging to be performed across per-class results
          in the multi-class case.
          Acceptable values are `None`, `"micro"`, `"macro"` and
          `"weighted"`. Defaults to `None`.
          If `None`, no averaging is performed and `result()` will return
          the score for each class.
          If `"micro"`, compute metrics globally by counting the total
          true positives, false negatives and false positives.
          If `"macro"`, compute metrics for each label,
          and return their unweighted mean.
          This does not take label imbalance into account.
          If `"weighted"`, compute metrics for each label,
          and return their average weighted by support
          (the number of true instances for each label).
          This alters `"macro"` to account for label imbalance.
          It can result in an score that is not between precision and recall.
      beta: Determines the weight of given to recall
          in the harmonic mean between precision and recall (see pseudocode
          equation above). Defaults to `1`.
      threshold: Elements of `y_pred` greater than `threshold` are
          converted to be 1, and the rest 0. If `threshold` is
          `None`, the argmax of `y_pred` is converted to 1, and the rest to 0.
      name: Optional. String name of the metric instance.
      dtype: Optional. Data type of the metric result.
  
  Returns:
      F-Beta Score: float.
  
  Example:
  
  >>> metric = keras.metrics.FBetaScore(beta=2.0, threshold=0.5)
  >>> y_true = np.array([[1, 1, 1],
  ...                    [1, 0, 0],
  ...                    [1, 1, 0]], np.int32)
  >>> y_pred = np.array([[0.2, 0.6, 0.7],
  ...                    [0.2, 0.6, 0.6],
  ...                    [0.6, 0.8, 0.0]], np.float32)
  >>> metric.update_state(y_true, y_pred)
  >>> result = metric.result()
  >>> result
  [0.3846154 , 0.90909094, 0.8333334 ]
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code: def reset_state(self):
      for v in self.variables:
          v.assign(ops.zeros(v.shape, dtype=v.dtype))
  
file_path: keras/keras/metrics/f_score_metrics.py
imports: from keras import backend
  from keras import initializers
  from keras import ops
  from keras.api_export import keras_export
  from keras.metrics.metric import Metric
label_desc: Use image node_id 6 for calling the FBetaScore obj's underlying member method code with example usage: obj.reset_state() without return types