code: def f1_score(
      y_true,
      y_pred,
      *,
      labels=None,
      pos_label=1,
      average="binary",
      sample_weight=None,
      zero_division="warn",
  ):
      """Compute the F1 score, also known as balanced F-score or F-measure.
  
      The F1 score can be interpreted as a harmonic mean of the precision and
      recall, where an F1 score reaches its best value at 1 and worst score at 0.
      The relative contribution of precision and recall to the F1 score are
      equal. The formula for the F1 score is::
  
          F1 = 2 * TP / (2 * TP + FN + FP)
  
      Where "TP" is the number of true positives, "FN" is the number of false
      negatives, and "FP" is the number of false positives. F1 is by default
      calculated as 0.0 when there are no true positives, false negatives, nor
      false positives.
  
      Support beyond term:`binary` targets is achieved by treating :term:`multiclass`
      and :term:`multilabel` data as a collection of binary problems, one for each
      label. For the :term:`binary` case, setting `average='binary'` will return
      F1 score for `pos_label`. If `average` is not `'binary'`, `pos_label` is ignored
      and F1 score for both classes are computed, then averaged or both returned (when
      `average=None`). Similarly, for :term:`multiclass` and :term:`multilabel` targets,
      F1 score for all `labels` are either returned or averaged depending on the
      `average` parameter. Use `labels` specify the set of labels to calculate F1 score
      for.
  
      Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
  
      Parameters
      ----------
      y_true : 1d array-like, or label indicator array / sparse matrix
          Ground truth (correct) target values.
  
      y_pred : 1d array-like, or label indicator array / sparse matrix
          Estimated targets as returned by a classifier.
  
      labels : array-like, default=None
          The set of labels to include when `average != 'binary'`, and their
          order if `average is None`. Labels present in the data can be
          excluded, for example in multiclass classification to exclude a "negative
          class". Labels not present in the data can be included and will be
          "assigned" 0 samples. For multilabel targets, labels are column indices.
          By default, all labels in `y_true` and `y_pred` are used in sorted order.
  
          .. versionchanged:: 0.17
             Parameter `labels` improved for multiclass problem.
  
      pos_label : int, float, bool or str, default=1
          The class to report if `average='binary'` and the data is binary,
          otherwise this parameter is ignored.
          For multiclass or multilabel targets, set `labels=[pos_label]` and
          `average != 'binary'` to report metrics for one label only.
  
      average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
              default='binary'
          This parameter is required for multiclass/multilabel targets.
          If ``None``, the scores for each class are returned. Otherwise, this
          determines the type of averaging performed on the data:
  
          ``'binary'``:
              Only report results for the class specified by ``pos_label``.
              This is applicable only if targets (``y_{true,pred}``) are binary.
          ``'micro'``:
              Calculate metrics globally by counting the total true positives,
              false negatives and false positives.
          ``'macro'``:
              Calculate metrics for each label, and find their unweighted
              mean.  This does not take label imbalance into account.
          ``'weighted'``:
              Calculate metrics for each label, and find 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
              F-score that is not between precision and recall.
          ``'samples'``:
              Calculate metrics for each instance, and find their average (only
              meaningful for multilabel classification where this differs from
              :func:`accuracy_score`).
  
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights.
  
      zero_division : {"warn", 0.0, 1.0, np.nan}, default="warn"
          Sets the value to return when there is a zero division, i.e. when all
          predictions and labels are negative.
  
          Notes:
          - If set to "warn", this acts like 0, but a warning is also raised.
          - If set to `np.nan`, such values will be excluded from the average.
  
          .. versionadded:: 1.3
             `np.nan` option was added.
  
      Returns
      -------
      f1_score : float or array of float, shape = [n_unique_labels]
          F1 score of the positive class in binary classification or weighted
          average of the F1 scores of each class for the multiclass task.
  
      See Also
      --------
      fbeta_score : Compute the F-beta score.
      precision_recall_fscore_support : Compute the precision, recall, F-score,
          and support.
      jaccard_score : Compute the Jaccard similarity coefficient score.
      multilabel_confusion_matrix : Compute a confusion matrix for each class or
          sample.
  
      Notes
      -----
      When ``true positive + false positive + false negative == 0`` (i.e. a class
      is completely absent from both ``y_true`` or ``y_pred``), f-score is
      undefined. In such cases, by default f-score will be set to 0.0, and
      ``UndefinedMetricWarning`` will be raised. This behavior can be modified by
      setting the ``zero_division`` parameter.
  
      References
      ----------
      .. [1] `Wikipedia entry for the F1-score
             <https://en.wikipedia.org/wiki/F1_score>`_.
  
      Examples
      --------
      >>> import numpy as np
      >>> from sklearn.metrics import f1_score
      >>> y_true = [0, 1, 2, 0, 1, 2]
      >>> y_pred = [0, 2, 1, 0, 0, 1]
      >>> f1_score(y_true, y_pred, average='macro')
      0.26...
      >>> f1_score(y_true, y_pred, average='micro')
      0.33...
      >>> f1_score(y_true, y_pred, average='weighted')
      0.26...
      >>> f1_score(y_true, y_pred, average=None)
      array([0.8, 0. , 0. ])
  
      >>> # binary classification
      >>> y_true_empty = [0, 0, 0, 0, 0, 0]
      >>> y_pred_empty = [0, 0, 0, 0, 0, 0]
      >>> f1_score(y_true_empty, y_pred_empty)
      0.0...
      >>> f1_score(y_true_empty, y_pred_empty, zero_division=1.0)
      1.0...
      >>> f1_score(y_true_empty, y_pred_empty, zero_division=np.nan)
      nan...
  
      >>> # multilabel classification
      >>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]]
      >>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]]
      >>> f1_score(y_true, y_pred, average=None)
      array([0.66666667, 1.        , 0.66666667])
      """
      return fbeta_score(
          y_true,
          y_pred,
          beta=1,
          labels=labels,
          pos_label=pos_label,
          average=average,
          sample_weight=sample_weight,
          zero_division=zero_division,
      )
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from scipy.sparse import coo_matrix, csr_matrix
  from scipy.special import xlogy
  from ..exceptions import UndefinedMetricWarning
  from ..preprocessing import LabelBinarizer, LabelEncoder
  from ..utils import assert_all_finite, check_array, check_consistent_length, column_or_1d
  from ..utils._array_api import _union1d, _weighted_sum, get_namespace
  from ..utils._param_validation import Interval, Options, StrOptions, validate_params
  from ..utils.extmath import _nanaverage
  from ..utils.multiclass import type_of_target, unique_labels
  from ..utils.sparsefuncs import count_nonzero
  from ..utils.validation import _check_pos_label_consistency, _num_samples
label_desc: Use image node_id 10 for calling a global function with example usage: f1_score(y_true, y_pred) and returns: fbeta_score