code: def test_multi_fbeta():
      fbeta = metrics.MultiFBeta(
          betas={0: 0.25, 1: 1, 2: 4}, weights={0: 1, 1: 1, 2: 2}
      )
      sk_fbeta = sk_metrics.fbeta_score
      y_true = [0, 1, 2, 2, 2]
      y_pred = [0, 1, 0, 2, 1]
  
      for i, (yt, yp) in enumerate(zip(y_true, y_pred)):
          fbeta.update(yt, yp)
  
          if i >= 2:
              fbeta_0, _, _ = sk_fbeta(
                  y_true[: i + 1],
                  y_pred[: i + 1],
                  beta=0.25,
                  average=None,
              )
              _, fbeta_1, _ = sk_fbeta(
                  y_true[: i + 1], y_pred[: i + 1], beta=1, average=None
              )
              _, _, fbeta_2 = sk_fbeta(
                  y_true[: i + 1], y_pred[: i + 1], beta=4, average=None
              )
  
              multi_fbeta = fbeta_0 * 1 + fbeta_1 * 1 + fbeta_2 * 2
              multi_fbeta /= 1 + 1 + 2
  
              assert math.isclose(fbeta.get(), multi_fbeta)
  
imports: from __future__ import annotations
  import collections
  import math
  from sklearn import metrics
  from river import metrics, utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_multi_fbeta() without return types