code: def test_sklearn_coherence(river_params, sklearn_params):
      """Checks that the sklearn and river implementations produce the same results."""
  
      rv = anomaly.OneClassSVM(**river_params)
      sk = sklm.SGDOneClassSVM(**sklearn_params)
  
      for x, _ in datasets.Phishing().take(100):
          rv.learn_one(x)
          sk.partial_fit([list(x.values())])
  
      for i, w in enumerate(rv.weights.values()):
          assert math.isclose(w, sk.coef_[i])
  
imports: from __future__ import annotations
  import math
  import pytest
  from sklearn import linear_model
  from river import anomaly, datasets, optim
label_desc: Use image node_id 1 for calling a global function with example usage: test_sklearn_coherence(river_params, sklearn_params) without return types