class_docstr: Dummy class used to turn off concept drift detection capabilities of adaptive models.
  It always signals that no concept drift was detected.
  Examples
  --------
  
  >>> from river import drift
  >>> from river import evaluate
  >>> from river import forest
  >>> from river import metrics
  >>> from river.datasets import synth
  
  >>> dataset = synth.ConceptDriftStream(
  ...     seed=8,
  ...     position=500,
  ...     width=40,
  ... ).take(700)
  
  We can turn off the warning detection capabilities of Adaptive Random Forest (ARF) or
  other similar models. Thus, the base models will reset immediately after identifying a drift,
  bypassing the background model building phase:
  
  >>> adaptive_model = forest.ARFClassifier(
  ...     leaf_prediction="mc",
  ...     warning_detector=drift.NoDrift(),
  ...     seed=8
  ... )
  
  We can also turn off the concept drift handling capabilities completely:
  
  >>> stationary_model = forest.ARFClassifier(
  ...     leaf_prediction="mc",
  ...     warning_detector=drift.NoDrift(),
  ...     drift_detector=drift.NoDrift(),
  ...     seed=8
  ... )
  
  Let's put that to test:
  
  >>> for x, y in dataset:
  ...     adaptive_model.learn_one(x, y)
  ...     stationary_model.learn_one(x, y)
  
  The adaptive model:
  
  >>> adaptive_model.n_drifts_detected()
  2
  
  >>> adaptive_model.n_warnings_detected()
  0
  
  The stationary one:
  
  >>> stationary_model.n_drifts_detected()
  0
  
  >>> stationary_model.n_warnings_detected()
  0
code: def drift_detected(self):
      return False
  
imports: from __future__ import annotations
  from river import base
label_desc: Use image node_id 3 for calling the NoDrift obj's underlying member method code with example usage: obj.drift_detected() and returns: False