class_docstr: A track evaluate a model's performance.
  
  The following metrics are recorded:
  
  - Time, which should be interpreted with wisdom. Indeed time can depend on the architecture
      and local resource situations. Comparison via FLOPS should be preferred.
  - The model's memory footprint.
  - The model's predictive performance on the track's dataset.
  
  Parameters
  ----------
  name
      The name of the track.
  datasets
      The datasets that compose the track.
  metric
      The metric(s) used to track performance.
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code: def run(self, model, dataset, n_checkpoints=10):
      yield from evaluate.iter_progressive_val_score(
          dataset=dataset,
          model=model.clone(),
          metric=self.metric.clone(),
          step=dataset.n_samples // n_checkpoints,
          measure_time=True,
          measure_memory=True,
      )
  
file_path: river/river/evaluate/tracks.py
imports: from __future__ import annotations
  from river import datasets, evaluate, metrics
label_desc: Use image node_id 3 for calling the Track obj's underlying member method code with example usage: obj.run(model, dataset, n_checkpoints) without return types