docstr: Log metrics at the end of the active learning step.
  
  Args:
      dataset_size: Current dataset size, if None, take state of `_active_dataset_size`.
      metrics: Metrics values
  
  Notes:
      Metrics can be overridden.
code: def active_step(
      self, dataset_size: Optional[int], metrics: Dict[str, Any]
  ):
      """
      Log metrics at the end of the active learning step.
  
      Args:
          dataset_size: Current dataset size, if None, take state of `_active_dataset_size`.
          metrics: Metrics values
  
      Notes:
          Metrics can be overridden.
      """
      if dataset_size is None:
          dataset_size = self._active_dataset_size
      self.active_learning_metrics[dataset_size].update(metrics)
  
file_path: baal/baal/metrics/mixin.py
imports: from typing import Callable, Dict, Any, Optional, DefaultDict, Union
  import numpy
  from torchmetrics import Metric
  from baal.utils.metrics import Metrics
label_desc: Use image node_id 1 for calling the MetricMixin obj's underlying member method code with example usage: obj.active_step(dataset_size, metrics) without return types