class_docstr: Base class for strategies to select representations.
  
  This base class handles sample and update frequencies, as well as logging
  selection statistics to TensorBoard. Sub-classes should implement _select()
  and _update().
docstr: Updates the strategy's state based on current validation losses.
  
  Args:
    epoch: Current epoch number.
    validation_losses: A list of numbers, one for each representation.
docstr_tok: ["Updates","the","strategy","'s","state","based","on","current","validation","losses",".","Args",":","epoch",":","Current","epoch","number",".","validation_losses",":","A","list","of","numbers",",","one","for","each","representation","."]
code: def update(self, epoch, validation_losses):
      """Updates the strategy's state based on current validation losses.
  
      Args:
        epoch: Current epoch number.
        validation_losses: A list of numbers, one for each representation.
      """
      self._update(epoch, validation_losses)
      self.last_update_epoch.assign(epoch)
      # Log the counts since last update to the summary and reset the counts.
      for i in range(self.num_representations):
          tf.summary.scalar(
              f"representations/selected/{self.representation_names[i]}",
              self.selection_counter[i],
              step=epoch,
          )
      self.selection_counter.assign([0] * self.num_representations)
  
imports: import gin
  import tensorflow.compat.v2
label_desc: Use image node_id 4 for calling the SelectionStrategy obj's underlying member method code with example usage: obj.update(epoch, validation_losses) without return types