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: Returns the index of the selected representation for a training step.
docstr_tok: ["Returns","the","index","of","the","selected","representation","for","a","training","step","."]
code: def select(self, step):
      """Returns the index of the selected representation for a training step."""
      if step - self.last_selection_step >= self.sample_freq:
          self.current_selection.assign(self._select())
          self.last_selection_step.assign(step)
      # Increment the counter for the newly selected item.
      self.selection_counter.scatter_add(
          tf.IndexedSlices(1, self.current_selection)
      )
      return self.current_selection.numpy()
  
imports: import gin
  import tensorflow.compat.v2
label_desc: Use image node_id 2 for calling the SelectionStrategy obj's underlying member method code with example usage: obj.select(step) and returns: self