class_docstr: Multiplicative-weight strategy which samples representations adaptively.
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      # Weights of each representation. Each selection is a sample drawn
      # proportionally to the weights.
      # TODO(csferng): Store the weights in logit space.
      self.weights = tf.Variable(tf.ones(self.num_representations))
      self.current_selection.assign(self._select())
  
imports: import gin
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new MultiplicativeWeightStrategy object from inherited base classes: SelectionStrategy with example: obj = MultiplicativeWeightStrategy()