class_docstr: Base class for optimizers that work hand in hand with Trax layers.
  
  To define an optimizer subclass, specify its behavior with respect to a
  single node in the network (e.g., a single dense layer):
  
    - `init`: how to create/initialize optimizer-internal parameters ("slots"),
        as a function of the node's weights.
    - `update`: how to use gradient information to update node weights and
        optimizer slots.
  
  The Trax runtime combines these node-local computations into layer weight
  updates and optimizer slot updates for the whole tree of layers in the model.
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code: def slots(self):
      return self._slots
  
file_path: trax/trax/optimizers/base.py
imports: from trax import fastmath
  from trax.fastmath import numpy
label_desc: Use image node_id 4 for calling the Optimizer obj's underlying member method code with example usage: obj.slots() and returns: self