(self, step, grads, weights, slots, opt_params)
Computes updated layer weights and optimizer slots for one training step.

    Args:
      step: Training step number.
      grads: Gradient values for this node (from back-propagation during a
          training step).
      weights: Current weight values for this node (i.e., layer weights).
      slots: Current slot values for this node.
      opt_params: Optimizer hyperparameters (e.g. learning rate, momentum),
          same across all nodes in the model.

    Returns:
      Tuple of (new_weights, new_slots), which the Trax runtime will use to
      update the model and optimizer within each training step.
Computes updated layer weights and optimizer slots for one training step.
Computes updated layer weights and optimizer slots for one training step .
def update(self, step, grads, weights, slots, opt_params):
    """Computes updated layer weights and optimizer slots for one training step.

    Args:
      step: Training step number.
      grads: Gradient values for this node (from back-propagation during a
          training step).
      weights: Current weight values for this node (i.e., layer weights).
      slots: Current slot values for this node.
      opt_params: Optimizer hyperparameters (e.g. learning rate, momentum),
          same across all nodes in the model.

    Returns:
      Tuple of (new_weights, new_slots), which the Trax runtime will use to
      update the model and optimizer within each training step.
    """
    raise NotImplementedError
def update ( self , step , grads , weights , slots , opt_params ) : raise NotImplementedError