class_docstr: Provides an inner loop optimizer for the implicitly defined output
  layer. It is based on an unrolled Nesterov momentum algorithm.
  
  Args:
      learning_rate (flaot): learning rate for optimizer.
      momentum (float): momentum for optimizer.
      learnable (bool): If :obj:`True` then the :obj:`learning_rate` and
          :obj:`momentum` will be learnable parameters. If False they
          are fixed. (default: :obj:`True`)
class_docstr_tok: ["Provides","an","inner","loop","optimizer","for","the","implicitly","defined","output","layer",".","It","is","based","on","an","unrolled","Nesterov","momentum","algorithm",".","Args",":","learning_rate","(","flaot",")",":","learning","rate","for","optimizer",".","momentum","(","float",")",":","momentum","for","optimizer",".","learnable","(","bool",")",":","If",":","obj",":","`","True","`","then","the",":","obj",":","`","learning_rate","`","and",":","obj",":","`","momentum","`","will","be","learnable","parameters",".","If","False","they","are","fixed",".","(","default",":",":","obj",":","`","True","`",")"]
code: def learning_rate(self):
      return self.softplus(self._lr)
  
imports: from typing import Callable, List, Optional, Tuple
  import torch
  from torch import Tensor
  from torch_geometric.nn.aggr import Aggregation
  from torch_geometric.nn.inits import reset
  from torch_geometric.utils import scatter
label_desc: Use image node_id 3 for calling the MomentumOptimizer obj's underlying member method code with example usage: obj.learning_rate() and returns: self