docstr: Update the learning rate after each update.
docstr_tok: ["Update","the","learning","rate","after","each","update","."]
code: def step_update(self, num_updates):
      """Update the learning rate after each update."""
      return self.optimizer.get_lr()
  
file_path: fairseq/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py
imports: from argparse import Namespace
  from fairseq.dataclass.utils import gen_parser_from_dataclass
  from fairseq.optim import FairseqOptimizer
label_desc: Use image node_id 7 for calling the FairseqLRScheduler obj's underlying member method code with example usage: obj.step_update(num_updates) and returns: self