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 {
          k: s.step_update(num_updates)
          for k, s in self.lr_schedulers.items()
      }
  
imports: import logging
  from collections import defaultdict
  from dataclasses import dataclass, field
  from typing import Dict, Any, List, Optional
  import torch.optim
  from fairseq.dataclass import FairseqDataclass
  from fairseq.optim import FairseqOptimizer, register_optimizer, _build_optimizer
  from fairseq.optim.lr_scheduler import FairseqLRScheduler, build_lr_scheduler
  from omegaconf import II, open_dict
  import copy
label_desc: Use image node_id 6 for calling the CompositeLRScheduler obj's underlying member method code with example usage: obj.step_update(num_updates) and returns: unknown