class_docstr: Do various flavours of stochastic gradient descent, with first and
  second order momentum. Currently support 'vanilla' SGD, Adam, and RAdam.
class_docstr_tok: ["Do","various","flavours","of","stochastic","gradient","descent",",","with","first","and","second","order","momentum",".","Currently","support","'vanilla","'","SGD",",","Adam",",","and","RAdam","."]
code: def step_schedules(self):
      for key, schedule in self.schedules.items():
          try:
              value = next(schedule)
          except StopIteration:  # schedule exhausted, use last value
              value = getattr(self, key)
          setattr(self, key, value)
  
file_path: thinc/thinc/optimizers.py
imports: import math
  from collections import defaultdict
  from typing import Dict, List, Optional, Tuple, Union, cast
  from .backends import get_array_ops
  from .config import registry
  from .types import FloatsXd, Generator
label_desc: Use image node_id 3 for calling the Optimizer obj's underlying member method code with example usage: obj.step_schedules() without return types