class_docstr: Anneals the learning rate.
code: def load_state_dict(self, sd):
      if "start_lr" in sd:
          max_lr_ = sd["start_lr"]
      else:
          max_lr_ = sd["max_lr"]
      self.max_lr = self._check_and_set(
          self.max_lr, max_lr_, "learning rate"
      )
  
      self.min_lr = self._check_and_set(
          self.min_lr, sd["min_lr"], "minimum learning rate"
      )
  
      if "warmup_iter" in sd:
          warmup_steps_ = sd["warmup_iter"]
      else:
          warmup_steps_ = sd["warmup_steps"]
      self.warmup_steps = self._check_and_set(
          self.warmup_steps, warmup_steps_, "warmup iterations"
      )
  
      if "end_iter" in sd:
          decay_steps_ = sd["end_iter"]
      else:
          decay_steps_ = sd["decay_steps"]
      self.decay_steps = self._check_and_set(
          self.decay_steps, decay_steps_, "total number of iterations"
      )
      self.decay_style = self._check_and_set(
          self.decay_style, sd["decay_style"], "decay style"
      )
  
      if "num_iters" in sd:
          num_steps = sd["num_iters"]
      else:
          num_steps = sd["num_steps"]
      self.step(increment=num_steps)
  
file_path: colossalai/examples/tutorial/sequence_parallel/lr_scheduler/annealing_lr.py
imports: import math
label_desc: Use image node_id 6 for calling the AnnealingLR obj's underlying member method code with example usage: obj.load_state_dict(sd) without return types