class_docstr: supports repeated iterations, configurable via lists
  note: use with a base_lr of 1.0.
class_docstr_tok: ["supports","repeated","iterations",",","configurable","via","lists","note",":","use","with","a","base_lr","of","1.0","."]
code: def __init__(
      self,
      warm_up_steps,
      f_min,
      f_max,
      f_start,
      cycle_lengths,
      verbosity_interval=0,
  ):
      assert (
          len(warm_up_steps)
          == len(f_min)
          == len(f_max)
          == len(f_start)
          == len(cycle_lengths)
      )
      self.lr_warm_up_steps = warm_up_steps
      self.f_start = f_start
      self.f_min = f_min
      self.f_max = f_max
      self.cycle_lengths = cycle_lengths
      self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
      self.last_f = 0.0
      self.verbosity_interval = verbosity_interval
  
file_path: generative-models/sgm/lr_scheduler.py
imports: import numpy
label_desc: Use image node_id 1 to create a new LambdaWarmUpCosineScheduler2 object  with example: obj = LambdaWarmUpCosineScheduler2(warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval)