class_docstr: Polynomial LR Scheduler w/ warmup, noise, and k-decay
  
  k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909
code: def get_cycle_length(self, cycles=0):
      cycles = max(1, cycles or self.cycle_limit)
      if self.cycle_mul == 1.0:
          return self.t_initial * cycles
      else:
          return int(
              math.floor(
                  -self.t_initial
                  * (self.cycle_mul**cycles - 1)
                  / (1 - self.cycle_mul)
              )
          )
  
file_path: pytorch-image-models/timm/scheduler/poly_lr.py
imports: import math
  import logging
  import torch
  from .scheduler import Scheduler
label_desc: Use image node_id 3 for calling the PolyLRScheduler obj's underlying member method code with example usage: obj.get_cycle_length(cycles) and returns: unknown, int