class_docstr: A scheduler that interpolates learning rates in a logarithmic fashion
code: def get_lr(self):
      if self.last_epoch <= 0:
          lrs = [self.min_lr for base_lr in self.base_lrs]
      elif self.last_epoch < self.num_steps:
          lrs = [
              self.min_lr * (self.q ** (self.last_epoch - 1))
              for base_lr in self.base_lrs
          ]
      else:
          lrs = [self.max_lr for base_lr in self.base_lrs]
  
      return lrs
  
file_path: axolotl/src/axolotl/utils/schedulers.py
imports: import math
  from functools import partial
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR, LRScheduler
label_desc: Use image node_id 2 for calling the InterpolatingLogScheduler obj's underlying member method code with example usage: obj.get_lr() and returns: lrs