class_docstr: Poly learning rate schedule used to train DeepLab.
  Paper: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
      Atrous Convolution, and Fully Connected CRFs.
  Reference: https://github.com/tensorflow/models/blob/21b73d22f3ed05b650e85ac50849408dd36de32e/research/deeplab/utils/train_utils.py#L337  # noqa
code: def get_lr(self) -> List[float]:
      warmup_factor = _get_warmup_factor_at_iter(
          self.warmup_method,
          self.last_epoch,
          self.warmup_iters,
          self.warmup_factor,
      )
      if self.constant_ending > 0 and warmup_factor == 1.0:
          # Constant ending lr.
          if (
              math.pow(
                  (1.0 - self.last_epoch / self.max_iters), self.power
              )
              < self.constant_ending
          ):
              return [
                  base_lr * self.constant_ending
                  for base_lr in self.base_lrs
              ]
      return [
          base_lr
          * warmup_factor
          * math.pow(
              (1.0 - self.last_epoch / self.max_iters), self.power
          )
          for base_lr in self.base_lrs
      ]
  
file_path: detectron2/projects/DeepLab/deeplab/lr_scheduler.py
imports: import math
  from typing import List
  import torch
  from detectron2.solver.lr_scheduler import LRScheduler, _get_warmup_factor_at_iter
label_desc: Use image node_id 2 for calling the WarmupPolyLR obj's underlying member method code with example usage: obj.get_lr() and returns: unknown, unknown