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 __init__(
      self,
      optimizer: torch.optim.Optimizer,
      max_iters: int,
      warmup_factor: float = 0.001,
      warmup_iters: int = 1000,
      warmup_method: str = "linear",
      last_epoch: int = -1,
      power: float = 0.9,
      constant_ending: float = 0.0,
  ):
      self.max_iters = max_iters
      self.warmup_factor = warmup_factor
      self.warmup_iters = warmup_iters
      self.warmup_method = warmup_method
      self.power = power
      self.constant_ending = constant_ending
      super().__init__(optimizer, last_epoch)
  
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 1 to create a new WarmupPolyLR object from inherited base classes: LRScheduler with example: obj = WarmupPolyLR(optimizer, max_iters, warmup_factor, warmup_iters, warmup_method, last_epoch, power, constant_ending)