class_docstr: Applies a warmup schedule on a given learning rate decay schedule.
class_docstr_tok: ["Applies","a","warmup","schedule","on","a","given","learning","rate","decay","schedule","."]
code: def get_config(self) -> Dict[str, Any]:
      return {
          "initial_learning_rate": self.initial_learning_rate,
          "decay_schedule_fn": self.decay_schedule_fn,
          "warmup_steps": self.warmup_steps,
          "name": self.name,
      }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tempfile
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import tensorflow
  from mediapipe.model_maker.python.core.data import dataset
  from mediapipe.model_maker.python.core.utils import quantization
label_desc: Use image node_id 3 for calling the WarmUp obj's underlying member method code with example usage: obj.get_config() and returns: dict