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","."]
docstr: Initializes a new instance of the `WarmUp` class.
  
  Args:
    initial_learning_rate: learning rate after the warmup.
    decay_schedule_fn: A function maps step to learning rate. Will be applied
      for values of step larger than 'warmup_steps'.
    warmup_steps: Number of steps to do warmup for.
    name: TF namescope under which to perform the learning rate calculation.
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code: def __init__(
      self,
      initial_learning_rate: float,
      decay_schedule_fn: Callable[[Any], Any],
      warmup_steps: int,
      name: Optional[str] = None,
  ):
      """Initializes a new instance of the `WarmUp` class.
  
      Args:
        initial_learning_rate: learning rate after the warmup.
        decay_schedule_fn: A function maps step to learning rate. Will be applied
          for values of step larger than 'warmup_steps'.
        warmup_steps: Number of steps to do warmup for.
        name: TF namescope under which to perform the learning rate calculation.
      """
      super(WarmUp, self).__init__()
      self.initial_learning_rate = initial_learning_rate
      self.warmup_steps = warmup_steps
      self.decay_schedule_fn = decay_schedule_fn
      self.name = 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 1 to create a new WarmUp object from inherited base classes: tf.keras.optimizers.schedules with example: obj = WarmUp(initial_learning_rate, decay_schedule_fn, warmup_steps, name)