docstr: Callback to update learning rate on every batch instead of epoch.
  
  Parameters
  ----------
  schedule
      Function taking the epoch and batch index as input which returns the new
      learning rate as output.
  batch_size
      Batch size.
  steps_per_epoch
      Number of batches or steps per epoch.
docstr_tok: ["Callback","to","update","learning","rate","on","every","batch","instead","of","epoch",".","Parameters","--","--","--","--","--","schedule","Function","taking","the","epoch","and","batch","index","as","input","which","returns","the","new","learning","rate","as","output",".","batch_size","Batch","size",".","steps_per_epoch","Number","of","batches","or","steps","per","epoch","."]
code: def __init__(
      self, schedule: Callable, batch_size: int, steps_per_epoch: int
  ):
      """
      Callback to update learning rate on every batch instead of epoch.
  
      Parameters
      ----------
      schedule
          Function taking the epoch and batch index as input which returns the new
          learning rate as output.
      batch_size
          Batch size.
      steps_per_epoch
          Number of batches or steps per epoch.
      """
      super(LearningRateBatchScheduler, self).__init__()
      self.schedule = schedule
      self.steps_per_epoch = steps_per_epoch
      self.batch_size = batch_size
      self.epochs = -1
      self.prev_lr = -1
  
imports: import argparse
  import numpy
  import os
  from pathlib import Path
  import tensorflow
  from tensorflow.keras.callbacks import Callback, ModelCheckpoint
  from tensorflow.keras.initializers import RandomNormal
  from tensorflow.keras.layers import Activation, Add, BatchNormalization, Conv2D, Dense, Input, ZeroPadding2D
  from tensorflow.keras.models import Model
  from tensorflow.keras.optimizers import SGD
  from tensorflow.keras.preprocessing.image import ImageDataGenerator
  from tensorflow.keras.regularizers import l2
  from typing import Callable, Tuple, Union
label_desc: Use image node_id 1 to create a new LearningRateBatchScheduler object from inherited base classes: Callback with example: obj = LearningRateBatchScheduler(schedule, batch_size, steps_per_epoch)