code: def learning_rate_schedule(
      current_epoch: int,
      current_batch: int,
      batches_per_epoch: int,
      batch_size: int,
  ) -> float:
      """
      Linear learning rate scaling and learning rate decay at specified epochs.
  
      Parameters
      ----------
      current_epoch
          Current training epoch.
      current_batch
          Current batch with current epoch, not used.
      batches_per_epoch
          Number of batches or steps in an epoch, not used.
      batch_size
          Batch size.
  
      Returns
      -------
      Adjusted learning rate.
      """
      del current_batch, batches_per_epoch  # not used
      initial_learning_rate = BASE_LEARNING_RATE * batch_size / 128
      learning_rate = initial_learning_rate
      for mult, start_epoch in LR_SCHEDULE:
          if current_epoch >= start_epoch:
              learning_rate = initial_learning_rate * mult
          else:
              break
      return learning_rate
  
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 6 for calling a global function with example usage: learning_rate_schedule(current_epoch, current_batch, batches_per_epoch, batch_size) and returns: learning_rate