code: def lr_normalizer(lr, optimizer):
      """Assuming a default learning rate 1, rescales the learning rate
      such that learning rates amongst different optimizers are more or less
      equivalent.
  
      Parameters
      ----------
      lr : float
          The learning rate.
      optimizer : keras optimizer
          The optimizer. For example, Adagrad, Adam, RMSprop.
      """
  
      from tensorflow.keras.optimizers import (
          SGD,
          Adam,
          Adadelta,
          Adagrad,
          Adamax,
          RMSprop,
      )
      from tensorflow.keras.optimizers import Nadam
      from talos.utils.exceptions import TalosModelError
  
      if optimizer == Adadelta:
          pass
      elif optimizer == SGD or optimizer == Adagrad:
          lr /= 100.0
      elif optimizer == Adam or optimizer == RMSprop:
          lr /= 1000.0
      elif optimizer == Adamax or optimizer == Nadam:
          lr /= 500.0
      else:
          raise TalosModelError(
              str(optimizer) + " is not supported by lr_normalizer"
          )
  
      return lr
  
label_desc: Use image node_id 1 for calling a global function with example usage: lr_normalizer(lr, optimizer) and returns: lr