class_docstr: Learning rate scheduler which sets the learning rate according to schedule.
  
  Arguments:
      schedule: a function that takes an epoch index
          (integer, indexed from 0) and current learning rate
          as inputs and returns a new learning rate as output (float).
class_docstr_tok: ["Learning","rate","scheduler","which","sets","the","learning","rate","according","to","schedule",".","Arguments",":","schedule",":","a","function","that","takes","an","epoch","index","(","integer",",","indexed","from","0",")","and","current","learning","rate","as","inputs","and","returns","a","new","learning","rate","as","output","(","float",")","."]
code: def __init__(self, schedule):
      super().__init__()
      self.schedule = schedule
  
imports: import numpy
  import keras
label_desc: Use image node_id 1 to create a new CustomLearningRateScheduler object from inherited base classes: keras.callbacks with example: obj = CustomLearningRateScheduler(schedule)