class_docstr: The learning rate schedule base class.
  
  You can use a learning rate schedule to modulate how the learning rate
  of your optimizer changes over time.
  
  Several built-in learning rate schedules are available, such as
  `tf.keras.optimizers.schedules.ExponentialDecay` or
  `tf.keras.optimizers.schedules.PiecewiseConstantDecay`:
  
  ```python
  lr_schedule = keras.optimizers.schedules.ExponentialDecay(
      initial_learning_rate=1e-2,
      decay_steps=10000,
      decay_rate=0.9)
  optimizer = keras.optimizers.SGD(learning_rate=lr_schedule)
  ```
  
  A `LearningRateSchedule` instance can be passed in as the `learning_rate`
  argument of any optimizer.
  
  To implement your own schedule object, you should implement the `__call__`
  method, which takes a `step` argument (scalar integer tensor, the
  current training step count).
  Like for any other Keras object, you can also optionally
  make your object serializable by implementing the `get_config`
  and `from_config` methods.
  
  Example:
  
  ```python
  class MyLRSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
  
    def __init__(self, initial_learning_rate):
      self.initial_learning_rate = initial_learning_rate
  
    def __call__(self, step):
       return self.initial_learning_rate / (step + 1)
  
  optimizer = tf.keras.optimizers.SGD(learning_rate=MyLRSchedule(0.1))
  ```
docstr: Instantiates a `LearningRateSchedule` from its config.
  
  Args:
      config: Output of `get_config()`.
  
  Returns:
      A `LearningRateSchedule` instance.
docstr_tok: ["Instantiates","a","`","LearningRateSchedule","`","from","its","config",".","Args",":","config",":","Output","of","`","get_config","(",")","`",".","Returns",":","A","`","LearningRateSchedule","`","instance","."]
code: def from_config(cls, config):
      """Instantiates a `LearningRateSchedule` from its config.
  
      Args:
          config: Output of `get_config()`.
  
      Returns:
          A `LearningRateSchedule` instance.
      """
      return cls(**config)
  
imports: import abc
  import math
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import control_flow_case
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling the LearningRateSchedule obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls