code: def exponential_decay(
      learning_rate,
      global_step,
      decay_steps,
      decay_rate,
      staircase=False,
      name=None,
  ):
      """Applies exponential decay to the learning rate.
  
      When training a model, it is often recommended to lower the learning rate as
      the training progresses.  This function applies an exponential decay function
      to a provided initial learning rate.  It requires a `global_step` value to
      compute the decayed learning rate.  You can just pass a TensorFlow variable
      that you increment at each training step.
  
      The function returns the decayed learning rate.  It is computed as:
  
      ```python
      decayed_learning_rate = learning_rate *
                              decay_rate ^ (global_step / decay_steps)
      ```
  
      If the argument `staircase` is `True`, then `global_step / decay_steps` is an
      integer division and the decayed learning rate follows a staircase function.
  
      Example: decay every 100000 steps with a base of 0.96:
  
      ```python
      ...
      global_step = tf.Variable(0, trainable=False)
      starter_learning_rate = 0.1
      learning_rate = tf.compat.v1.train.exponential_decay(starter_learning_rate,
      global_step,
                                                 100000, 0.96, staircase=True)
      # Passing global_step to minimize() will increment it at each step.
      learning_step = (
          tf.compat.v1.train.GradientDescentOptimizer(learning_rate)
          .minimize(...my loss..., global_step=global_step)
      )
      ```
  
      Args:
        learning_rate: A scalar `float32` or `float64` `Tensor` or a Python number.
          The initial learning rate.
        global_step: A scalar `int32` or `int64` `Tensor` or a Python number. Global
          step to use for the decay computation.  Must not be negative.
        decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. Must
          be positive.  See the decay computation above.
        decay_rate: A scalar `float32` or `float64` `Tensor` or a Python number.
          The decay rate.
        staircase: Boolean.  If `True` decay the learning rate at discrete intervals
        name: String.  Optional name of the operation.  Defaults to
          'ExponentialDecay'.
  
      Returns:
        A scalar `Tensor` of the same type as `learning_rate`.  The decayed
        learning rate.
  
      Raises:
        ValueError: if `global_step` is not supplied.
  
      @compatibility(eager)
      When eager execution is enabled, this function returns a function which in
      turn returns the decayed learning rate Tensor. This can be useful for changing
      the learning rate value across different invocations of optimizer functions.
      @end_compatibility
      """
      decayed_lr = learning_rate_schedule.ExponentialDecay(
          learning_rate,
          decay_steps,
          decay_rate,
          staircase=staircase,
          name=name,
      )
      if not context.executing_eagerly():
          decayed_lr = decayed_lr(global_step)
      else:
          decayed_lr = functools.partial(decayed_lr, global_step)
      return decayed_lr
  
file_path: tensorflow-upstream/tensorflow/python/keras/optimizer_v2/legacy_learning_rate_decay.py
imports: import functools
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: exponential_decay(learning_rate, global_step, decay_steps, decay_rate, staircase, name) and returns: decayed_lr