parameters: (learning_rate,
                         global_step,
                         decay_steps,
                         decay_rate,
                         staircase=False,
                         name=None)
return_statement: return decayed_lr
docstring: Applies inverse time decay to the initial learning rate.
  
    When training a model, it is often recommended to lower the learning rate as
    the training progresses.  This function applies an inverse decay function
    to a provided initial learning rate.  It requires an `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 / (1 + decay_rate * global_step /
    decay_step)
    ```
  
    or, if `staircase` is `True`, as:
  
    ```python
    decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step /
    decay_step))
    ```
  
    Example: decay 1/t with a rate of 0.5:
  
    ```python
    ...
    global_step = tf.Variable(0, trainable=False)
    learning_rate = 0.1
    decay_steps = 1.0
    decay_rate = 0.5
    learning_rate = tf.compat.v1.train.inverse_time_decay(learning_rate,
    global_step,
    decay_steps, decay_rate)
  
    # 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 Python number. Global step to use for the decay computation.
        Must not be negative.
      decay_steps: How often to apply decay.
      decay_rate: A Python number.  The decay rate.
      staircase: Whether to apply decay in a discrete staircase, as opposed to
        continuous, fashion.
      name: String.  Optional name of the operation.  Defaults to
        'InverseTimeDecay'.
  
    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
docstring_summary: Applies inverse time decay to the initial learning rate.
docstring_tokens: Applies inverse time decay to the initial learning rate .
function: def inverse_time_decay(learning_rate,
                         global_step,
                         decay_steps,
                         decay_rate,
                         staircase=False,
                         name=None):
    """Applies inverse time decay to the initial learning rate.
  
    When training a model, it is often recommended to lower the learning rate as
    the training progresses.  This function applies an inverse decay function
    to a provided initial learning rate.  It requires an `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 / (1 + decay_rate * global_step /
    decay_step)
    ```
  
    or, if `staircase` is `True`, as:
  
    ```python
    decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step /
    decay_step))
    ```
  
    Example: decay 1/t with a rate of 0.5:
  
    ```python
    ...
    global_step = tf.Variable(0, trainable=False)
    learning_rate = 0.1
    decay_steps = 1.0
    decay_rate = 0.5
    learning_rate = tf.compat.v1.train.inverse_time_decay(learning_rate,
    global_step,
    decay_steps, decay_rate)
  
    # 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 Python number. Global step to use for the decay computation.
        Must not be negative.
      decay_steps: How often to apply decay.
      decay_rate: A Python number.  The decay rate.
      staircase: Whether to apply decay in a discrete staircase, as opposed to
        continuous, fashion.
      name: String.  Optional name of the operation.  Defaults to
        'InverseTimeDecay'.
  
    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.InverseTimeDecay(
        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
function_tokens: def inverse_time_decay ( learning_rate , global_step , decay_steps , decay_rate , staircase = False , name = None ) : decayed_lr = learning_rate_schedule . InverseTimeDecay ( 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