parameters: (learning_rate,
                          global_step,
                          decay_steps,
                          num_periods=0.5,
                          alpha=0.0,
                          beta=0.001,
                          name=None)
return_statement: return decayed_lr
docstring: Applies linear cosine decay to the learning rate.
  
    See [Bello et al., ICML2017] Neural Optimizer Search with RL.
    https://arxiv.org/abs/1709.07417
  
    For the idea of warm starts here controlled by `num_periods`,
    see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent
    with Warm Restarts. https://arxiv.org/abs/1608.03983
  
    Note that linear cosine decay is more aggressive than cosine decay and
    larger initial learning rates can typically be used.
  
    When training a model, it is often recommended to lower the learning rate as
    the training progresses.  This function applies a linear cosine 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
    global_step = min(global_step, decay_steps)
    linear_decay = (decay_steps - global_step) / decay_steps)
    cosine_decay = 0.5 * (
        1 + cos(pi * 2 * num_periods * global_step / decay_steps))
    decayed = (alpha + linear_decay) * cosine_decay + beta
    decayed_learning_rate = learning_rate * decayed
    ```
  
    Example usage:
    ```python
    decay_steps = 1000
    lr_decayed = linear_cosine_decay(learning_rate, global_step, decay_steps)
    ```
  
    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.
      decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. Number
        of steps to decay over.
      num_periods: Number of periods in the cosine part of the decay. See
        computation above.
      alpha: See computation above.
      beta: See computation above.
      name: String.  Optional name of the operation.  Defaults to
        'LinearCosineDecay'.
  
    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 linear cosine decay to the learning rate.
docstring_tokens: Applies linear cosine decay to the learning rate .
function: def linear_cosine_decay(learning_rate,
                          global_step,
                          decay_steps,
                          num_periods=0.5,
                          alpha=0.0,
                          beta=0.001,
                          name=None):
    """Applies linear cosine decay to the learning rate.
  
    See [Bello et al., ICML2017] Neural Optimizer Search with RL.
    https://arxiv.org/abs/1709.07417
  
    For the idea of warm starts here controlled by `num_periods`,
    see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent
    with Warm Restarts. https://arxiv.org/abs/1608.03983
  
    Note that linear cosine decay is more aggressive than cosine decay and
    larger initial learning rates can typically be used.
  
    When training a model, it is often recommended to lower the learning rate as
    the training progresses.  This function applies a linear cosine 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
    global_step = min(global_step, decay_steps)
    linear_decay = (decay_steps - global_step) / decay_steps)
    cosine_decay = 0.5 * (
        1 + cos(pi * 2 * num_periods * global_step / decay_steps))
    decayed = (alpha + linear_decay) * cosine_decay + beta
    decayed_learning_rate = learning_rate * decayed
    ```
  
    Example usage:
    ```python
    decay_steps = 1000
    lr_decayed = linear_cosine_decay(learning_rate, global_step, decay_steps)
    ```
  
    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.
      decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. Number
        of steps to decay over.
      num_periods: Number of periods in the cosine part of the decay. See
        computation above.
      alpha: See computation above.
      beta: See computation above.
      name: String.  Optional name of the operation.  Defaults to
        'LinearCosineDecay'.
  
    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.LinearCosineDecay(
        learning_rate,
        decay_steps,
        num_periods=num_periods,
        alpha=alpha,
        beta=beta,
        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 linear_cosine_decay ( learning_rate , global_step , decay_steps , num_periods = 0.5 , alpha = 0.0 , beta = 0.001 , name = None ) : decayed_lr = learning_rate_schedule . LinearCosineDecay ( learning_rate , decay_steps , num_periods = num_periods , alpha = alpha , beta = beta , 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