class_docstr: Optimizer that implements COCOB Backprop Algorithm
  
      Reference:
          - [COntinuos COin Betting (COCOB) Backprop optimizer
  ](https://arxiv.org/abs/1705.07795)
docstr: Constructs a new COCOB-Backprop optimizer
  
  Arguments:
      `aplha`: Default value is set to 100 as per paper.
               This has the effect of restricting the value of the
               parameters in the first iterations of the algorithm.
               (Refer to Paper for indepth understanding)
  
  Rasies:
      `ValueError`: If the value of `alpha` is less than 1.
      `NotImplementedError`: If the data is in sparse format.
code: def __init__(
      self,
      alpha: float = 100,
      use_locking: bool = False,
      name: str = "COCOB",
      **kwargs,
  ):
      """Constructs a new COCOB-Backprop optimizer
  
      Arguments:
          `aplha`: Default value is set to 100 as per paper.
                   This has the effect of restricting the value of the
                   parameters in the first iterations of the algorithm.
                   (Refer to Paper for indepth understanding)
  
      Rasies:
          `ValueError`: If the value of `alpha` is less than 1.
          `NotImplementedError`: If the data is in sparse format.
      """
  
      if alpha < 1:
          raise ValueError("`alpha` must be greater than Zero")
  
      super().__init__(name, **kwargs)
      self._set_hyper("alpha", alpha)
      self._alpha = alpha
  
file_path: addons/tensorflow_addons/optimizers/cocob.py
imports: from typeguard import typechecked
  import tensorflow
  from tensorflow_addons.optimizers import KerasLegacyOptimizer
label_desc: Use image node_id 1 to create a new COCOB object from inherited base classes: KerasLegacyOptimizer with example: obj = COCOB(alpha, use_locking, name)