class_docstr: Optimizer that implements the AdamW algorithm.
  
  AdamW optimization is a stochastic gradient descent method that is based on
  adaptive estimation of first-order and second-order moments with an added
  method to decay weights per the techniques discussed in the paper,
  'Decoupled Weight Decay Regularization' by
  [Loshchilov, Hutter et al., 2019](https://arxiv.org/abs/1711.05101).
  
  According to
  [Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
  the underying Adam method is "*computationally
  efficient, has little memory requirement, invariant to diagonal rescaling of
  gradients, and is well suited for problems that are large in terms of
  data/parameters*".
  
  Args:
      learning_rate: A float, a
          `keras.optimizers.schedules.LearningRateSchedule` instance, or
          a callable that takes no arguments and returns the actual value to
          use. The learning rate. Defaults to `0.001`.
      beta_1: A float value or a constant float tensor, or a callable
          that takes no arguments and returns the actual value to use. The
          exponential decay rate for the 1st moment estimates.
          Defaults to `0.9`.
      beta_2: A float value or a constant float tensor, or a callable
          that takes no arguments and returns the actual value to use. The
          exponential decay rate for the 2nd moment estimates.
          Defaults to `0.999`.
      epsilon: A small constant for numerical stability. This epsilon is
          "epsilon hat" in the Kingma and Ba paper (in the formula just
          before Section 2.1), not the epsilon in Algorithm 1 of the paper.
          Defaults to 1e-7.
      amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm
          from the paper "On the Convergence of Adam and beyond".
          Defaults to `False`.
      {{base_optimizer_keyword_args}}
  
  References:
  
  - [Loshchilov et al., 2019](https://arxiv.org/abs/1711.05101)
  - [Kingma et al., 2014](http://arxiv.org/abs/1412.6980) for `adam`
  - [Reddi et al., 2018](
      https://openreview.net/pdf?id=ryQu7f-RZ) for `amsgrad`.
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code: def __init__(
      self,
      learning_rate=0.001,
      weight_decay=0.004,
      beta_1=0.9,
      beta_2=0.999,
      epsilon=1e-7,
      amsgrad=False,
      clipnorm=None,
      clipvalue=None,
      global_clipnorm=None,
      use_ema=False,
      ema_momentum=0.99,
      ema_overwrite_frequency=None,
      name="adamw",
      **kwargs,
  ):
      super().__init__(
          learning_rate=learning_rate,
          beta_1=beta_1,
          beta_2=beta_2,
          epsilon=epsilon,
          amsgrad=amsgrad,
          name=name,
          weight_decay=weight_decay,
          clipnorm=clipnorm,
          clipvalue=clipvalue,
          global_clipnorm=global_clipnorm,
          use_ema=use_ema,
          ema_momentum=ema_momentum,
          ema_overwrite_frequency=ema_overwrite_frequency,
          **kwargs,
      )
  
      if self.weight_decay is None:
          raise ValueError(
              "Argument `weight_decay` must be a float. Received: "
              "weight_decay=None"
          )
  
imports: from keras.api_export import keras_export
  from keras.optimizers import adam
  from keras.optimizers import optimizer
label_desc: Use image node_id 1 to create a new AdamW object from inherited base classes: adam with example: obj = AdamW(learning_rate, weight_decay, beta_1, beta_2, epsilon, amsgrad, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, name)