class_docstr: Implements AdamW algorithm.
  
  The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
  The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
  
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups
      lr (float, optional): learning rate (default: 1e-3)
      betas (Tuple[float, float], optional): coefficients used for computing
          running averages of gradient and its square (default: (0.9, 0.999))
      eps (float, optional): term added to the denominator to improve
          numerical stability (default: 1e-8)
      weight_decay (float, optional): weight decay coefficient (default: 1e-2)
      amsgrad (boolean, optional): whether to use the AMSGrad variant of this
          algorithm from the paper `On the Convergence of Adam and Beyond`_
          (default: False)
  
  .. _Adam\: A Method for Stochastic Optimization:
      https://arxiv.org/abs/1412.6980
  .. _Decoupled Weight Decay Regularization:
      https://arxiv.org/abs/1711.05101
  .. _On the Convergence of Adam and Beyond:
      https://openreview.net/forum?id=ryQu7f-RZ
code: def __init__(
      self,
      params,
      lr=1e-3,
      betas=(0.9, 0.999),
      eps=1e-8,
      weight_decay=1e-2,
      amsgrad=False,
  ):
      if not 0.0 <= lr:
          raise ValueError("Invalid learning rate: {}".format(lr))
      if not 0.0 <= eps:
          raise ValueError("Invalid epsilon value: {}".format(eps))
      if not 0.0 <= betas[0] < 1.0:
          raise ValueError(
              "Invalid beta parameter at index 0: {}".format(betas[0])
          )
      if not 0.0 <= betas[1] < 1.0:
          raise ValueError(
              "Invalid beta parameter at index 1: {}".format(betas[1])
          )
      defaults = dict(
          lr=lr,
          betas=betas,
          eps=eps,
          weight_decay=weight_decay,
          amsgrad=amsgrad,
      )
      super(AdamW, self).__init__(params, defaults)
  
file_path: pytorch-image-models/timm/optim/adamw.py
imports: import math
  import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 1 to create a new AdamW object from inherited base classes: Optimizer with example: obj = AdamW(params, lr, betas, eps, weight_decay, amsgrad)