src/transformers/optimization.py/AdamW/__init__
class AdamW:    def __init__(
        self,
        params: Iterable[nn.parameter.Parameter],
        lr: float = 1e-3,
        betas: Tuple[float, float] = (0.9, 0.999),
        eps: float = 1e-6,
        weight_decay: float = 0.0,
        correct_bias: bool = True,
        no_deprecation_warning: bool = False,
    ):
        if not no_deprecation_warning:
            warnings.warn(
                "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch"
                " implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this"
                " warning",
                FutureWarning,
            )
        require_version("torch>=1.5.0")  # add_ with alpha
        if lr < 0.0:
            raise ValueError(f"Invalid learning rate: {lr} - should be >= 0.0")
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError(f"Invalid beta parameter: {betas[0]} - should be in [0.0, 1.0)")
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError(f"Invalid beta parameter: {betas[1]} - should be in [0.0, 1.0)")
        if not 0.0 <= eps:
            raise ValueError(f"Invalid epsilon value: {eps} - should be >= 0.0")
        defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay, "correct_bias": correct_bias}
        super().__init__(params, defaults)