torch.optim.nadam 的源代码

```html

import torch
from torch import Tensor
from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _dispatch_sqrt,
                        _stack_if_compiling, _get_scalar_dtype, _default_to_fused_or_foreach,
                        _view_as_real, _capturable_doc, _differentiable_doc, _foreach_doc,)
from typing import List, Optional

__all__ = ['NAdam', 'nadam']

class NAdam(Optimizer):
    def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0, momentum_decay=4e-3, decoupled_weight_decay: bool = False,
                 *, foreach: Optional[bool] = None, capturable: bool = False,
                 differentiable: bool = False):
        if not 0.0 <= lr:
            raise ValueError(f"Invalid learning rate: {lr}")
        if not 0.0 <= eps:
            raise ValueError(f"Invalid epsilon value: {eps}")
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
        if not 0.0 <= weight_decay:
            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
        if not 0.0 <= momentum_decay:
            raise ValueError(f"Invalid momentum_decay value: {momentum_decay}")
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, momentum_decay=momentum_decay,
                        decoupled_weight_decay=decoupled_weight_decay,
                        foreach=foreach, capturable=capturable, differentiable=differentiable)
        super().__init__(params, defaults)

    def __setstate__(self, state):
        super().__setstate__(state)
        for group in self.param_groups:
            group.setdefault('foreach', None)
            group.setdefault('capturable', False)
            group.setdefault('differentiable', False)
            group.setdefault('decoupled_weight_decay', False)
            for p in group["params"]:
                p_state = self.state.get(p, [])
                if len(p_state) != 0:
                    if not torch.is_tensor(p_state['step']):
                        step_val = float(p_state["step"])
                        p_state["step"] = (torch.tensor(step_val, dtype=_get_scalar_dtype(), device=p.device)
                                           if group['capturable'] else torch.tensor(step_val, dtype=_get_scalar_dtype()))
                    if not torch.is_tensor(p_state['mu_product']):
                        mu_prod_val = p_state["mu_product"]
                        p_state["mu_product"]</