text: import torch
  import math
  from torch.optim.optimizer import Optimizer
  
  
  class AdamW(Optimizer):
      """Implements Adam algorithm.
      It has been proposed in `Adam: A Method for Stochastic Optimization`_.
      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 using the method from
              the paper `Fixing Weight Decay Regularization in Adam` (default: 0)
          amsgrad (boolean, optional): whether to use the AMSGrad variant of this
              algorithm from the paper `On the Convergence of Adam and Beyond`_
      .. _Adam\: A Method for Stochastic Optimization:
          https://arxiv.org/abs/1412.6980
      .. _Fixing Weight Decay Regularization in Adam:
          https://arxiv.org/abs/1711.05101
      .. _On the Convergence of Adam and Beyond:
          https://openreview.net/forum?id=ryQu7f-RZ
      """
  
      def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                   weight_decay=0, 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().__init__(params, defaults)
          self._initial_lr = lr
  
      def __setstate__(self, state):
          super().__setstate__(state)
          for group in self.param_groups:
              group.setdefault('amsgrad', False)
  
      def step(self, closure=None):
          """Performs a single optimization step.
          Arguments:
              closure (callable, optional): A closure that reevaluates the model
                  and returns the loss.
          """
          loss = None
          if closure is not None:
              loss = closure()
  
          for group in self.param_groups:
              for p in group['params']:
                  if p.grad is None:
                      continue
                  grad = p.grad.data
                  if grad.is_sparse:
                      raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
                  amsgrad = group['amsgrad']
  
                  state = self.state[p]
  
                  # State initialization
                  if len(state) == 0:
                      state['step'] = 0
                      # Exponential moving average of gradient values
                      state['exp_avg'] = torch.zeros_like(p.data)
                      # Exponential moving average of squared gradient values
                      state['exp_avg_sq'] = torch.zeros_like(p.data)
                      if amsgrad:
                          # Maintains max of all exp. moving avg. of sq. grad. values
                          state['max_exp_avg_sq'] = torch.zeros_like(p.data)
  
                  exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                  if amsgrad:
                      max_exp_avg_sq = state['max_exp_avg_sq']
                  beta1, beta2 = group['betas']
  
                  state['step'] += 1
  
                  # Decay the first and second moment running average coefficient
                  exp_avg.mul_(beta1).add_(1 - beta1, grad)
                  exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                  if amsgrad:
                      # Maintains the maximum of all 2nd moment running avg. till now
                      torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                      # Use the max. for normalizing running avg. of gradient
                      denom = max_exp_avg_sq.sqrt().add_(group['eps'])
                  else:
                      denom = exp_avg_sq.sqrt().add_(group['eps'])
  
                  bias_correction1 = 1 - beta1 ** state['step']
                  bias_correction2 = 1 - beta2 ** state['step']
                  step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
  
                  if group['weight_decay'] != 0:
                      #eta = group['lr'] / self._initial_lr # scheduler changes lr only
                      #p.data.add_(-group['weight_decay'] * eta, p.data)
                      w = group['weight_decay'] * group['lr']
                      p.data.add_(-w, p.data)
  
                  p.data.addcdiv_(-step_size, exp_avg, denom)
  
          return loss
  
  
  def optimizer(optimizer: str, params: list, lr: float, momentum=0.9, weight_decay=0.0001):
      opt = None
      if optimizer == 'sgd':
          opt = torch.optim.SGD(params, lr=lr, momentum=momentum, weight_decay=weight_decay)
      elif optimizer == 'adamw':
          opt = AdamW(params, lr=lr, momentum=momentum, weight_decay=weight_decay)
      else:
          opt = torch.optim.Adam(params, lr=lr)
      return opt