hunk.code_window:             depth (int): depth of transformer
               num_heads (int): number of attention heads
               mlp_ratio (int): ratio of mlp hidden dim to embedding dim
               qkv_bias (bool): enable bias for qkv if True
               init_values: (float): layer-scale init values
               drop_rate (float): dropout rate
               attn_drop_rate (float): attention dropout rate
               drop_path_rate (float): stochastic depth rate
  
hunk.labels: keep keep keep keep add keep keep keep
hunk.after_edit:             class_token (bool): use class token
               fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
  
hunk.file_path: timm/models/vision_transformer.py
hunk.type: add
file: """
  SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py
  
  Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
  Code: https://github.com/clovaai/AdamP
  
  Copyright (c) 2020-present NAVER Corp.
  MIT license
  """
  
  import torch
  import torch.nn.functional as F
  from torch.optim.optimizer import Optimizer, required
  import math
  
  from .adamp import projection
  
  
  class SGDP(Optimizer):
      def __init__(self, params, lr=required, momentum=0, dampening=0,
                   weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1):
          defaults = dict(
              lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,
              nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio)
          super(SGDP, self).__init__(params, defaults)
  
      @torch.no_grad()
      def step(self, closure=None):
          loss = None
          if closure is not None:
              with torch.enable_grad():
                  loss = closure()
  
          for group in self.param_groups:
              weight_decay = group['weight_decay']
              momentum = group['momentum']
              dampening = group['dampening']
              nesterov = group['nesterov']
  
              for p in group['params']:
                  if p.grad is None:
                      continue
                  grad = p.grad
                  state = self.state[p]
  
                  # State initialization
                  if len(state) == 0:
                      state['momentum'] = torch.zeros_like(p)
  
                  # SGD
                  buf = state['momentum']
                  buf.mul_(momentum).add_(grad, alpha=1. - dampening)
                  if nesterov:
                      d_p = grad + momentum * buf
                  else:
                      d_p = buf
  
                  # Projection
                  wd_ratio = 1.
                  if len(p.shape) > 1:
                      d_p, wd_ratio = projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps'])
  
                  # Weight decay
                  if weight_decay != 0:
                      p.mul_(1. - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum))
  
                  # Step
                  p.add_(d_p, alpha=-group['lr'])
  
          return loss
  