class_docstr: Implements SGDP algorithm.
  
  It has been proposed in `Slowing Down the Weight Norm Increase in
  Momentum-based Optimizers`__
  
  Arguments:
      params: iterable of parameters to optimize or dicts defining
          parameter groups
      lr: learning rate (default: 1e-3)
      momentum: momentum factor (default: 0)
      dampening: dampening for momentum (default: 0)
      eps: term added to the denominator to improve
          numerical stability (default: 1e-8)
      weight_decay: weight decay (L2 penalty) (default: 0)
      delta: threhold that determines whether a set of parameters is scale
          invariant or not (default: 0.1)
      wd_ratio: relative weight decay applied on scale-invariant parameters
          compared to that applied on scale-variant parameters (default: 0.1)
      nesterov: enables Nesterov momentum (default: False)
  
  
  Example:
      >>> import torch_optimizer as optim
      >>> optimizer = optim.SGDP(model.parameters(), lr=0.1)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.step()
  
   __ https://arxiv.org/abs/2006.08217
  
  Note:
      Reference code: https://github.com/clovaai/AdamP
code: def _cosine_similarity(x, y, eps, view_func):
      x = view_func(x)
      y = view_func(y)
  
      x_norm = x.norm(dim=1).add_(eps)
      y_norm = y.norm(dim=1).add_(eps)
      dot = (x * y).sum(dim=1)
  
      return dot.abs() / x_norm / y_norm
  
file_path: pytorch-optimizer/torch_optimizer/sgdp.py
imports: import math
  import torch
  from torch.optim.optimizer import Optimizer
  from .types import OptFloat, OptLossClosure, Params
label_desc: Use image node_id 4 for calling the SGDP obj's underlying member method code with example usage: obj._cosine_similarity(x, y, eps, view_func) and returns: unknown