code: def _init_group(
      self, group, params_with_grad, d_p_list, momentum_buffer_list
  ):
      has_sparse_grad = False
  
      for p in group["params"]:
          if p.grad is not None:
              params_with_grad.append(p)
              d_p_list.append(p.grad)
              if p.grad.is_sparse:
                  has_sparse_grad = True
  
              state = self.state[p]
              if "momentum_buffer" not in state:
                  momentum_buffer_list.append(None)
              else:
                  momentum_buffer_list.append(state["momentum_buffer"])
  
      return has_sparse_grad
  
file_path: pytorch-image-models/timm/optim/sgdw.py
imports: from functools import update_wrapper, wraps
  import torch
  from torch import Tensor
  from torch.optim.optimizer import Optimizer
  from typing import List, Optional
label_desc: Use image node_id 3 for calling the SGDW obj's underlying member method code with example usage: obj._init_group(group, params_with_grad, d_p_list, momentum_buffer_list) and returns: has_sparse_grad