code: def add_loop_sparse(self, adj, fill_value=1):
      # make identify sparse tensor
      row = torch.range(0, int(adj.shape[0] - 1), dtype=torch.int64)
      i = torch.stack((row, row), dim=0)
      v = torch.ones(adj.shape[0], dtype=torch.float32)
      shape = adj.shape
      I_n = torch.sparse.FloatTensor(i, v, shape)
      return adj + I_n.to(self.device)
  
imports: import numpy
  from copy import deepcopy
  from numba import njit
  import torch.nn
  import torch.nn.functional
  import math
  import torch
  import torch.optim
  import typing
  from torch_geometric.data import Data
  from sklearn.metrics.pairwise import euclidean_distances, cosine_similarity
  from scipy.sparse import lil_matrix
  from sklearn.preprocessing import normalize
  import scipy.sparse
  import autogl.data
  from ..None import register_model
  from .None import utils
  from ..gcn import GCN
  from .nn import GCNConv
  from ..base import BaseAutoModel
  from .....utils import get_logger
  from .utils import accuracy
label_desc: Use image node_id 4 for calling the GCN4GNNGuard obj's underlying member method code with example usage: obj.add_loop_sparse(adj, fill_value) and returns: unknown