code: def feature_drop_weights_dense(x, node_c):
      """Same as `feature_drop_weights` but for dense continuous features"""
      x = x.abs()
      w = x.t() @ node_c
      w = w.log()
      s = (w.max() - w) / (w.max() - w.mean())
  
      return s
  
file_path: DIG/dig/sslgraph/utils/adaptive.py
imports: import torch
  from torch.utils.data import random_split
  from torch_geometric.utils import degree, remove_self_loops, get_laplacian, to_undirected, to_dense_adj, to_networkx, degree, to_undirected
  from torch_scatter import scatter
  import networkx
label_desc: Use image node_id 5 for calling a global function with example usage: feature_drop_weights_dense(x, node_c) and returns: s