class_docstr: Edge perturbation on the given graph or batched graphs. Class objects callable via 
  method :meth:`views_fn`.
  
  Args:
      add (bool, optional): Set :obj:`True` if randomly add edges in a given graph.
          (default: :obj:`True`)
      drop (bool, optional): Set :obj:`True` if randomly drop edges in a given graph.
          (default: :obj:`False`)
      ratio (float, optional): Percentage of edges to add or drop. (default: :obj:`0.1`)
code: def __call__(self, data):
      return self.views_fn(data)
  
imports: import torch
  from torch_geometric.utils import to_dense_adj, dense_to_sparse, dropout_adj, degree, add_remaining_self_loops
  from sklearn.preprocessing import MinMaxScaler
  from torch_geometric.data import Batch, Data
  from dig.sslgraph.utils.adaptive import degree_drop_weights, pr_drop_weights, evc_drop_weights, drop_edge_weighted
label_desc: Use image node_id 2 for calling the EdgePerturbation obj's underlying member method code with example usage: obj.__call__(data) and returns: self