class_docstr: Proabilistic edge perturbation on the given graph or batched graphs. Perturbations are adaptive i.e. critical edges have lower probability of being removed.
  Adaptations based on the paper `Graph Contrastive Learning with Adaptive Augmentation <https://arxiv.org/abs/2010.14945>`
  Refer to `source implementation <https://github.com/CRIPAC-DIG/GCA>` for more details.
  Class objects callable via method :meth:`views_fn`.
  
  Args:
      centrality_measure (str): Method for computing edge centrality. Set `degree` for degree centrality,
      `pr` for PageRank centrality and `evc` for eigen-vector centrality
      prob (float): Probability factor used for calculating edge-drop probability
      threshold (float): Upper-bound probability for dropping any edge, defaults to 0.7
docstr: Method to be called when :class:`AdaEdgePerturbation` object is called.
  
  Args:
      data (:class:`torch_geometric.data.Data`): The input graph or batched graphs.
      
  :rtype: :class:`torch_geometric.data.Data`.  
docstr_tok: ["Method","to","be","called","when",":","class",":","`","AdaEdgePerturbation","`","object","is","called",".","Args",":","data","(",":","class",":","`","torch_geometric.data.Data","`",")",":","The","input","graph","or","batched","graphs",".",":","rtype",":",":","class",":","`","torch_geometric.data.Data","`","."]
code: def views_fn(self, data):
      r"""Method to be called when :class:`AdaEdgePerturbation` object is called.
  
      Args:
          data (:class:`torch_geometric.data.Data`): The input graph or batched graphs.
  
      :rtype: :class:`torch_geometric.data.Data`.
      """
      if isinstance(data, Batch):
          dlist = [self.do_trans(d) for d in data.to_data_list()]
          return Batch.from_data_list(dlist)
      elif isinstance(data, Data):
          return self.do_trans(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 5 for calling the AdaEdgePerturbation obj's underlying member method code with example usage: obj.views_fn(data) and returns: Batch, self