class_docstr: Subgraph sampling based on random walk on the given graph or batched graphs.
  Class objects callable via method :meth:`views_fn`.
  
  Args:
      ratio (float, optional): Percentage of nodes to sample from the graph.
          (default: :obj:`0.1`)
      add_self_loop (bool, optional): Set True to add self-loop to edge_index.
          (default: :obj:`False`)
code: def __call__(self, data):
      return self.views_fn(data)
  
file_path: DIG/dig/sslgraph/method/contrastive/views_fn/sample.py
imports: import torch
  import numpy
  from torch_geometric.utils import to_dense_adj, dense_to_sparse, subgraph
  from torch_geometric.data import Batch, Data
label_desc: Use image node_id 2 for calling the RWSample obj's underlying member method code with example usage: obj.__call__(data) and returns: self