class_docstr: The GraphSAINT sampler base class from the `"GraphSAINT: Graph
  Sampling Based Inductive Learning Method"
  <https://arxiv.org/abs/1907.04931>`_ paper.
  Given a graph in a :obj:`data` object, this class samples nodes and
  constructs subgraphs that can be processed in a mini-batch fashion.
  Normalization coefficients for each mini-batch are given via
  :obj:`node_norm` and :obj:`edge_norm` data attributes.
  
  .. note::
  
      See :class:`~torch_geometric.loader.GraphSAINTNodeSampler`,
      :class:`~torch_geometric.loader.GraphSAINTEdgeSampler` and
      :class:`~torch_geometric.loader.GraphSAINTRandomWalkSampler` for
      currently supported samplers.
      For an example of using GraphSAINT sampling, see
      `examples/graph_saint.py <https://github.com/pyg-team/
      pytorch_geometric/blob/master/examples/graph_saint.py>`_.
  
  Args:
      data (torch_geometric.data.Data): The graph data object.
      batch_size (int): The approximate number of samples per batch.
      num_steps (int, optional): The number of iterations per epoch.
          (default: :obj:`1`)
      sample_coverage (int): How many samples per node should be used to
          compute normalization statistics. (default: :obj:`0`)
      save_dir (str, optional): If set, will save normalization statistics to
          the :obj:`save_dir` directory for faster re-use.
          (default: :obj:`None`)
      log (bool, optional): If set to :obj:`False`, will not log any
          pre-processing progress. (default: :obj:`True`)
      **kwargs (optional): Additional arguments of
          :class:`torch.utils.data.DataLoader`, such as :obj:`batch_size` or
          :obj:`num_workers`.
code: def _filename(self):
      return (
          f"{self.__class__.__name__.lower()}_{self.sample_coverage}.pt"
      )
  
imports: import os.path
  from typing import Optional
  import torch
  from tqdm import tqdm
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 2 for calling the GraphSAINTSampler obj's underlying member method code with example usage: obj._filename() and returns: self, str, self, str