class_docstr: The GraphSAINT random walk sampler class (see
  :class:`~torch_geometric.loader.GraphSAINTSampler`).
  
  Args:
      walk_length (int): The length of each random walk.
code: def __init__(
      self,
      data,
      batch_size: int,
      walk_length: int,
      num_steps: int = 1,
      sample_coverage: int = 0,
      save_dir: Optional[str] = None,
      log: bool = True,
      **kwargs,
  ):
      self.walk_length = walk_length
      super().__init__(
          data,
          batch_size,
          num_steps,
          sample_coverage,
          save_dir,
          log,
          **kwargs,
      )
  
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 1 to create a new GraphSAINTRandomWalkSampler object from inherited base classes: GraphSAINTSampler with example: obj = GraphSAINTRandomWalkSampler(data, batch_size, walk_length, num_steps, sample_coverage, save_dir, log)