class_docstr: A sampler with given order, specially for IBMB loaders.
  
  Args:
      data_source (np.ndarray, torch.Tensor, List): A :obj:`np.ndarray`,
          :obj:`torch.Tensor`, or :obj:`List` data object. Contains the
          order of the batches.
class_docstr_tok: ["A","sampler","with","given","order",",","specially","for","IBMB","loaders",".","Args",":","data_source","(","np.ndarray",",","torch.Tensor",",","List",")",":","A",":","obj",":","`","np.ndarray","`",",",":","obj",":","`","torch.Tensor","`",",","or",":","obj",":","`","List","`","data","object",".","Contains","the","order","of","the","batches","."]
code: def __len__(self) -> int:
      return len(self.data_source)
  
imports: import logging
  import math
  from typing import Callable, Iterator, List, NamedTuple, Optional, Tuple, Union
  import numpy
  import scipy.sparse
  import torch
  from torch import Tensor
  from tqdm import tqdm
  from torch_geometric.data import Data
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import get_ppr, is_undirected, subgraph
label_desc: Use image node_id 3 for calling the IBMBOrderedSampler obj's underlying member method code with example usage: obj.__len__() and returns: len