class_docstr: A distributed loader that preform sampling from edges.
  
  Args:
      data: A (:class:`~torch_geometric.data.FeatureStore`,
          :class:`~torch_geometric.data.GraphStore`) data object.
      num_neighbors (List[int] or Dict[Tuple[str, str, str], List[int]]):
          The number of neighbors to sample for each node in each iteration.
          If an entry is set to :obj:`-1`, all neighbors will be included.
          In heterogeneous graphs, may also take in a dictionary denoting
          the amount of neighbors to sample for each individual edge type.
      master_addr (str): RPC address for distributed loader communication,
          *i.e.* the IP address of the master node.
      master_port (Union[int, str]): Open port for RPC communication with
          the master node.
      current_ctx (DistContext): Distributed context information of the
          current process.
      concurrency (int, optional): RPC concurrency used for defining the
          maximum size of the asynchronous processing queue.
          (default: :obj:`1`)
  
      All other arguments follow the interface of
      :class:`torch_geometric.loader.LinkNeighborLoader`.
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code: def __repr__(self) -> str:
      return DistLoader.__repr__(self)
  
file_path: pytorch_geometric/torch_geometric/distributed/dist_link_neighbor_loader.py
imports: from typing import Callable, Dict, List, Optional, Tuple, Union
  import torch
  from torch_geometric.distributed import DistLoader, DistNeighborSampler, LocalFeatureStore, LocalGraphStore
  from torch_geometric.distributed.dist_context import DistContext
  from torch_geometric.loader import LinkLoader
  from torch_geometric.sampler.base import NegativeSampling, SubgraphType
  from torch_geometric.typing import EdgeType, InputEdges, OptTensor
label_desc: Use image node_id 2 for calling the DistLinkNeighborLoader obj's underlying member method code with example usage: obj.__repr__() and returns: DistLoader