class_docstr: A distributed retriever built on top of the ``torch.distributed`` communication package. During training all workers
  initialize their own instance of the retriever, however, only the main worker loads the index into memory. The index is stored
  in cpu memory. The index will also work well in a non-distributed setup.
  
  Args:
      config (:class:`~transformers.RagConfig`):
          The configuration of the RAG model this Retriever is used with. Contains parameters indicating which ``Index`` to build.
      question_encoder_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
          The tokenizer that was used to tokenize the question.
          It is used to decode the question and then use the generator_tokenizer.
      generator_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
          The tokenizer used for the generator part of the RagModel.
      index (:class:`~transformers.models.rag.retrieval_rag.Index`, optional, defaults to the one defined by the configuration):
          If specified, use this index instead of the one built using the configuration
code: def _infer_socket_ifname(self):
      addrs = psutil.net_if_addrs()
      # a hacky way to deal with varying network interface names
      ifname = next(
          (addr for addr in addrs if addr.startswith("e")), None
      )
      return ifname
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/rag/distributed_pytorch_retriever.py
imports: import logging
  import os
  from typing import List, Tuple
  import numpy
  import psutil
  import torch
  import torch.distributed
  from transformers import RagRetriever
label_desc: Use image node_id 5 for calling the RagPyTorchDistributedRetriever obj's underlying member method code with example usage: obj._infer_socket_ifname() and returns: ifname