code: def _add_biencoder_args(parser):
      group = parser.add_argument_group(title="biencoder")
  
      # network size
      group.add_argument(
          "--ict-head-size",
          type=int,
          default=None,
          help="Size of block embeddings to be used in ICT and "
          "REALM (paper default: 128)",
      )
      group.add_argument(
          "--biencoder-projection-dim",
          type=int,
          default=0,
          help="Size of projection head used in biencoder (paper"
          " default: 128)",
      )
      group.add_argument(
          "--biencoder-shared-query-context-model",
          action="store_true",
          help="Whether to share the parameters of the query "
          "and context models or not",
      )
  
      # checkpointing
      group.add_argument(
          "--ict-load",
          type=str,
          default=None,
          help="Directory containing an ICTBertModel checkpoint",
      )
      group.add_argument(
          "--bert-load",
          type=str,
          default=None,
          help="Directory containing an BertModel checkpoint "
          "(needed to start ICT and REALM)",
      )
  
      # data
      group.add_argument(
          "--titles-data-path",
          type=str,
          default=None,
          help="Path to titles dataset used for ICT",
      )
      group.add_argument(
          "--query-in-block-prob",
          type=float,
          default=0.1,
          help="Probability of keeping query in block for "
          "ICT dataset",
      )
      group.add_argument(
          "--use-one-sent-docs",
          action="store_true",
          help="Whether to use one sentence documents in ICT",
      )
      group.add_argument(
          "--evidence-data-path",
          type=str,
          default=None,
          help="Path to Wikipedia Evidence frm DPR paper",
      )
  
      # training
      group.add_argument(
          "--retriever-report-topk-accuracies",
          nargs="+",
          type=int,
          default=[],
          help="Which top-k accuracies to report " "(e.g. '1 5 20')",
      )
      group.add_argument(
          "--retriever-score-scaling",
          action="store_true",
          help="Whether to scale retriever scores by inverse "
          "square root of hidden size",
      )
  
      # faiss index
      group.add_argument(
          "--block-data-path",
          type=str,
          default=None,
          help="Where to save/load BlockData to/from",
      )
      group.add_argument(
          "--embedding-path",
          type=str,
          default=None,
          help="Where to save/load Open-Retrieval Embedding"
          " data to/from",
      )
  
      # indexer
      group.add_argument(
          "--indexer-batch-size",
          type=int,
          default=128,
          help="How large of batches to use when doing indexing "
          "jobs",
      )
      group.add_argument(
          "--indexer-log-interval",
          type=int,
          default=1000,
          help="After how many batches should the indexer "
          "report progress",
      )
      return parser
  
imports: import argparse
  import dataclasses
  import json
  import os
  import torch
  import types
  import torch.nn.functional
  from megatron.global_vars import set_retro_args, get_retro_args
  from tools.retro.utils import get_args_path
  from megatron.core.models.retro import RetroConfig
  from megatron.core.transformer import TransformerConfig
label_desc: Use image node_id 21 for calling a global function with example usage: _add_biencoder_args(parser) and returns: parser