code: def expand_generation_args(group, train=False):
      """Expands the generation related arguments with pytorch_translate
      specific arguments"""
      group.add_argument(
          "--word-reward",
          type=float,
          default=0.0,
          help=(
              "Value to add to (log-prob) score for each token except EOS. "
              "Value < 0 encourages shorter translations, while > 0 "
              "(the usual case) encourages longer translations "
              "(similar to --length-penalty)."
          ),
      )
      group.add_argument(
          "--unk-reward",
          type=float,
          default=0.0,
          help=(
              "Value to add to (log-prob) score for UNK tokens. "
              "Value < 0 (the usual case) encourages fewer UNKs, while > 0 "
              "encourages more UNKs."
          ),
      )
      group.add_argument(
          "--length-penalty",
          type=float,
          default=0.0,
          help=(
              "When >0 scores are normalized according to length (divided by "
              "length^length_penalty). Effectively overrides word_reward when"
              "in use. NOTE: supersedes --lenpen."
          ),
      )
      group.add_argument(
          "--model-weights",
          default="",
          help=(
              "Interpolation weights for ensembles. Comma-separated list of "
              "floats with length equal to the number of models in the ensemble."
          ),
      )
      group.add_argument(
          "--report-oracle-bleu",
          type=utils.bool_flag,
          nargs="?",
          const=True,
          default=False,
          help=(
              "During evaluation, determine best among top-k outputs (where k "
              "is controlled by --nbest) for each sentence by smoothed "
              "sentence-level BLEU and report overall BLEU score for these "
              "sentences."
          ),
      )
      group.add_argument(
          "--output-hypos-binary-path",
          default=None,
          type=str,
          help=(
              "Optional filename to save output hypotheses (binary format "
              "and EOS-terminated, suitable for use as training targets)"
          ),
      )
      group.add_argument(
          "--max-examples-to-evaluate",
          default=-1,
          type=int,
          help=(
              "If >0 and smaller than size of evaluation data set, randomly "
              "sample this many examples to evaluate"
          ),
      )
  
      group.add_argument(
          "--max-examples-to-evaluate-seed",
          default=-1,
          type=int,
          help=(
              "If not -1, set seed for random sample as the given seed value, "
              "so we can replicate result on random sample."
          ),
      )
  
      group.add_argument(
          "--output-source-binary-path",
          default=None,
          type=str,
          help=(
              "Optional filename to save binarized source after evaluation "
              "(primary use case being that this source dataset will be after "
              "any filtering due to --max-examples-to-evaluate)"
          ),
      )
      group.add_argument(
          "--translation-info-export-path",
          default=None,
          type=str,
          help=(
              "Optional path to save translation info output in pickled format"
          ),
      )
      group.add_argument(
          "--diversity-sibling-gamma",
          type=float,
          default=0.0,
          help=(
              "The diversity rate of sibling_rank for generating diverse beams"
          ),
      )
      group.add_argument(
          "--hypotheses-export-path",
          default=None,
          type=str,
          help=(
              "Optional path to save all generated hypotheses to external file"
          ),
      )
  
      # These arguments are only used during training
      if train:
          group.add_argument(
              "--multi-model-restore-files",
              default=None,
              type=str,
              nargs="+",
              help=(
                  "If --multi-encoder = --multi-decoder > 1, this option makes "
                  "it possible to initialize individual model weights from "
                  "existing checkpoints of separate training runs."
              ),
          )
      return group
  
file_path: translate/pytorch_translate/options.py
imports: import os
  import torch
  from fairseq.data.indexed_dataset import get_available_dataset_impl
  from pytorch_translate import constants, utils
label_desc: Use image node_id 7 for calling a global function with example usage: expand_generation_args(group, train) and returns: group