code: def get_parser_with_args():
      parser = options.get_parser(
          "Generation", default_task="pytorch_translate"
      )
      pytorch_translate_options.add_verbosity_args(parser)
      pytorch_translate_options.add_dataset_args(parser, gen=True)
      generation_group = options.add_generation_args(parser)
      pytorch_translate_options.expand_generation_args(generation_group)
  
      # Adds args used by the standalone generate binary.
      generation_group.add_argument(
          "--source-vocab-file",
          default="",
          metavar="FILE",
          help="Path to text file representing the Dictionary to use.",
      )
      generation_group.add_argument(
          "--char-source-vocab-file",
          default="",
          metavar="FILE",
          help=(
              "Same as --source-vocab-file except using characters. "
              "(For use with char_source and char_aware models only.)"
          ),
      )
      generation_group.add_argument(
          "--target-vocab-file",
          default="",
          metavar="FILE",
          help="Path to text file representing the Dictionary to use.",
      )
      generation_group.add_argument(
          "--char-target-vocab-file",
          default="",
          metavar="FILE",
          help=(
              "Same as --target-vocab-file except using characters. "
              "(For use with char_aware models only.)"
          ),
      )
      generation_group.add_argument(
          "--source-text-file",
          default="",
          nargs="+",
          metavar="FILE",
          help="Path to raw text file containing examples in source dialect. "
          "This overrides what would be loaded from the data dir. "
          "You can specify multiple source files (eg. for use in combination "
          "with --source-ensembling). By default this will only translate the "
          "first source file",
      )
      generation_group.add_argument(
          "--target-text-file",
          default="",
          metavar="FILE",
          help="Path to raw text file containing examples in target dialect. "
          "This overrides what would be loaded from the data dir.",
      )
      generation_group.add_argument(
          "--source-binary-file",
          default="",
          help="Path for the binary file containing source eval examples. "
          "(Overrides --source-text-file. Must be used in conjunction with "
          "--target-binary-file).",
      )
      generation_group.add_argument(
          "--target-binary-file",
          default="",
          help="Path for the binary file containing target eval examples. "
          "(Overrides --target-text-file. Must be used in conjunction with "
          "--source-binary-file).",
      )
      generation_group.add_argument(
          "--translation-output-file",
          default="",
          type=str,
          metavar="FILE",
          help="Path to text file to store the output of the model. ",
      )
      generation_group.add_argument(
          "--translation-probs-file",
          default="",
          type=str,
          metavar="FILE",
          help="Path to text file to store the probs of translation output. ",
      )
      generation_group.add_argument(
          "--multiling-source-lang",
          action="append",
          metavar="SRC",
          help=(
              "Must be set for decoding with multilingual models. "
              "Must match an entry from --multiling-encoder-lang from training."
          ),
      )
      generation_group.add_argument(
          "--multiling-target-lang",
          action="append",
          metavar="TARGET",
          help=(
              "Must be set for decoding with multilingual models. "
              "Must match an entry from --multiling-decoder-lang from training."
          ),
      )
      generation_group.add_argument(
          "--source-ensembling",
          action="store_true",
          help="If this flag is present, the model will ensemble the predictions "
          "conditioned on multiple source sentences (one per source-text-file)",
      )
      generation_group.add_argument(
          "--competing-completed-beam-search",
          action="store_true",
          help="If this flag is present, use the alternative beam search "
          "implementation in research/beam_search. This beam search keeps completed "
          "hypos in the beam and let them compete against hypo expansions in the "
          "next time step.",
      )
      generation_group.add_argument(
          "--iterative-refinement",
          action="store_true",
          help="If this flag is present, use iterative refinement.",
      )
  
      return parser
  
imports: import argparse
  import collections
  import os
  import pickle
  from typing import List, NamedTuple, Optional
  import numpy
  import torch
  from fairseq import data, iterative_refinement_generator, options, progress_bar, tasks, utils
  from fairseq.meters import StopwatchMeter, TimeMeter
  from fairseq.models import FairseqEncoderDecoderModel, FairseqMultiModel
  from fairseq.scoring import bleu
  from pytorch_translate import beam_decode, char_aware_hybrid, char_source_hybrid, char_source_model, char_source_transformer_model, hybrid_transformer_rnn, options, rnn, transformer, utils
  from pytorch_translate.constants import CHECKPOINT_PATHS_DELIMITER
  from pytorch_translate.data import data
  from pytorch_translate.dual_learning.dual_learning_models import DualLearningModel
  from pytorch_translate.research.beam_search import competing_completed
  from pytorch_translate.research.multisource import multisource_data, multisource_decode
  from pytorch_translate.tasks.pytorch_translate_multi_task import PyTorchTranslateMultiTask
  from pytorch_translate.tasks.pytorch_translate_task import PytorchTranslateTask
label_desc: Use image node_id 7 for calling a global function with example usage: get_parser_with_args() and returns: parser