code: def main():
      parser = ArgumentParser()
      parser.add_argument(
          "--model",
          type=str,
          required=True,
          help="Path to .nemo model file(s). If ensembling, provide comma separated paths to multiple models.",
      )
      parser.add_argument(
          "--srctext",
          type=str,
          required=True,
          help="Path to the file to translate.",
      )
      parser.add_argument(
          "--tgtout",
          type=str,
          required=True,
          help="Path to the file where translations are to be written.",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=256,
          help="Number of sentences to batch together while translatiing.",
      )
      parser.add_argument(
          "--beam_size", type=int, default=4, help="Beam size."
      )
      parser.add_argument(
          "--len_pen",
          type=float,
          default=0.6,
          help="Length Penalty. Ref: https://arxiv.org/abs/1609.08144",
      )
      parser.add_argument(
          "--max_delta_length",
          type=int,
          default=5,
          help="Stop generating if target sequence length exceeds source length by this number.",
      )
      parser.add_argument(
          "--target_lang",
          type=str,
          default=None,
          help="Target language identifier ex: en,de,fr,es etc. If both `--target_lang` and `--source_lang` are "
          "not set, then target language processing will be done the same way as during model training. If "
          "`--target_lang` parameter is not set but `--source_lang` parameter is set, then target language "
          "processing will not be performed. If `--target_lang` equals 'ignore', then target language processing "
          "will not be performed regardless of value of `--source_lang` parameter.",
      )
      parser.add_argument(
          "--source_lang",
          type=str,
          default=None,
          help="Source language identifier ex: en,de,fr,es etc. If both `--target_lang` and `--source_lang` are "
          "not set, then source language processing will be done the same way as during model training. If "
          "`--source_lang` parameter is not set but `--target_lang` parameter is set, then source language "
          "processing will not be performed. If `--source_lang` equals 'ignore', then source language processing "
          "will not be performed regardless of value of `--target_lang` parameter.",
      )
      parser.add_argument(
          "--write_scores",
          action="store_true",
          help="Whether to write a separate file with scores not including length penalties corresponding to each beam hypothesis (.score suffix)",
      )
      parser.add_argument(
          "--write_timing",
          action="store_true",
          help="Whether to write a separate file with detailed timing info (.timing.json suffix)",
      )
      # shallow fusion specific parameters
      parser.add_argument(
          "--lm_model",
          type=str,
          default=None,
          help="Optional path to an LM model that has the same tokenizer as NMT models for shallow fuison. Note: If using --write_scores, it will add LM scores as well.",
      )
      parser.add_argument(
          "--fusion_coef",
          type=float,
          default=0.07,
          help="Weight assigned to LM scores during shallow fusion.",
      )
  
      args = parser.parse_args()
      torch.set_grad_enabled(False)
      logging.info("Attempting to initialize from .nemo file")
      models = []
      for model_path in args.model.split(","):
          if not model_path.endswith(".nemo"):
              raise NotImplementedError(
                  f"Only support .nemo files, but got: {model_path}"
              )
          model = nemo_nlp.models.machine_translation.MTEncDecModel.restore_from(
              restore_path=model_path
          ).eval()
          models.append(model)
  
      if (len(models) > 1) and (args.write_timing):
          raise RuntimeError(
              "Cannot measure timing when more than 1 model is used"
          )
  
      src_text = []
      tgt_text = []
      tgt_text_all = []
      src_texts = []
      all_scores = []
      all_timing = []
  
      if torch.cuda.is_available():
          models = [model.cuda() for model in models]
  
      if args.lm_model is not None:
          lm_model = nemo_nlp.models.language_modeling.TransformerLMModel.restore_from(
              restore_path=args.lm_model
          ).eval()
      else:
          lm_model = None
  
      if len(models) > 1:
          ensemble_generator = EnsembleBeamSearchSequenceGenerator(
              encoders=[model.encoder for model in models],
              embeddings=[model.decoder.embedding for model in models],
              decoders=[model.decoder.decoder for model in models],
              log_softmaxes=[model.log_softmax for model in models],
              max_sequence_length=512,
              beam_size=args.beam_size,
              bos=models[0].decoder_tokenizer.bos_id,
              pad=models[0].decoder_tokenizer.pad_id,
              eos=models[0].decoder_tokenizer.eos_id,
              len_pen=args.len_pen,
              max_delta_length=args.max_delta_length,
              language_model=lm_model,
              fusion_coef=args.fusion_coef,
          )
      else:
          model = models[0]
          ensemble_generator = None
          if lm_model is not None:
              model.beam_search = BeamSearchSequenceGeneratorWithLanguageModel(
                  embedding=model.decoder.embedding,
                  decoder=model.decoder.decoder,
                  log_softmax=model.log_softmax,
                  bos=model.decoder_tokenizer.bos_id,
                  pad=model.decoder_tokenizer.pad_id,
                  eos=model.decoder_tokenizer.eos_id,
                  language_model=lm_model,
                  fusion_coef=args.fusion_coef,
                  max_sequence_length=model.decoder.max_sequence_length,
                  beam_size=args.beam_size,
                  len_pen=args.len_pen,
                  max_delta_length=args.max_delta_length,
              )
          else:
              model.beam_search = BeamSearchSequenceGenerator(
                  embedding=model.decoder.embedding,
                  decoder=model.decoder.decoder,
                  log_softmax=model.log_softmax,
                  bos=model.decoder_tokenizer.bos_id,
                  pad=model.decoder_tokenizer.pad_id,
                  eos=model.decoder_tokenizer.eos_id,
                  max_sequence_length=model.decoder.max_sequence_length,
                  beam_size=args.beam_size,
                  len_pen=args.len_pen,
                  max_delta_length=args.max_delta_length,
              )
  
      logging.info(f"Translating: {args.srctext}")
  
      with open(args.srctext, "r") as src_f:
          for line in src_f:
              src_text.append(line.strip())
              if len(src_text) == args.batch_size:
                  # warmup when measuring timing
                  if args.write_timing and (not all_timing):
                      print("running a warmup batch")
                      translate_text(
                          models=models,
                          args=args,
                          src_text=src_text,
                          tgt_text=[],
                          tgt_text_all=[],
                          src_texts=[],
                          all_scores=[],
                          all_timing=[],
                          ensemble_generator=ensemble_generator,
                      )
                  translate_text(
                      models=models,
                      args=args,
                      src_text=src_text,
                      tgt_text=tgt_text,
                      tgt_text_all=tgt_text_all,
                      src_texts=src_texts,
                      all_scores=all_scores,
                      all_timing=all_timing,
                      ensemble_generator=ensemble_generator,
                  )
                  src_text = []
  
          if len(src_text) > 0:
              translate_text(
                  models=models,
                  args=args,
                  src_text=src_text,
                  tgt_text=tgt_text,
                  tgt_text_all=tgt_text_all,
                  src_texts=src_texts,
                  all_scores=all_scores,
                  all_timing=all_timing,
                  ensemble_generator=ensemble_generator,
              )
  
      with open(args.tgtout, "w") as tgt_f:
          for line in tgt_text:
              tgt_f.write(line + "\n")
  
      if args.write_scores:
          with open(args.tgtout + ".score", "w") as tgt_f_scores:
              for line, score, inp in zip(
                  tgt_text_all, all_scores, src_texts
              ):
                  tgt_f_scores.write(
                      inp + "\t" + line + "\t" + str(score) + "\n"
                  )
  
      if args.write_timing:
          # collect list of dicts to a dict of lists
          timing_dict = {}
          if len(all_timing):
              for k in all_timing[0].keys():
                  timing_dict[k] = [t[k] for t in all_timing]
  
          with open(args.tgtout + ".timing.json", "w") as timing_fh:
              json.dump(timing_dict, timing_fh)
  
imports: import json
  from argparse import ArgumentParser
  import torch
  import nemo.collections.nlp
  from nemo.collections.nlp.modules.common.transformer import BeamSearchSequenceGenerator, BeamSearchSequenceGeneratorWithLanguageModel, EnsembleBeamSearchSequenceGenerator
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types