code: def predict_on_stream(
      config: Union[str, Path, dict],
      batch_size: Optional[int] = None,
      file_path: Optional[str] = None,
  ) -> None:
      """Make a prediction with the component described in corresponding configuration file."""
  
      batch_size = batch_size or 1
      if file_path is None or file_path == "-":
          if sys.stdin.isatty():
              raise RuntimeError(
                  "To process data from terminal please use interact mode"
              )
          f = sys.stdin
      else:
          f = open(file_path, encoding="utf8")
  
      model: Chainer = build_model(config)
  
      args_count = len(model.in_x)
      while True:
          batch = list(
              (l.strip() for l in islice(f, batch_size * args_count))
          )
  
          if not batch:
              break
  
          args = []
          for i in range(args_count):
              args.append(batch[i::args_count])
  
          res = model(*args)
          if len(model.out_params) == 1:
              res = [res]
          for res in zip(*res):
              res = json.dumps(jsonify_data(res), ensure_ascii=False)
              print(res, flush=True)
  
      if f is not sys.stdin:
          f.close()
  
file_path: DeepPavlov/deeppavlov/core/commands/infer.py
imports: import json
  import sys
  from itertools import islice
  from logging import getLogger
  from pathlib import Path
  from typing import Optional, Union
  from deeppavlov.core.commands.utils import import_packages, parse_config
  from deeppavlov.core.common.chainer import Chainer
  from deeppavlov.core.common.params import from_params
  from deeppavlov.core.data.utils import jsonify_data
  from deeppavlov.download import deep_download
  from deeppavlov.utils.pip_wrapper import install_from_config
label_desc: Use image node_id 3 for calling a global function with example usage: predict_on_stream(config, batch_size, file_path) without return types