code: def batch_predict_caffe2_model(
      pytext_model_file: str,
      caffe2_model_file: str,
      db_type: str = CAFFE2_DB_TYPE,
      data_source: Optional[DataSource] = None,
      use_cuda=False,
      task: Optional[NewTask] = None,
      train_config: Optional[PyTextConfig] = None,
      cache_size: int = 0,
  ):
      """
      Gets predictions from caffe2 model from a batch of examples.
  
      Args:
          pytext_model_file: Path to pytext model file (required if task and
              training config is not specified)
          caffe2_model_file: Path to caffe2 model file
          db_type: DB type to use for caffe2
          data_source: Data source for test examples
          use_cuda: Whether to turn on cuda processing
          task: The pytext task object
          train_config: The pytext training config
          cache_size: The LRU cache size to use for prediction. 0 = no cache,
              -1 = boundless cache, [1, inf) = size of cache
      """
      logging.info(
          f"Loading data processing config from {pytext_model_file}"
      )
  
      _set_cuda(use_cuda)
      if task is None or train_config is None:
          task, train_config, _ = load(pytext_model_file)
  
      data_source = data_source or task.data.data_source
      logging.info(f"Loading Caffe2 model: {caffe2_model_file}")
      predictor = create_predictor(
          train_config,
          PathManager.get_local_path(caffe2_model_file),
          db_type,
          task,
          cache_size,
      )
      logging.info(f"Model loaded, start testing")
      predictions = [predictor(example) for example in data_source.test]
      return predictions
  
file_path: pytext/pytext/__init__.py
imports: import json
  import logging
  import uuid
  from functools import lru_cache
  from typing import Callable, Mapping, Optional
  import numpy
  from caffe2.python import workspace
  from caffe2.python.predictor import predictor_exporter
  from pytext.data.sources.data_source import DataSource
  from pytext.task import load
  from pytext.task.new_task import NewTask
  from pytext.utils.file_io import PathManager, register_http_url_handler
  from pytext.workflow import _set_cuda
  from .builtin_task import register_builtin_tasks
  from .config import pytext_config_from_json, PyTextConfig
  from .utils.onnx import CAFFE2_DB_TYPE, convert_caffe2_blob_name
label_desc: Use image node_id 4 for calling a global function with example usage: batch_predict_caffe2_model(pytext_model_file, caffe2_model_file, db_type, data_source, use_cuda, task, train_config, cache_size) and returns: predictions