class_docstr: Model converted to ONNX and run with Microsoft's onnxruntime.
  
  Attributes:
      network_parameters (ModelParams): The model parameters as batch
              size, input and output sizes.
      onnx_path (str or Path): Path to the onnx model.
      input_names (List[str]): Input names used when the onnx model
          was produced.
      output_names (List[str]): Output names used when the onnx model
          was produced.
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code: def _predict_arrays(
      self, input_arrays: Generator[np.ndarray, None, None]
  ):
      input_dict = {
          name: input_array
          for name, input_array in zip(self.input_names, input_arrays)
      }
      outputs = self._session.run(self.output_names, input_dict)
      return outputs
  
imports: import multiprocessing
  import os
  import shutil
  from abc import ABC
  from pathlib import Path
  from typing import Union, List, Generator, Tuple, Dict, Type
  import cpuinfo
  import numpy
  from loguru import logger
  from nebullvm.config import ONNX_FILENAMES, ONNX_PROVIDERS
  from nebullvm.core.models import QuantizationType, Device, DeviceType, ModelParams, DeepLearningFramework
  from nebullvm.operations.inference_learners.base import BaseInferenceLearner, LearnerMetadata, PytorchBaseInferenceLearner, TensorflowBaseInferenceLearner, NumpyBaseInferenceLearner
  from nebullvm.operations.optimizations.compilers.utils import tensorrt_is_available
  from nebullvm.optional_modules.onnx import onnx
  from nebullvm.optional_modules.onnxruntime import onnxruntime
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 8 for calling the ONNXInferenceLearner obj's underlying member method code with example usage: obj._predict_arrays(input_arrays) and returns: outputs