class_docstr: Model optimized using TensorRT.
  
  The class cannot be directly instantiated, but implements all the core
  methods needed for using TensorRT at inference time.
  
  Attributes:
      network_parameters (ModelParams): The model parameters as batch
              size, input and output sizes.
      engine (any): The tensorRT engine.
      input_names (List[str]): Names associated to the model input tensors.
      output_names (List[str]): Names associated to the model output tensors.
      cuda_stream (any, optional): Stream used for communication with Nvidia
          GPUs.
      nvidia_logger (any, optional): Logger used by the Nvidia service
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code: def _synchronize_stream(self):
      raise NotImplementedError()
  
imports: import json
  import os
  from abc import ABC
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import Any, Union, Dict, Type, List, Tuple, Generator, Optional
  import numpy
  from loguru import logger
  from nebullvm.config import NVIDIA_FILENAMES
  from nebullvm.core.models import Device, DeviceType, ModelParams, DeepLearningFramework
  from nebullvm.operations.inference_learners.base import BaseInferenceLearner, LearnerMetadata, PytorchBaseInferenceLearner, TensorflowBaseInferenceLearner, NumpyBaseInferenceLearner
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.tensor_rt import tensorrt, polygraphy
  from nebullvm.optional_modules.torch import torch, ScriptModule
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation, VerifyContiguity
label_desc: Use image node_id 3 for calling the ONNXTensorRTInferenceLearner obj's underlying member method code with example usage: obj._synchronize_stream() without return types