class_docstr: Parameters
  ----------
  model : pytorch model
      The model to speedup by quantization.
  input_shape : tuple
      The input shape of the model, shall pass it to torch.onnx.export.
      Note, the batch size of input_shape is the inference batch of the created trt engine,
      it should be equal to the batch size of running test with the engine.
  config : dict
      Config recording bits number and name of layers.
  onnx_path : str
      The path user want to store onnx model which is converted from pytorch model.
code: def __init__(
      self,
      model,
      input_shape,
      config=None,
      onnx_path="default_model.onnx",
  ):
      super().__init__(model, config)
      self.model = model
      self.input_shape = input_shape
      self.config = config
      self.onnx_path = onnx_path
      # Input name of onnx model providing for torch.onnx.export to generate onnx model
      # Output name of onnx model providing for torch.onnx.export to generate onnx model
      self.input_names = ["actual_input_1"]
      self.output_names = ["output1"]
  
      self.engine = None
      self.context = None
      self.inputs = None
      self.outputs = None
      self.bindings = None
      self.stream = None
  
      trt_version = int(trt.__version__[0])
      assert (
          trt_version >= TRT8
      ), "Version of TensorRT is too old, please \
              update TensorRT to version >= 8.0"
  
imports: import time
  import logging
  import tensorrt
  import numpy
  import torch
  from .None import frontend_to_onnx
  from .None import trt_pycuda
  from .backend import BaseModelSpeedup
label_desc: Use image node_id 1 to create a new ModelSpeedupTensorRT object from inherited base classes: BaseModelSpeedup with example: obj = ModelSpeedupTensorRT(model, input_shape, config, onnx_path)