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.
docstr: Load TensorRT quantized model engine from specific path.
  
  Parameters
  ----------
  path : str
      The path of export model
docstr_tok: ["Load","TensorRT","quantized","model","engine","from","specific","path",".","Parameters","--","--","--","--","--","path",":","str","The","path","of","export","model"]
code: def load_quantized_model(self, path):
      """
      Load TensorRT quantized model engine from specific path.
  
      Parameters
      ----------
      path : str
          The path of export model
      """
      assert path is not None
      with open(path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
          engine = runtime.deserialize_cuda_engine(f.read())
          self.context = engine.create_execution_context()
          logger.info(
              "Load TensorRT engine from %s successfully.", path
          )
  
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 6 for calling the ModelSpeedupTensorRT obj's underlying member method code with example usage: obj.load_quantized_model(path) without return types