How to use the onnxruntime.capi._pybind_state function in onnxruntime

def _load_model(self, providers=[]):
        if isinstance(self._path_or_bytes, str): 
            self._sess = C.InferenceSession(
                self._sess_options if self._sess_options else C.get_default_session_options(), 
                self._path_or_bytes, True)
        elif isinstance(self._path_or_bytes, bytes):
            self._sess = C.InferenceSession(
                self._sess_options if self._sess_options else C.get_default_session_options(), 
                self._path_or_bytes, False)
        # elif isinstance(self._path_or_bytes, tuple):
            # to remove, hidden trick
        #   self._sess.load_model_no_init(self._path_or_bytes[0], providers)
        else:
            raise TypeError("Unable to load from type '{0}'".format(type(self._path_or_bytes)))

        self._sess.load_model(providers)
        
        self._session_options = self._sess.session_options
        self._inputs_meta = self._sess.inputs_meta
        self._outputs_meta = self._sess.outputs_meta
        self._overridable_initializers = self._sess.overridable_initializers
        self._model_meta = self._sess.model_meta
        self._providers = self._sess.get_providers()

def _load_model(self, providers=[]):
        if isinstance(self._path_or_bytes, str): 
            self._sess = C.InferenceSession(
                self._sess_options if self._sess_options else C.get_default_session_options(), 
                self._path_or_bytes, True)
        elif isinstance(self._path_or_bytes, bytes):
            self._sess = C.InferenceSession(
                self._sess_options if self._sess_options else C.get_default_session_options(), 
                self._path_or_bytes, False)
        # elif isinstance(self._path_or_bytes, tuple):
            # to remove, hidden trick
        #   self._sess.load_model_no_init(self._path_or_bytes[0], providers)
        else:
            raise TypeError("Unable to load from type '{0}'".format(type(self._path_or_bytes)))

        self._sess.load_model(providers)
        
        self._session_options = self._sess.session_options
        self._inputs_meta = self._sess.inputs_meta

# to remove, hidden trick
        #   self._sess.load_model_no_init(self._path_or_bytes[0], providers)
        else:
            raise TypeError("Unable to load from type '{0}'".format(type(self._path_or_bytes)))

        self._sess.load_model(providers)
        
        self._session_options = self._sess.session_options
        self._inputs_meta = self._sess.inputs_meta
        self._outputs_meta = self._sess.outputs_meta
        self._overridable_initializers = self._sess.overridable_initializers
        self._model_meta = self._sess.model_meta
        self._providers = self._sess.get_providers()

        # Tensorrt can fall back to CUDA. All others fall back to CPU.
        if 'TensorrtExecutionProvider' in C.get_available_providers():
          self._fallback_providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
        else:
          self._fallback_providers = ['CPUExecutionProvider']

def _load_model(self, providers=[]):
        if isinstance(self._path_or_bytes, str): 
            self._sess = C.InferenceSession(
                self._sess_options if self._sess_options else C.get_default_session_options(), 
                self._path_or_bytes, True)
        elif isinstance(self._path_or_bytes, bytes):
            self._sess = C.InferenceSession(
                self._sess_options if self._sess_options else C.get_default_session_options(), 
                self._path_or_bytes, False)
        # elif isinstance(self._path_or_bytes, tuple):
            # to remove, hidden trick
        #   self._sess.load_model_no_init(self._path_or_bytes[0], providers)
        else:
            raise TypeError("Unable to load from type '{0}'".format(type(self._path_or_bytes)))

        self._sess.load_model(providers)
        
        self._session_options = self._sess.session_options
        self._inputs_meta = self._sess.inputs_meta
        self._outputs_meta = self._sess.outputs_meta
        self._overridable_initializers = self._sess.overridable_initializers
        self._model_meta = self._sess.model_meta
        self._providers = self._sess.get_providers()

def set_providers(self, providers):
        """
        Register the input list of execution providers. The underlying session is re-created.

        :param providers: list of execution providers

        The list of providers is ordered by Priority. For example ['CUDAExecutionProvider', 'CPUExecutionProvider'] means
        execute a node using CUDAExecutionProvider if capable, otherwise execute using CPUExecutionProvider.
        """
        if not set(providers).issubset(C.get_available_providers()):
            raise ValueError("{} does not contain a subset of available providers {}".format(providers, C.get_available_providers()))
        self._reset_session()
        self._load_model(providers)

:param run_options: See :class:`onnxruntime.RunOptions`.

        ::

            sess.run([output_name], {input_name: x})
        """
        num_required_inputs = len(self._inputs_meta)
        num_inputs = len(input_feed)
        # the graph may have optional inputs used to override initializers. allow for that.
        if num_inputs < num_required_inputs:
            raise ValueError("Model requires {} inputs. Input Feed contains {}".format(num_required_inputs, num_inputs))
        if not output_names:
            output_names = [output.name for output in self._outputs_meta]
        try:
            return self._sess.run(output_names, input_feed, run_options)
        except C.EPFail as err:
            if self._enable_fallback:
                print("EP Error: {} using {}".format(str(err), self._providers))
                print("Falling back to {} and retrying.".format(self._fallback_providers))
                self.set_providers(self._fallback_providers)
                # Fallback only once.
                self.disable_fallback()
                return self._sess.run(output_names, input_feed, run_options)
            else:
                raise