    def run(
        self,
        inputs: Union[Dict[str, AITData], List[AITData]],
        outputs: Union[Dict[str, AITData], List[AITData]],
        stream_ptr: Optional[int] = None,
        sync: bool = True,
        graph_mode: bool = False,
    ) -> Dict[str, AITData]:
        """
        Run the model.

        Parameters
        ----------
        inputs: Union[Dict[str, AITData], List[AITData]]
            The inputs to use. AITData is a named tuple containing
            the tensor's data_ptr, size, and dtype. If inputs is a list,
            it must be ordered correctly (as specified by GetInputNameToIndexMap).
            This parameter can also be a dictionary (name -> AITData).
        outputs: Union[Dict[str, AITData], List[AITData]]
            The outputs to use. Similar to inputs, can either be a list of ordered
            outputs, or a dictionary (output name -> AITData).
            These should be allocated with enough memory to store their maximum
            size (which can be queried with GetOutputMaximumSize).
        stream_ptr: int
            A pointer to CUDA stream to run on. If None, use the legacy stream.
        sync: bool:
            If True, synchronize the stream at the end of the run
        graph_mode: bool
            If True, use a CUDA graph kernel (experimental)

        Returns
        -------
        AITDatas with output shapes that are computed by shape inference. This may not be
        the maximum shape. The output memory blobs that are passed in to Run()
        should be interpreted and possibly truncated according to these sizes.
        """
        return self._run_impl(
            inputs, outputs, stream_ptr, sync, graph_mode, outputs_on_host=False
        )
Run the model.

Parameters
----------
inputs: Union[Dict[str, AITData], List[AITData]]
The inputs to use. AITData is a named tuple containing
the tensor's data_ptr, size, and dtype. If inputs is a list,
it must be ordered correctly (as specified by GetInputNameToIndexMap).
This parameter can also be a dictionary (name -> AITData).
outputs: Union[Dict[str, AITData], List[AITData]]
The outputs to use. Similar to inputs, can either be a list of ordered
outputs, or a dictionary (output name -> AITData).
These should be allocated with enough memory to store their maximum
size (which can be queried with GetOutputMaximumSize).
stream_ptr: int
A pointer to CUDA stream to run on. If None, use the legacy stream.
sync: bool:
If True, synchronize the stream at the end of the run
graph_mode: bool
If True, use a CUDA graph kernel (experimental)

Returns
-------
AITDatas with output shapes that are computed by shape inference. This may not be
the maximum shape. The output memory blobs that are passed in to Run()
should be interpreted and possibly truncated according to these sizes.
python/aitemplate/compiler/model.py