docstr: This function run through the onnxruntime forwarding step
docstr_tok: ["This","function","run","through","the","onnxruntime","forwarding","step"]
code: def forward_step(self, *inputs, **kwargs):
      """
      This function run through the onnxruntime forwarding step
      """
      flattened_inputs = []
      _flatten(inputs, flattened_inputs)
      zipped_inputs = dict(zip(self.forward_args, flattened_inputs))
      if kwargs is not None and len(kwargs) > 0:
          zipped_inputs.update(kwargs)
      # TODO: kwargs has tuple or dict?
      if len(self._forward_args) != len(zipped_inputs):
          # formatting a Tensor will cost much time,
          # so we put it in this `if` statement
          invalidInputError(
              False,
              "The length of inputs is "
              "inconsistent with the length of ONNX Runtime session's inputs, "
              f"got model_forward_args: {self._forward_args}, "
              f"and flattened inputs: {flattened_inputs}",
          )
      ort_outs = self.ortsess.run(None, zipped_inputs)
      return ort_outs
  
file_path: BigDL/python/nano/src/bigdl/nano/deps/onnxruntime/core/onnxruntime_model.py
imports: from pathlib import Path
  import onnxruntime
  import onnx
  from bigdl.nano.utils.common import invalidInputError, _flatten
  import numpy
label_desc: Use image node_id 2 for calling the ONNXRuntimeModel obj's underlying member method code with example usage: obj.forward_step() and returns: ort_outs