code: def run(self, inputs, **kwargs):
      if isinstance(inputs, numpy.ndarray):
          inputs = [inputs]
      if isinstance(inputs, list):
          if len(inputs) == len(self._session.input_names):
              feeds = dict(zip(self._session.input_names, inputs))
          else:
              feeds = {}
              pos_inputs = 0
              for inp, tshape in zip(
                  self._session.input_names, self._session.input_types
              ):
                  shape = tuple(
                      d.dim_value for d in tshape.tensor_type.shape.dim
                  )
                  if shape == inputs[pos_inputs].shape:
                      feeds[inp] = inputs[pos_inputs]
                      pos_inputs += 1
                      if pos_inputs >= len(inputs):
                          break
      elif isinstance(inputs, dict):
          feeds = inputs
      else:
          raise TypeError(f"Unexpected input type {type(inputs)!r}.")
      outs = self._session.run(None, feeds)
      return outs
  
imports: import os
  import platform
  import sys
  import unittest
  from typing import Any
  import numpy
  import version_utils
  import onnx.backend.base
  import onnx.backend.test
  import onnx.shape_inference
  import onnx.version_converter
  from onnx import ModelProto
  from onnx.backend.base import Device, DeviceType
  from onnx.reference import ReferenceEvaluator
label_desc: Use image node_id 2 for calling the ReferenceEvaluatorBackendRep obj's underlying member method code with example usage: obj.run(inputs) and returns: outs