code: def run(self, inputs, **kwargs):
      neoml_inputs = dict()
  
      def _get_dtype(orig_dtype):
          if np.issubdtype(orig_dtype, np.floating):
              return np.float32
          elif (
              np.issubdtype(orig_dtype, np.integer)
              or orig_dtype == bool
          ):
              return np.int32
          raise ValueError(
              f"{orig_dtype} is not supported by neoml.Onnx.BackendRep"
          )
  
      input_infos = self.info["inputs"]
      for idx, onnx_input in enumerate(inputs):
          onnx_input = onnx_input.astype(
              _get_dtype(onnx_input.dtype), copy=False
          )
          neoml_shape = list(onnx_input.shape) + [1] * (
              7 - len(onnx_input.shape)
          )
          neoml_blob = neoml.Blob.asblob(
              self.dnn.math_engine, onnx_input, neoml_shape
          )
          neoml_inputs[input_infos[idx]] = neoml_blob
      neoml_outputs = self.dnn.run(neoml_inputs)
      result = list()
      for output, onnx_dtype, dim_count in zip(
          self.info["outputs"],
          self.output_dtypes,
          self.output_dim_count,
      ):
          out_blob = neoml_outputs[output]
          result.append(out_blob.asarray())
          if dim_count is not None:
              result[-1].resize(out_blob.shape[:dim_count])
          if onnx_dtype is not None:
              result[-1] = result[-1].astype(onnx_dtype, copy=False)
      return result
  
file_path: neoml/NeoML/Python/neoml/Onnx/Backend.py
imports: from .API import load_from_buffer
  import onnx.backend.base
  import neoml.MathEngine
  import neoml.Blob
  import numpy
label_desc: Use image node_id 2 for calling the BackendRep obj's underlying member method code with example usage: obj.run(inputs) and returns: result, np, np