code: def __init__(self, model, device):
      if device == "CPU":
          math_engine = neoml.MathEngine.CpuMathEngine(1)
      else:
          math_engine = neoml.MathEngine.GpuMathEngine()
      self.dnn, self.info = load_from_buffer(
          model.SerializeToString(), math_engine
      )
      output_infos = self.info["outputs"]
      self.output_dtypes = [None] * len(output_infos)
      self.output_dim_count = [None] * len(output_infos)
      for value_info in model.graph.output:
          for idx, out in enumerate(output_infos):
              if out == value_info.name:
                  try:
                      self.output_dtypes[idx] = _onnx_type_to_np[
                          value_info.type.tensor_type.elem_type
                      ]
                  except:
                      pass
                  try:
                      self.output_dim_count[idx] = len(
                          value_info.type.tensor_type.shape.dim
                      )
                  except:
                      pass
  
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 1 to create a new BackendRep object  with example: obj = BackendRep(model, device)