docstr: Create a network definition object used to create the gradient-boosted trees
  formulation in Pyomo
  
  Args:
     onnx_model : ONNX Model
        An ONNX model that is generated by the ONNX convert function for
        lightgbm.
     scaling_object : ScalingInterface or None
        A scaling object to specify the scaling parameters for the
        tree ensemble inputs and outputs. If None, then no
        scaling is performed.
     scaled_input_bounds : dict or None
        A dict that contains the bounds on the scaled variables (the
        direct inputs to the tree ensemble). If None, then no bounds
        are specified or they are generated using unscaled bounds.
docstr_tok: ["Create","a","network","definition","object","used","to","create","the","gradient-boosted","trees","formulation","in","Pyomo","Args",":","onnx_model",":","ONNX","Model","An","ONNX","model","that","is","generated","by","the","ONNX","convert","function","for","lightgbm",".","scaling_object",":","ScalingInterface","or","None","A","scaling","object","to","specify","the","scaling","parameters","for","the","tree","ensemble","inputs","and","outputs",".","If","None",",","then","no","scaling","is","performed",".","scaled_input_bounds",":","dict","or","None","A","dict","that","contains","the","bounds","on","the","scaled","variables","(","the","direct","inputs","to","the","tree","ensemble",")",".","If","None",",","then","no","bounds","are","specified","or","they","are","generated","using","unscaled","bounds","."]
code: def __init__(
      self, onnx_model, scaling_object=None, scaled_input_bounds=None
  ):
      """
      Create a network definition object used to create the gradient-boosted trees
      formulation in Pyomo
  
      Args:
         onnx_model : ONNX Model
            An ONNX model that is generated by the ONNX convert function for
            lightgbm.
         scaling_object : ScalingInterface or None
            A scaling object to specify the scaling parameters for the
            tree ensemble inputs and outputs. If None, then no
            scaling is performed.
         scaled_input_bounds : dict or None
            A dict that contains the bounds on the scaled variables (the
            direct inputs to the tree ensemble). If None, then no bounds
            are specified or they are generated using unscaled bounds.
      """
      self.__model = onnx_model
      self.__n_inputs = _model_num_inputs(onnx_model)
      self.__n_outputs = _model_num_outputs(onnx_model)
      self.__scaling_object = scaling_object
      self.__scaled_input_bounds = scaled_input_bounds
  
file_path: OMLT/src/omlt/gbt/model.py
label_desc: Use image node_id 1 to create a new GradientBoostedTreeModel object  with example: obj = GradientBoostedTreeModel(onnx_model, scaling_object, scaled_input_bounds)