code: def add_initializer(self, name, onnx_type, shape, content):
          '''
          Add a TensorProto into the initializer list of the final ONNX model
  
          :param name: Variable name in the produced ONNX model.
          :param onnx_type: Element types allowed in ONNX tensor, e.g., TensorProto.FLOAT and TensorProto.STRING.
          :param shape: Tensor shape, a list of integers.
          :param content: Flattened tensor values (i.e., a float list or a float array).
          '''
          if any(d is None for d in shape):
              raise ValueError('Shape of initializer cannot contain None')
          tensor = helper.make_tensor(name, onnx_type, shape, content)
          self.initializers.append(tensorf add_initializer(self, name, onnx_type, shape, content):
          '''
          Add a TensorProto into the initializer list of the final ONNX model
  
          :param name: Variable name in the produced ONNX model.
          :param onnx_type: Element types allowed in ONNX tensor, e.g., TensorProto.FLOAT and TensorProto.STRING.
          :param shape: Tensor shape, a list of integers.
          :param content: Flattened tensor values (i.e., a float list or a float array).
          '''
          if any(d is None for d in shape):
              raise ValueError('Shape of initializer cannot contain None')
          tensor = helper.make_tensor(name, onnx_type, shape, content)
          self.initializers.append(tensor)
docs: Add a TensorProto into the initializer list of the final ONNX model
  
          :param name: Variable name in the produced ONNX model.
          :param onnx_type: Element types allowed in ONNX tensor, e.g., TensorProto.FLOAT and TensorProto.STRING.
          :param shape: Tensor shape, a list of integers.
          :param content: Flattened tensor values (i.e., a float list or a float array).