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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

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(tensor)