7ee672fda5a01e29375439b6a18fb1971f90d3c5,pymanopt/autodiff/backends/_tensorflow.py,_TensorFlowBackend,compute_hessian,#_TensorFlowBackend#,92

Before Change

    @Backend._assert_backend_available
    def compute_hessian(self, objective, argument):
        if not isinstance(argument, list):
            argA = tf.zeros_like(argument)
            tfhess = _hessian_vector_product(objective, [argument], [argA])

            def hess(x, a):
                feed_dict = {argument: x, argA: a}
                return self._session.run(tfhess[0], feed_dict)
        else:
            argA = [tf.zeros_like(arg) for arg in argument]
            tfhess = _hessian_vector_product(objective, argument, argA)

            def hess(x, a):
                feed_dict = {i: d for i, d in zip(argument+argA, x+a)}
                return self._session.run(tfhess, feed_dict)

        return hess


TensorFlow = make_graph_backend_decorator(_TensorFlowBackend)

After Change

    @Backend._assert_backend_available
    def compute_hessian(self, function, arguments):
        flattened_arguments = flatten_arguments(arguments)

        if len(flattened_arguments) == 1:
            (argument,) = flattened_arguments
            zeros = tf.zeros_like(argument)
            hessian = _hessian_vector_product(function, [argument], [zeros])

            def unary_hessian(point, vector):
                feed_dict = {argument: point, zeros: vector}
                return self._session.run(hessian[0], feed_dict)
            return unary_hessian

        zeros = [tf.zeros_like(argument) for argument in flattened_arguments]
        hessian = _hessian_vector_product(function, flattened_arguments, zeros)

        def nary_hessian(points, vectors):

Instances