':param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:param gradients: symbolic expressions for the gradients of trainable parameters of the target. By default
this will be computed by calling theano.grad
:return: No return value.'

def update_opt(self, loss, target, inputs, extra_inputs=None, gradients=None, *args, **kwargs):

    self._target = target
    def get_opt_output(gradients):
        if (gradients is None):
            gradients = theano.grad(loss, target.get_params(trainable=True))
        flat_grad = flatten_tensor_variables(gradients)
        return [loss.astype('float64'), flat_grad.astype('float64')]
    if (extra_inputs is None):
        extra_inputs = list()
    self._opt_fun = lazydict(f_loss=(lambda : compile_function((inputs + extra_inputs), loss)), f_opt=(lambda : compile_function(inputs=(inputs + extra_inputs), outputs=get_opt_output(gradients))))
