':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
:return: No return value.'

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

    self._target = target
    self._train_op = self._tf_optimizer.minimize(loss, var_list=target.get_params(trainable=True))
    if (extra_inputs is None):
        extra_inputs = list()
    self._input_vars = (inputs + extra_inputs)
    self._opt_fun = ext.lazydict(f_loss=(lambda : tensor_utils.compile_function((inputs + extra_inputs), loss)))
