@neural_computation
    def get_step_inputs(self, input_var, states=None, mask=None, additional_inputs=None):
        step_inputs = {}
        if self._input_type == "sequence":
            if not additional_inputs:
                additional_inputs = []
            step_inputs.update(self.merge_inputs(input_var, additional_inputs=additional_inputs))
        else:
            # step_inputs["mask"] = mask.dimshuffle((1,0)) if mask else None
            if additional_inputs:
                step_inputs.update(self.merge_inputs(None, additional_inputs=additional_inputs))
        if states:
            for name in self.state_names:
                step_inputs[name] = states[name]

        return step_inputs
Tests if the LandmarkGroup contains ``nan`` values or
dataset/ETHPy150Open zomux/deepy/deepy/layers/recurrent.py/RecurrentLayer.get_step_inputs--crossover with--dataset/ETHPy150Open menpo/menpo/menpo/landmark/base.py/LandmarkGroup.has_nan_values