

@neural_computation
def get_step_inputs(self, input_var, states=None, mask=None, additional_inputs=None):
    '\n        :type input_var: T.var\n        :rtype: dict\n        '
    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))
    elif 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

dataset/ETHPy150Open zomux/deepy/deepy/layers/recurrent.py RecurrentLayer.get_step_inputs/original