keras/layers/recurrent.py [678:696]:
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        if self.return_state:
            states = to_list(states, allow_tuple=True)
            return [output] + states
        else:
            return output

    def reset_states(self, states=None):
        if not self.stateful:
            raise AttributeError('Layer must be stateful.')
        batch_size = self.input_spec[0].shape[0]
        if not batch_size:
            raise ValueError('If a RNN is stateful, it needs to know '
                             'its batch size. Specify the batch size '
                             'of your input tensors: \n'
                             '- If using a Sequential model, '
                             'specify the batch size by passing '
                             'a `batch_input_shape` '
                             'argument to your first layer.\n'
                             '- If using the functional API, specify '
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keras/legacy/layers.py [607:625]:
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        if self.return_state:
            states = to_list(states, allow_tuple=True)
            return [output] + states
        else:
            return output

    def reset_states(self, states=None):
        if not self.stateful:
            raise AttributeError('Layer must be stateful.')
        batch_size = self.input_spec[0].shape[0]
        if not batch_size:
            raise ValueError('If a RNN is stateful, it needs to know '
                             'its batch size. Specify the batch size '
                             'of your input tensors: \n'
                             '- If using a Sequential model, '
                             'specify the batch size by passing '
                             'a `batch_input_shape` '
                             'argument to your first layer.\n'
                             '- If using the functional API, specify '
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