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### `tf.nn.state_saving_rnn(cell, inputs, state_saver, state_name, sequence_length=None, scope=None)` {#state_saving_rnn}
RNN that accepts a state saver for time-truncated RNN calculation.
##### Args:
* <b>`cell`</b>: An instance of `RNNCell`.
* <b>`inputs`</b>: A length T list of inputs, each a tensor of shape
`[batch_size, input_size]`.
* <b>`state_saver`</b>: A state saver object with methods `state` and `save_state`.
* <b>`state_name`</b>: Python string or tuple of strings. The name to use with the
state_saver. If the cell returns tuples of states (i.e.,
`cell.state_size` is a tuple) then `state_name` should be a tuple of
strings having the same length as `cell.state_size`. Otherwise it should
be a single string.
* <b>`sequence_length`</b>: (optional) An int32/int64 vector size [batch_size].
See the documentation for rnn() for more details about sequence_length.
* <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
##### Returns:
A pair (outputs, state) where:
outputs is a length T list of outputs (one for each input)
states is the final state
##### Raises:
* <b>`TypeError`</b>: If `cell` is not an instance of RNNCell.
* <b>`ValueError`</b>: If `inputs` is `None` or an empty list, or if the arity and
type of `state_name` does not match that of `cell.state_size`.
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