### `tf.contrib.rnn.static_bidirectional_rnn(cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None, dtype=None, sequence_length=None, scope=None)` {#static_bidirectional_rnn}

Creates a bidirectional recurrent neural network.

Similar to the unidirectional case above (rnn) but takes input and builds
independent forward and backward RNNs with the final forward and backward
outputs depth-concatenated, such that the output will have the format
[time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
forward and backward cell must match. The initial state for both directions
is zero by default (but can be set optionally) and no intermediate states are
ever returned -- the network is fully unrolled for the given (passed in)
length(s) of the sequence(s) or completely unrolled if length(s) is not given.

##### Args:


*  <b>`cell_fw`</b>: An instance of RNNCell, to be used for forward direction.
*  <b>`cell_bw`</b>: An instance of RNNCell, to be used for backward direction.
*  <b>`inputs`</b>: A length T list of inputs, each a tensor of shape
    [batch_size, input_size], or a nested tuple of such elements.
*  <b>`initial_state_fw`</b>: (optional) An initial state for the forward RNN.
    This must be a tensor of appropriate type and shape
    `[batch_size, cell_fw.state_size]`.
    If `cell_fw.state_size` is a tuple, this should be a tuple of
    tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
*  <b>`initial_state_bw`</b>: (optional) Same as for `initial_state_fw`, but using
    the corresponding properties of `cell_bw`.
*  <b>`dtype`</b>: (optional) The data type for the initial state.  Required if
    either of the initial states are not provided.
*  <b>`sequence_length`</b>: (optional) An int32/int64 vector, size `[batch_size]`,
    containing the actual lengths for each of the sequences.
*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to
    "bidirectional_rnn"

##### Returns:

  A tuple (outputs, output_state_fw, output_state_bw) where:
    outputs is a length `T` list of outputs (one for each input), which
      are depth-concatenated forward and backward outputs.
    output_state_fw is the final state of the forward rnn.
    output_state_bw is the final state of the backward rnn.

##### Raises:


*  <b>`TypeError`</b>: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
*  <b>`ValueError`</b>: If inputs is None or an empty list.