```
import tensorflow as tf
dims, layers = 32, 2
# Creating the forward and backwards cells
lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(dims, forget_bias=1.0)
lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(dims, forget_bias=1.0)
# Pass lstm_fw_cell / lstm_bw_cell directly to tf.nn.bidrectional_rnn
# if only a single layer is needed
lstm_fw_multicell = tf.nn.rnn_cell.MultiRNNCell([lstm_fw_cell]*layers)
lstm_bw_multicell = tf.nn.rnn_cell.MultiRNNCell([lstm_bw_cell]*layers)
# tf.nn.bidirectional_rnn takes a list of tensors with shape
# [batch_size x cell_fw.state_size], so separate the input into discrete
# timesteps.
_X = tf.unpack(state_below, axis=1)
# state_fw and state_bw are the final states of the forwards/backwards LSTM, respectively
outputs, state_fw, state_bw = tf.nn.bidirectional_rnn(lstm_fw_multicell, lstm_bw_multicell, _X, dtype='float32')
```

**Parameters**

`state_below`

is a 3D tensor of with the following dimensions: [`batch_size`

, maximum sequence index,`dims`

]. This comes from a previous operation, such as looking up a word embedding.`dims`

is the number of hidden units.`layers`

can be adjusted above 1 to create a*stacked LSTM network*.

**Notes**

`tf.unpack`

may not be able to determine the size of a given axis (use the`nums`

argument if this is the case).- It may be helpful to add an additional weight + bias multiplication beneath the LSTM (e.g.
`tf.matmul(state_below, U) + b`

.