def transduce(self, es):
for e in es:<EOL><INDENT>ensure_freshness(e)<EOL><DEDENT>for (fb,bb) in self.builder_layers:<EOL><INDENT>fs = fb.initial_state().transduce(es)<EOL>bs = bb.initial_state().transduce(reversed(es))<EOL>es = [concatenate([f,b]) for f,b in zip(fs, reversed(bs))]<EOL><DEDENT>return es<EOL>
returns the list of output Expressions obtained by adding the given inputs
to the current state, one by one, to both the forward and backward RNNs, 
and concatenating.

@param es: a list of Expression

see also add_inputs(xs)

.transduce(xs) is different from .add_inputs(xs) in the following way:

    .add_inputs(xs) returns a list of RNNState pairs. RNNState objects can be
     queried in various ways. In particular, they allow access to the previous
     state, as well as to the state-vectors (h() and s() )

    .transduce(xs) returns a list of Expression. These are just the output
     expressions. For many cases, this suffices. 
     transduce is much more memory efficient than add_inputs.