def transduce(self, es):
        """
        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. 
        """
        for e in es:
            ensure_freshness(e)
        for (fb,bb) in self.builder_layers:
            fs = fb.initial_state().transduce(es)
            bs = bb.initial_state().transduce(reversed(es))
            es = [concatenate([f,b]) for f,b in zip(fs, reversed(bs))]
        return es
def transduce(self, es):
        """
        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. 
        """
        for e in es:
            ensure_freshness(e)
        for (fb,bb) in self.builder_layers:
            fs = fb.initial_state().transduce(es)
            bs = bb.initial_state().transduce(reversed(es))
            es = [concatenate([f,b]) for f,b in zip(fs, reversed(bs))]
        return es
def transduce ( self , es ) : for e in es : ensure_freshness ( e ) for ( fb , bb ) in self . builder_layers : fs = fb . initial_state ( ) . transduce ( es ) bs = bb . initial_state ( ) . transduce ( reversed ( es ) ) es = [ concatenate ( [ f , b ] ) for f , b in zip ( fs , reversed ( bs ) ) ] return es
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.
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