def connect(self, inputs):
outputs, updates = self.transform(inputs)<EOL>if isinstance(outputs, dict):<EOL><INDENT>outputs = sorted(outputs.items())<EOL><DEDENT>if isinstance(outputs, (TT.TensorVariable, SS.SparseVariable)):<EOL><INDENT>outputs = [('<STR_LIT>', outputs)]<EOL><DEDENT>outs = {self.full_name(name): expr for name, expr in outputs}<EOL>return outs, updates<EOL>
Create Theano variables representing the outputs of this layer.

        Parameters
        ----------
        inputs : dict of Theano expressions
            Symbolic inputs to this layer, given as a dictionary mapping string
            names to Theano expressions. Each string key should be of the form
            "{layer_name}:{output_name}" and refers to a specific output from
            a specific layer in the graph.

        Returns
        -------
        outputs : dict
            A dictionary mapping names to Theano expressions for the outputs
            from this layer.
        updates : sequence of (parameter, expression) tuples
            Updates that should be performed by a Theano function that computes
            something using this layer.