def get_output_for(self, *in_expr: TfExpression, return_as_list: bool = False, **dynamic_kwargs) -> Union[TfExpression, List[TfExpression]]:
        """Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s).
        The graph is placed on the current TensorFlow device."""
        assert len(in_expr) == self.num_inputs
        assert not all(expr is None for expr in in_expr)
        self._get_vars()  # ensure that all variables have been created

        # Choose build func kwargs.
        build_kwargs = dict(self.static_kwargs)
        build_kwargs.update(dynamic_kwargs)
        build_kwargs["is_template_graph"] = False
        build_kwargs["components"] = self._components

        # Build TensorFlow graph to evaluate the network.
        with tfutil.absolute_variable_scope(self.scope, reuse=True), tf.name_scope(self.name):
            assert tf.get_variable_scope().name == self.scope
            valid_inputs = [expr for expr in in_expr if expr is not None]
            final_inputs = []
            for expr, name, shape in zip(in_expr, self.input_names, self.input_shapes):
                if expr is not None:
                    expr = tf.identity(expr, name=name)
                else:
                    expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name)
                final_inputs.append(expr)
            out_expr = self._build_func(*final_inputs, **build_kwargs)

        # Propagate input shapes back to the user-specified expressions.
        for expr, final in zip(in_expr, final_inputs):
            if isinstance(expr, tf.Tensor):
                expr.set_shape(final.shape)

        # Express outputs in the desired format.
        assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
        if return_as_list:
            out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
        return out_expr
Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s).
        The graph is placed on the current TensorFlow device.
converters/stylegan2ada_tf_official/dnnlib/tflib/network.py