def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpression]]]:
        """Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to
        individual layers of the network. Mainly intended to be used for reporting."""
        layers = []

        def recurse(scope, parent_ops, parent_vars, level):
            if len(parent_ops) == 0 and len(parent_vars) == 0:
                return

            # Ignore specific patterns.
            if any(p in scope for p in ["/Shape", "/strided_slice", "/Cast", "/concat", "/Assign"]):
                return

            # Filter ops and vars by scope.
            global_prefix = scope + "/"
            local_prefix = global_prefix[len(self.scope) + 1:]
            cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]]
            cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]]
            if not cur_ops and not cur_vars:
                return

            # Filter out all ops related to variables.
            for var in [op for op in cur_ops if op.type.startswith("Variable")]:
                var_prefix = var.name + "/"
                cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)]

            # Scope does not contain ops as immediate children => recurse deeper.
            contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type not in ["Identity", "Cast", "Transpose"] for op in cur_ops)
            if (level == 0 or not contains_direct_ops) and (len(cur_ops) != 0 or len(cur_vars) != 0):
                visited = set()
                for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]:
                    token = rel_name.split("/")[0]
                    if token not in visited:
                        recurse(global_prefix + token, cur_ops, cur_vars, level + 1)
                        visited.add(token)
                return

            # Report layer.
            layer_name = scope[len(self.scope) + 1:]
            layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1]
            layer_trainables = [var for _name, var in cur_vars if var.trainable]
            layers.append((layer_name, layer_output, layer_trainables))

        recurse(self.scope, self.list_ops(), list(self._get_vars().items()), 0)
        return layers
Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to
        individual layers of the network. Mainly intended to be used for reporting.
converters/stylegan2ada_tf_official/dnnlib/tflib/network.py