code: def _construct_default_feature_mask(
      inputs: Tuple[Tensor, ...]
  ) -> Tuple[Tuple[Tensor, ...], int]:
      feature_mask = []
      current_num_features = 0
      for i in range(len(inputs)):
          num_features = torch.numel(inputs[i][0])
          feature_mask.append(
              current_num_features
              + torch.reshape(
                  torch.arange(num_features, device=inputs[i].device),
                  inputs[i][0:1].shape,
              )
          )
          current_num_features += num_features
      total_features = current_num_features
      feature_mask = tuple(feature_mask)
      return feature_mask, total_features
  
imports: import typing
  from inspect import signature
  from typing import Any, Callable, List, Tuple, TYPE_CHECKING, Union
  import torch
  from captum._utils.common import _format_baseline, _format_output, _format_tensor_into_tuples, _validate_input
  from captum._utils.typing import BaselineType, Literal, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.approximation_methods import SUPPORTED_METHODS
  from torch import Tensor
label_desc: Use image node_id 20 for calling a global function with example usage: _construct_default_feature_mask(inputs) and returns: feature_mask,  total_features