code: def _hypothetical_contrib_func(
      multipliers: Tuple[Tensor, ...],
      inputs: Tuple[Tensor, ...],
      baselines: Tuple[Tensor, ...],
  ) -> Tuple[Tensor, ...]:
      r"""
      Implements hypothetical input contributions based on the logic described here:
      https://github.com/kundajelab/deeplift/pull/36/files
      This is using a dummy model for test purposes
      """
      # we assume that multiplies, inputs and baselines have the following shape:
      # tuple((bsz x len x channel), )
      assert len(multipliers[0].shape) == 3, multipliers[0].shape
      assert len(inputs[0].shape) == 3, inputs[0].shape
      assert len(baselines[0].shape) == 3, baselines[0].shape
      assert len(multipliers) == len(inputs) and len(inputs) == len(
          baselines
      ), (
          "multipliers, inputs and baselines must have the same shape but"
          "multipliers: {}, inputs: {}, baselines: {}".format(
              len(multipliers), len(inputs), len(baselines)
          )
      )
  
      attributions = []
      for k in range(len(multipliers)):
          sub_attributions = torch.zeros_like(inputs[k])
          for i in range(inputs[k].shape[-1]):
              hypothetical_input = torch.zeros_like(inputs[k])
              hypothetical_input[:, :, i] = 1.0
              hypothetical_input_ref_diff = (
                  hypothetical_input - baselines[k]
              )
              sub_attributions[:, :, i] = torch.sum(
                  hypothetical_input_ref_diff * multipliers[k], dim=-1
              )
          attributions.append(sub_attributions)
      return tuple(attributions)
  
imports: from inspect import signature
  from typing import Callable, List, Tuple, Union
  import torch
  from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from tests.helpers.basic import assertAttributionComparision, assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModelWithReusedModules, Conv1dSeqModel, LinearMaxPoolLinearModel, ReLUDeepLiftModel, ReLULinearModel, TanhDeepLiftModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 1 for calling a global function with example usage: _hypothetical_contrib_func(multipliers, inputs, baselines) and returns: tuple