code: def test_gradient_target_tuple(self) -> None:
      model = BasicModel()
      input = torch.tensor(
          [[[4.0, 2.0], [-1.0, -2.0]], [[3.0, -4.0], [10.0, 5.0]]],
          requires_grad=True,
      )
      grads = compute_gradients(model, input, target_ind=(0, 1))[0]
      assertTensorAlmostEqual(
          self,
          grads,
          [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]]],
          delta=0.01,
          mode="max",
      )
  
imports: from typing import List, Tuple
  import torch
  from captum._utils.gradient import apply_gradient_requirements, compute_gradients, compute_layer_gradients_and_eval, undo_gradient_requirements
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel, BasicModel2, BasicModel4_MultiArgs, BasicModel5_MultiArgs, BasicModel6_MultiTensor, BasicModel_MultiLayer
label_desc: Use image node_id 10 for calling the Test obj's underlying member method code with example usage: obj.test_gradient_target_tuple() without return types