code: def test_gradient_activation_embedding_no_grad(self) -> None:
      input1 = torch.tensor([2, 5, 0, 1])
      input2 = torch.tensor([3, 0, 0, 2])
      model = BasicEmbeddingModel()
      for param in model.parameters():
          param.requires_grad = False
  
      with torch.no_grad():
          layer_act = LayerGradientXActivation(model, model.embedding1)
          self.assertEqual(
              list(layer_act.attribute(inputs=(input1, input2)).shape),
              [4, 100],
          )
  
imports: import unittest
  from typing import Any, List, Tuple, Union
  import torch
  from captum._utils.typing import ModuleOrModuleList
  from captum.attr._core.layer.layer_activation import LayerActivation
  from captum.attr._core.layer.layer_gradient_x_activation import LayerGradientXActivation
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicEmbeddingModel, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 13 for calling the Test obj's underlying member method code with example usage: obj.test_gradient_activation_embedding_no_grad() without return types