code: def _get_rule_config() -> Tuple[Tensor, Module, Tensor, Tensor]:
      relevance = torch.tensor([[[-0.0, 3.0]]])
      layer = nn.modules.Conv1d(1, 1, 2, bias=False)
      nn.init.constant_(layer.weight.data, 2)
      activations = torch.tensor([[[1.0, 5.0, 7.0]]])
      input = torch.tensor([[2, 0, -2]])
      return relevance, layer, activations, input
  
imports: from typing import cast, Tuple
  import torch
  import torch.nn
  from captum.attr import InputXGradient, LRP
  from captum.attr._utils.lrp_rules import Alpha1_Beta0_Rule, EpsilonRule, GammaRule, IdentityRule
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer, BasicModelWithReusedLinear, SimpleLRPModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling a global function with example usage: _get_rule_config() and returns: relevance,  layer,  activations,  input