code: def _get_simple_model2(
      inplace: bool = False,
  ) -> Tuple[Module, Tensor]:
      class MyModel(nn.Module):
          def __init__(self, inplace) -> None:
              super().__init__()
              self.lin = nn.Linear(2, 2)
              self.lin.weight = nn.Parameter(torch.ones(2, 2))
              self.relu = torch.nn.ReLU(inplace=inplace)
  
          def forward(self, input):
              return self.relu(self.lin(input))[0].unsqueeze(0)
  
      input = torch.tensor([[1.0, 2.0], [1.0, 3.0]])
      model = MyModel(inplace)
  
      return model, 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 4 for calling a global function with example usage: _get_simple_model2(inplace) and returns: model,  input, self