code: def _test_keras_linear_131(keras_fname, reduced_space=False):
      x, y, x_test = get_neural_network_data("131")
  
      nn = keras.models.load_model(keras_fname, compile=False)
      net = load_keras_sequential(nn, scaled_input_bounds=[(-1, 1)])
      m = pyo.ConcreteModel()
      m.neural_net_block = OmltBlock()
      if reduced_space:
          formulation = ReducedSpaceNNFormulation(net)
      else:
          formulation = FullSpaceNNFormulation(net)
      m.neural_net_block.build_formulation(formulation)
  
      nn_outputs = nn.predict(x=x_test)
      for d in range(len(x_test)):
          m.neural_net_block.inputs[0].fix(x_test[d])
          status = pyo.SolverFactory("ipopt").solve(m, tee=False)
          pyo.assert_optimal_termination(status)
          assert (
              abs(
                  pyo.value(m.neural_net_block.outputs[0])
                  - nn_outputs[d][0]
              )
              < 1e-5
          )
  
imports: import numpy
  import pyomo.environ
  import pytest
  from pyomo.common.dependencies import DeferredImportError
  from omlt.dependencies import keras, keras_available
  from conftest import get_neural_network_data
  from omlt.block import OmltBlock
  from omlt.neuralnet import FullSpaceNNFormulation, ReducedSpaceNNFormulation
  from omlt.neuralnet.activations import ComplementarityReLUActivation
  from omlt.scaling import OffsetScaling
label_desc: Use image node_id 2 for calling a global function with example usage: _test_keras_linear_131(keras_fname, reduced_space) without return types