code: def test_graph_reg_model_evaluate(self):
      weight = np.array([[4.0], [-3.0]])
      bias = np.array([0.0], dtype=np.float32)
  
      train_example = """
                  features {
                    feature {
                      key: "x"
                      value: { float_list { value: [ 2.0, 3.0 ] } }
                    }
                    feature {
                      key: "NL_nbr_0_x"
                      value: { float_list { value: [ 2.5, 3.0 ] } }
                    }
                    feature {
                      key: "NL_nbr_0_weight"
                      value: { float_list { value: 1.0 } }
                    }
                    feature {
                      key: "NL_nbr_1_x"
                      value: { float_list { value: [ 2.0, 2.0 ] } }
                    }
                    feature {
                      key: "NL_nbr_1_weight"
                      value: { float_list { value: 1.0 } }
                    }
                    feature {
                      key: "y"
                      value: { float_list { value: 0.0 } }
                    }
                  }
                """
  
      test_example = """
                  features {
                    feature {
                      key: "x"
                      value: { float_list { value: [ 4.0, 2.0 ] } }
                    }
                    feature {
                      key: "y"
                      value: { float_list { value: 4.0 } }
                    }
                  }
                """
      self._train_and_check_eval_results(
          train_example,
          test_example,
          max_neighbors=2,
          weight=weight,
          bias=bias,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import shutil
  import tempfile
  import neural_structured_learning.configs
  import neural_structured_learning.estimator
  import numpy
  import tensorflow
  from tensorflow import estimator
  from google.protobuf import text_format
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 9 for calling the GraphRegularizationTest obj's underlying member method code with example usage: obj.test_graph_reg_model_evaluate() without return types