docstr: Test that predictions are unaffected when there is no training.
docstr_tok: ["Test","that","predictions","are","unaffected","when","there","is","no","training","."]
code: def test_graph_reg_wrapper_no_training(self):
      """Test that predictions are unaffected when there is no training."""
      # Base model: y = x + 2
      base_est = self.build_linear_regressor(
          weight=[[1.0]],
          weight_shape=[1, 1],
          bias=[2.0],
          bias_shape=[1],
      )
  
      def embedding_fn(features, unused_mode):
          # Apply the same model, i.e, y = x + 2.
          # Use broadcasting to do element-wise addition.
          return tf.math.add(features[FEATURE_NAME], [2.0])
  
      graph_reg_config = nsl_configs.make_graph_reg_config(
          max_neighbors=1
      )
      graph_reg_est = nsl_estimator.add_graph_regularization(
          base_est, embedding_fn, graph_reg_config=graph_reg_config
      )
  
      # Consider only one neighbor for the input sample.
      example = """
                  features {
                    feature {
                      key: "x"
                      value: { float_list { value: [ 1.0 ] } }
                    }
                    feature {
                      key: "NL_nbr_0_x"
                      value: { float_list { value: [ 2.0 ] } }
                    }
                    feature {
                      key: "NL_nbr_0_weight"
                      value: { float_list { value: 1.0 } }
                    }
                 }
                """
  
      input_fn = single_example_input_fn(
          example, input_shape=[1], max_neighbors=0
      )
      predictions = graph_reg_est.predict(input_fn=input_fn)
      predicted_scores = [x["predictions"] for x in predictions]
      self.assertAllClose([[3.0]], predicted_scores)
  
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 4 for calling the GraphRegularizationTest obj's underlying member method code with example usage: obj.test_graph_reg_wrapper_no_training() and returns: tf