code: def create_fpls():
      fpl1 = types.FeaturesPredictionsLabels(
          input_ref=0,
          features=make_features_dict(
              {"gender": ["f"], "age": [13], "interest": ["cars"]}
          ),
          predictions=make_features_dict(
              {
                  "kb": [1],
              }
          ),
          labels=make_features_dict({"ad_risk_score": [0]}),
      )
      fpl2 = types.FeaturesPredictionsLabels(
          input_ref=0,
          features=make_features_dict(
              {"gender": ["m"], "age": [10], "interest": ["cars"]}
          ),
          predictions=make_features_dict(
              {
                  "kb": [1],
              }
          ),
          labels=make_features_dict({"ad_risk_score": [0]}),
      )
      return [fpl1, fpl2]
  
file_path: model-analysis/tensorflow_model_analysis/extractors/slice_key_extractor_test.py
imports: from absl.testing import parameterized
  import apache_beam
  from apache_beam.testing import util
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.extractors import slice_key_extractor
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
label_desc: Use image node_id 2 for calling a global function with example usage: create_fpls() and returns: list