class_docstr: Test class that extends tf.test.TestCase with extra functionality.
docstr: Run predict for a single example for a injective model.
  
  Args:
    eval_saved_model: EvalSavedModel
    raw_example_bytes: Raw example bytes for the example
  
  Returns:
    The singular FPL returned by eval_saved_model.predict on the given
    raw_example_bytes.
docstr_tok: ["Run","predict","for","a","single","example","for","a","injective","model",".","Args",":","eval_saved_model",":","EvalSavedModel","raw_example_bytes",":","Raw","example","bytes","for","the","example","Returns",":","The","singular","FPL","returned","by","eval_saved_model.predict","on","the","given","raw_example_bytes","."]
code: def predict_injective_single_example(
      self,
      eval_saved_model: load.EvalSavedModel,
      raw_example_bytes: bytes,
  ) -> types.FeaturesPredictionsLabels:
      """Run predict for a single example for a injective model.
  
      Args:
        eval_saved_model: EvalSavedModel
        raw_example_bytes: Raw example bytes for the example
  
      Returns:
        The singular FPL returned by eval_saved_model.predict on the given
        raw_example_bytes.
      """
      fetched_list = eval_saved_model.predict(raw_example_bytes)
      self.assertEqual(1, len(fetched_list))
      self.assertEqual(0, fetched_list[0].input_ref)
      return eval_saved_model.as_features_predictions_labels(
          fetched_list
      )[0]
  
imports: import math
  import tempfile
  from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import constants
  from tensorflow_model_analysis.eval_saved_model import load
  from tensorflow_model_analysis.eval_saved_model import util
  from tensorflow_model_analysis.utils import model_util
label_desc: Use image node_id 12 for calling the TensorflowModelAnalysisTest obj's underlying member method code with example usage: obj.predict_injective_single_example(eval_saved_model, raw_example_bytes) and returns: eval_saved_model