code: def testParseExampleStringsAndFloats(self):
      # Tests parsing examples of string and float features
      examples = []
      for i in range(50):
          example = tf.train.Example()
          example.features.feature["str"].bytes_list.value.append(b"hi")
          example.features.feature["float"].float_list.value.append(i)
          examples.append(example)
  
      entries = {}
      for i, example in enumerate(examples):
          self.fs._ParseExample(
              example.features.feature, [], entries, i
          )
  
      self.assertEqual(2, len(entries))
      self.assertEqual(self.fs.fs_proto.FLOAT, entries["float"]["type"])
      self.assertEqual(self.fs.fs_proto.STRING, entries["str"]["type"])
      for i in range(len(examples)):
          self.assertEqual(1, entries["str"]["counts"][i])
          self.assertEqual(1, entries["float"]["counts"][i])
          self.assertEqual(i, entries["float"]["vals"][i])
          self.assertEqual(
              "hi", entries["str"]["vals"][i].decode("UTF-8", "strict")
          )
  
imports: from facets_overview.feature_statistics_generator import FeatureStatisticsGenerator
  import numpy
  import tensorflow
  from tensorflow.python.platform import googletest
label_desc: Use image node_id 10 for calling the FeatureStatisticsGeneratorTest obj's underlying member method code with example usage: obj.testParseExampleStringsAndFloats() without return types