code: def runTest(self):
      data = self.spark.createDataFrame(
          [
              [1, "query: how much protein should a female eat"],
              [2, "query: summit define"],
              [
                  3,
                  "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 "
                  "is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're "
                  "expecting or training for a marathon. Check out the chart below to see how much protein you should "
                  "be eating each day.",
              ],
              [
                  4,
                  "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain :"
                  " the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the "
                  "leaders of two or more governments.",
              ],
          ]
      ).toDF("id", "text")
  
      document_assembler = (
          DocumentAssembler()
          .setInputCol("text")
          .setOutputCol("documents")
      )
  
      e5 = self.tested_annotator
  
      pipeline = Pipeline().setStages([document_assembler, e5])
      results = pipeline.fit(data).transform(data)
  
      results.select("e5.embeddings").show(truncate=False)
  
file_path: spark-nlp/python/test/annotator/embeddings/e5_embeddings_test.py
imports: import os
  import unittest
  import pytest
  from sparknlp.annotator import *
  from sparknlp.base import *
  from test.annotator.common.has_max_sentence_length_test import HasMaxSentenceLengthTests
  from test.util import SparkContextForTest
label_desc: Use image node_id 2 for calling the E5EmbeddingsTestSpec obj's underlying member method code with example usage: obj.runTest() without return types