code: def test_spacy_dependency_parsing(self, mock_handler):
      self.run_sql("CREATE DATABASE proj")
  
      text = ["Apple is looking at buying U.K. startup for $1 billion"]
      df = pd.DataFrame(text, columns=["text"])
  
      self.set_handler(mock_handler, name="pg", tables={"df": df})
  
      self.run_sql(
          """
              CREATE MODEL proj.spacy__dependency_parsing__model
              PREDICT recognition
              USING
                engine = 'spacy'
                linguistic_feature = 'dependency-parsing',
                target_column = 'text';
              """
      )
  
      result_df = self.run_sql(
          """
              SELECT recognition
              FROM proj.spacy__dependency_parsing__model
              WHERE
              text='Apple is looking at buying U.K. startup for $1 billion';
              """
      )
  
      assert (
          "{("
          "', 'punct', 'looking', 'VERB', '[]'), ('for', 'prep', 'startup', 'NOUN', '[billion]'), ('1', 'compound', 'billion', 'NUM', '[]'), ('Apple', 'nsubj', 'looking', 'VERB', '[]'), ('buying', 'pcomp', 'at', 'ADP', '[U.K.]'), ('looking', 'ROOT', 'looking', 'VERB', '[\", Apple, is, at, startup, \"]'), ('$', 'quantmod', 'billion', 'NUM', '[]'), ('is', 'aux', 'looking', 'VERB', '[]'), ('billion', 'pobj', 'for', 'ADP', '[$, 1]'), ('startup', 'dep', 'looking', 'VERB', '[for]'), ('at', 'prep', 'looking', 'VERB', '[buying]'), ('U.K.', 'dobj', 'buying', 'VERB', '[]')}"
          in result_df["recognition"].iloc[0]
      )
  
file_path: mindsdb/tests/unit/ml_handlers/test_spacy.py
imports: from unittest.mock import patch
  import pandas
  from mindsdb_sql import parse_sql
  from tests.unit.executor_test_base import BaseExecutorTest
label_desc: Use image node_id 4 for calling the TestSpacy obj's underlying member method code with example usage: obj.test_spacy_dependency_parsing(mock_handler) without return types