docstr: Returns all models currently registered that belong to the ML engine.
docstr_tok: ["Returns","all","models","currently","registered","that","belong","to","the","ML","engine","."]
code: def get_tables(self) -> Response:
      """Returns all models currently registered that belong to the ML engine."""
      all_models = self.model_controller.get_models(
          integration_id=self.integration_id
      )
      all_models_names = [[x["name"]] for x in all_models]
      response = Response(
          RESPONSE_TYPE.TABLE,
          pd.DataFrame(all_models_names, columns=["table_name"]),
      )
      return response
  
imports: import socket
  import datetime
  from typing import Optional
  import pandas
  from sqlalchemy import func, null
  from sqlalchemy.sql.functions import coalesce
  from mindsdb_sql import parse_sql
  from mindsdb.utilities.config import Config
  import mindsdb.interfaces.storage.db
  from mindsdb.integrations.libs.response import HandlerResponse, RESPONSE_TYPE
  from mindsdb.__about__ import __version__
  from mindsdb.utilities.hooks import after_predict
  from mindsdb.interfaces.model.model_controller import ModelController
  from mindsdb.interfaces.model.functions import get_model_record
  from mindsdb.integrations.libs.const import PREDICTOR_STATUS
  from mindsdb.interfaces.database.database import DatabaseController
  from mindsdb.interfaces.storage.model_fs import ModelStorage, HandlerStorage
  from mindsdb.utilities.context import context
  from mindsdb.interfaces.model.functions import get_model_records
  from mindsdb.utilities.functions import mark_process
  import mindsdb.utilities.profiler
  from mindsdb.utilities.ml_task_queue.producer import MLTaskProducer
  from mindsdb.utilities.ml_task_queue.const import ML_TASK_TYPE
  from mindsdb.integrations.libs.process_cache import process_cache, empty_callback
label_desc: Use image node_id 3 for calling the BaseMLEngineExec obj's underlying member method code with example usage: obj.get_tables() and returns: response