docstr: Trains a model given some data-gathering SQL statement. 
docstr_tok: ["Trains","a","model","given","some","data-gathering","SQL","statement","."]
code: def learn(
      self,
      model_name,
      project_name,
      data_integration_ref=None,
      fetch_data_query=None,
      problem_definition=None,
      join_learn_process=False,
      label=None,
      is_retrain=False,
      set_active=True,
  ):
      # TODO move to model_controller
      """Trains a model given some data-gathering SQL statement."""
  
      # may or may not be provided (e.g. 0-shot models do not need it), so engine will handle it
      target = problem_definition.get(
          "target", [""]
      )  # db.Predictor expects Column(Array(String))
  
      project = self.database_controller.get_project(name=project_name)
  
      if hasattr(self.handler_class, "create_validation"):
          self.handler_class.create_validation(
              target,
              args=problem_definition,
              handler_storage=HandlerStorage(self.integration_id),
          )
  
      predictor_record = db.Predictor(
          company_id=ctx.company_id,
          name=model_name,
          integration_id=self.integration_id,
          data_integration_ref=data_integration_ref,
          fetch_data_query=fetch_data_query,
          mindsdb_version=mindsdb_version,
          to_predict=target,
          learn_args=problem_definition,
          data={"name": model_name},
          project_id=project.id,
          training_data_columns_count=None,
          training_data_rows_count=None,
          training_start_at=dt.datetime.now(),
          status=PREDICTOR_STATUS.GENERATING,
          label=label,
          hostname=socket.gethostname(),
          version=(
              db.session.query(
                  coalesce(func.max(db.Predictor.version), 1)
                  + (1 if is_retrain else 0)
              )
              .filter_by(
                  company_id=ctx.company_id,
                  name=model_name,
                  project_id=project.id,
                  deleted_at=null(),
              )
              .scalar_subquery()
          ),
          active=(not is_retrain),  # if create then active
      )
  
      db.serializable_insert(predictor_record)
  
      task = self.base_ml_executor.apply_async(
          task_type=ML_TASK_TYPE.LEARN,
          model_id=predictor_record.id,
          payload={
              "handler_meta": {
                  "module_path": self.handler_class.__module__,
                  "class_name": self.handler_class.__name__,
                  "engine": self.engine,
                  "integration_id": self.integration_id,
              },
              "context": ctx.dump(),
              "model_id": predictor_record.id,
              "problem_definition": problem_definition,
              "set_active": set_active,
              "data_integration_ref": data_integration_ref,
              "fetch_data_query": fetch_data_query,
              "project_name": project_name,
          },
      )
  
      if join_learn_process is True:
          task.result()
          predictor_record = db.Predictor.query.get(predictor_record.id)
          db.session.refresh(predictor_record)
      else:
          # to prevent memory leak need to add any callback
          task.add_done_callback(empty_callback)
  
      return predictor_record
  
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 6 for calling the BaseMLEngineExec obj's underlying member method code with example usage: obj.learn(model_name, project_name, data_integration_ref, fetch_data_query, problem_definition, join_learn_process, label, is_retrain, set_active) and returns: predictor_record