docstr: Generates predictions with some model and input data. 
docstr_tok: ["Generates","predictions","with","some","model","and","input","data","."]
code: def predict(
      self,
      model_name: str,
      data: list,
      pred_format: str = "dict",
      project_name: str = None,
      version=None,
      params: dict = None,
  ):
      """Generates predictions with some model and input data."""
      if isinstance(data, dict):
          data = [data]
      df = pd.DataFrame(data)
      kwargs = {
          "name": model_name,
          "ml_handler_name": self.name,
          "project_name": project_name,
      }
      if version is None:
          kwargs["active"] = True
      else:
          kwargs["active"] = None
          kwargs["version"] = version
      predictor_record = get_model_record(**kwargs)
      if predictor_record is None:
          if version is not None:
              model_name = f"{model_name}.{version}"
          raise Exception(
              f"Error: model '{model_name}' does not exists!"
          )
      if predictor_record.status != PREDICTOR_STATUS.COMPLETE:
          raise Exception("Error: model creation not completed")
  
      args = {
          "pred_format": pred_format,
          "predict_params": {} if params is None else params,
      }
  
      try:
          task = self.base_ml_executor.apply_async(
              task_type=ML_TASK_TYPE.PREDICT,
              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(),
                  "predictor_record": predictor_record,
                  "args": args,
              },
              dataframe=df,
          )
          predictions = task.result()
      except Exception as e:
          msg = str(e).strip()
          if msg == "":
              msg = e.__class__.__name__
          msg = f"[{self.name}/{model_name}]: {msg}"
          raise MLEngineException(msg) from e
  
      # mdb indexes
      if (
          "__mindsdb_row_id" not in predictions.columns
          and "__mindsdb_row_id" in df.columns
      ):
          predictions["__mindsdb_row_id"] = df["__mindsdb_row_id"]
  
      after_predict_hook(
          company_id=ctx.company_id,
          predictor_id=predictor_record.id,
          rows_in_count=df.shape[0],
          columns_in_count=df.shape[1],
          rows_out_count=len(predictions),
      )
      return predictions
  
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 7 for calling the BaseMLEngineExec obj's underlying member method code with example usage: obj.predict(model_name, data, pred_format, project_name, version, params) and returns: predictions