code: def create_docker(
      api_name: str,
      base_image: str = "python:3.8-slim",
      expose_port: int = 8000,
  ) -> None:
      """
      This function creates a ``Dockerfile`` and ``requirements.txt`` for
      productionalizing API end-point.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> boston = get_data('boston')
      >>> from pycaret.regression import *
      >>> exp_name = setup(data = boston,  target = 'medv')
      >>> lr = create_model('lr')
      >>> create_api(lr, 'lr_api')
      >>> create_docker('lr_api')
  
  
      api_name: str
          Name of API. Must be saved as a .py file in the same folder.
  
  
      base_image: str, default = "python:3.8-slim"
          Name of the base image for Dockerfile.
  
  
      expose_port: int, default = 8000
          port for expose for API in the Dockerfile.
  
  
      Returns:
          None
      """
      return _CURRENT_EXPERIMENT.create_docker(
          api_name=api_name,
          base_image=base_image,
          expose_port=expose_port,
      )
  
imports: import logging
  import os
  from typing import Any, BinaryIO, Callable, Dict, List, Optional, Union
  import pandas
  from joblib.memory import Memory
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.regression.oop import RegressionExperiment
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 35 for calling a global function with example usage: create_docker(api_name, base_image, expose_port) and returns: _CURRENT_EXPERIMENT