class_docstr: Configuration for the current :class:`TableEnvironment` session to adjust Table & SQL API
  programs.
  
  This class is a pure API class that abstracts configuration from various sources. Currently,
  configuration can be set in any of the following layers (in the given order):
  
  - flink-conf.yaml
  - CLI parameters
  - :class:`~pyflink.datastream.StreamExecutionEnvironment` when bridging to DataStream API
  - :func:`~EnvironmentSettings.Builder.with_configuration`
  - :func:`~TableConfig.set`
  
  The latter two represent the application-specific part of the configuration. They initialize
  and directly modify :func:`~TableConfig.get_configuration`. Other layers represent the
  configuration of the execution context and are immutable.
  
  The getter :func:`~TableConfig.get` gives read-only access to the full configuration. However,
  application-specific configuration has precedence. Configuration of outer layers is used for
  defaults and fallbacks. The setter :func:`~TableConfig.set` will only affect
  application-specific configuration.
  
  For common or important configuration options, this class provides getters and setters methods
  with detailed inline documentation.
  
  For more advanced configuration, users can directly access the underlying key-value map via
  :func:`~pyflink.table.TableConfig.get_configuration`.
  
  Example:
  ::
  
      >>> table_config = t_env.get_config()
      >>> config = Configuration()
      >>> config.set_string("parallelism.default", "128") \
      ...       .set_string("pipeline.auto-watermark-interval", "800ms") \
      ...       .set_string("execution.checkpointing.interval", "30s")
      >>> table_config.add_configuration(config)
  
  .. note::
  
      Because options are read at different point in time when performing operations, it is
      recommended to set configuration options early after instantiating a table environment.
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docstr: Sets the path of the python interpreter which is used to execute the python udf workers.
  
  e.g. "/usr/local/bin/python3".
  
  If python UDF depends on a specific python version which does not exist in the cluster,
  the method :func:`pyflink.table.TableEnvironment.add_python_archive` can be used to upload
  a virtual environment. The path of the python interpreter contained in the uploaded
  environment can be specified via this method.
  
  Example:
  ::
  
      # command executed in shell
      # assume that the relative path of python interpreter is py_env/bin/python
      $ zip -r py_env.zip py_env
  
      # python code
      >>> table_env.add_python_archive("py_env.zip")
      >>> table_env.get_config().set_python_executable("py_env.zip/py_env/bin/python")
  
  .. note::
  
      Please make sure the uploaded python environment matches the platform that the cluster
      is running on and that the python version must be 3.7 or higher.
  
  .. note::
  
      The python udf worker depends on Apache Beam (version == 2.43.0).
      Please ensure that the specified environment meets the above requirements.
  
  :param python_exec: The path of python interpreter.
  
  .. versionadded:: 1.10.0
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code: def set_python_executable(self, python_exec: str):
      """
      Sets the path of the python interpreter which is used to execute the python udf workers.
  
      e.g. "/usr/local/bin/python3".
  
      If python UDF depends on a specific python version which does not exist in the cluster,
      the method :func:`pyflink.table.TableEnvironment.add_python_archive` can be used to upload
      a virtual environment. The path of the python interpreter contained in the uploaded
      environment can be specified via this method.
  
      Example:
      ::
  
          # command executed in shell
          # assume that the relative path of python interpreter is py_env/bin/python
          $ zip -r py_env.zip py_env
  
          # python code
          >>> table_env.add_python_archive("py_env.zip")
          >>> table_env.get_config().set_python_executable("py_env.zip/py_env/bin/python")
  
      .. note::
  
          Please make sure the uploaded python environment matches the platform that the cluster
          is running on and that the python version must be 3.7 or higher.
  
      .. note::
  
          The python udf worker depends on Apache Beam (version == 2.43.0).
          Please ensure that the specified environment meets the above requirements.
  
      :param python_exec: The path of python interpreter.
  
      .. versionadded:: 1.10.0
      """
      jvm = get_gateway().jvm
      self.set(jvm.PythonOptions.PYTHON_EXECUTABLE.key(), python_exec)
  
imports: import datetime
  from py4j.compat import long
  from pyflink.common.configuration import Configuration
  from pyflink.java_gateway import get_gateway
  from pyflink.table.sql_dialect import SqlDialect
  from pyflink.util.java_utils import add_jars_to_context_class_loader
label_desc: Use image node_id 18 for calling the TableConfig obj's underlying member method code with example usage: obj.set_python_executable(python_exec) without return types