docs: def prepare_pyspark_yarn_interactive(env_name, env_archive, spark_conf):
      """
      This ASSUMES that you have a compatible python environment running on the other side.
  
      WARNING: Injects "PYSPARK_DRIVER_PYTHON" and "PYSPARK_PYTHON" as
      environmental variables into your current environment
  
      Parameters
      ----------
      env_name : str
      env_archive : str
      spark_conf : SparkConfiguration
  
      Examples
      --------
      >>> from spylon.spark import SparkConfiguration
      >>> conf = SparkConfiguration()
      >>> import spylon.spark.yarn_launcher as yl
      >>> conf = yl.prepare_pyspark_yarn_interactive(
      ...    env_name="yarn-pyspark-env", env_archive="hdfs:///path/to/conda_envs/yarn-pyspark-env.zip",
      ...    spark_conf=conf
      ... )
      ... # Create our context
      ... sc, sqlC = conf.sql_context("conda-test")
      ... # Example of it working
      ... rdd = sc.parallelize(range(10), 10)
      ...
      ... def pandas_test(x):
      ...    import numpy
      ...    import pandas
      ...    import sys
      ...    import socket
      ...    return [{"numpy": numpy.__version__, "pandas": pandas.__version__,
      ...             "host": socket.getfqdn(), "python": sys.executable}]
      ...
      ... rdd.mapPartitions(pandas_test).collect()
  
      Returns
      -------
      SparkConfiguration
          Copy of `spark_conf` input with added Yarn requirements.
      """
  
      from .launcher import SparkConfiguration
  
      assert isinstance(spark_conf, SparkConfiguration)
  
      yarn_python = os.path.join(".", "CONDA", env_name, "bin", "python")
      archives = env_archive + "#CONDA"
  
      new_spark_conf = copy.deepcopy(spark_conf)
      new_spark_conf.master = "yarn"
      new_spark_conf.deploy_mode = "client"
      new_spark_conf.archives = [archives]
      new_spark_conf.conf.set("spark.executorEnv.PYSPARK_PYTHON", yarn_python)
      new_spark_conf._python_path = yarn_python
  
      env_update = {
          "PYSPARK_DRIVER_PYTHON": sys.executable,
          "PYSPARK_PYTHON": yarn_python
      }
  
      os.environ.update(env_update)
  
      return new_spark_conf