While using `databricks-sdk` in my code, I've found that checking PySpark objects types is not reliable anymore.  
I've used to do the following: 

from pyspark.sql import Column, DataFrame, SparkSession

isinstance(spark, SparkSession)
isinstance(a_df, DataFrame)
isinstance(a_col, Column)

And so I've found that this isn't reliable as it dependes on the context in which I'm running Spark.  
That is to say: 

type(spark) -> pyspark.sql.(connect).session.SparkSession
type(a_df)  -> pyspark.sql.(connect).dataframe.DataFrame
type(a_col) -> pyspark.sql.(connect).column.Column

Where depending on the context Databricks may add it's `connect` module to the object in question.  
Am I not supposed to check types when using PySpark in Databricks? 
Is there a reference for this change that I can follow? 
And more practically, how do I check if the dataframe is a dataframe? 
The only way I can think of at the moment is: 

from pyspark.sql import SparkSession, Column, DataFrame
from pyspark.sql.connect import session, dataframe, column

isinstance(spark, (SparkSession, session.SparkSession))
isinstance(a_df,  (DataFrame, dataframe.DataFrame))
isinstance(a_col, (Column, column.Column))

 Is there a more natural way to do this type of checking?