class_docstr: Downloads and manages resources, pretrained models/pipelines.
  
  Usually you will not need to use this class directly. It is called by the
  `pretrained()` function of annotators.
  
  However, you can use this class to list the available pretrained resources.
  
  Examples
  --------
  If you want to list all NerDLModels for the english language you can run:
  
  >>> ResourceDownloader.showPublicModels("NerDLModel", "en")
  +-------------+------+---------+
  | Model       | lang | version |
  +-------------+------+---------+
  | onto_100    | en   | 2.1.0   |
  | onto_300    | en   | 2.1.0   |
  | ner_dl_bert | en   | 2.2.0   |
  |  ...        | ...  | ...     |
  
  
  Similarly for Pipelines:
  
  >>> ResourceDownloader.showPublicPipelines("en")
  +------------------+------+---------+
  | Pipeline         | lang | version |
  +------------------+------+---------+
  | dependency_parse | en   | 2.0.2   |
  | check_spelling   | en   | 2.1.0   |
  | match_datetime   | en   | 2.1.0   |
  |  ...             | ...  | ...     |
docstr: Shows all available annotators in Spark NLP.
          
docstr_tok: ["Shows","all","available","annotators","in","Spark","NLP","."]
code: def showAvailableAnnotators():
      """Shows all available annotators in Spark NLP."""
      print(_internal._ShowAvailableAnnotators().apply())
  
file_path: spark-nlp/python/sparknlp/pretrained/resource_downloader.py
imports: import sys
  import threading
  from py4j.protocol import Py4JJavaError
  from pyspark.ml import PipelineModel
  import sparknlp.internal
  from sparknlp.pretrained.utils import printProgress
label_desc: Use image node_id 8 for calling the ResourceDownloader obj's underlying member method code with example usage: obj.showAvailableAnnotators() without return types