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: Prints all pretrained models for a particular annotator model, that are
  compatible with a version of Spark NLP. If any of the optional arguments are not
  set, the filter is not considered.
  
  Parameters
  ----------
  lang : str, optional
      Language of the models to filter, by default None
  version : str, optional
      Version of Spark NLP to filter, by default None
docstr_tok: ["Prints","all","pretrained","models","for","a","particular","annotator","model",",","that","are","compatible","with","a","version","of","Spark","NLP",".","If","any","of","the","optional","arguments","are","not","set",",","the","filter","is","not","considered",".","Parameters","--","--","--","--","--","lang",":","str",",","optional","Language","of","the","models","to","filter",",","by","default","None","version",":","str",",","optional","Version","of","Spark","NLP","to","filter",",","by","default","None"]
code: def showPublicPipelines(lang=None, version=None):
      """Prints all pretrained models for a particular annotator model, that are
      compatible with a version of Spark NLP. If any of the optional arguments are not
      set, the filter is not considered.
  
      Parameters
      ----------
      lang : str, optional
          Language of the models to filter, by default None
      version : str, optional
          Version of Spark NLP to filter, by default None
      """
      print(_internal._ShowPublicPipelines(lang, version).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 6 for calling the ResourceDownloader obj's underlying member method code with example usage: obj.showPublicPipelines(lang, version) without return types