class_docstr: A wrapper around registered implementations of beam nodes.
class_docstr_tok: ["A","wrapper","around","registered","implementations","of","beam","nodes","."]
docstr: Retrieves ptransform for `tag`.
  
  Args:
    tag: A string key (or None) to retrieve corresponding ptransform.
  
  Returns:
    A tuple of a registered beam.PTransform implementation and the tag it was
    registered with.
  
  Raises:
    KeyError: If no registered PTransform implementation could be found.
docstr_tok: ["Retrieves","ptransform","for","`","tag","`",".","Args",":","tag",":","A","string","key","(","or","None",")","to","retrieve","corresponding","ptransform",".","Returns",":","A","tuple","of","a","registered","beam.PTransform","implementation","and","the","tag","it","was","registered","with",".","Raises",":","KeyError",":","If","no","registered","PTransform","implementation","could","be","found","."]
code: def get_ptransform(self, tag):
      """Retrieves ptransform for `tag`.
  
      Args:
        tag: A string key (or None) to retrieve corresponding ptransform.
  
      Returns:
        A tuple of a registered beam.PTransform implementation and the tag it was
        registered with.
  
      Raises:
        KeyError: If no registered PTransform implementation could be found.
  
      """
      if tag is None or tag not in self._ptransform_by_tag:
          return (
              self._ptransform_by_tag[self._GENERAL_ENVIRONMENT_TAG],
              None,
          )
      return self._ptransform_by_tag[tag], tag.value
  
imports: import collections
  import dataclasses
  import enum
  import os
  from typing import Any, Dict, Mapping, Optional
  import uuid
  import apache_beam
  from tensorflow_transform import common_types
  from tensorflow_transform import nodes
  from tfx_bsl.telemetry import util
label_desc: Use image node_id 3 for calling the _PtransformWrapper obj's underlying member method code with example usage: obj.get_ptransform(tag) and returns: self,  tag, self,  None