code: def visualize_ner_str(text, pipe, select=None, colors=None):
      """
      Takes in a text string and visualizes the named entities within the text.
  
      Required args also include a pipeline code, the two-letter code for a language defined by Universal Dependencies (ex: "en" for English).
  
      Lastly, the user must provide an NLP pipeline - we recommend Stanza (ex: pipe = stanza.Pipeline('en')).
  
      Optionally, the 'select' argument allows for specific NER tags to be highlighted; the 'color' argument allows
      for specific NER tags to have certain color(s).
      """
      doc = pipe(text)
      visualize_ner_doc(doc, pipe.lang, select, colors)
  
file_path: stanza/stanza/utils/visualization/ner_visualization.py
imports: from spacy import displacy
  from spacy.tokens import Doc
  from spacy.tokens import Span
  from stanza.models.common.constant import is_right_to_left
  import stanza
  import spacy
  import copy
label_desc: Use image node_id 2 for calling a global function with example usage: visualize_ner_str(text, pipe, select, colors) without return types