code: def noun_chunks(s: TextSeries) -> pd.Series:
      """
      Return noun chunks (noun phrases).
  
      Return a Pandas Series where each row contains a tuple that has information
      regarding the noun chunk.
  
      Tuple: (`chunk'text`, `chunk'label`, `starting index`, `ending index`)
  
      Noun chunks or noun phrases are phrases that have noun at their head or
      nucleus i.e., they ontain the noun and other words that describe that noun.
      A detailed explanation on noun chunks: https://en.wikipedia.org/wiki/Noun
      phrase. Internally `noun_chunks` makes use of Spacy's dependency parsing:
      https://spacy.io/usage/linguistic-features#dependency-parse
  
      Examples
      --------
      >>> import texthero as hero
      >>> import pandas as pd
      >>> s = pd.Series("The spotted puppy is sleeping.")
      >>> hero.noun_chunks(s)
      0    [(The spotted puppy, NP, 0, 17)]
      dtype: object
      """
  
      noun_chunks = []
  
      nlp = en_core_web_sm.load(disable=["ner"])
  
      # nlp.pipe is now "tagger", "parser"
      for doc in nlp.pipe(s.astype("unicode").values, batch_size=32):
          noun_chunks.append(
              [
                  (
                      chunk.text,
                      chunk.label_,
                      chunk.start_char,
                      chunk.end_char,
                  )
                  for chunk in doc.noun_chunks
              ]
          )
  
      return pd.Series(noun_chunks, index=s.index)
  
file_path: texthero/texthero/nlp.py
imports: import spacy
  import pandas
  from nltk.stem import PorterStemmer, SnowballStemmer
  from texthero._types import TextSeries, InputSeries
label_desc: Use image node_id 2 for calling a global function with example usage: noun_chunks(s) and returns: pd