class_docstr: Create a doc instance of text, obtain cleaned, readable text and
  metadata from this doc.
  
  Properties:
  raw: incoming, unedited text
  language: 2-letter code for the language of the text
  is_detected_language: is the language detected or specified beforehand
  is_reliable_language: is the language specified or was it reliably detected
  hint_language: language you expect your text to be
  _spacy_nlps: nested dictionary {lang: {model_id: model}} with loaded spacy language modules
docstr: Returns word embeddings for the words in the document.
  The default spacy models don't have "true" word vectors
  but only context-sensitive tensors that are within the document.
  
  Returns:
  A dictionary mapping words from the document to a dict with the
  corresponding values of the following variables:
  
  has vector: Does the token have a vector representation?
  vector norm: The L2 norm of the token's vector (the square root of the
              sum of the values squared)
  OOV: Out-of-vocabulary (This variable always gets the value True since
                          there are no vectors included in the model)
  vector: The vector representation of the word
  
  >>> from textpipe.doc import Doc
  >>> doc = Doc('Test sentence')
  >>> doc.word_vectors['Test']['is_oov']
  True
  >>> len(doc.word_vectors['Test']['vector'])
  96
  >>> doc.word_vectors['Test']['vector_norm'] == doc.word_vectors['sentence']['vector_norm']
  False
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code: def generate_word_vectors(self, model_name=None):
      """
      Returns word embeddings for the words in the document.
      The default spacy models don't have "true" word vectors
      but only context-sensitive tensors that are within the document.
  
      Returns:
      A dictionary mapping words from the document to a dict with the
      corresponding values of the following variables:
  
      has vector: Does the token have a vector representation?
      vector norm: The L2 norm of the token's vector (the square root of the
                  sum of the values squared)
      OOV: Out-of-vocabulary (This variable always gets the value True since
                              there are no vectors included in the model)
      vector: The vector representation of the word
  
      >>> from textpipe.doc import Doc
      >>> doc = Doc('Test sentence')
      >>> doc.word_vectors['Test']['is_oov']
      True
      >>> len(doc.word_vectors['Test']['vector'])
      96
      >>> doc.word_vectors['Test']['vector_norm'] == doc.word_vectors['sentence']['vector_norm']
      False
      """
      lang = (
          self.language
          if self.is_reliable_language
          else self.hint_language
      )
      return {
          token.text: {
              "has_vector": token.has_vector,
              "vector_norm": token.vector_norm,
              "is_oov": token.is_oov,
              "vector": token.vector.tolist(),
          }
          for token in self._load_spacy_doc(lang, model_name)
      }
  
imports: import functools
  import re
  import unicodedata
  from collections import Counter
  from urllib.parse import urlparse
  import cld2
  import numpy
  import spacy
  import spacy.matcher
  import textacy
  import textacy.ke
  import textacy.text_utils
  from bs4 import BeautifulSoup
  from datasketch import MinHash
  from gensim.models.keyedvectors import KeyedVectors
  from gensim.summarization.summarizer import summarize
  from textpipe.data.emoji import EMOJI_TO_UNICODE_NAME, EMOJI_TO_SENTIMENT
  from textpipe.wrappers import RedisKeyedVectors
  from textpipe.util import getattr_
label_desc: Use image node_id 27 for calling the Doc obj's underlying member method code with example usage: obj.generate_word_vectors(model_name) and returns: unknown