class_docstr: Default encoder class with inbuilt function for decoding text that
  has been encoded by the same object. Also supports label encoding.
  Can be used as a skeleton to build more sophisticated encoders on top.
class_docstr_tok: ["Default","encoder","class","with","inbuilt","function","for","decoding","text","that","has","been","encoded","by","the","same","object",".","Also","supports","label","encoding",".","Can","be","used","as","a","skeleton","to","build","more","sophisticated","encoders","on","top","."]
docstr: Initializes the Encoder object and sets internal tokenizer,
  labelEncoder and vectorizer using predefined objects.
docstr_tok: ["Initializes","the","Encoder","object","and","sets","internal","tokenizer",",","labelEncoder","and","vectorizer","using","predefined","objects","."]
code: def __init__(self):
      """Initializes the Encoder object and sets internal tokenizer,
      labelEncoder and vectorizer using predefined objects.
      """
      self.tokenizer = BOWTokenizer(
          nlp
      )  # the tokenizer must have a tokenize() and parse() function.
      self.labelEncoder = LabelEncoder()
      self.vectorizer = CountVectorizer(
          tokenizer=self.tokenizer.tokenize,
          ngram_range=(1, 1),
          min_df=1,
      )
      self.decode_params = {}
  
file_path: interpret-text/python/interpret_text/experimental/common/utils_classical.py
imports: import spacy
  import html
  import string
  import numpy
  import matplotlib.pyplot
  from sklearn.preprocessing import LabelEncoder
  from IPython.core.display import display, HTML
  from sklearn.feature_extraction.text import CountVectorizer
label_desc: Use image node_id 1 to create a new BOWEncoder object  with example: obj = BOWEncoder()