code: def __init__(
      self,
      input="content",
      encoding="utf-8",
      decode_error="strict",
      strip_accents=None,
      lowercase=True,
      preprocessor=None,
      tokenizer=None,
      stop_words=None,
      token_pattern=r"(?u)\b\w\w+\b",
      ngram_range=(1, 1),
      analyzer="word",
      max_df=1.0,
      min_df=1,
      max_features=None,
      vocabulary=None,
      binary=False,
      dtype=np.int64,
  ):
      self.input = input
      self.encoding = encoding
      self.decode_error = decode_error
      self.strip_accents = strip_accents
      self.preprocessor = preprocessor
      self.tokenizer = tokenizer
      self.analyzer = analyzer
      self.lowercase = lowercase
      self.token_pattern = token_pattern
      self.stop_words = stop_words
      self.max_df = max_df
      self.min_df = min_df
      self.max_features = max_features
      self.ngram_range = ngram_range
      self.vocabulary = vocabulary
      self.binary = binary
      self.dtype = dtype
  
imports: import re
  from pprint import PrettyPrinter
  import numpy
  from sklearn.utils._pprint import _EstimatorPrettyPrinter
  from sklearn.linear_model import LogisticRegressionCV
  from sklearn.pipeline import make_pipeline
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest, chi2
  from sklearn import config_context
label_desc: Use image node_id 1 to create a new CountVectorizer object from inherited base classes: BaseEstimator with example: obj = CountVectorizer(input, encoding, decode_error, strip_accents, lowercase, preprocessor, tokenizer, stop_words, token_pattern, ngram_range, analyzer, max_df, min_df, max_features, vocabulary, binary, dtype)