code: def __init__(
      self,
      column_descriptions=None,
      dtype=np.float32,
      separator="=",
      sparse=True,
      keep_cat_features=False,
  ):
      self.dtype = dtype
      self.separator = separator
      self.sparse = sparse
      if column_descriptions == None:
          column_descriptions = {}
      self.column_descriptions = column_descriptions
      self.vals_to_drop = set(
          ["ignore", "output", "regressor", "classifier"]
      )
      self.has_been_restricted = False
      self.keep_cat_features = keep_cat_features
      self.label_encoders = {}
      self.numerical_columns = None
      self.num_numerical_cols = None
      self.categorical_columns = None
      self.numeric_col_types = [
          "int8",
          "int16",
          "int32",
          "int64",
          "float16",
          "float32",
          "float64",
      ]
      self.additional_numerical_cols = []
  
imports: from array import array
  import numbers
  from operator import itemgetter
  import numpy
  import pandas
  import scipy.sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.externals import six
  from auto_ml.utils import CustomLabelEncoder
label_desc: Use image node_id 1 to create a new DataFrameVectorizer object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = DataFrameVectorizer(column_descriptions, dtype, separator, sparse, keep_cat_features)