code: def __init__(
      self,
      ngram_range_word: int = 1,
      ngram_range_char: int = 4,
      min_df: float = 0.0,
      max_df: float = 1.0,
      binary: bool = False,
      norm: str = "l2",
      sublinear_tf: bool = False,
      per_column: bool = False,
      analyzer: str = "char",
      random_state: Optional[Union[int, np.random.RandomState]] = None,
  ) -> None:
      self.random_state = random_state
      self.min_df = min_df
      self.max_df = max_df
      self.binary = binary
      self.norm = norm
      self.sublinear_tf = sublinear_tf
      self.per_column = per_column
      self.analyzer = analyzer
      self.ngram_range_word = ngram_range_word
      self.ngram_range_char = ngram_range_char
  
imports: from typing import Dict, Optional, Tuple, Union
  import itertools
  import ConfigSpace.hyperparameters
  import numpy
  import pandas
  from ConfigSpace import EqualsCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from scipy.sparse import hstack
  from sklearn.feature_extraction.text import TfidfVectorizer
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 1 to create a new TfidfEncoder object from inherited base classes: AutoSklearnPreprocessingAlgorithm with example: obj = TfidfEncoder(ngram_range_word, ngram_range_char, min_df, max_df, binary, norm, sublinear_tf, per_column, analyzer, random_state)