class_docstr: Substitute missing values by constant:
      When strategy == “constant”, fill_value is used to replace all
      occurrences of missing_values.
      If left to the default, fill_value will be 0 when imputing
      numerical data and “missing_value” for strings or object data types.
code: def __init__(
      self,
      random_state: Optional[Union[int, np.random.RandomState]] = None,
  ) -> None:
      self.random_state = random_state
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from scipy.sparse import spmatrix
  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
label_desc: Use image node_id 1 to create a new CategoricalImputation object from inherited base classes: AutoSklearnPreprocessingAlgorithm with example: obj = CategoricalImputation(random_state)