code: def fit(
      self,
      X: PIPELINE_DATA_DTYPE,
      y: Optional[PIPELINE_DATA_DTYPE] = None,
  ) -> "VarianceThreshold":
      self.preprocessor = sklearn.feature_selection.VarianceThreshold(
          threshold=0.0
      )
      self.preprocessor = self.preprocessor.fit(X)
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  import sklearn.feature_selection
  from ConfigSpace.configuration_space import ConfigurationSpace
  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 2 for calling the VarianceThreshold obj's underlying member method code with example usage: obj.fit(X, y) and returns: self