code: def fit(
      self,
      X: PIPELINE_DATA_DTYPE,
      y: Optional[PIPELINE_DATA_DTYPE] = None,
  ) -> "AutoSklearnPreprocessingAlgorithm":
      if self.preprocessor is None:
          raise NotFittedError()
      if sparse.isspmatrix(X):
          self.preprocessor.set_params(with_mean=False)
  
      return super(StandardScalerComponent, self).fit(X, y)
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  from scipy import sparse
  from sklearn.exceptions import NotFittedError
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import Rescaling
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 3 for calling the StandardScalerComponent obj's underlying member method code with example usage: obj.fit(X, y) and returns: super