code: def fit_transform(self, X, y=None, **fit_params):
      return X - X.sum(axis=0)
  
imports: import pandas
  import scipy
  from pandas import DataFrame
  from scipy.sparse import issparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import RobustScaler, StandardScaler
  from scattertext.representations.Doc2VecBuilder import Doc2VecBuilder
  from scattertext.termscoring.RankDifference import RankDifference
  from scattertext.categoryprojector.CategoryProjection import CategoryProjection, CategoryProjectionWithDoc2Vec
  from scattertext.termcompaction.AssociationCompactor import AssociationCompactor
  from sklearn.decomposition import PCA
label_desc: Use image node_id 1 for calling the LengthNormalizer obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: unknown