docstr: :param weighter: instance of an sklearn class with fit_transform to weight X category corpus.
  :param normalizer: instance of an sklearn class with fit_transform to normalize term X category corpus.
  :param selector: instance of a compactor class, if None, no compaction will be done.
  :param projector: instance an sklearn class with fit_transform
  :param fit_transform_kwargs: optional, dict of kwargs to fit_transform
  :param use_metadata: bool, use metadata features
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code: def __init__(
      self,
      weighter=LengthNormalizer(),
      normalizer=StandardScaler(),
      selector=AssociationCompactor(1000, RankDifference),
      projector=PCA(2),
      fit_transform_kwargs=None,
      use_metadata=False,
  ):
      """
  
      :param weighter: instance of an sklearn class with fit_transform to weight X category corpus.
      :param normalizer: instance of an sklearn class with fit_transform to normalize term X category corpus.
      :param selector: instance of a compactor class, if None, no compaction will be done.
      :param projector: instance an sklearn class with fit_transform
      :param fit_transform_kwargs: optional, dict of kwargs to fit_transform
      :param use_metadata: bool, use metadata features
      """
      self.weighter_ = weighter
      self.normalizer_ = normalizer
      self.selector_ = selector
      self.projector_ = projector
      self.fit_transform_kwargs_ = (
          {} if fit_transform_kwargs is None else fit_transform_kwargs
      )
      self.use_metadata_ = use_metadata
  
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 to create a new CategoryProjector object from inherited base classes: CategoryProjectorBase with example: obj = CategoryProjector(weighter, normalizer, selector, projector, fit_transform_kwargs, use_metadata)