code: def __init__(
      self,
      category_order: List = None,
      correlation_type: str = "spearmanr",
      term_ranker: Optional[TermRanker] = None,
      frequency_scaler: Optional[Callable[[np.array], np.array]] = None,
  ):
      TrendPlotSettings.__init__(self, category_order=category_order)
      self.correlation_type = correlation_type
      self.term_ranker = (
          AbsoluteFrequencyRanker
          if term_ranker is None
          else term_ranker
      )
      self.frequency_scaler = (
          dense_rank if frequency_scaler is None else frequency_scaler
      )
  
imports: import dataclasses
  from abc import ABC, abstractmethod
  from typing import List, Dict, Optional, Callable
  from sklearn.neighbors import KNeighborsRegressor
  import numpy
  import pandas
  from scattertext.dispersion.dispersion import Dispersion
  from scattertext.indexstore import IndexStoreFromList
  from scattertext.continuous.correlations import Correlations
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scattertext.termranking.TermRanker import TermRanker
  from scattertext.TermDocMatrix import TermDocMatrix
  from scattertext.Scalers import get_scaler_name, scale_center_zero_abs, scale, dense_rank
  from scattertext.smoothing.mean_isotonic import MeanIsotonic
label_desc: Use image node_id 1 to create a new CorrelationPlotSettings object from inherited base classes: TrendPlotSettings with example: obj = CorrelationPlotSettings(category_order, correlation_type, term_ranker, frequency_scaler)