code: def __init__(
      self,
      n_components=None,
      copy=True,
      whiten=False,
      svd_solver="auto",
      tol=0.0,
      iterated_power="auto",
      random_state=None,
  ):
      self.n_components = n_components
      self.copy = copy
      self.whiten = whiten
      self.svd_solver = svd_solver
      self.tol = tol
      self.iterated_power = iterated_power
      self.random_state = random_state
  
imports: import re
  from pprint import PrettyPrinter
  import numpy
  from sklearn.utils._pprint import _EstimatorPrettyPrinter
  from sklearn.linear_model import LogisticRegressionCV
  from sklearn.pipeline import make_pipeline
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest, chi2
  from sklearn import config_context
label_desc: Use image node_id 1 to create a new PCA object from inherited base classes: BaseEstimator with example: obj = PCA(n_components, copy, whiten, svd_solver, tol, iterated_power, random_state)