code: def __init__(self, X):
      # we check the array-like preprocessor here, and we as much permissive
      # as possible (because the user will check for the desired
      # format with arguments in check_input, and only this latter function
      # should return the appropriate errors). We do this only to have a numpy
      # array object which can be indexed by another numpy array object.
      X = check_array(
          X,
          accept_sparse=True,
          dtype=None,
          force_all_finite=False,
          ensure_2d=False,
          allow_nd=True,
          ensure_min_samples=0,
          ensure_min_features=0,
          estimator=None,
      )
      self.X = X
  
imports: import numpy
  from numpy.linalg import LinAlgError
  from sklearn.datasets import make_spd_matrix
  from sklearn.decomposition import PCA
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_X_y, check_random_state
  from .exceptions import PreprocessorError, NonPSDError
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from scipy.linalg import pinvh, eigh
  import sys
  import time
  import warnings
label_desc: Use image node_id 1 to create a new ArrayIndexer object  with example: obj = ArrayIndexer(X)