docstr: Pad the time series with additional points since the method works better if the anomaly point
  is towards the center of the sliding window.
  
  Parameters
  ----------
  X
      Uniformly sampled time series instances.
  t
      Equidistant timestamps corresponding to each input instances (i.e, the array should contain
      numerical values in increasing order).
  
  Returns
  -------
  Padded version of X.
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code: def add_est_points(self, X: np.ndarray, t: np.ndarray) -> np.ndarray:
      """
      Pad the time series with additional points since the method works better if the anomaly point
      is towards the center of the sliding window.
  
      Parameters
      ----------
      X
          Uniformly sampled time series instances.
      t
          Equidistant timestamps corresponding to each input instances (i.e, the array should contain
          numerical values in increasing order).
  
      Returns
      -------
      Padded version of X.
      """
      grads = self.compute_grads(X, t)
      X_add = X[-self.n_grad_points] + grads
      X_pad = np.concatenate([X, np.tile(X_add, self.n_est_points)])
      return X_pad
  
imports: import enum
  import logging
  import numpy
  from typing import Dict
  from alibi_detect.base import BaseDetector, ThresholdMixin, outlier_prediction_dict
  from alibi_detect.utils._types import Literal
label_desc: Use image node_id 6 for calling the SpectralResidual obj's underlying member method code with example usage: obj.add_est_points(X, t) and returns: X_pad