docstr: Imputes p-values from the Z-scores of `ScaledFScore` scores.  Assuming incorrectly
  that the scaled f-scores are normally distributed.
  
  Parameters
  ----------
  X : np.array
          Array of word counts, shape (N, 2) where N is the vocab size.  X[:,0] is the
          positive class, while X[:,1] is the negative class.
  
  Returns
  -------
  np.array of p-values
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code: def get_p_vals(self, X):
      """
      Imputes p-values from the Z-scores of `ScaledFScore` scores.  Assuming incorrectly
      that the scaled f-scores are normally distributed.
  
      Parameters
      ----------
      X : np.array
              Array of word counts, shape (N, 2) where N is the vocab size.  X[:,0] is the
              positive class, while X[:,1] is the negative class.
  
      Returns
      -------
      np.array of p-values
  
      """
      z_scores = ScaledFZScore(self.scaler_algo, self.beta).get_scores(
          X[:, 0], X[:, 1]
      )
      return norm.cdf(z_scores)
  
file_path: scattertext/scattertext/termsignificance/ScaledFScoreSignificance.py
imports: import numpy
  from scattertext.termscoring.ScaledFScore import ScaledFZScore
  from scattertext.Common import DEFAULT_SCALER_ALGO, DEFAULT_BETA
  from scattertext.termscoring import ScaledFScore
  from scipy.stats import norm
  from scattertext.termsignificance.TermSignificance import TermSignificance
label_desc: Use image node_id 3 for calling the ScaledFScoreSignificance obj's underlying member method code with example usage: obj.get_p_vals(X) and returns: norm