code: def trim(self):
  
  		
  
  		if 0 < self.cutoff <= 0.5:
  			pscore = self.raw_data['pscore']
  			keep = (pscore >= self.cutoff) & (pscore <= 1-self.cutoff)
  			Y_trimmed = self.raw_data['Y'][keep]
  			D_trimmed = self.raw_data['D'][keep]
  			X_trimmed = self.raw_data['X'][keep]
  			self.raw_data = Data(Y_trimmed, D_trimmed, X_trimmed)
  			self.raw_data._dict['pscore'] = pscore[keep]
  			self.summary_stats = Summary(self.raw_data)
  			self.strata = None
  			self.estimates = Estimators()
  		elif self.cutoff == 0:
  			pass
  		else:
  			raise ValueError('Invalid cutoff.')
docs: Trims data based on propensity score to create a subsample with
  		better covariate balance.
  		
  		The default cutoff value is set to 0.1. To set a custom cutoff
  		value, modify the object attribute named cutoff directly.
  
  		This method should only be executed after the propensity score
  		has been estimated.