code: def set_params(self, **params):
  		
  		if 'bias' in params.keys():
  			self.intercept_ = params['bias']
  		if 'weights' in params.keys():
  			self.coef_ = params['weights']
  		for key in params.keys():
  			if 'b_' == key[:2]:
  				self.B[int(key[2:])] = params[key]
  
  		return self
docs: Set the parameters of the estimator.
  
  		Args:
  			bias (array-like) : bias of the estimator. Also known as the intercept in a linear model.
  			weights (array-like) : weights of the features. Also known as coeficients.
  			NER biases (array-like) : NER entities infering column position on X and bias value. Ex: `b_4=10, b_5=6`.
  
  		Example:
  			>>> cls = VTT()
  			>>> cls.set_params(b_4=10, b_5=6, b_6=8)