class_docstr: Algorithm predicting the baseline estimate for given user and item.
  
  :math:`\hat{r}_{ui} = b_{ui} = \mu + b_u + b_i`
  
  If user :math:`u` is unknown, then the bias :math:`b_u` is assumed to be
  zero. The same applies for item :math:`i` with :math:`b_i`.
  
  See section 2.1 of :cite:`Koren:2010` for details.
  
  Args:
      bsl_options(dict): A dictionary of options for the baseline estimates
          computation. See :ref:`baseline_estimates_configuration` for
          accepted options.
      verbose(bool): Whether to print trace messages of bias estimation,
          similarity, etc.  Default is True.
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code: def estimate(self, u, i):
      est = self.trainset.global_mean
      if self.trainset.knows_user(u):
          est += self.bu[u]
      if self.trainset.knows_item(i):
          est += self.bi[i]
  
      return est
  
imports: from .algo_base import AlgoBase
label_desc: Use image node_id 3 for calling the BaselineOnly obj's underlying member method code with example usage: obj.estimate(u, i) and returns: est