code: def get_intercept_only_candidate_models(data, weights_col):
      
      model_type = "intercept_only"
      formula = "meter_value ~ 1"
  
      if weights_col is None:
          weights = 1
      else:
          weights = data[weights_col]
  
      try:
          model = smf.wls(formula=formula, data=data, weights=weights)
      except Exception as e:
          return [get_fit_failed_candidate_model(model_type, formula)]
  
      result = model.fit()
  
      # CalTrack 3.3.1.3
      model_params = {"intercept": result.params["Intercept"]}
  
      model_warnings = []
  
      # CalTrack 3.4.3.2
      for parameter in ["intercept"]:
          model_warnings.extend(
              get_parameter_negative_warning(model_type, model_params, parameter)
          )
  
      if len(model_warnings) > 0:
          status = "DISQUALIFIED"
      else:
          status = "QUALIFIED"
  
      return [
          CalTRACKUsagePerDayCandidateModel(
              model_type=model_type,
              formula=formula,
              status=status,
              warnings=model_warnings,
              model_params=model_params,
              model=model,
              result=result,
              r_squared_adj=0,
          )
      ]
docs: Return a list of a single candidate intercept-only model.
  
      Parameters
      ----------
      data : :any:`pandas.DataFrame`
          A DataFrame containing at least the column ``meter_value``.
          DataFrames of this form can be made using the
          :any:`eemeter.create_caltrack_daily_design_matrix` or
          :any:`eemeter.create_caltrack_billing_design_matrix` methods.
      weights_col : :any:`str` or None
          The name of the column (if any) in ``data`` to use as weights.
  
      Returns
      -------
      candidate_models : :any:`list` of :any:`CalTRACKUsagePerDayCandidateModel`
          List containing a single intercept-only candidate model.