class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE (GAS) RANK MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame
      Field to specify the univariate time series data that will be used.
  
  team_1 : str (pd.DataFrame)        
      Specifies which column contains the home team
  
  team_2 : str (pd.DataFrame)        
      Specifies which column contains the away team
  
  family : GAS family object
      Which distribution to use, e.g. GASNormal()
  
  score_diff : str (pd.DataFrame)        
      Specifies which column contains the score
  
  gradient_only : Boolean (default: True)
      If true, will only use gradient rather than second-order terms
      to construct the modified score.
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code: def _match_count_2(self):
      home_count, away_count = np.zeros(len(self.home_2_id)), np.zeros(
          len(self.away_2_id)
      )
  
      for t in range(0, len(home_count)):
          home_count[t] = len(
              self.home_2_id[0 : t + 1][
                  self.home_2_id[0 : t + 1] == self.home_2_id[t]
              ]
          ) + len(
              self.away_2_id[0 : t + 1][
                  self.away_2_id[0 : t + 1] == self.home_2_id[t]
              ]
          )
          away_count[t] = len(
              self.home_2_id[0 : t + 1][
                  self.home_2_id[0 : t + 1] == self.away_2_id[t]
              ]
          ) + len(
              self.away_2_id[0 : t + 1][
                  self.away_2_id[0 : t + 1] == self.away_2_id[t]
              ]
          )
  
      return home_count, away_count
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .scores import *
  from .gas_core_recursions import gas_recursion
label_desc: Use image node_id 4 for calling the GASRank obj's underlying member method code with example usage: obj._match_count_2() and returns: home_count,  away_count