code: def fit(fqdn, result, *argl, **argd):
      
      #Check the arguments to see what kind of data we are working with, then
      #choose the appropriate function below to return the analysis dictionary.
      #The first positional argument will be the instance of the machine that was
      #used. Check its name against a list.
      global _machines
      out = None
      if len(argl) > 0:
          machine = argl[0]
          #We save pointers to the machine that was just fit so that we can figure
          #out later what training data was used for analysis purposes.
          key = id(machine)
          _machines[key] = (machine, argl[0], argl[1])
          
          if isclassifier(machine):
              out = classify_fit(fqdn, result, *argl, **argd)
          elif isregressor(machine):
              out = regress_fit(fqdn, result, *argl, **argd)
          
      return out
docs: Analyzes the result of a generic fit operation performed by `sklearn`.
  
      Args:
          fqdn (str): full-qualified name of the method that was called.
          result: result of calling the method with `fqdn`.
          argl (tuple): positional arguments passed to the method call.
          argd (dict): keyword arguments passed to the method call.