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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fit(fqdn, result, *argl, **argd):
 """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. """

    #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