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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(self, fixed=None, random=None, priors=None, family='gaussian',
            link=None, run=True, categorical=None, backend=None, **kwargs):
        '''Fit the model using the specified BackEnd.

        Args:
            fixed (str): Optional formula specification of fixed effects.
            random (list): Optional list-based specification of random effects.
            priors (dict): Optional specification of priors for one or more
                terms. A dict where the keys are the names of terms in the
                model, and the values are either instances of class Prior or
                ints, floats, or strings that specify the width of the priors
                on a standardized scale.
            family (str, Family): A specification of the model family
                (analogous to the family object in R). Either a string, or an
                instance of class priors.Family. If a string is passed, a
                family with the corresponding name must be defined in the
                defaults loaded at Model initialization. Valid pre-defined
                families are 'gaussian', 'bernoulli', 'poisson', and 't'.
            link (str): The model link function to use. Can be either a string
                (must be one of the options defined in the current backend;
                typically this will include at least 'identity', 'logit',
                'inverse', and 'log'), or a callable that takes a 1D ndarray
                or theano tensor as the sole argument and returns one with
                the same shape.
            run (bool): Whether or not to immediately begin fitting the model
                once any set up of passed arguments is complete.
            categorical (str, list): The names of any variables to treat as
                categorical. Can be either a single variable name, or a list
                of names. If categorical is None, the data type of the columns
                in the DataFrame will be used to infer handling. In cases where
                numeric columns are to be treated as categoricals (e.g., random
                factors coded as numerical IDs), explicitly passing variable
                names via this argument is recommended.
            backend (str): The name of the BackEnd to use. Currently only
                'pymc' and 'stan' backends are supported. Defaults to PyMC3.
        '''

        if fixed is not None or random is not None:
            self.add(fixed=fixed, random=random, priors=priors, family=family,
                     link=link, categorical=categorical, append=False)

        ''' Run the BackEnd to fit the model. '''
        if backend is None:
            backend = 'pymc' if self._backend_name is None else self._backend_name

        if run:
            if not self.built or backend != self._backend_name:
                self.build(backend)
            return self.backend.run(**kwargs)

        self._backend_name = backend