How to use the pmdarima.utils.is_iterable function in pmdarima

def _post_ppc_arima(a):
    """If there are no suitable models, raise a ValueError.
    Otherwise, return ``a``. In the case that ``a`` is an iterable
    (i.e., it made it to the end of the function), this method will
    filter out the None values and assess whether the list is empty.

    Parameters
    ----------
    a : ARIMA or iterable
        The list or ARIMAs, or an ARIMA
    """
    # if it's a result of making it to the end, it will
    # be a list of ARIMA models. Filter out the Nones
    # (the failed models)...
    if is_iterable(a):
        a = [m for m in a if m is not None]

    # if the list is empty, or if it was an ARIMA and it's None
    if not a:  # check for truthiness rather than None explicitly
        raise ValueError('Could not successfully fit ARIMA to input data. '
                         'It is likely your data is non-stationary. Please '
                         'induce stationarity or try a different '
                         'range of model order params. If your data is '
                         'seasonal, check the period (m) of the data.')
    # good to return
    return a

def _post_ppc_arima(a):
    """If there are no suitable models, raise a ValueError.
    Otherwise, return ``a``. In the case that ``a`` is an iterable
    (i.e., it made it to the end of the function), this method will
    filter out the None values and assess whether the list is empty.

    Parameters
    ----------
    a : ARIMA or iterable
        The list or ARIMAs, or an ARIMA
    """
    # if it's a result of making it to the end, it will
    # be a list of ARIMA models. Filter out the Nones
    # (the failed models)...
    if is_iterable(a):
        a = [m for m in a if m is not None]

    # if the list is empty, or if it was an ARIMA and it's None
    if not a:  # check for truthiness rather than None explicitly
        raise ValueError('Could not successfully fit ARIMA to input data. '
                         'It is likely your data is non-stationary. Please '
                         'induce stationarity or try a different '
                         'range of model order params. If your data is '
                         'seasonal, check the period (m) of the data.')
    # good to return
    return a

the model. If this is called from the end of the function, ``fits``
    will already be a list.

    We *know* that if a function call makes it here, ``fits`` is NOT None
    or it would have thrown an exception in :func:`_post_ppc_arima`.

    Parameters
    ----------
    fits : iterable or ARIMA
        The ARIMA(s)

    return_all : bool
        Whether to return all.
    """
    # make sure it's an iterable
    if not is_iterable(fits):
        fits = [fits]

    # whether to print the final runtime
    if trace:
        print('Total fit time: %.3f seconds' % (time.time() - start))

    # which to return? if not all, then first index (assume sorted)
    if not return_all:
        return fits[0]
    return fits

the model. If this is called from the end of the function, ``fits``
    will already be a list.

    We *know* that if a function call makes it here, ``fits`` is NOT None
    or it would have thrown an exception in :func:`_post_ppc_arima`.

    Parameters
    ----------
    fits : iterable or ARIMA
        The ARIMA(s)

    return_all : bool
        Whether to return all.
    """
    # make sure it's an iterable
    if not is_iterable(fits):
        fits = [fits]

    # whether to print the final runtime
    if trace:
        print('Total fit time: %.3f seconds' % (time.time() - start))

    # which to return? if not all, then first index (assume sorted)
    if not return_all:
        return fits[0]
    return fits

**kwargs : keyword args
            Any keyword args that should be passed as ``**fit_kwargs`` in the
            new model fit.

        Notes
        -----
        * Internally, this calls ``fit`` again using the OLD model parameters
          as the starting parameters for the new model's MLE computation.
        """
        get_compatible_check_is_fitted(self, 'arima_res_')
        model_res = self.arima_res_

        # Allow updating with a scalar if the user is just adding a single
        # sample.
        if not is_iterable(y):
            y = [y]

        # validate the new samples to add
        y = check_endog(y, dtype=DTYPE)
        n_samples = y.shape[0]

        # if exogenous is None and new exog provided, or vice versa, raise
        exogenous = self._check_exog(exogenous)  # type: np.ndarray

        # ensure the k_exog matches
        if exogenous is not None:
            k_exog = model_res.model.k_exog
            n_exog, exog_dim = exogenous.shape

            if exogenous.shape[1] != k_exog:
                raise ValueError("Dim mismatch in fit exogenous (%i) and new "