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

if stationary:
        d = D = 0

    # now for seasonality
    if m == 1:
        D = max_P = max_Q = 0

    # m must be > 1 for nsdiffs
    elif D is None:  # we don't have a D yet and we need one (seasonal)
        seasonal_test_args = seasonal_test_args \
            if seasonal_test_args is not None else dict()
        D = nsdiffs(xx, m=m, test=seasonal_test, max_D=max_D,
                    **seasonal_test_args)

        if D > 0 and exogenous is not None:
            diffxreg = diff(exogenous, differences=D, lag=m)
            # check for constance on any column
            if np.apply_along_axis(is_constant, arr=diffxreg, axis=0).any():
                D -= 1

    # D might still be None if not seasonal. Py 3 will throw and error for that
    # unless we explicitly check for ``seasonal``
    if D > 0:
        dx = diff(xx, differences=D, lag=m)
    else:
        dx = xx

    # If D was too big, we might have gotten rid of x altogether!
    if dx.shape[0] == 0:
        raise ValueError("The seasonal differencing order, D=%i, was too "
                         "large for your time series, and after differencing, "
                         "there are no samples remaining in your data. "

dx = xx

    # If D was too big, we might have gotten rid of x altogether!
    if dx.shape[0] == 0:
        raise ValueError("The seasonal differencing order, D=%i, was too "
                         "large for your time series, and after differencing, "
                         "there are no samples remaining in your data. "
                         "Try a smaller value for D, or if you didn't set D "
                         "to begin with, try setting it explicitly. This can "
                         "also occur in seasonal settings when m is too large."
                         % D)

    # difference the exogenous matrix
    if exogenous is not None:
        if D > 0:
            diffxreg = diff(exogenous, differences=D, lag=m)
        else:
            diffxreg = exogenous
    else:
        # here's the thing... we're only going to use diffxreg if exogenous
        # was not None in the first place. However, PyCharm doesn't know that
        # and it thinks we might use it before assigning it. Therefore, assign
        # it to None as a default value and it won't raise the warning anymore.
        diffxreg = None

    # determine/set the order of differencing by estimating the number of
    # orders it would take in order to make the TS stationary.
    if d is None:
        offset_test_args = offset_test_args \
            if offset_test_args is not None else dict()
        d = ndiffs(dx, test=test, alpha=alpha, max_d=max_d, **offset_test_args)

d -= 1

    # check differences (do we want to warn?...)
    if error_action == 'warn' and not suppress_warnings:
        if D >= 2:
            warnings.warn("Having more than one seasonal differences is "
                          "not recommended. Please consider using only one "
                          "seasonal difference.")
        # if D is -1, this will be off, so we include the OR
        elif D + d > 2 or d > 2:
            warnings.warn("Having 3 or more differencing operations is not "
                          "recommended. Please consider reducing the total "
                          "number of differences.")

    if d > 0:
        dx = diff(dx, differences=d, lag=1)

    # check for constance
    if is_constant(dx):
        if exogenous is None and not (D > 0 or d < 2):
            raise ValueError('data follow a simple polynomial and are not '
                             'suitable for ARIMA modeling')

        # perfect regression
        ssn = None if not seasonal else (0, D, 0, m)
        return _return_wrapper(
            _post_ppc_arima(_fit_arima(y, xreg=exogenous, order=(0, d, 0),
                                       seasonal_order=ssn,
                                       start_params=start_params, trend=trend,
                                       method=method, transparams=transparams,
                                       solver=solver, maxiter=maxiter,
                                       disp=disp, callback=callback,

d -= 1

    # check differences (do we want to warn?...)
    if error_action == 'warn' and not suppress_warnings:
        if D >= 2:
            warnings.warn("Having more than one seasonal differences is "
                          "not recommended. Please consider using only one "
                          "seasonal difference.", ModelFitWarning)
        # if D is -1, this will be off, so we include the OR
        elif D + d > 2 or d > 2:
            warnings.warn("Having 3 or more differencing operations is not "
                          "recommended. Please consider reducing the total "
                          "number of differences.", ModelFitWarning)

    if d > 0:
        dx = diff(dx, differences=d, lag=1)

    # check for constance
    if is_constant(dx):
        if exogenous is None and not (D > 0 or d < 2):
            raise ValueError('data follow a simple polynomial and are not '
                             'suitable for ARIMA modeling')

        # perfect regression
        ssn = (0, 0, 0, 0) if not seasonal else (0, D, 0, m)
        return _return_wrapper(
            _post_ppc_arima(
                solvers._fit_arima(
                    y, xreg=exogenous, order=(0, d, 0),
                    seasonal_order=ssn,
                    start_params=start_params, trend=trend,
                    method=method, maxiter=maxiter,

elif D is None:  # we don't have a D yet and we need one (seasonal)
        seasonal_test_args = seasonal_test_args \
            if seasonal_test_args is not None else dict()
        D = nsdiffs(xx, m=m, test=seasonal_test, max_D=max_D,
                    **seasonal_test_args)

        if D > 0 and exogenous is not None:
            diffxreg = diff(exogenous, differences=D, lag=m)
            # check for constance on any column
            if np.apply_along_axis(is_constant, arr=diffxreg, axis=0).any():
                D -= 1

    # D might still be None if not seasonal. Py 3 will throw and error for that
    # unless we explicitly check for ``seasonal``
    if D > 0:
        dx = diff(xx, differences=D, lag=m)
    else:
        dx = xx

    # If D was too big, we might have gotten rid of x altogether!
    if dx.shape[0] == 0:
        raise ValueError("The seasonal differencing order, D=%i, was too "
                         "large for your time series, and after differencing, "
                         "there are no samples remaining in your data. "
                         "Try a smaller value for D, or if you didn't set D "
                         "to begin with, try setting it explicitly. This can "
                         "also occur in seasonal settings when m is too large."
                         % D)

    # difference the exogenous matrix
    if exogenous is not None:
        if D > 0:

dx = xx

    # If D was too big, we might have gotten rid of x altogether!
    if dx.shape[0] == 0:
        raise ValueError("The seasonal differencing order, D=%i, was too "
                         "large for your time series, and after differencing, "
                         "there are no samples remaining in your data. "
                         "Try a smaller value for D, or if you didn't set D "
                         "to begin with, try setting it explicitly. This can "
                         "also occur in seasonal settings when m is too large."
                         % D)

    # difference the exogenous matrix
    if exogenous is not None:
        if D > 0:
            diffxreg = diff(exogenous, differences=D, lag=m)
        else:
            diffxreg = exogenous
    else:
        # here's the thing... we're only going to use diffxreg if exogenous
        # was not None in the first place. However, PyCharm doesn't know that
        # and it thinks we might use it before assigning it. Therefore, assign
        # it to None as a default value and it won't raise the warning anymore.
        diffxreg = None

    # determine/set the order of differencing by estimating the number of
    # orders it would take in order to make the TS stationary.
    if d is None:
        offset_test_args = offset_test_args \
            if offset_test_args is not None else dict()
        d = ndiffs(dx, test=test, alpha=alpha, max_d=max_d, **offset_test_args)

elif D is None:  # we don't have a D yet and we need one (seasonal)
        seasonal_test_args = seasonal_test_args \
            if seasonal_test_args is not None else dict()
        D = nsdiffs(xx, m=m, test=seasonal_test, max_D=max_D,
                    **seasonal_test_args)

        if D > 0 and exogenous is not None:
            diffxreg = diff(exogenous, differences=D, lag=m)
            # check for constance on any column
            if np.apply_along_axis(is_constant, arr=diffxreg, axis=0).any():
                D -= 1

    # D might still be None if not seasonal. Py 3 will throw and error for that
    # unless we explicitly check for ``seasonal``
    if D > 0:
        dx = diff(xx, differences=D, lag=m)
    else:
        dx = xx

    # If D was too big, we might have gotten rid of x altogether!
    if dx.shape[0] == 0:
        raise ValueError("The seasonal differencing order, D=%i, was too "
                         "large for your time series, and after differencing, "
                         "there are no samples remaining in your data. "
                         "Try a smaller value for D, or if you didn't set D "
                         "to begin with, try setting it explicitly. This can "
                         "also occur in seasonal settings when m is too large."
                         % D)

    # difference the exogenous matrix
    if exogenous is not None:
        if D > 0:

else:
        # here's the thing... we're only going to use diffxreg if exogenous
        # was not None in the first place. However, PyCharm doesn't know that
        # and it thinks we might use it before assigning it. Therefore, assign
        # it to None as a default value and it won't raise the warning anymore.
        diffxreg = None

    # determine/set the order of differencing by estimating the number of
    # orders it would take in order to make the TS stationary.
    if d is None:
        offset_test_args = offset_test_args \
            if offset_test_args is not None else dict()
        d = ndiffs(dx, test=test, alpha=alpha, max_d=max_d, **offset_test_args)

        if d > 0 and exogenous is not None:
            diffxreg = diff(diffxreg, differences=d, lag=1)

            # if any columns are constant, subtract one order of differencing
            if np.apply_along_axis(is_constant, arr=diffxreg, axis=0).any():
                d -= 1

    # check differences (do we want to warn?...)
    if error_action == 'warn' and not suppress_warnings:
        if D >= 2:
            warnings.warn("Having more than one seasonal differences is "
                          "not recommended. Please consider using only one "
                          "seasonal difference.")
        # if D is -1, this will be off, so we include the OR
        elif D + d > 2 or d > 2:
            warnings.warn("Having 3 or more differencing operations is not "
                          "recommended. Please consider reducing the total "
                          "number of differences.")