How to use the nimare.meta.ibma.IBMAEstimator function in NiMARE

_required_inputs = {
        'z_maps': ('image', 'z'),
        'sample_sizes': ('metadata', 'sample_sizes')
    }

    def __init__(self, two_sided=True, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.two_sided = two_sided

    def _fit(self, dataset):
        z_maps = self.inputs_['z_maps']
        sample_sizes = np.array([np.mean(n) for n in self.inputs_['sample_sizes']])
        return weighted_stouffers(z_maps, sample_sizes, two_sided=self.two_sided)


class RFX_GLM(IBMAEstimator):
    """
    A t-test on contrast images. Requires contrast images.

    Parameters
    ----------
    null : {'theoretical', 'empirical'}, optional
        Whether to use a theoretical null T distribution or an empirically-
        derived null distribution determined via sign flipping.
        Default is 'theoretical'.
    n_iters : :obj:`int` or :obj:`None`, optional
        The number of iterations to run in estimating the null distribution.
        Only used if ``null = 'empirical'``.
    two_sided : :obj:`bool`, optional
        Whether to do a two- or one-sided test. Default is True.
    """
    _required_inputs = {

-----
    Sum of -log P-values (from T/Zs converted to Ps)
    """
    _required_inputs = {
        'z_maps': ('image', 'z')
    }

    def __init__(self, two_sided=True, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.two_sided = two_sided

    def _fit(self, dataset):
        return fishers(self.inputs_['z_maps'], two_sided=self.two_sided)


class Stouffers(IBMAEstimator):
    """
    A t-test on z-statistic images. Requires z-statistic images.

    Parameters
    ----------
    inference : {'ffx', 'rfx'}, optional
        Whether to use fixed-effects inference (default) or random-effects
        inference.
    null : {'theoretical', 'empirical'}, optional
        Whether to use a theoretical null T distribution or an empirically-
        derived null distribution determined via sign flipping. Empirical null
        is only possible if ``inference = 'rfx'``.
    n_iters : :obj:`int` or :obj:`None`, optional
        The number of iterations to run in estimating the null distribution.
        Only used if ``inference = 'rfx'`` and ``null = 'empirical'``.
    two_sided : :obj:`bool`, optional

def __init__(self, inference='ffx', null='theoretical', n_iters=None,
                 two_sided=True, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.inference = inference
        self.null = null
        self.n_iters = n_iters
        self.two_sided = two_sided

    def _fit(self, dataset):
        return stouffers(self.inputs_['z_maps'], inference=self.inference,
                         null=self.null, n_iters=self.n_iters,
                         two_sided=self.two_sided)


class WeightedStouffers(IBMAEstimator):
    """
    An image-based meta-analytic test using z-statistic images and
    sample sizes. Zs from bigger studies get bigger weights.

    Parameters
    ----------
    two_sided : :obj:`bool`, optional
        Whether to do a two- or one-sided test. Default is True.
    """
    _required_inputs = {
        'z_maps': ('image', 'z'),
        'sample_sizes': ('metadata', 'sample_sizes')
    }

    def __init__(self, two_sided=True, *args, **kwargs):
        super().__init__(*args, **kwargs)

def __init__(self, *args, **kwargs):
        mask = kwargs.get('mask')
        if mask is not None:
            mask = get_masker(mask)
        self.masker = mask

    def _preprocess_input(self, dataset):
        """ Mask required input images using either the dataset's mask or the
        estimator's. """
        masker = self.masker or dataset.masker
        for name, (type_, _) in self._required_inputs.items():
            if type_ == 'image':
                self.inputs_[name] = masker.transform(self.inputs_[name])


class Fishers(IBMAEstimator):
    """
    An image-based meta-analytic test using t- or z-statistic images.
    Requires z-statistic images, but will be extended to work with t-statistic
    images as well.

    Parameters
    ----------
    two_sided : :obj:`bool`, optional
        Whether to do a two- or one-sided test. Default is True.

    Notes
    -----
    Sum of -log P-values (from T/Zs converted to Ps)
    """
    _required_inputs = {
        'z_maps': ('image', 'z')

Working directory for FSL flameo outputs.
    two_sided : :obj:`bool`, optional
        Whether analysis should be two-sided (True) or one-sided (False).

    Returns
    -------
    result : :obj:`dict`
        Dictionary containing maps for test statistics, p-values, and
        negative log(p) values.
    """
    result = fsl_glm(con_maps, se_maps, sample_sizes, mask, inference='mfx',
                     cdt=cdt, q=q, work_dir=work_dir, two_sided=two_sided)
    return result


class MFX_GLM(IBMAEstimator):
    """
    The gold standard image-based meta-analytic test. Uses contrast and
    standard error images.

    Parameters
    ----------
    cdt : :obj:`float`, optional
        Cluster-defining p-value threshold.
    q : :obj:`float`, optional
        Alpha for multiple comparisons correction.
    two_sided : :obj:`bool`, optional
        Whether analysis should be two-sided (True) or one-sided (False).
    """
    _required_inputs = {
        'con_maps': ('image', 'con'),
        'se_maps': ('image', 'se'),

Working directory for FSL flameo outputs.
    two_sided : :obj:`bool`, optional
        Whether analysis should be two-sided (True) or one-sided (False).

    Returns
    -------
    result : :obj:`dict`
        Dictionary containing maps for test statistics, p-values, and
        negative log(p) values.
    """
    result = fsl_glm(con_maps, se_maps, sample_sizes, mask, inference='ffx',
                     cdt=cdt, q=q, work_dir=work_dir, two_sided=two_sided)
    return result


class FFX_GLM(IBMAEstimator):
    """
    An image-based meta-analytic test using contrast and standard error images.
    Don't estimate variance, just take from first level.

    Parameters
    ----------
    cdt : :obj:`float`, optional
        Cluster-defining p-value threshold.
    q : :obj:`float`, optional
        Alpha for multiple comparisons correction.
    two_sided : :obj:`bool`, optional
        Whether analysis should be two-sided (True) or one-sided (False).
    """
    _required_inputs = {
        'con_maps': ('image', 'con'),
        'se_maps': ('image', 'se'),