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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 cuts_from_bbox(mask_nii, cuts=3):
 """Finds equi-spaced cuts for presenting images"""

    from nibabel.affines import apply_affine

    mask_data = mask_nii.get_data() > 0.0

    # First, project the number of masked voxels on each axes
    ijk_counts = [
        mask_data.sum(2).sum(1),  # project sagittal planes to transverse (i) axis
        mask_data.sum(2).sum(0),  # project coronal planes to to longitudinal (j) axis
        mask_data.sum(1).sum(0),  # project axial planes to vertical (k) axis
    ]

    # If all voxels are masked in a slice (say that happens at k=10),
    # then the value for ijk_counts for the projection to k (ie. ijk_counts[2])
    # at that element of the orthogonal axes (ijk_counts[2][10]) is
    # the total number of voxels in that slice (ie. Ni x Nj).
    # Here we define some thresholds to consider the plane as "masked"
    # The thresholds vary because of the shape of the brain
    # I have manually found that for the axial view requiring 30%
    # of the slice elements to be masked drops almost empty boxes
    # in the mosaic of axial planes (and also addresses #281)
    ijk_th = [
        int((mask_data.shape[1] * mask_data.shape[2]) * 0.2),   # sagittal
        int((mask_data.shape[0] * mask_data.shape[2]) * 0.0),   # coronal
        int((mask_data.shape[0] * mask_data.shape[1]) * 0.3),   # axial
    ]

    vox_coords = []
    for ax, (c, th) in enumerate(zip(ijk_counts, ijk_th)):
        B = np.argwhere(c > th)
        if B.size:
            smin, smax = B.min(), B.max()

        # Avoid too narrow selections of cuts (very small masks)
        if not B.size or (th > 0 and (smin + cuts + 1) >= smax):
            B = np.argwhere(c > 0)

        # Resort to full plane if mask is seemingly empty
        smin, smax = B.min(), B.max() if B.size else (0, mask_data.shape[ax])
        inc = (smax - smin) / (cuts + 1)
        vox_coords.append([smin + (i + 1) * inc for i in range(cuts)])

    ras_coords = []
    for cross in np.array(vox_coords).T:
        ras_coords.append(apply_affine(
            mask_nii.affine, cross).tolist())
    ras_cuts = [list(coords) for coords in np.transpose(ras_coords)]
    return {k: v for k, v in zip(['x', 'y', 'z'], ras_cuts)}