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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 _reorient_3d(image):
 """ Reorganize the data for a 3d nifti """

    # Create empty array where x,y,z correspond to LR (sagittal), PA (coronal), IS (axial) directions and the size
    # of the array in each direction is the same with the corresponding direction of the input image.
    new_image = numpy.zeros([image.dimensions[image.sagittal_orientation.normal_component],
                             image.dimensions[image.coronal_orientation.normal_component],
                             image.dimensions[image.axial_orientation.normal_component]],
                            dtype=image.nifti_data.dtype)

    # Fill the new image with the values of the input image but with matching the orientation with x,y,z
    if image.coronal_orientation.y_inverted:
        for i in range(new_image.shape[2]):
            new_image[:, :, i] = numpy.fliplr(numpy.squeeze(image.get_slice(SliceType.AXIAL,
                                                                            new_image.shape[2] - 1 - i).original_data))
    else:
        for i in range(new_image.shape[2]):
            new_image[:, :, i] = numpy.fliplr(numpy.squeeze(image.get_slice(SliceType.AXIAL,
                                                                            i).original_data))

    return new_image