def distribute_fpn_proposals(fpn_rois, min_level, max_level, refer_level, refer_scale, name=None):
    "\n\t:alias_main: paddle.nn.functional.distribute_fpn_proposals\n\t:alias: paddle.nn.functional.distribute_fpn_proposals,paddle.nn.functional.vision.distribute_fpn_proposals\n\t:old_api: paddle.fluid.layers.distribute_fpn_proposals\n\t\n    **This op only takes LoDTensor as input.** In Feature Pyramid Networks \n    (FPN) models, it is needed to distribute all proposals into different FPN \n    level, with respect to scale of the proposals, the referring scale and the \n    referring level. Besides, to restore the order of proposals, we return an \n    array which indicates the original index of rois in current proposals. \n    To compute FPN level for each roi, the formula is given as follows:\n    \n    .. math::\n\n        roi\\_scale &= \\sqrt{BBoxArea(fpn\\_roi)}\n\n        level = floor(&\\log(\\frac{roi\\_scale}{refer\\_scale}) + refer\\_level)\n\n    where BBoxArea is a function to compute the area of each roi.\n\n    Args:\n\n        fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is \n            float32 or float64. The input fpn_rois.\n        min_level(int32): The lowest level of FPN layer where the proposals come \n            from.\n        max_level(int32): The highest level of FPN layer where the proposals\n            come from.\n        refer_level(int32): The referring level of FPN layer with specified scale.\n        refer_scale(int32): The referring scale of FPN layer with specified level.\n        name(str, optional): For detailed information, please refer \n            to :ref:`api_guide_Name`. Usually name is no need to set and \n            None by default. \n\n    Returns:\n        Tuple:\n\n        multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4] \n        and data type of float32 and float64. The length is \n        max_level-min_level+1. The proposals in each FPN level.\n\n        restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is \n        the number of total rois. The data type is int32. It is\n        used to restore the order of fpn_rois.\n\n\n    Examples:\n        .. code-block:: python\n\n            import paddle.fluid as fluid\n            fpn_rois = fluid.data(\n                name='data', shape=[None, 4], dtype='float32', lod_level=1)\n            multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(\n                fpn_rois=fpn_rois,\n                min_level=2,\n                max_level=5,\n                refer_level=4,\n                refer_scale=224)\n    "
    check_variable_and_dtype(fpn_rois, 'fpn_rois', ['float32', 'float64'], 'distribute_fpn_proposals')
    helper = LayerHelper('distribute_fpn_proposals', **locals())
    dtype = helper.input_dtype('fpn_rois')
    num_lvl = ((max_level - min_level) + 1)
    multi_rois = [helper.create_variable_for_type_inference(dtype) for i in range(num_lvl)]
    restore_ind = helper.create_variable_for_type_inference(dtype='int32')
    helper.append_op(type='distribute_fpn_proposals', inputs={'FpnRois': fpn_rois}, outputs={'MultiFpnRois': multi_rois, 'RestoreIndex': restore_ind}, attrs={'min_level': min_level, 'max_level': max_level, 'refer_level': refer_level, 'refer_scale': refer_scale})
    return (multi_rois, restore_ind)
:alias_main: paddle.nn.functional.distribute_fpn_proposals
    :alias: paddle.nn.functional.distribute_fpn_proposals,paddle.nn.functional.vision.distribute_fpn_proposals
    :old_api: paddle.fluid.layers.distribute_fpn_proposals
    
**This op only takes LoDTensor as input.** In Feature Pyramid Networks 
(FPN) models, it is needed to distribute all proposals into different FPN 
level, with respect to scale of the proposals, the referring scale and the 
referring level. Besides, to restore the order of proposals, we return an 
array which indicates the original index of rois in current proposals. 
To compute FPN level for each roi, the formula is given as follows:

.. math::

    roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}

    level = floor(&\log(\frac{roi\_scale}{refer\_scale}) + refer\_level)

where BBoxArea is a function to compute the area of each roi.

Args:

    fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is 
        float32 or float64. The input fpn_rois.
    min_level(int32): The lowest level of FPN layer where the proposals come 
        from.
    max_level(int32): The highest level of FPN layer where the proposals
        come from.
    refer_level(int32): The referring level of FPN layer with specified scale.
    refer_scale(int32): The referring scale of FPN layer with specified level.
    name(str, optional): For detailed information, please refer 
        to :ref:`api_guide_Name`. Usually name is no need to set and 
        None by default. 

Returns:
    Tuple:

    multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4] 
    and data type of float32 and float64. The length is 
    max_level-min_level+1. The proposals in each FPN level.

    restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is 
    the number of total rois. The data type is int32. It is
    used to restore the order of fpn_rois.


Examples:
    .. code-block:: python

        import paddle.fluid as fluid
        fpn_rois = fluid.data(
            name='data', shape=[None, 4], dtype='float32', lod_level=1)
        multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
            fpn_rois=fpn_rois,
            min_level=2,
            max_level=5,
            refer_level=4,
            refer_scale=224)
def distribute_fpn_proposals(fpn_rois, min_level, max_level, refer_level, refer_scale, name=None):
    "\n\t:alias_main: paddle.nn.functional.distribute_fpn_proposals\n\t:alias: paddle.nn.functional.distribute_fpn_proposals,paddle.nn.functional.vision.distribute_fpn_proposals\n\t:old_api: paddle.fluid.layers.distribute_fpn_proposals\n\t\n    **This op only takes LoDTensor as input.** In Feature Pyramid Networks \n    (FPN) models, it is needed to distribute all proposals into different FPN \n    level, with respect to scale of the proposals, the referring scale and the \n    referring level. Besides, to restore the order of proposals, we return an \n    array which indicates the original index of rois in current proposals. \n    To compute FPN level for each roi, the formula is given as follows:\n    \n    .. math::\n\n        roi\\_scale &= \\sqrt{BBoxArea(fpn\\_roi)}\n\n        level = floor(&\\log(\\frac{roi\\_scale}{refer\\_scale}) + refer\\_level)\n\n    where BBoxArea is a function to compute the area of each roi.\n\n    Args:\n\n        fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is \n            float32 or float64. The input fpn_rois.\n        min_level(int32): The lowest level of FPN layer where the proposals come \n            from.\n        max_level(int32): The highest level of FPN layer where the proposals\n            come from.\n        refer_level(int32): The referring level of FPN layer with specified scale.\n        refer_scale(int32): The referring scale of FPN layer with specified level.\n        name(str, optional): For detailed information, please refer \n            to :ref:`api_guide_Name`. Usually name is no need to set and \n            None by default. \n\n    Returns:\n        Tuple:\n\n        multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4] \n        and data type of float32 and float64. The length is \n        max_level-min_level+1. The proposals in each FPN level.\n\n        restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is \n        the number of total rois. The data type is int32. It is\n        used to restore the order of fpn_rois.\n\n\n    Examples:\n        .. code-block:: python\n\n            import paddle.fluid as fluid\n            fpn_rois = fluid.data(\n                name='data', shape=[None, 4], dtype='float32', lod_level=1)\n            multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(\n                fpn_rois=fpn_rois,\n                min_level=2,\n                max_level=5,\n                refer_level=4,\n                refer_scale=224)\n    "
    check_variable_and_dtype(fpn_rois, 'fpn_rois', ['float32', 'float64'], 'distribute_fpn_proposals')
    helper = LayerHelper('distribute_fpn_proposals', **locals())
    dtype = helper.input_dtype('fpn_rois')
    num_lvl = ((max_level - min_level) + 1)
    multi_rois = [helper.create_variable_for_type_inference(dtype) for i in range(num_lvl)]
    restore_ind = helper.create_variable_for_type_inference(dtype='int32')
    helper.append_op(type='distribute_fpn_proposals', inputs={'FpnRois': fpn_rois}, outputs={'MultiFpnRois': multi_rois, 'RestoreIndex': restore_ind}, attrs={'min_level': min_level, 'max_level': max_level, 'refer_level': refer_level, 'refer_scale': refer_scale})
    return (multi_rois, restore_ind)