def box_clip(input, im_info, name=None):
    "\n\t:alias_main: paddle.nn.functional.box_clip\n\t:alias: paddle.nn.functional.box_clip,paddle.nn.functional.vision.box_clip\n\t:old_api: paddle.fluid.layers.box_clip\n\t\n    Clip the box into the size given by im_info\n    For each input box, The formula is given as follows:\n        \n    .. code-block:: text\n\n        xmin = max(min(xmin, im_w - 1), 0)\n        ymin = max(min(ymin, im_h - 1), 0) \n        xmax = max(min(xmax, im_w - 1), 0)\n        ymax = max(min(ymax, im_h - 1), 0)\n    \n    where im_w and im_h are computed from im_info:\n \n    .. code-block:: text\n\n        im_h = round(height / scale)\n        im_w = round(weight / scale)\n\n    Args:\n        input(Variable): The input Tensor with shape :math:`[N_1, N_2, ..., N_k, 4]`,\n            the last dimension is 4 and data type is float32 or float64.\n        im_info(Variable): The 2-D Tensor with shape [N, 3] with layout \n            (height, width, scale) representing the information of image. \n            Height and width are the input sizes and scale is the ratio of network input\n            size and original size. The data type is float32 or float64.\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        Variable:\n\n        output(Variable): The clipped tensor with data type float32 or float64. \n        The shape is same as input.\n\n        \n    Examples:\n        .. code-block:: python\n        \n            import paddle.fluid as fluid\n            boxes = fluid.data(\n                name='boxes', shape=[None, 8, 4], dtype='float32', lod_level=1)\n            im_info = fluid.data(name='im_info', shape=[-1 ,3])\n            out = fluid.layers.box_clip(\n                input=boxes, im_info=im_info)\n    "
    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'box_clip')
    check_variable_and_dtype(im_info, 'im_info', ['float32', 'float64'], 'box_clip')
    helper = LayerHelper('box_clip', **locals())
    output = helper.create_variable_for_type_inference(dtype=input.dtype)
    inputs = {'Input': input, 'ImInfo': im_info}
    helper.append_op(type='box_clip', inputs=inputs, outputs={'Output': output})
    return output
:alias_main: paddle.nn.functional.box_clip
    :alias: paddle.nn.functional.box_clip,paddle.nn.functional.vision.box_clip
    :old_api: paddle.fluid.layers.box_clip
    
Clip the box into the size given by im_info
For each input box, The formula is given as follows:
    
.. code-block:: text

    xmin = max(min(xmin, im_w - 1), 0)
    ymin = max(min(ymin, im_h - 1), 0) 
    xmax = max(min(xmax, im_w - 1), 0)
    ymax = max(min(ymax, im_h - 1), 0)

where im_w and im_h are computed from im_info:

.. code-block:: text

    im_h = round(height / scale)
    im_w = round(weight / scale)

Args:
    input(Variable): The input Tensor with shape :math:`[N_1, N_2, ..., N_k, 4]`,
        the last dimension is 4 and data type is float32 or float64.
    im_info(Variable): The 2-D Tensor with shape [N, 3] with layout 
        (height, width, scale) representing the information of image. 
        Height and width are the input sizes and scale is the ratio of network input
        size and original size. The data type is float32 or float64.
    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:
    Variable:

    output(Variable): The clipped tensor with data type float32 or float64. 
    The shape is same as input.

    
Examples:
    .. code-block:: python
    
        import paddle.fluid as fluid
        boxes = fluid.data(
            name='boxes', shape=[None, 8, 4], dtype='float32', lod_level=1)
        im_info = fluid.data(name='im_info', shape=[-1 ,3])
        out = fluid.layers.box_clip(
            input=boxes, im_info=im_info)
def box_clip(input, im_info, name=None):
    "\n\t:alias_main: paddle.nn.functional.box_clip\n\t:alias: paddle.nn.functional.box_clip,paddle.nn.functional.vision.box_clip\n\t:old_api: paddle.fluid.layers.box_clip\n\t\n    Clip the box into the size given by im_info\n    For each input box, The formula is given as follows:\n        \n    .. code-block:: text\n\n        xmin = max(min(xmin, im_w - 1), 0)\n        ymin = max(min(ymin, im_h - 1), 0) \n        xmax = max(min(xmax, im_w - 1), 0)\n        ymax = max(min(ymax, im_h - 1), 0)\n    \n    where im_w and im_h are computed from im_info:\n \n    .. code-block:: text\n\n        im_h = round(height / scale)\n        im_w = round(weight / scale)\n\n    Args:\n        input(Variable): The input Tensor with shape :math:`[N_1, N_2, ..., N_k, 4]`,\n            the last dimension is 4 and data type is float32 or float64.\n        im_info(Variable): The 2-D Tensor with shape [N, 3] with layout \n            (height, width, scale) representing the information of image. \n            Height and width are the input sizes and scale is the ratio of network input\n            size and original size. The data type is float32 or float64.\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        Variable:\n\n        output(Variable): The clipped tensor with data type float32 or float64. \n        The shape is same as input.\n\n        \n    Examples:\n        .. code-block:: python\n        \n            import paddle.fluid as fluid\n            boxes = fluid.data(\n                name='boxes', shape=[None, 8, 4], dtype='float32', lod_level=1)\n            im_info = fluid.data(name='im_info', shape=[-1 ,3])\n            out = fluid.layers.box_clip(\n                input=boxes, im_info=im_info)\n    "
    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'box_clip')
    check_variable_and_dtype(im_info, 'im_info', ['float32', 'float64'], 'box_clip')
    helper = LayerHelper('box_clip', **locals())
    output = helper.create_variable_for_type_inference(dtype=input.dtype)
    inputs = {'Input': input, 'ImInfo': im_info}
    helper.append_op(type='box_clip', inputs=inputs, outputs={'Output': output})
    return output