Support static graph code-gen for temporal_shift

[sanbuphy]

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#51842

[paddle-bot]

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[heavyrain-lzy]

请rerun Window-infer CI

[luotao1]

@heavyrain-lzy

请rerun Window-infer CI

CI 已经通过

[heavyrain-lzy]

根据PR-CI-Static-Check的输出，这里需要把原始.cc文件中的comment拷贝到Python端。

1. python/paddle/nn/functional/extension.py中的temporal_shift，删除@templatedoc(),替换${comment}：

def temporal_shift(x, seg_num, shift_ratio=0.25, name=None, data_format="NCHW"):
    r"""

    **Temporal Shift Operator**

    Calculate the temporal shifting features for Input(X).

    Input(X) should be in shape of [N*T, C, H, W] or [N*T, H, W, C], while
    N is the batch size, T is the temporal segment number specified by
    :attr:`seg_num`, C is the channel number, H and W is the height and
    width of features.

    Temporal Shifting is calculated as follows when data format is NCHW:

    Step 1: Reshape Input(X) to [N, T, C, H, W].

    Step 2: Pad 0 to reshaping result in the 2nd(T) dimension with
    padding width as 1 on each side, padding result will be in shape
    of [N, T+2, C, H, W].

    Step 3: Assume :attr:`shift_ratio` is :math:`1/4`, slice padding
    result as follows:

    $$
    slice1 = x[:, :T, :C/4, :, :]
    $$
    $$
    slice2 = x[:, 2:T+2, C/4:C/2, :, :]
    $$
    $$
    slice3 = x[:, 1:T+1, C/2:, :, :]
    $$

    Step 4: Concatenate three slices along the 3rd(C) dimension and
    reshape result to [N*T, C, H, W].

    For details of temporal shifting, please refer to paper:
    `Temporal Shift Module <http://arxiv.org/abs/1811.08383>`_ .

    Args:
        x(Tensor): The input tensor of temporal shift operator.
                   This is a 4-D tensor with shape of [N*T, C, H, W] or
                   [N*T, H, W, C]. While N is the batch size,
                   T is the temporal segment number, C is the channel number,
                   H is the height of features and W is the width of features.
                   The data type is float16, float32 and float64
        seg_num(int): The temporal segment number, this should be a positive integer.
        shift_ratio(float): The shift ratio of the channels, the first :attr:`shift_ratio` part
                            of channels will be shifted by -1 along the temporal dimension,
                            and the second :attr:`shift_ratio` part of channels will be shifted
                            by 1 along the temporal dimension. :attr:`shift_ratio` should be in
                            range [0, 0.5]. Default 0.25.
        name(str, optional): For detailed information, please refer
                             to :ref:`api_guide_Name`. Usually name is no need to set and
                             None by default.
        data_format(str, optional): Data format that specifies the layout of input.
            It can be "NCHW" or "NHWC". Default: "NCHW".

    Returns:
        out(Tensor): The temporal shifting result is a tensor with the
        same shape and same data type as the input.

    Examples:
        .. code-block:: python

            import paddle
            import paddle.nn.functional as F

            input = paddle.randn([6, 4, 2, 2])
            out = F.temporal_shift(x=input, seg_num=2, shift_ratio=0.2)
    """

[heavyrain-lzy]

LGTM

[heavyrain-lzy]

@sunzhongkai 麻烦review一下PR

[heavyrain-lzy]

是不是没有安装pre-commit，代码格式错误，ci-converage可能是随机挂

[sanbuphy]

是不是没有安装pre-commit，代码格式错误，ci-converage可能是随机挂

commit过了，我再commit一次吧

[sanbuphy]

是不是没有安装pre-commit，代码格式错误，ci-converage可能是随机挂

precommit的话 是提示没有可更新的，明天我再看看具体出在哪

[heavyrain-lzy]

LGTM

[luotao1]

请 @sunzhongkai588 审核下文档～

[sunzhongkai588]

LGTM