(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda')
return _simple_upfirdn_2d(x, k, up=factor, pad0=(p+1)//2+factor-1, pad1=p//2, data_format=data_format, impl=impl)
r"""Upsample a batch of 2D images with the given filter.

    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
    and upsamples each image with the given filter. The filter is normalized so that
    if the input pixels are constant, they will be scaled by the specified `gain`.
    Pixels outside the image are assumed to be zero, and the filter is padded with
    zeros so that its shape is a multiple of the upsampling factor.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                      The default is `[1] * factor`, which corresponds to nearest-neighbor
                      upsampling.
        factor:       Integer upsampling factor (default: 2).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).

    Returns:
        Tensor of the shape `[N, C, H * factor, W * factor]` or
        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
r"""Upsample a batch of 2D images with the given filter.
r Upsample a batch of 2D images with the given filter .
def upsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
    r"""Upsample a batch of 2D images with the given filter.

    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
    and upsamples each image with the given filter. The filter is normalized so that
    if the input pixels are constant, they will be scaled by the specified `gain`.
    Pixels outside the image are assumed to be zero, and the filter is padded with
    zeros so that its shape is a multiple of the upsampling factor.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                      The default is `[1] * factor`, which corresponds to nearest-neighbor
                      upsampling.
        factor:       Integer upsampling factor (default: 2).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).

    Returns:
        Tensor of the shape `[N, C, H * factor, W * factor]` or
        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
    """

    assert isinstance(factor, int) and factor >= 1
    if k is None:
        k = [1] * factor
    k = _setup_kernel(k) * (gain * (factor ** 2))
    p = k.shape[0] - factor
    return _simple_upfirdn_2d(x, k, up=factor, pad0=(p+1)//2+factor-1, pad1=p//2, data_format=data_format, impl=impl)
def upsample_2d ( x , k = None , factor = 2 , gain = 1 , data_format = 'NCHW' , impl = 'cuda' ) : assert isinstance ( factor , int ) and factor >= 1 if k is None : k = [ 1 ] * factor k = _setup_kernel ( k ) * ( gain * ( factor ** 2 ) ) p = k . shape [ 0 ] - factor return _simple_upfirdn_2d ( x , k , up = factor , pad0 = ( p + 1 ) // 2 + factor - 1 , pad1 = p // 2 , data_format = data_format , impl = impl )