def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
    r"""2D convolution with optional up/downsampling.

    Padding is performed only once at the beginning, not between the operations.

    Args:
        x:              Input tensor of shape
                        `[batch_size, in_channels, in_height, in_width]`.
        w:              Weight tensor of shape
                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
                        calling upfirdn2d.setup_filter(). None = identity (default).
        up:             Integer upsampling factor (default: 1).
        down:           Integer downsampling factor (default: 1).
        padding:        Padding with respect to the upsampled image. Can be a single number
                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
                        (default: 0).
        groups:         Split input channels into N groups (default: 1).
        flip_weight:    False = convolution, True = correlation (default: True).
        flip_filter:    False = convolution, True = correlation (default: False).

    Returns:
        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
    """
    # Validate arguments.
    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
    assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
    assert isinstance(up, int) and (up >= 1)
    assert isinstance(down, int) and (down >= 1)
    assert isinstance(groups, int) and (groups >= 1)
    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
    fw, fh = _get_filter_size(f)
    px0, px1, py0, py1 = _parse_padding(padding)

    # Adjust padding to account for up/downsampling.
    if up > 1:
        px0 += (fw + up - 1) // 2
        px1 += (fw - up) // 2
        py0 += (fh + up - 1) // 2
        py1 += (fh - up) // 2
    if down > 1:
        px0 += (fw - down + 1) // 2
        px1 += (fw - down) // 2
        py0 += (fh - down + 1) // 2
        py1 += (fh - down) // 2

    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
    if kw == 1 and kh == 1 and (down > 1 and up == 1):
        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
        return x

    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
    if kw == 1 and kh == 1 and (up > 1 and down == 1):
        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
        return x

    # Fast path: downsampling only => use strided convolution.
    if down > 1 and up == 1:
        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
        x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
        return x

    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
    if up > 1:
        if groups == 1:
            w = w.transpose(0, 1)
        else:
            w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
            w = w.transpose(1, 2)
            w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
        px0 -= kw - 1
        px1 -= kw - up
        py0 -= kh - 1
        py1 -= kh - up
        pxt = max(min(-px0, -px1), 0)
        pyt = max(min(-py0, -py1), 0)
        x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
        if down > 1:
            x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
        return x

    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
    if up == 1 and down == 1:
        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
            return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)

    # Fallback: Generic reference implementation.
    x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
    if down > 1:
        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
    return x
2D convolution with optional up/downsampling.

    Padding is performed only once at the beginning, not between the operations.

    Args:
        x:              Input tensor of shape
                        `[batch_size, in_channels, in_height, in_width]`.
        w:              Weight tensor of shape
                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
                        calling upfirdn2d.setup_filter(). None = identity (default).
        up:             Integer upsampling factor (default: 1).
        down:           Integer downsampling factor (default: 1).
        padding:        Padding with respect to the upsampled image. Can be a single number
                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
                        (default: 0).
        groups:         Split input channels into N groups (default: 1).
        flip_weight:    False = convolution, True = correlation (default: True).
        flip_filter:    False = convolution, True = correlation (default: False).

    Returns:
        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
    
converters/stylegan2ada_pth_official/torch_utils/ops/conv2d_resample.py