def generate_images(
    network_pkl: str,
    seeds: List[int],
    truncation_psi: float,
    noise_mode: str,
    outdir: str,
    translate: Tuple[float,float],
    rotate: float,
    class_idx: Optional[int]
):
    """Generate images using pretrained network pickle.

    Examples:

    \b
    # Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
    python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl

    \b
    # Generate uncurated images with truncation using the MetFaces-U dataset
    python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
    """

    print('Loading networks from "%s"...' % network_pkl)
    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
    with dnnlib.util.open_url(network_pkl) as f:
        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore

    os.makedirs(outdir, exist_ok=True)

    # Labels.
    label = torch.zeros([1, G.c_dim], device=device)
    if G.c_dim != 0:
        if class_idx is None:
            raise click.ClickException('Must specify class label with --class when using a conditional network')
        label[:, class_idx] = 1
    else:
        if class_idx is not None:
            print ('warn: --class=lbl ignored when running on an unconditional network')

    # Generate images.
    for seed_idx, seed in enumerate(seeds):
        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device).float()

        # Construct an inverse rotation/translation matrix and pass to the generator.  The
        # generator expects this matrix as an inverse to avoid potentially failing numerical
        # operations in the network.
        if hasattr(G.synthesis, 'input'):
            m = make_transform(translate, rotate)
            m = np.linalg.inv(m)
            G.synthesis.input.transform.copy_(torch.from_numpy(m))

        img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
        PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
Generate images using pretrained network pickle.

    Examples:

    
    # Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
    python gen_images.py --outdir=out --trunc=1 --seeds=2 \
        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl

    
    # Generate uncurated images with truncation using the MetFaces-U dataset
    python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \
        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
    
gen_images.py