How to use the lpips.PerceptualLoss function in lpips

imgs = torch.stack(imgs, 0).to(device)

    g_ema = Generator(args.size, 512, 8)
    g_ema.load_state_dict(torch.load(args.ckpt)["g_ema"], strict=False)
    g_ema.eval()
    g_ema = g_ema.to(device)

    with torch.no_grad():
        noise_sample = torch.randn(n_mean_latent, 512, device=device)
        latent_out = g_ema.style(noise_sample)

        latent_mean = latent_out.mean(0)
        latent_std = ((latent_out - latent_mean).pow(2).sum() / n_mean_latent) ** 0.5

    percept = lpips.PerceptualLoss(
        model="net-lin", net="vgg", use_gpu=device.startswith("cuda")
    )

    noises_single = g_ema.make_noise()
    noises = []
    for noise in noises_single:
        noises.append(noise.repeat(imgs.shape[0], 1, 1, 1).normal_())

    latent_in = latent_mean.detach().clone().unsqueeze(0).repeat(imgs.shape[0], 1)

    if args.w_plus:
        latent_in = latent_in.unsqueeze(1).repeat(1, g_ema.n_latent, 1)

    latent_in.requires_grad = True

    for noise in noises: