def METHOD_NAME(
        x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0,
        with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
    runs with success, but needs further validation and possibly optimization for lower runtime impact.
    """
    B, C, H, W = x.shape
    total_size = W * H
    clipped_block_size = min(block_size, min(W, H))
    # seed_drop_rate, the gamma parameter
    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
            (W - block_size + 1) * (H - block_size + 1))
    # Forces the block to be inside the feature map.
    w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))
    valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
                  ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
    if batchwise:
        # one mask for whole batch, quite a bit faster
        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
    else:
        uniform_noise = torch.rand_like(x)
    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
    block_mask = -F.max_pool2d(
        -block_mask,
        kernel_size=clipped_block_size,  # block_size,
        stride=1,
        padding=clipped_block_size // 2)
    if with_noise:
        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
        if inplace:
            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
        else:
            x = x * block_mask + normal_noise * (1 - block_mask)
    else:
        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
        if inplace:
            x.mul_(block_mask * normalize_scale)
        else:
            x = x * block_mask * normalize_scale
    return x