def drop_path(x, drop_prob=0., training=False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of
    residual blocks).

    We follow the implementation
    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    # handle tensors with different dimensions, not just 4D tensors.
    shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(
        shape, dtype=x.dtype, device=x.device)
    output = x.div(keep_prob) * random_tensor.floor()
    return output
Drop paths (Stochastic Depth) per sample (when applied in main path of
    residual blocks).

    We follow the implementation
    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
    
ControlNet-v1-1-nightly/annotator/uniformer/mmcv/cnn/bricks/drop.py