def sample_random_indices(
    n_indices: int, n_samples: int, device: Optional[torch.device] = None
) -> Optional[torch.Tensor]:
    """
    Samples `n_samples` random indices from range `[0..n_indices - 1]`.
    If `n_indices` is smaller than `n_samples`, returns `None` meaning that all indices
    are selected.
    Args:
        n_indices (int): total number of indices
        n_samples (int): number of indices to sample
        device (torch.device): the desired device of returned tensor
    Return:
        Tensor of selected vertex indices, or `None`, if all vertices are selected
    """
    if (n_samples <= 0) or (n_indices <= n_samples):
        return None
    indices = torch.randperm(n_indices, device=device)[:n_samples]
    return indices

    Samples `n_samples` random indices from range `[0..n_indices - 1]`.
    If `n_indices` is smaller than `n_samples`, returns `None` meaning that all indices
    are selected.
    Args:
        n_indices (int): total number of indices
        n_samples (int): number of indices to sample
        device (torch.device): the desired device of returned tensor
    Return:
        Tensor of selected vertex indices, or `None`, if all vertices are selected
    
EVA-02/det/projects/DensePose/densepose/modeling/losses/utils.py