import torch

def apply_masks(x, masks):
    """
    :param x: [B, N, D]
    :param masks: [B, 2, Nq], containing indices (int64) to select from N patches
    :return: [B * 2, Nq, D]
    """
    B, N, D = x.shape
    _, V, Nq = masks.shape  # V = 2 views

    all_x = []

    for v in range(V):
        # masks[:, v, :] -> [B, Nq]
        # We need to expand it for gather:
        # [B, Nq] -> [B, Nq, D] for gather
        idx = masks[:, v, :].unsqueeze(-1).expand(-1, -1, D)  # [B, Nq, D]
        
        # Gather along dim=1 (patch dimension)
        gathered = torch.gather(x, dim=1, index=idx)  # [B, Nq, D]
        
        all_x.append(gathered)

    # Concatenate along batch dimension:
    # [B, Nq, D] * 2 -> [B*2, Nq, D]
    return torch.cat(all_x, dim=0)