code: def maximum_path(neg_cent, mask):
      """Numba version.
      neg_cent: [b, t_t, t_s]
      mask: [b, t_t, t_s]
      """
      device = neg_cent.device
      dtype = neg_cent.dtype
      neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
      path = np.zeros(neg_cent.shape, dtype=np.int32)
  
      t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
      t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
      maximum_path_c(path, neg_cent, t_t_max, t_s_max)
      return torch.from_numpy(path).to(device=device, dtype=dtype)
  
imports: import numba
  import numpy
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: maximum_path(neg_cent, mask) and returns: torch