code: def cat(seq: List[Optional[Tensor]]) -> Optional[Tensor]:
      values = [v for v in seq if v is not None]
      values = [v for v in values if v.numel() > 0]
      values = [v.unsqueeze(-1) if v.dim() == 1 else v for v in values]
      return torch.cat(values, dim=-1) if len(values) > 0 else None
  
file_path: pytorch_geometric/torch_geometric/io/tu.py
imports: import os.path
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  from torch import Tensor
  from torch_geometric.data import Data
  from torch_geometric.io import fs, read_txt_array
  from torch_geometric.utils import coalesce, cumsum, one_hot, remove_self_loops
label_desc: Use image node_id 3 for calling a global function with example usage: cat(seq) and returns: unknown