code: def cat(
      tensors: List[Tensor], dim: Union[int, Tuple[int, int]]
  ) -> Tensor:
      assert is_torch_sparse_tensor(tensors[0])
  
      if tensors[0].layout == torch.sparse_coo:
          return cat_coo(tensors, dim)
      elif tensors[0].layout == torch.sparse_csr:
          return cat_csr(tensors, dim)
      else:
          return cat_csc(tensors, dim)
  
imports: import typing
  import warnings
  from typing import Any, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  import torch_geometric.typing
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import coalesce, cumsum
label_desc: Use image node_id 16 for calling a global function with example usage: cat(tensors, dim) and returns: cat_coo, cat_csr, cat_csc