code: def spmm(
      src: SparseTensor, other: torch.Tensor, reduce: str = "sum"
  ) -> torch.Tensor:
      if reduce == "sum" or reduce == "add":
          return spmm_sum(src, other)
      elif reduce == "mean":
          return spmm_mean(src, other)
      elif reduce == "min":
          return spmm_min(src, other)[0]
      elif reduce == "max":
          return spmm_max(src, other)[0]
      else:
          raise ValueError
  
file_path: pytorch_sparse/torch_sparse/matmul.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch_sparse.tensor import SparseTensor
label_desc: Use image node_id 6 for calling a global function with example usage: spmm(src, other, reduce) and returns: spmm_sum, spmm_mean, spmm_min, spmm_max