code: def group(xs: List[Tensor], aggr: Optional[str]) -> Optional[Tensor]:
      if len(xs) == 0:
          return None
      elif aggr is None:
          return torch.stack(xs, dim=1)
      elif len(xs) == 1:
          return xs[0]
      elif aggr == "cat":
          return torch.cat(xs, dim=-1)
      else:
          out = torch.stack(xs, dim=0)
          out = getattr(torch, aggr)(out, dim=0)
          out = out[0] if isinstance(out, tuple) else out
          return out
  
file_path: pytorch_geometric/torch_geometric/nn/conv/hetero_conv.py
imports: import warnings
  from typing import Dict, List, Optional
  import torch
  from torch import Tensor
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.module_dict import ModuleDict
  from torch_geometric.typing import EdgeType, NodeType
  from torch_geometric.utils.hetero import check_add_self_loops
label_desc: Use image node_id 1 for calling a global function with example usage: group(xs, aggr) and returns: None, torch, xs, torch, out