code: def group_edge_placeholder(
      input_dict: Dict[EdgeType, Union[Tensor, SparseTensor]],
      type2id: Dict[EdgeType, int],
      offset_dict: Dict[NodeType, int] = None,
  ) -> Union[Tensor, SparseTensor]:
      inputs = [input_dict[key] for key in type2id.keys()]
  
      if len(inputs) == 1:
          return inputs[0]
  
      # In case of grouping a graph connectivity tensor `edge_index` or `adj_t`,
      # we need to increment its indices:
      elif inputs[0].size(0) == 2 and inputs[0].dtype == torch.long:
          if offset_dict is None:
              raise AttributeError(
                  "Can not infer node-level offsets. Please ensure that there "
                  "exists a node-level argument before the 'edge_index' "
                  "argument in your forward header."
              )
  
          outputs = []
          for value, (src_type, _, dst_type) in zip(inputs, type2id):
              value = value.clone()
              value[0, :] += offset_dict[src_type]
              value[1, :] += offset_dict[dst_type]
              outputs.append(value)
  
          return torch.cat(outputs, dim=-1)
  
      elif isinstance(inputs[0], SparseTensor):
          if offset_dict is None:
              raise AttributeError(
                  "Can not infer node-level offsets. Please ensure that there "
                  "exists a node-level argument before the 'SparseTensor' "
                  "argument in your forward header."
              )
  
          # For grouping a list of SparseTensors, we convert them into a
          # unified `edge_index` representation in order to avoid conflicts
          # induced by re-shuffling the data.
          rows, cols = [], []
          for value, (src_type, _, dst_type) in zip(inputs, type2id):
              col, row, value = value.coo()
              assert value is None
              rows.append(row + offset_dict[src_type])
              cols.append(col + offset_dict[dst_type])
  
          row = torch.cat(rows, dim=0)
          col = torch.cat(cols, dim=0)
          return torch.stack([row, col], dim=0)
  
      else:
          return torch.cat(inputs, dim=-2)
  
imports: import copy
  import warnings
  from typing import Any, Dict, List, Optional, Union
  import torch
  from torch import Tensor
  from torch.nn import Module, Parameter
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.dense import Linear
  from torch_geometric.nn.fx import Transformer
  from torch_geometric.typing import EdgeType, Metadata, NodeType, SparseTensor
  from torch_geometric.utils.hetero import get_unused_node_types
label_desc: Use image node_id 6 for calling a global function with example usage: group_edge_placeholder(input_dict, type2id, offset_dict) and returns: inputs, torch, torch, torch