code: def placeholder(self, node: Node, target: Any, name: str):
      if node.type is not None:
          Type = EdgeType if self.is_edge_level(node) else NodeType
          node.type = Dict[Type, node.type]
  
      out = node
  
      # Create `node_offset_dict` and `edge_offset_dict` dictionaries in case
      # they are not yet initialized. These dictionaries hold the cumulated
      # sizes used to create a unified graph representation and to split the
      # output data.
      if (
          self.is_edge_level(node)
          and not self._edge_offset_dict_initialized
      ):
          self.graph.inserting_after(out)
          out = self.graph.create_node(
              "call_function",
              target=get_edge_offset_dict,
              args=(node, self.edge_type2id),
              name="edge_offset_dict",
          )
          self._edge_offset_dict_initialized = True
  
      elif not self._node_offset_dict_initialized:
          self.graph.inserting_after(out)
          out = self.graph.create_node(
              "call_function",
              target=get_node_offset_dict,
              args=(node, self.node_type2id),
              name="node_offset_dict",
          )
          self._node_offset_dict_initialized = True
  
      # Create a `edge_type` tensor used as input to `HeteroBasisConv`:
      if self.is_edge_level(node) and not self._edge_type_initialized:
          self.graph.inserting_after(out)
          out = self.graph.create_node(
              "call_function",
              target=get_edge_type,
              args=(node, self.edge_type2id),
              name="edge_type",
          )
          self._edge_type_initialized = True
  
      # Add `Linear` operation to align features to the same dimensionality:
      if name in self.in_channels:
          self.graph.inserting_after(out)
          out = self.graph.create_node(
              "call_module",
              target=f"align_lin__{name}",
              args=(node,),
              name=f"{name}__aligned",
          )
          self._state[out.name] = self._state[name]
  
          lin = LinearAlign(
              self.metadata[int(self.is_edge_level(node))],
              self.in_channels[name],
          )
          setattr(self.module, f"align_lin__{name}", lin)
  
      # Perform grouping of type-wise values into a single tensor:
      if self.is_edge_level(node):
          self.graph.inserting_after(out)
          out = self.graph.create_node(
              "call_function",
              target=group_edge_placeholder,
              args=(
                  out if name in self.in_channels else node,
                  self.edge_type2id,
                  self.find_by_name("node_offset_dict"),
              ),
              name=f"{name}__grouped",
          )
          self._state[out.name] = "edge"
  
      else:
          self.graph.inserting_after(out)
          out = self.graph.create_node(
              "call_function",
              target=group_node_placeholder,
              args=(
                  out if name in self.in_channels else node,
                  self.node_type2id,
              ),
              name=f"{name}__grouped",
          )
          self._state[out.name] = "node"
  
      self.replace_all_uses_with(node, out)
  
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 4 for calling the ToHeteroWithBasesTransformer obj's underlying member method code with example usage: obj.placeholder(node, target, name) without return types