code: def rel_graph_embed(graph, embed_size):
      """Initialize a heterogenous embedding layer for all node types in the
      graph, except for the "paper" node type.
  
      The function constructs a dictionary 'node_num', where the keys are node
      types (ntype) and the values are the number of nodes for each type. This
      dictionary is used to create a HeteroEmbedding instance.
  
      (HIGHLIGHT)
      A HeteroEmbedding instance holds separate embedding layers for each node
      type, each with its own feature space of dimensionality
      (node_num[ntype], embed_size), where 'node_num[ntype]' is the number of
      nodes of type 'ntype' and 'embed_size' is the embedding dimension.
  
      The "paper" node type is specifically excluded, possibly because these nodes
      might already have predefined feature representations, and therefore, do not
      require an additional embedding layer.
  
      Parameters
      ----------
      graph : FusedCSCSamplingGraph
          The graph for which to create the heterogenous embedding layer.
      embed_size : int
          The size of the embedding vectors.
  
      Returns
      --------
      HeteroEmbedding
          A heterogenous embedding layer for all node types in the graph, except
          for the "paper" node type.
      """
      node_num = {}
      node_type_to_id = graph.node_type_to_id
      node_type_offset = graph.node_type_offset
      for ntype, ntype_id in node_type_to_id.items():
          # Skip the "paper" node type.
          if ntype == "paper":
              continue
          node_num[ntype] = (
              node_type_offset[ntype_id + 1]
              - node_type_offset[ntype_id]
          )
      print(f"node_num for rel_graph_embed: {node_num}")
      return HeteroEmbedding(node_num, embed_size)
  
imports: import argparse
  import itertools
  import sys
  import time
  import dgl
  import dgl.graphbolt
  import dgl.nn
  import psutil
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import HeteroEmbedding
  from ogb.lsc import MAG240MEvaluator
  from ogb.nodeproppred import Evaluator
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling a global function with example usage: rel_graph_embed(graph, embed_size) and returns: HeteroEmbedding