docstr: Parameters
  ----------
  g : DGLGraph
      Input graph.
  inputs : dict[str, torch.Tensor]
      Node feature for each node type.
  
  Returns
  -------
  dict[str, torch.Tensor]
      New node features for each node type.
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code: def forward(self, g, inputs):
      """
      Parameters
      ----------
      g : DGLGraph
          Input graph.
      inputs : dict[str, torch.Tensor]
          Node feature for each node type.
  
      Returns
      -------
      dict[str, torch.Tensor]
          New node features for each node type.
      """
      # Create a deep copy of the graph g with features saved in local
      # frames to prevent side effects from modifying the graph.
      g = g.local_var()
  
      # Create a dictionary of weights for each relationship. The weights
      # are retrieved from the Linear layers defined earlier.
      weight_dict = {
          rel_name: {"weight": self.weight[rel_name].weight.T}
          for rel_name in self.relation_names
      }
  
      # Create a dictionary of node features for the destination nodes in
      # the graph. We slice the node features according to the number of
      # destination nodes of each type. This is necessary because when
      # incorporating the effect of self-loop edges, we perform computations
      # only on the destination nodes' features. By doing so, we ensure the
      # feature dimensions match and prevent any misuse of incorrect node
      # features.
      inputs_dst = {
          k: v[: g.number_of_dst_nodes(k)] for k, v in inputs.items()
      }
      # Apply the convolution operation on the graph. mod_kwargs are
      # additional arguments for each relation function defined in the
      # HeteroGraphConv. In this case, it's the weights for each relation.
      hs = self.conv(g, inputs, mod_kwargs=weight_dict)
  
      def _apply(ntype, h):
          # Apply the `loop_weight` to the input node features, effectively
          # acting as a residual connection. This allows the model to refine
          # node embeddings based on its current features.
          h = h + self.loop_weights[ntype](inputs_dst[ntype])
          if self.activation:
              h = self.activation(h)
          return self.dropout(h)
  
      # Apply the function defined above for each node type. This will update
      # the node features using the `loop_weights`, apply the activation
      # function and dropout.
      return {ntype: _apply(ntype, h) for ntype, h in hs.items()}
  
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 3 for calling the RelGraphConvLayer obj's underlying member method code with example usage: obj.forward(g, inputs) and returns: unknown, self