code: def forward(
      self,
      node_feat,
      in_degree,
      out_degree,
      path_data,
      dist,
      attn_mask=None,
  ):
      num_graphs, max_num_nodes, _ = node_feat.shape
      deg_emb = self.degree_encoder(th.stack((in_degree, out_degree)))
  
      # node feature + degree encoding as input
      node_feat = self.atom_encoder(node_feat.int()).sum(dim=-2)
      node_feat = node_feat + deg_emb
      graph_token_feat = self.graph_token.weight.unsqueeze(0).repeat(
          num_graphs, 1, 1
      )
      x = th.cat([graph_token_feat, node_feat], dim=1)
  
      # spatial encoding and path encoding serve as attention bias
      attn_bias = th.zeros(
          num_graphs,
          max_num_nodes + 1,
          max_num_nodes + 1,
          self.num_heads,
          device=dist.device,
      )
      path_encoding = self.path_encoder(dist, path_data)
      spatial_encoding = self.spatial_encoder(dist)
      attn_bias[:, 1:, 1:, :] = path_encoding + spatial_encoding
  
      # spatial encoding of the virtual node
      t = self.graph_token_virtual_distance.weight.reshape(
          1, 1, self.num_heads
      )
      # Since the virtual node comes first, the spatial encodings between it
      # and other nodes will fill the 1st row and 1st column (omit num_graphs
      # and num_heads dimensions) of attn_bias matrix by broadcasting.
      attn_bias[:, 1:, 0, :] = attn_bias[:, 1:, 0, :] + t
      attn_bias[:, 0, :, :] = attn_bias[:, 0, :, :] + t
  
      x = self.emb_layer_norm(x)
  
      for layer in self.layers:
          x = layer(
              x,
              attn_mask=attn_mask,
              attn_bias=attn_bias,
          )
  
      graph_rep = x[:, 0, :]
      graph_rep = self.layer_norm(
          self.activation_fn(self.lm_head_transform_weight(graph_rep))
      )
      graph_rep = (
          self.embed_out(graph_rep) + self.lm_output_learned_bias
      )
  
      return graph_rep
  
file_path: dgl/examples/core/Graphormer/model.py
imports: import torch
  import torch.nn
  from dgl.nn import DegreeEncoder, GraphormerLayer, PathEncoder, SpatialEncoder
label_desc: Use image node_id 3 for calling the Graphormer obj's underlying member method code with example usage: obj.forward(node_feat, in_degree, out_degree, path_data, dist, attn_mask) and returns: graph_rep