code: def forward(
      self,
      x,
      edge_index,
      edge_type=None,
      batch_size=1,
      output_nodes=None,
      special_node_input=None,
  ):
      # x is the input node features num_nodesxin_feat
      # edge_index is a 2xnum_edges matrix of which nodes each edge connects
      # edge_type is a num_edges of what the edge type is for each of those types
      if self.num_nodes is not None:
          assert x.size(0) == self.num_nodes * batch_size
  
      # Set optional spec_out to None
      spec_out = None
  
      # Check type and inputs match
      if self.gcn_type == "RGCN":
          assert edge_type is not None
      elif self.gcn_type in ["GCN", "SAGE"]:
          assert edge_type is None
      else:
          raise Exception("GCN type %s not implemented" % self.gcn_type)
  
      # First GCN conv
      if edge_type is not None:
          x = self.conv1(x, edge_index, edge_type)
      else:
          x = self.conv1(x, edge_index)
      if self.num_gcn_conv > 1:
          # Transfer layers + bn/drop
          if self.use_bn:
              x = self.bn1(x)
          x = F.relu(x)
          if self.use_drop:
              x = F.dropout(x, p=self.drop_p, training=self.training)
  
          # Second layer
          if edge_type is not None:
              x = self.conv2(x, edge_index, edge_type)
          else:
              x = self.conv2(x, edge_index)
  
      if self.num_gcn_conv > 2:
          # Transfer layers + bn/drop
          if self.use_bn:
              x = self.bn2(x)
          x = F.relu(x)
          if self.use_drop:
              x = F.dropout(x, p=self.drop_p, training=self.training)
  
          # Third layer
          if edge_type is not None:
              x = self.conv3(x, edge_index, edge_type)
          else:
              x = self.conv3(x, edge_index)
  
      if self.num_gcn_conv > 3:
          # Transfer layers + bn/drop
          if self.use_bn:
              x = self.bn3(x)
          x = F.relu(x)
          if self.use_drop:
              x = F.dropout(x, p=self.drop_p, training=self.training)
  
          # Third layer
          if edge_type is not None:
              x = self.conv4(x, edge_index, edge_type)
          else:
              x = self.conv4(x, edge_index)
  
      if self.num_gcn_conv > 4:
          # Transfer layers + bn/drop
          if self.use_bn:
              x = self.bn4(x)
          x = F.relu(x)
          if self.use_drop:
              x = F.dropout(x, p=self.drop_p, training=self.training)
  
          # Third layer
          if edge_type is not None:
              x = self.conv5(x, edge_index, edge_type)
          else:
              x = self.conv5(x, edge_index)
  
      if self.num_gcn_conv > 5:
          # Transfer layers + bn/drop
          if self.use_bn:
              x = self.bn5(x)
          x = F.relu(x)
          if self.use_drop:
              x = F.dropout(x, p=self.drop_p, training=self.training)
  
          # Third layer
          if edge_type is not None:
              x = self.conv6(x, edge_index, edge_type)
          else:
              x = self.conv6(x, edge_index)
  
      assert self.num_gcn_conv <= 6
  
      # Add special conv layer for special node input/output
      if self.output_special_node or self.special_input_node:
          # Encode special input
          if self.special_input_node:
              assert special_node_input is not None
              special_node_input = self.spec_input_fc(
                  special_node_input
              )
          # Or zero-pad it
          else:
              special_node_input = torch.zeros(
                  batch_size, self.node_hid_dim
              ).to(x.device)
  
          # Create special edge_index, edge_type matrices
          if (
              self.edge_index_special is None
              or self.special_bs != batch_size
          ):
              # Set special_bs
              # This makes sure the prebuild edge_index/type has right batch size
              self.special_bs = batch_size
  
              # Figure out the special node edges
              # Do bidirectional just to be safe
              spec_edges = []
              for batch_ind in range(batch_size):
                  spec_node_idx = (
                      self.num_nodes * batch_size + batch_ind
                  )
                  spec_edges += [
                      [node_idx, spec_node_idx]
                      for node_idx in range(
                          self.num_nodes * batch_ind,
                          self.num_nodes * (batch_ind + 1),
                      )
                  ]
                  spec_edges += [
                      [spec_node_idx, node_idx]
                      for node_idx in range(
                          self.num_nodes * batch_ind,
                          self.num_nodes * (batch_ind + 1),
                      )
                  ]
              assert len(spec_edges) == self.num_nodes * batch_size * 2
              self.edge_index_special = (
                  torch.LongTensor(spec_edges)
                  .transpose(0, 1)
                  .to(x.device)
              )
  
              # Make edge type (if necessary)
              if self.gcn_type == "RGCN":
                  self.edge_type_special = (
                      torch.LongTensor(len(spec_edges))
                      .fill_(self.num_relations)
                      .to(x.device)
                  )  # edge type is special n+1 edge type
  
          # Forward through final special conv
          # Transfer layers + bn/drop
          if self.use_bn:
              x = self.bn_spec(x)
          x = F.relu(x)
          if self.use_drop:
              x = F.dropout(x, p=self.drop_p, training=self.training)
  
          # Special conv layer
          edge_index_tmp = torch.cat(
              [edge_index, self.edge_index_special], dim=1
          )
          x = torch.cat([x, special_node_input], dim=0)
          if edge_type is not None:
              edge_type_tmp = torch.cat(
                  [edge_type, self.edge_type_special], dim=0
              )
              x = self.conv_spec(x, edge_index_tmp, edge_type_tmp)
          else:
              x = self.conv_spec(x, edge_index_tmp)
  
          # Output
          if self.num_nodes is not None:
              assert (
                  x.size(0) == self.num_nodes * batch_size + batch_size
              )
          # If it's output special, get the output as those special
          # node hidden states
          if self.output_special_node:
              # Should be just the last (batch_size) nodes
              spec_out = x[self.num_nodes * batch_size :]
              assert spec_out.size(0) == batch_size
  
          # Otherwise, we want to remove the last batch_size nodes
          # (since we don't use them)
          x = x[: self.num_nodes * batch_size]
          assert x.size(0) == self.num_nodes * batch_size
      # Reshape output to batch size now
      # For dynamic graph, we don't do the reshape. It's the class
      # above's job to reshape this properly
      if self.num_nodes is not None:
          x = x.reshape(batch_size, self.num_nodes, self.node_hid_dim)
  
      # Prepare final output
      if self.output_type in [
          "hidden",
          "hidden_ans",
          "hidden_subindex",
      ]:
          # Don't really need anything here, either passing all of G
          # Subindexing happens a level up
          pass
      elif self.output_type in [
          "graph_level",
          "graph_level_ansonly",
          "graph_level_inputonly",
      ]:
          # Relu
          x = F.relu(x)
  
          # Check shape of x is num_nodes x hid_size
          assert x.shape[2] == self.node_hid_dim
  
          # For ansonly or inputonly, use input output_nodes (LongTensor) to reindex x
          if self.output_type in [
              "graph_level_ansonly",
              "graph_level_inputonly",
          ]:
              assert output_nodes is not None
              x = x[:, output_nodes, :]
  
          bs, num_node, _ = x.shape
          x = x.reshape(bs * num_node, self.node_hid_dim)
  
          # Get feat
          feat = self.feat_layer(x)
          feat = feat.reshape(bs, num_node, self.output_dim)
  
          # Forward through linear to 1
          logit = self.logit_pred(x)
          logit = logit.reshape(bs, num_node)
          logit = F.softmax(logit)
  
          # Get weighted sum of x!
          x = torch.bmm(logit.unsqueeze(1), feat).squeeze()
      elif self.output_type in ["graph_prediction"]:
          # Need just a final logits prediction
          x = F.relu(x)
          x = self.logit_pred(x)
          x = x.squeeze()  # Remove final dim
      else:
          raise Exception("output type not known %s" % self.output_type)
  
      # Return output
      return x, spec_out
  
imports: import os
  import pickle
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from gensim.models import KeyedVectors
  from gensim.scripts.glove2word2vec import glove2word2vec
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.utils.text import VocabDict
  from networkx import convert_node_labels_to_integers
  from torch_geometric.nn import BatchNorm, GCNConv, RGCNConv, SAGEConv
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the GraphNetwork obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_type, batch_size, output_nodes, special_node_input) and returns: x,  spec_out