code: def forward(self, g, out_key):
      h_dict = {
          ntype: g.nodes[ntype].data["feat"] for ntype in g.ntypes
      }  # prepare input feature dict
      for layer in self.film_layers:
          h_dict = layer(g, h_dict)
      h = self.predict(
          h_dict[out_key]
      )  # use the final embed to predict, out_size = num_classes
      h = torch.sigmoid(h)
      return h
  
file_path: dgl/examples/pytorch/GNN-FiLM/main.py
imports: import argparse
  import os
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from data_loader import load_PPI
  from utils import evaluate_f1_score
label_desc: Use image node_id 2 for calling the GNNFiLM obj's underlying member method code with example usage: obj.forward(g, out_key) and returns: h