code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout=0.0,
  ):
      super().__init__()
      self.dropout = dropout
  
      self.convs = torch.nn.ModuleList()
      self.convs.append(FiLMConv(in_channels, hidden_channels))
      for _ in range(num_layers - 2):
          self.convs.append(FiLMConv(hidden_channels, hidden_channels))
      self.convs.append(
          FiLMConv(hidden_channels, out_channels, act=None)
      )
  
      self.norms = torch.nn.ModuleList()
      for _ in range(num_layers - 1):
          self.norms.append(BatchNorm1d(hidden_channels))
  
file_path: pytorch_geometric/examples/film.py
imports: import os.path
  import torch
  import torch.nn.functional
  from sklearn.metrics import f1_score
  from torch.nn import BatchNorm1d
  from torch_geometric.datasets import PPI
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import FiLMConv
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net(in_channels, hidden_channels, out_channels, num_layers, dropout)