code: def __init__(self, in_feats, hidden, out_feats, n_layers, dropout):
      super(FeedForwardNet, self).__init__()
      self.layers = nn.ModuleList()
      self.n_layers = n_layers
      if n_layers == 1:
          self.layers.append(nn.Linear(in_feats, out_feats))
      else:
          self.layers.append(nn.Linear(in_feats, hidden))
          for i in range(n_layers - 2):
              self.layers.append(nn.Linear(hidden, hidden))
          self.layers.append(nn.Linear(hidden, out_feats))
      if self.n_layers > 1:
          self.prelu = nn.PReLU()
          self.dropout = nn.Dropout(dropout)
      self.reset_parameters()
  
imports: import argparse
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  from dataset import load_dataset
label_desc: Use image node_id 1 to create a new FeedForwardNet object from inherited base classes: nn with example: obj = FeedForwardNet(in_feats, hidden, out_feats, n_layers, dropout)