code: def forward(self, x):
      for i, lin in enumerate(self.lins[:-1]):
          x = lin(x)
          x = self.bns[i](x)
          x = F.relu(x)
          x = F.dropout(x, p=self.dropout, training=self.training)
      x = self.lins[-1](x)
      return torch.log_softmax(x, dim=-1)
  
file_path: ogb/examples/nodeproppred/papers100M/mlp.py
imports: import argparse
  from tqdm.auto import tqdm
  import torch
  import torch.nn.functional
  from torch.utils.data import Dataset, DataLoader
  from ogb.nodeproppred import Evaluator
  from logger import Logger
label_desc: Use image node_id 3 for calling the MLP obj's underlying member method code with example usage: obj.forward(x) and returns: torch