code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout,
  ):
      super(MLP, self).__init__()
  
      self.lins = torch.nn.ModuleList()
      for _ in range(num_layers + 1):
          self.lins.append(
              torch.nn.Linear(in_channels, hidden_channels)
          )
      self.lin = torch.nn.Linear(
          (num_layers + 1) * hidden_channels, out_channels
      )
  
      self.dropout = dropout
  
file_path: ogb/examples/nodeproppred/products/sign.py
imports: import argparse
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.transforms import SIGN
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: torch.nn with example: obj = MLP(in_channels, hidden_channels, out_channels, num_layers, dropout)