code: def forward(self, x):
      # feature
      # [N, F]
      out = self.mlp(x).squeeze()
      out = self.softmax(out)
      return out
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  import copy
  import random
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 2 for calling the MLPModel obj's underlying member method code with example usage: obj.forward(x) and returns: out