code: def __init__(
      self,
      d_feat,
      hidden_size=256,
      num_layers=3,
      dropout=0.0,
      output_dim=1,
  ):
      super().__init__()
  
      self.mlp = nn.Sequential()
      self.softmax = nn.Softmax(dim=1)
  
      for i in range(num_layers):
          if i > 0:
              self.mlp.add_module("drop_%d" % i, nn.Dropout(dropout))
          self.mlp.add_module(
              "fc_%d" % i,
              nn.Linear(d_feat if i == 0 else hidden_size, hidden_size),
          )
          self.mlp.add_module("relu_%d" % i, nn.ReLU())
  
      self.mlp.add_module("fc_out", nn.Linear(hidden_size, output_dim))
  
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 1 to create a new MLPModel object from inherited base classes: nn with example: obj = MLPModel(d_feat, hidden_size, num_layers, dropout, output_dim)