class_docstr: A generalized MLP model that can act as either a 2-layer MLPDecoder or a 4-layer MLPDecoder based on the include_decoder_layers parameter.
  
  Args:
      in_dim (int): the dimension of input feature.
      hidden_dim (int): the dimension of hidden layers.
      out_dim (int): the dimension of output layer.
      dropout_rate (float): the dropout rate during training.
      include_decoder_layers (bool): whether or not to include the additional layers that are part of the MLPDecoder
code: def forward(self, x):
      x = F.relu(self.fc1(x))
      x = self.dropout(x)
      x = self.fc2(x)
  
      if self.include_decoder_layers:
          x = self.dropout(F.relu(x))
          x = F.relu(self.fc3(x))
          x = self.fc4(x)
  
      return x
  
imports: from typing import List, Tuple
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torch.nn.functional
  from kale.embed.gripnet import GripNet
  from kale.evaluate.metrics import auprc_auroc_ap
  from kale.prepdata.graph_negative_sampling import typed_negative_sampling
  from kale.prepdata.supergraph_construct import SuperGraph
  from kale.utils.initialize_nn import bias_init, xavier_init
label_desc: Use image node_id 2 for calling the MLPDecoder obj's underlying member method code with example usage: obj.forward(x) and returns: x