class_docstr: Specific class for link prediction task.
  
  Parameters
  ----------
  
  input_size : int
               The length of input node embeddings
  num_class : int
             The number of node catrgoriey for classification
  hidden_size : list of int type values
                Example for two layers's FeedforwardNN: [50, 20]
  activation: the activation function class for each fully connected layer
              Default: nn.ReLU()
              Example: nn.ReLU(),nn.Sigmoid().
code: def __init__(
      self, input_size, hidden_size, num_class, activation=None
  ):
      super(ConcatFeedForwardNN, self).__init__()
  
      if activation is None:
          activation = nn.ReLU()
      self.classifier = ConcatFeedForwardNNLayer(
          input_size, num_class, hidden_size, activation
      )
  
imports: import torch
  from torch import nn
  from ..base import LinkPredictionBase
  from .ConcatFeedForwardNNLayer import ConcatFeedForwardNNLayer
label_desc: Use image node_id 1 to create a new ConcatFeedForwardNN object from inherited base classes: LinkPredictionBase with example: obj = ConcatFeedForwardNN(input_size, hidden_size, num_class, activation)