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(ConcatFeedForwardNNLayer, self).__init__()
  
      # build the linear module list
      self.activation = activation
      self.ffnn_all1 = nn.Linear(2 * input_size, hidden_size)
      self.ffnn_all2 = nn.Linear(hidden_size, num_class)
      if activation is None:
          activation = nn.ReLU()
  
imports: import torch
  from torch import nn
  from ..base import LinkPredictionLayerBase
label_desc: Use image node_id 1 to create a new ConcatFeedForwardNNLayer object from inherited base classes: LinkPredictionLayerBase with example: obj = ConcatFeedForwardNNLayer(input_size, hidden_size, num_class, activation)