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
  num_channel: int
             The number of channels for node embeddings to be used for link prediction
  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, num_channel):
      super(StackedElementProdLayer, self).__init__()
  
      # build the linear module list
      self.num_channel = num_channel
      self.ffnn = nn.Linear(num_channel * hidden_size, num_class)
  
imports: import torch
  from torch import nn
  from ..base import LinkPredictionLayerBase
label_desc: Use image node_id 1 to create a new StackedElementProdLayer object from inherited base classes: LinkPredictionLayerBase with example: obj = StackedElementProdLayer(input_size, hidden_size, num_class, num_channel)