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]
class_docstr_tok: ["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","]"]
code: def __init__(self, input_size, hidden_size, num_class):
      super(StackedElementProd, self).__init__()
  
      self.num_channel = num_class
      self.classifier = StackedElementProdLayer(
          input_size, num_class, self.num_channel, hidden_size
      )
  
imports: import torch
  from .StackedElementProdLayer import StackedElementProdLayer
label_desc: Use image node_id 1 to create a new StackedElementProd object from inherited base classes: StackedElementProdLayer with example: obj = StackedElementProd(input_size, hidden_size, num_class)