class_docstr: Specific class for node classification 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, num_class, hidden_size, activation=None
  ):
      super(FeedForwardNN, self).__init__()
  
      if activation is None:
          activation = nn.ReLU()
      self.classifier = FeedForwardNNLayer(
          input_size, num_class, hidden_size, activation
      )
  
imports: from torch import nn
  from ..base import NodeClassifierBase
  from .FeedForwardNNLayer import FeedForwardNNLayer
label_desc: Use image node_id 1 to create a new FeedForwardNN object from inherited base classes: NodeClassifierBase with example: obj = FeedForwardNN(input_size, num_class, hidden_size, activation)