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(FeedForwardNNLayer, self).__init__()
  
      if activation is None:
          activation = nn.ReLU()
  
      # build the linear module list
      module_seq = []
  
      for layer_idx in range(len(hidden_size)):
          if layer_idx == 0:
              module_seq.append(
                  (
                      "linear" + str(layer_idx),
                      nn.Linear(input_size, hidden_size[layer_idx]),
                  )
              )
          else:
              module_seq.append(
                  (
                      "linear" + str(layer_idx),
                      nn.Linear(
                          hidden_size[layer_idx - 1],
                          self.hidden_size[layer_idx],
                      ),
                  )
              )
          module_seq.append(("activate" + str(layer_idx), activation))
  
      module_seq.append(
          ("linear_end", nn.Linear(hidden_size[-1], num_class))
      )
  
      self.classifier = nn.Sequential(
          collections.OrderedDict(module_seq)
      )
  
imports: import collections
  import torch
  from torch import nn
  from ..base import NodeClassifierLayerBase
label_desc: Use image node_id 1 to create a new FeedForwardNNLayer object from inherited base classes: NodeClassifierLayerBase with example: obj = FeedForwardNNLayer(input_size, num_class, hidden_size, activation)