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().
docstr: Forward functions to compute the logits tensor for node classification.
  
  
  
  Parameters
  ----------
  
  node_emb : tensor [N,H]
             N: number of nodes
             H: length of the node embeddings
  node_idx : a list of index of nodes that needs classification.
             Default: 'None'
  
  Returns
  -------
       logit tensor: [N, num_class] The score logits for all nodes preidcted.
docstr_tok: ["Forward","functions","to","compute","the","logits","tensor","for","node","classification",".","Parameters","--","--","--","--","--","node_emb",":","tensor","[","N",",","H","]","N",":","number","of","nodes","H",":","length","of","the","node","embeddings","node_idx",":","a","list","of","index","of","nodes","that","needs","classification",".","Default",":","'None'","Returns","--","--","--","-","logit","tensor",":","[","N",",","num_class","]","The","score","logits","for","all","nodes","preidcted","."]
code: def forward(self, node_emb, node_idx=None):
      r"""
      Forward functions to compute the logits tensor for node classification.
  
  
  
      Parameters
      ----------
  
      node_emb : tensor [N,H]
                 N: number of nodes
                 H: length of the node embeddings
      node_idx : a list of index of nodes that needs classification.
                 Default: 'None'
  
      Returns
      -------
           logit tensor: [N, num_class] The score logits for all nodes preidcted.
      """
      if node_idx is None:
          return self.classifier(node_emb)
      else:
          new_emb_new = node_emb[
              th.tensor(node_idx), :
          ]  # get the required node embeddings.
          return self.classifier(new_emb_new)
  
imports: import collections
  import torch
  from torch import nn
  from ..base import NodeClassifierLayerBase
label_desc: Use image node_id 2 for calling the FeedForwardNNLayer obj's underlying member method code with example usage: obj.forward(node_emb, node_idx) and returns: self, self