class_docstr: Specific class for node classification layer.
  
  
  Parameters
  ----------
  
  input_size : int
               The length of input node embeddings
  output_size : int
             The number of node catrgoriey for classification
  hidden_size : int
                the hidden size of the linear layer
docstr: Forward functions for classification task.
  
  ...
  
  Parameters
  ----------
  
  node_emb : padded tensor [B,N,H]
             B: batch size
             N: max number of nodes
             H: length of the node embeddings
  node_idx : a list of index of nodes that needs classification.
             Default: 'None'
             Example: [1,3,5]
  
  Returns
  -------
       logit tensor: [B,N, num_class] The score logits for all nodes preidcted.
code: def forward(self, node_emb, node_idx=None):
      r"""
      Forward functions for classification task.
  
      ...
  
      Parameters
      ----------
  
      node_emb : padded tensor [B,N,H]
                 B: batch size
                 N: max number of nodes
                 H: length of the node embeddings
      node_idx : a list of index of nodes that needs classification.
                 Default: 'None'
                 Example: [1,3,5]
  
      Returns
      -------
           logit tensor: [B,N, num_class] The score logits for all nodes preidcted.
      """
      if node_idx:
          node_emb = node_emb[
              th.tensor(node_idx), :
          ]  # get the required node embeddings.
      batch_size = node_emb.size(0)
  
      self.hidden = self.init_hidden(batch_size)
      lstm_out, self.hidden = self.lstm(node_emb, self.hidden)
      if self.hidden_size is None:
          return lstm_out
      else:
          lstm_feats = self.linear(lstm_out)
          return lstm_feats
  
file_path: graph4nlp/graph4nlp/pytorch/modules/prediction/classification/node_classification/BiLSTMFeedForwardNNLayer.py
imports: import torch
  import torch.autograd
  from torch import nn
  from ..base import NodeClassifierLayerBase
label_desc: Use image node_id 3 for calling the BiLSTMFeedForwardNNLayer obj's underlying member method code with example usage: obj.forward(node_emb, node_idx) and returns: lstm_out, lstm_feats