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
code: def init_hidden(self, batch_size):
      if self.hidden_size is not None:
          return (
              autograd.Variable(
                  th.randn(2, batch_size, self.hidden_size // 2)
              ).to(self.device),
              autograd.Variable(
                  th.randn(2, batch_size, self.hidden_size // 2)
              ).to(self.device),
          )
      else:
          return (
              autograd.Variable(
                  th.randn(2, batch_size, self.output_size // 2)
              ).to(self.device),
              autograd.Variable(
                  th.randn(2, batch_size, self.output_size // 2)
              ).to(self.device),
          )
  
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 2 for calling the BiLSTMFeedForwardNNLayer obj's underlying member method code with example usage: obj.init_hidden(batch_size) and returns: autograd,  autograd, autograd,  autograd