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
  ----------
  
  input graph : GraphData
               The tensors stored in the node feature field named "node_emb"  in the
               input_graph are used  for classification.
  
  
  Returns
  ---------
  
  output_graph : GraphData
                The computed logit tensor for each nodes in the graph are stored
                in the node feature field named "node_logits".
                logit tensor shape is: [num_class]
docstr_tok: ["Forward","functions","to","compute","the","logits","tensor","for","node","classification",".","Parameters","--","--","--","--","--","input","graph",":","GraphData","The","tensors","stored","in","the","node","feature","field","named","``","node_emb","''","in","the","input_graph","are","used","for","classification",".","Returns","--","--","--","--","-","output_graph",":","GraphData","The","computed","logit","tensor","for","each","nodes","in","the","graph","are","stored","in","the","node","feature","field","named","``","node_logits","''",".","logit","tensor","shape","is",":","[","num_class","]"]
code: def forward(self, input_graph):
      r"""
      Forward functions to compute the logits tensor for node classification.
  
  
  
      Parameters
      ----------
  
      input graph : GraphData
                   The tensors stored in the node feature field named "node_emb"  in the
                   input_graph are used  for classification.
  
  
      Returns
      ---------
  
      output_graph : GraphData
                    The computed logit tensor for each nodes in the graph are stored
                    in the node feature field named "node_logits".
                    logit tensor shape is: [num_class]
      """
  
      node_emb = input_graph.node_features["node_emb"]
      input_graph.node_features["logits"] = self.classifier(node_emb)
  
      return input_graph
  
imports: from torch import nn
  from ..base import NodeClassifierBase
  from .FeedForwardNNLayer import FeedForwardNNLayer
label_desc: Use image node_id 2 for calling the FeedForwardNN obj's underlying member method code with example usage: obj.forward(input_graph) and returns: input_graph