class_docstr: FeedForwardNN class for graph classification task.
  
  Parameters
  ----------
  input_size : int
      The dimension of input graph embeddings.
  num_class : int
      The number of classes for classification.
  hidden_size : list of int
      Hidden size per NN layer.
  activation: nn.Module, optional
      The activation function, default: `nn.ReLU()`.
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docstr: Compute the logits tensor for graph classification.
  
  Parameters
  ----------
  graph : GraphData
      The graph data containing graph embeddings.
  
  Returns
  -------
  list of GraphData
      The output graph data containing logits tensor for graph classification.
docstr_tok: ["Compute","the","logits","tensor","for","graph","classification",".","Parameters","--","--","--","--","--","graph",":","GraphData","The","graph","data","containing","graph","embeddings",".","Returns","--","--","--","-","list","of","GraphData","The","output","graph","data","containing","logits","tensor","for","graph","classification","."]
code: def forward(self, graph):
      r"""Compute the logits tensor for graph classification.
  
      Parameters
      ----------
      graph : GraphData
          The graph data containing graph embeddings.
  
      Returns
      -------
      list of GraphData
          The output graph data containing logits tensor for graph classification.
      """
      graph_emb = self.graph_pool(graph, "node_emb")
      logits = self.classifier(graph_emb)
      graph.graph_attributes["logits"] = logits
  
      return graph
  
file_path: graph4nlp/graph4nlp/pytorch/modules/prediction/classification/graph_classification/feedforward_nn.py
imports: import collections
  from torch import nn
  from ..base import GraphClassifierBase, GraphClassifierLayerBase
  from .avg_pooling import AvgPooling
  from .max_pooling import MaxPooling
label_desc: Use image node_id 2 for calling the FeedForwardNN obj's underlying member method code with example usage: obj.forward(graph) and returns: graph