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()`.
class_docstr_tok: ["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","(",")","`","."]
code: def __init__(
      self,
      input_size,
      num_class,
      hidden_size,
      activation=None,
      graph_pool_type="max_pool",
      **kwargs,
  ):
      super(FeedForwardNN, self).__init__()
  
      if not activation:
          activation = nn.ReLU()
  
      if graph_pool_type == "avg_pool":
          self.graph_pool = AvgPooling()
      elif graph_pool_type == "max_pool":
          self.graph_pool = MaxPooling(**kwargs)
      else:
          raise RuntimeError(
              "Unknown graph pooling type: {}".format(graph_pool_type)
          )
  
      self.classifier = FeedForwardNNLayer(
          input_size, num_class, hidden_size, activation
      )
  
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 1 to create a new FeedForwardNN object from inherited base classes: GraphClassifierBase with example: obj = FeedForwardNN(input_size, num_class, hidden_size, activation, graph_pool_type)