class_docstr: Compute importance weights for atoms and perform a weighted sum.
  
  Parameters
  ----------
  in_feats : int
      Input atom feature size
class_docstr_tok: ["Compute","importance","weights","for","atoms","and","perform","a","weighted","sum",".","Parameters","--","--","--","--","--","in_feats",":","int","Input","atom","feature","size"]
code: def __init__(self, in_feats):
      super(WeightAndSum, self).__init__()
      self.in_feats = in_feats
      self.atom_weighting = tf.keras.Sequential(
          layers.Dense(1), layers.Activation(tf.nn.sigmoid)
      )
  
imports: import tensorflow
  from tensorflow.keras import layers
  from ...readout import max_nodes, mean_nodes, softmax_nodes, sum_nodes, topk_nodes
label_desc: Use image node_id 1 to create a new WeightAndSum object from inherited base classes: layers with example: obj = WeightAndSum(in_feats)