class_docstr: Apply sum pooling over the nodes in the graph.
  
  .. math::
      r^{(i)} = \sum_{k=1}^{N_i} x^{(i)}_k
class_docstr_tok: ["Apply","sum","pooling","over","the","nodes","in","the","graph",".","..","math",":",":","r^","{","(","i",")","}","=","\\sum_","{","k=1","}","^","{","N_i","}","x^","{","(","i",")","}","_k"]
docstr: Compute sum pooling.
  
  
  Parameters
  ----------
  graph : DGLGraph
      The graph.
  feat : tf.Tensor
      The input feature with shape :math:`(N, *)` where
      :math:`N` is the number of nodes in the graph.
  
  Returns
  -------
  tf.Tensor
      The output feature with shape :math:`(B, *)`, where
      :math:`B` refers to the batch size.
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code: def call(self, graph, feat):
      r"""Compute sum pooling.
  
  
      Parameters
      ----------
      graph : DGLGraph
          The graph.
      feat : tf.Tensor
          The input feature with shape :math:`(N, *)` where
          :math:`N` is the number of nodes in the graph.
  
      Returns
      -------
      tf.Tensor
          The output feature with shape :math:`(B, *)`, where
          :math:`B` refers to the batch size.
      """
      with graph.local_scope():
          graph.ndata["h"] = feat
          readout = sum_nodes(graph, "h")
          return readout
  
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 2 for calling the SumPooling obj's underlying member method code with example usage: obj.call(graph, feat) and returns: readout