class_docstr: An analog of Keras Sequential class for Graph data.
  
  Like the Sequential class from Keras, but automatically passes topology
  placeholders from GraphTopology to each graph layer (from layers) added
  to the network. Non graph layers don't get the extra placeholders. 
class_docstr_tok: ["An","analog","of","Keras","Sequential","class","for","Graph","data",".","Like","the","Sequential","class","from","Keras",",","but","automatically","passes","topology","placeholders","from","GraphTopology","to","each","graph","layer","(","from","layers",")","added","to","the","network",".","Non","graph","layers","do","n't","get","the","extra","placeholders","."]
code: def return_inputs(self):
      return self.graph_topology.get_input_placeholders()
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import warnings
  import tensorflow
  from deepchem.nn.layers import GraphGather
  from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology, AlternateWeaveGraphTopology
label_desc: Use image node_id 6 for calling the SequentialGraph obj's underlying member method code with example usage: obj.return_inputs() and returns: self