class_docstr: An analog of Keras Sequential class for Coulomb Matrix data.
  
  automatically generates and passes topology placeholders to each layer. 
class_docstr_tok: ["An","analog","of","Keras","Sequential","class","for","Coulomb","Matrix","data",".","automatically","generates","and","passes","topology","placeholders","to","each","layer","."]
docstr: Adds a new layer to model.
code: def add(self, layer):
      """Adds a new layer to model."""
      with self.graph.as_default():
          if type(layer).__name__ in ["DTNNStep"]:
              self.output = layer(
                  [self.output]
                  + self.graph_topology.get_topology_placeholders()
              )
          elif type(layer).__name__ in ["DTNNGather"]:
              self.output = layer(
                  [
                      self.output,
                      self.graph_topology.atom_membership_placeholder,
                  ]
              )
          else:
              self.output = layer(self.output)
          self.layers.append(layer)
  
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 2 for calling the SequentialDTNNGraph obj's underlying member method code with example usage: obj.add(layer) without return types