class_docstr: An analog of Keras Sequential model for test/support models.
class_docstr_tok: ["An","analog","of","Keras","Sequential","model","for","test\/support","models","."]
docstr: Parameters
  ----------
  n_feat: int
    Number of atomic features.
code: def __init__(self, n_feat):
      """
      Parameters
      ----------
      n_feat: int
        Number of atomic features.
      """
      warnings.warn(
          "SequentialSupportWeaveGraph is deprecated. "
          "Will be removed in DeepChem 1.4.",
          DeprecationWarning,
      )
      self.graph = tf.Graph()
      with self.graph.as_default():
          # Create graph topology and x
          self.test_graph_topology = GraphTopology(n_feat, name="test")
          self.support_graph_topology = GraphTopology(
              n_feat, name="support"
          )
          self.test = (
              self.test_graph_topology.get_atom_features_placeholder()
          )
          self.support = (
              self.support_graph_topology.get_atom_features_placeholder()
          )
  
      # Keep track of the layers
      self.layers = []
      # Whether or not we have used the GraphGather layer yet
      self.bool_pre_gather = True
  
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 1 to create a new SequentialSupportGraph object from inherited base classes: object with example: obj = SequentialSupportGraph(n_feat)