class_docstr: SequentialGraph for DAG models
    
class_docstr_tok: ["SequentialGraph","for","DAG","models"]
docstr: Parameters
  ----------
  n_atom_feat: int, optional
    Number of features per atom.
  max_atoms: int, optional
    Maximum number of atoms in a molecule, should be defined based on dataset
code: def __init__(self, n_atom_feat=75, max_atoms=50):
      """
      Parameters
      ----------
      n_atom_feat: int, optional
        Number of features per atom.
      max_atoms: int, optional
        Maximum number of atoms in a molecule, should be defined based on dataset
      """
      warnings.warn(
          "SequentialDAGGraph is deprecated. "
          "Will be removed in DeepChem 1.4.",
          DeprecationWarning,
      )
      self.graph = tf.Graph()
      with self.graph.as_default():
          self.graph_topology = DAGGraphTopology(
              n_atom_feat=n_atom_feat, max_atoms=max_atoms
          )
          self.output = (
              self.graph_topology.get_atom_features_placeholder()
          )
      self.layers = []
  
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 SequentialDAGGraph object from inherited base classes: SequentialGraph with example: obj = SequentialDAGGraph(n_atom_feat, max_atoms)