class_docstr: SequentialGraph for Weave models
    
class_docstr_tok: ["SequentialGraph","for","Weave","models"]
docstr: Parameters
  ----------
  max_atoms: int, optional
    Maximum number of atoms in a molecule, should be defined based on dataset
  n_atom_feat: int, optional
    Number of features per atom.
  n_pair_feat: int, optional
    Number of features per pair of atoms.
code: def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
      """
      Parameters
      ----------
      max_atoms: int, optional
        Maximum number of atoms in a molecule, should be defined based on dataset
      n_atom_feat: int, optional
        Number of features per atom.
      n_pair_feat: int, optional
        Number of features per pair of atoms.
      """
      warnings.warn(
          "SequentialWeaveGraph is deprecated. "
          "Will be removed in DeepChem 1.4.",
          DeprecationWarning,
      )
      self.graph = tf.Graph()
      self.max_atoms = max_atoms
      self.n_atom_feat = n_atom_feat
      self.n_pair_feat = n_pair_feat
      with self.graph.as_default():
          self.graph_topology = WeaveGraphTopology(
              self.max_atoms, self.n_atom_feat, self.n_pair_feat
          )
          self.output = (
              self.graph_topology.get_atom_features_placeholder()
          )
          self.output_P = (
              self.graph_topology.get_pair_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 SequentialWeaveGraph object from inherited base classes: SequentialGraph with example: obj = SequentialWeaveGraph(max_atoms, n_atom_feat, n_pair_feat)