class_docstr: Manages placeholders associated with batch of graphs and their topology
docstr: Note that batch size is not specified in a GraphTopology object. A batch
  of molecules must be combined into a disconnected graph and fed to topology
  directly to handle batches.
  
  Parameters
  ----------
  n_feat: int
    Number of features per atom.
  name: str, optional
    Name of this manager.
  max_deg: int, optional
    Maximum #bonds for atoms in molecules.
  min_deg: int, optional
    Minimum #bonds for atoms in molecules.
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code: def __init__(self, n_feat, name="topology", max_deg=10, min_deg=0):
      """
      Note that batch size is not specified in a GraphTopology object. A batch
      of molecules must be combined into a disconnected graph and fed to topology
      directly to handle batches.
  
      Parameters
      ----------
      n_feat: int
        Number of features per atom.
      name: str, optional
        Name of this manager.
      max_deg: int, optional
        Maximum #bonds for atoms in molecules.
      min_deg: int, optional
        Minimum #bonds for atoms in molecules.
      """
      warnings.warn(
          "GraphTopology is deprecated. "
          "Will be removed in DeepChem 1.4.",
          DeprecationWarning,
      )
  
      # self.n_atoms = n_atoms
      self.n_feat = n_feat
  
      self.name = name
      self.max_deg = max_deg
      self.min_deg = min_deg
  
      self.atom_features_placeholder = tensor = tf.placeholder(
          dtype="float32",
          shape=(None, self.n_feat),
          name=self.name + "_atom_features",
      )
      self.deg_adj_lists_placeholders = [
          tf.placeholder(
              dtype="int32",
              shape=(None, deg),
              name=self.name + "_deg_adj" + str(deg),
          )
          for deg in range(1, self.max_deg + 1)
      ]
      self.deg_slice_placeholder = tf.placeholder(
          dtype="int32",
          shape=(self.max_deg - self.min_deg + 1, 2),
          name=self.name + "_deg_slice",
      )
      self.membership_placeholder = tf.placeholder(
          dtype="int32", shape=(None,), name=self.name + "_membership"
      )
  
      # Define the list of tensors to be used as topology
      self.topology = [
          self.deg_slice_placeholder,
          self.membership_placeholder,
      ]
      self.topology += self.deg_adj_lists_placeholders
  
      self.inputs = [self.atom_features_placeholder]
      self.inputs += self.topology
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import warnings
  import numpy
  import tensorflow
  from deepchem.nn.copy import Input
  from deepchem.feat.mol_graphs import ConvMol
label_desc: Use image node_id 1 to create a new GraphTopology object from inherited base classes: object with example: obj = GraphTopology(n_feat, name, max_deg, min_deg)