class_docstr: Model from Robust Spatial Filtering with Graph Convolutional Neural Networks
  https://arxiv.org/abs/1703.00792
class_docstr_tok: ["Model","from","Robust","Spatial","Filtering","with","Graph","Convolutional","Neural","Networks","https",":","\/\/arxiv.org\/abs\/1703.00792"]
docstr: Parameters
  ----------
  n_tasks: int
    Number of tasks
  mode: str
    Either "classification" or "regression"
docstr_tok: ["Parameters","--","--","--","--","--","n_tasks",":","int","Number","of","tasks","mode",":","str","Either","``","classification","''","or","``","regression","''"]
code: def __init__(
      self,
      n_tasks,
      max_atoms=200,
      dropout=0.0,
      mode="classification",
      **kwargs,
  ):
      """
      Parameters
      ----------
      n_tasks: int
        Number of tasks
      mode: str
        Either "classification" or "regression"
      """
      self.n_tasks = n_tasks
      self.mode = mode
      self.max_atoms = max_atoms
      self.error_bars = (
          True
          if "error_bars" in kwargs and kwargs["error_bars"]
          else False
      )
      self.dropout = dropout
      kwargs["use_queue"] = False
      super(PetroskiSuchModel, self).__init__(**kwargs)
      self.build_graph()
  
imports: import collections
  import numpy
  import six
  import tensorflow
  from deepchem.data import NumpyDataset
  from deepchem.feat.graph_features import ConvMolFeaturizer
  from deepchem.feat.mol_graphs import ConvMol
  from deepchem.metrics import to_one_hot
  from deepchem.models.tensorgraph.graph_layers import WeaveGather, DTNNEmbedding, DTNNStep, DTNNGather, DAGLayer, DAGGather, DTNNExtract, MessagePassing, SetGather
  from deepchem.models.tensorgraph.graph_layers import WeaveLayerFactory
  from deepchem.models.tensorgraph.layers import Dense, SoftMax, SoftMaxCrossEntropy, GraphConv, BatchNorm, GraphPool, GraphGather, WeightedError, Dropout, BatchNorm, Stack, Flatten, GraphCNN, GraphCNNPool
  from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature
  from deepchem.models.tensorgraph.tensor_graph import TensorGraph
  from deepchem.trans import undo_transforms
label_desc: Use image node_id 1 to create a new PetroskiSuchModel object from inherited base classes: TensorGraph with example: obj = PetroskiSuchModel(n_tasks, max_atoms, dropout, mode)