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"]
code: def evaluate(
      self, dataset, metrics, transformers=[], per_task_metrics=False
  ):
      if not self.built:
          self.build()
      return self.evaluate_generator(
          self.default_generator(dataset, predict=True),
          metrics,
          labels=self.my_labels,
          weights=[self.my_task_weights],
          per_task_metrics=per_task_metrics,
      )
  
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 5 for calling the PetroskiSuchModel obj's underlying member method code with example usage: obj.evaluate(dataset, metrics, transformers, per_task_metrics) and returns: self