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 predict_proba_on_generator(self, generator, transformers=[]):
      if not self.built:
          self.build()
      with self._get_tf("Graph").as_default():
          out_tensors = [x.out_tensor for x in self.outputs]
          results = []
          for feed_dict in generator:
              feed_dict = {
                  self.layers[k.name].out_tensor: v
                  for k, v in six.iteritems(feed_dict)
              }
              feed_dict[self._training_placeholder] = 1.0  ##
              result = np.array(
                  self.session.run(out_tensors, feed_dict=feed_dict)
              )
              if len(result.shape) == 3:
                  result = np.transpose(result, axes=[1, 0, 2])
              if len(transformers) > 0:
                  result = undo_transforms(result, transformers)
              results.append(result)
          return np.concatenate(results, axis=0)
  
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 4 for calling the PetroskiSuchModel obj's underlying member method code with example usage: obj.predict_proba_on_generator(generator, transformers) and returns: np