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 default_generator(
      self,
      dataset,
      epochs=1,
      predict=False,
      deterministic=True,
      pad_batches=True,
  ):
      for epoch in range(epochs):
          if not predict:
              print("Starting epoch %i" % epoch)
          for ind, (X_b, y_b, w_b, ids_b) in enumerate(
              dataset.iterbatches(
                  self.batch_size,
                  pad_batches=True,
                  deterministic=deterministic,
              )
          ):
              d = {}
              for index, label in enumerate(self.my_labels):
                  if self.mode == "classification":
                      d[label] = to_one_hot(y_b[:, index])
                  if self.mode == "regression":
                      d[label] = np.expand_dims(y_b[:, index], -1)
              d[self.my_task_weights] = w_b
              d[self.adj_matrix] = np.expand_dims(
                  np.array([x[0] for x in X_b]), -2
              )
              d[self.vertex_features] = np.array([x[1] for x in X_b])
              mask = np.zeros(
                  shape=(self.batch_size, self.max_atoms, 1)
              )
              for i in range(self.batch_size):
                  mask_size = X_b[i][2]
                  mask[i][:mask_size][0] = 1
              d[self.mask] = mask
              yield d
  
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 3 for calling the PetroskiSuchModel obj's underlying member method code with example usage: obj.default_generator(dataset, epochs, predict, deterministic, pad_batches) without return types