class_docstr: Model for graph property prediction
  
  This model proceeds as follows:
  
  * Combine latest node representations and edge features in updating node representations,
      which involves multiple rounds of message passing
  * For each graph, compute its representation by combining the representations
      of all nodes in it, which involves a Set2Set layer.
  * Perform the final prediction using an MLP
  Examples
  --------
  >>> import deepchem as dc
  >>> from deepchem.models.torch_models import MPNNModel
  >>> # preparing dataset
  >>> smiles = ["C1CCC1", "CCC"]
  >>> labels = [0., 1.]
  >>> featurizer = dc.feat.MolGraphConvFeaturizer(use_edges=True)
  >>> X = featurizer.featurize(smiles)
  >>> dataset = dc.data.NumpyDataset(X=X, y=labels)
  >>> # training model
  >>> model = MPNNModel(mode='classification', n_tasks=1,
  ...                  batch_size=16, learning_rate=0.001)
  >>> loss =  model.fit(dataset, nb_epoch=5)
  
  References
  ----------
  .. [1] Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl.
      "Neural Message Passing for Quantum Chemistry." ICML 2017.
  
  Notes
  -----
  This class requires DGL (https://github.com/dmlc/dgl) and DGL-LifeSci
  (https://github.com/awslabs/dgl-lifesci) to be installed.
  
  The featurizer used with MPNNModel must produce a GraphData object which should have both 'edge' and 'node' features.
docstr: Create batch data for MPNN.
  
  Parameters
  ----------
  batch: tuple
      The tuple is ``(inputs, labels, weights)``.
  
  Returns
  -------
  inputs: DGLGraph
      DGLGraph for a batch of graphs.
  labels: list of torch.Tensor or None
      The graph labels.
  weights: list of torch.Tensor or None
      The weights for each sample or sample/task pair converted to torch.Tensor.
code: def _prepare_batch(self, batch):
      """Create batch data for MPNN.
  
      Parameters
      ----------
      batch: tuple
          The tuple is ``(inputs, labels, weights)``.
  
      Returns
      -------
      inputs: DGLGraph
          DGLGraph for a batch of graphs.
      labels: list of torch.Tensor or None
          The graph labels.
      weights: list of torch.Tensor or None
          The weights for each sample or sample/task pair converted to torch.Tensor.
      """
      try:
          import dgl
      except:
          raise ImportError("This class requires dgl.")
  
      inputs, labels, weights = batch
      dgl_graphs = [
          graph.to_dgl_graph(self_loop=self._self_loop)
          for graph in inputs[0]
      ]
      inputs = dgl.batch(dgl_graphs).to(self.device)
      _, labels, weights = super(MPNNModel, self)._prepare_batch(
          ([], labels, weights)
      )
      return inputs, labels, weights
  
file_path: deepchem/deepchem/models/torch_models/mpnn.py
imports: import torch.nn
  import torch.nn.functional
  from deepchem.models.losses import Loss, L2Loss, SparseSoftmaxCrossEntropy
  from deepchem.models.torch_models.torch_model import TorchModel
label_desc: Use image node_id 2 for calling the MPNNModel obj's underlying member method code with example usage: obj._prepare_batch(batch) and returns: inputs,  labels,  weights