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
  >>> import dgl
  >>> from deepchem.models.torch_models import MPNN
  >>> smiles = ["C1CCC1", "C1=CC=CN=C1"]
  >>> featurizer = dc.feat.MolGraphConvFeaturizer(use_edges=True)
  >>> graphs = featurizer.featurize(smiles)
  >>> print(type(graphs[0]))
  <class 'deepchem.feat.graph_data.GraphData'>
  >>> dgl_graphs = [graphs[i].to_dgl_graph(self_loop=True) for i in range(len(graphs))]
  >>> # Batch two graphs into a graph of two connected components
  >>> batch_dgl_graph = dgl.batch(dgl_graphs)
  >>> model = MPNN(n_tasks=1, mode='regression')
  >>> preds = model(batch_dgl_graph)
  >>> print(type(preds))
  <class 'torch.Tensor'>
  >>> preds.shape == (2, 1)
  True
  
  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.
docstr: Parameters
  ----------
  n_tasks: int
      Number of tasks.
  node_out_feats: int
      The length of the final node representation vectors. Default to 64.
  edge_hidden_feats: int
      The length of the hidden edge representation vectors. Default to 128.
  num_step_message_passing: int
      The number of rounds of message passing. Default to 3.
  num_step_set2set: int
      The number of set2set steps. Default to 6.
  num_layer_set2set: int
      The number of set2set layers. Default to 3.
  mode: str
      The model type, 'classification' or 'regression'. Default to 'regression'.
  number_atom_features: int
      The length of the initial atom feature vectors. Default to 30.
  number_bond_features: int
      The length of the initial bond feature vectors. Default to 11.
  n_classes: int
      The number of classes to predict per task
      (only used when ``mode`` is 'classification'). Default to 2.
  nfeat_name: str
      For an input graph ``g``, the model assumes that it stores node features in
      ``g.ndata[nfeat_name]`` and will retrieve input node features from that.
      Default to 'x'.
  efeat_name: str
      For an input graph ``g``, the model assumes that it stores edge features in
      ``g.edata[efeat_name]`` and will retrieve input edge features from that.
      Default to 'edge_attr'.
code: def __init__(
      self,
      n_tasks: int,
      node_out_feats: int = 64,
      edge_hidden_feats: int = 128,
      num_step_message_passing: int = 3,
      num_step_set2set: int = 6,
      num_layer_set2set: int = 3,
      mode: str = "regression",
      number_atom_features: int = 30,
      number_bond_features: int = 11,
      n_classes: int = 2,
      nfeat_name: str = "x",
      efeat_name: str = "edge_attr",
  ):
      """
      Parameters
      ----------
      n_tasks: int
          Number of tasks.
      node_out_feats: int
          The length of the final node representation vectors. Default to 64.
      edge_hidden_feats: int
          The length of the hidden edge representation vectors. Default to 128.
      num_step_message_passing: int
          The number of rounds of message passing. Default to 3.
      num_step_set2set: int
          The number of set2set steps. Default to 6.
      num_layer_set2set: int
          The number of set2set layers. Default to 3.
      mode: str
          The model type, 'classification' or 'regression'. Default to 'regression'.
      number_atom_features: int
          The length of the initial atom feature vectors. Default to 30.
      number_bond_features: int
          The length of the initial bond feature vectors. Default to 11.
      n_classes: int
          The number of classes to predict per task
          (only used when ``mode`` is 'classification'). Default to 2.
      nfeat_name: str
          For an input graph ``g``, the model assumes that it stores node features in
          ``g.ndata[nfeat_name]`` and will retrieve input node features from that.
          Default to 'x'.
      efeat_name: str
          For an input graph ``g``, the model assumes that it stores edge features in
          ``g.edata[efeat_name]`` and will retrieve input edge features from that.
          Default to 'edge_attr'.
      """
      try:
          import dgl  # noqa: F401
      except:
          raise ImportError("This class requires dgl.")
      try:
          import dgllife  # noqa: F401
      except:
          raise ImportError("This class requires dgllife.")
  
      if mode not in ["classification", "regression"]:
          raise ValueError(
              "mode must be either 'classification' or 'regression'"
          )
  
      super(MPNN, self).__init__()
  
      self.n_tasks = n_tasks
      self.mode = mode
      self.n_classes = n_classes
      self.nfeat_name = nfeat_name
      self.efeat_name = efeat_name
      if mode == "classification":
          out_size = n_tasks * n_classes
      else:
          out_size = n_tasks
  
      from dgllife.model import MPNNPredictor as DGLMPNNPredictor
  
      self.model = DGLMPNNPredictor(
          node_in_feats=number_atom_features,
          edge_in_feats=number_bond_features,
          node_out_feats=node_out_feats,
          edge_hidden_feats=edge_hidden_feats,
          n_tasks=out_size,
          num_step_message_passing=num_step_message_passing,
          num_step_set2set=num_step_set2set,
          num_layer_set2set=num_layer_set2set,
      )
  
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 1 to create a new MPNN object from inherited base classes: nn with example: obj = MPNN(n_tasks, node_out_feats, edge_hidden_feats, num_step_message_passing, num_step_set2set, num_layer_set2set, mode, number_atom_features, number_bond_features, n_classes, nfeat_name, efeat_name)