class_docstr: Modified version of Data class of pytorch-geometric to enable batching process to
  custom increment values in certain keys.
class_docstr_tok: ["Modified","version","of","Data","class","of","pytorch-geometric","to","enable","batching","process","to","custom","increment","values","in","certain","keys","."]
docstr: Modified __inc__() to increment 'atom_to_incoming_bonds' and 'mapping' keys
  based given required increment value (example, number of bonds in the molecule)
docstr_tok: ["Modified","__inc__","(",")","to","increment","'atom_to_incoming_bonds","'","and","'mapping","'","keys","based","given","required","increment","value","(","example",",","number","of","bonds","in","the","molecule",")"]
code: def __inc__(self, key, value, *args, **kwargs):
      """
      Modified __inc__() to increment 'atom_to_incoming_bonds' and 'mapping' keys
      based given required increment value (example, number of bonds in the molecule)
      """
      if key in ["atom_to_incoming_bonds", "mapping"]:
          return self.required_inc
      else:
          return super().__inc__(key, value, *args, **kwargs)
  
imports: import numpy
  import torch
  import torch.nn
  from deepchem.models.losses import Loss, L2Loss, SparseSoftmaxCrossEntropy
  from deepchem.models.torch_models import layers
  from deepchem.models.torch_models import TorchModel
  from torch_geometric.data import Data, Batch
  from deepchem.feat import GraphData
  from deepchem.data import Dataset
  from typing import Union, List, Sequence, Optional, Iterable, Tuple
label_desc: Use image node_id 2 for calling the _ModData obj's underlying member method code with example usage: obj.__inc__(key, value) and returns: self, super