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: Initialize the _ModData class
docstr_tok: ["Initialize","the","_ModData","class"]
code: def __init__(self, required_inc, *args, **kwargs):
      """Initialize the _ModData class"""
      super().__init__(*args, **kwargs)
      self.required_inc = required_inc
  
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 1 to create a new _ModData object from inherited base classes: Data with example: obj = _ModData(required_inc)