class_docstr: DeepChem Lightning Module to be used with Lightning trainer.
  
  TODO: Add dataloader, example code and fit, once datasetmodule
  is ready
  The lightning module is a wrapper over deepchem's torch model.
  This module directly works with pytorch lightning trainer
  which runs training for multiple epochs and also is responsible
  for setting up and training models on multiple GPUs.
  https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.core.LightningModule.html?highlight=LightningModule
  
  Notes
  -----
  This class requires PyTorch to be installed.
docstr: Create a new DCLightningModule.
  
  Parameters
  ----------
  dc_model: deepchem.models.torch_models.torch_model.TorchModel
      TorchModel to be wrapped inside the lightning module.
docstr_tok: ["Create","a","new","DCLightningModule",".","Parameters","--","--","--","--","--","dc_model",":","deepchem.models.torch_models.torch_model.TorchModel","TorchModel","to","be","wrapped","inside","the","lightning","module","."]
code: def __init__(self, dc_model):
      """Create a new DCLightningModule.
  
      Parameters
      ----------
      dc_model: deepchem.models.torch_models.torch_model.TorchModel
          TorchModel to be wrapped inside the lightning module.
      """
      super().__init__()
      self.dc_model = dc_model
  
      self.pt_model = self.dc_model.model
      # This will not work for ModularTorchModel as it is directly uses `loss_func` to compute loss.
      self.loss = self.dc_model._loss_fn
  
file_path: deepchem/deepchem/models/lightning/dc_lightning_module.py
imports: import torch
  import lightning
  from deepchem.models.torch_models import ModularTorchModel, TorchModel
label_desc: Use image node_id 1 to create a new DCLightningModule object from inherited base class: L with example: obj = DCLightningModule(dc_model)