class_docstr: An implementation of DeepDTA model based on BaseDTATrainer.
  Args:
      drug_encoder: drug CNN encoder.
      target_encoder: target CNN encoder.
      decoder: drug-target MLP decoder.
      lr: learning rate.
class_docstr_tok: ["An","implementation","of","DeepDTA","model","based","on","BaseDTATrainer",".","Args",":","drug_encoder",":","drug","CNN","encoder",".","target_encoder",":","target","CNN","encoder",".","decoder",":","drug-target","MLP","decoder",".","lr",":","learning","rate","."]
code: def __init__(
      self,
      drug_encoder,
      target_encoder,
      decoder,
      lr=0.001,
      ci_metric=False,
      **kwargs,
  ):
      super().__init__(
          drug_encoder, target_encoder, decoder, lr, ci_metric, **kwargs
      )
  
file_path: pykale/kale/pipeline/deepdta.py
imports: import pytorch_lightning
  import torch
  from torch.nn import functional
  from kale.evaluate.metrics import concord_index
label_desc: Use image node_id 1 to create a new DeepDTATrainer object from inherited base classes: BaseDTATrainer with example: obj = DeepDTATrainer(drug_encoder, target_encoder, decoder, lr, ci_metric)