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","."]
docstr: Forward propagation in DeepDTA architecture.
  
  Args:
      x_drug: drug sequence encoding.
      x_target: target protein sequence encoding.
docstr_tok: ["Forward","propagation","in","DeepDTA","architecture",".","Args",":","x_drug",":","drug","sequence","encoding",".","x_target",":","target","protein","sequence","encoding","."]
code: def forward(self, x_drug, x_target):
      """
      Forward propagation in DeepDTA architecture.
  
      Args:
          x_drug: drug sequence encoding.
          x_target: target protein sequence encoding.
      """
      drug_emb = self.drug_encoder(x_drug)
      target_emb = self.target_encoder(x_target)
      comb_emb = torch.cat((drug_emb, target_emb), dim=1)
      output = self.decoder(comb_emb)
      return output
  
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 2 for calling the DeepDTATrainer obj's underlying member method code with example usage: obj.forward(x_drug, x_target) and returns: output