code: def _build_optimizer(self):
      parameters = [
          p for p in self.model.parameters() if p.requires_grad
      ]
      self.optimizer = optim.Adam(
          parameters, lr=self.opt["learning_rate"]
      )
  
imports: import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.jobs import JobsDataset
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from args import get_args
  from build_model import get_model
  from evaluation import ExpressionAccuracy
  from utils import get_log, wordid2str
label_desc: Use image node_id 6 for calling the Jobs obj's underlying member method code with example usage: obj._build_optimizer() without return types