code: def _step(self, batch, mode="train"):
      if mode == "dev":  # dev: compute metric
          (
              src_tokens,
              tgt_tokens,
              src_seq_len,
              tgt_seq_len,
              first,
              target_span,
          ) = batch
          pred = self.model.predict(src_tokens, src_seq_len, first)
          self.metrics.evaluate(target_span, pred, tgt_tokens)
          return
      elif mode == "test":  # test: just get pred
          src_tokens, src_seq_len, first = batch
          pred = self.model.predict(src_tokens, src_seq_len, first)
          return pred
      else:  # train: get loss
          (
              src_tokens,
              tgt_tokens,
              src_seq_len,
              tgt_seq_len,
              first,
              target_span,
          ) = batch
          pred = self.model(src_tokens, tgt_tokens, src_seq_len, first)
          loss = self.loss(tgt_tokens, tgt_seq_len, pred)
          return loss
  
file_path: DeepKE/src/deepke/name_entity_re/few_shot/module/train.py
imports: import torch
  from torch import optim
  from tqdm import tqdm
  from ..utils import convert_preds_to_outputs, write_predictions
  import random
label_desc: Use image node_id 5 for calling the Trainer obj's underlying member method code with example usage: obj._step(batch, mode) and returns: pred, loss