code: def prepare_parser_data():
      index = "index"
      ds = DataSet({index: list(range(N_SAMPLES))})
      ds.apply_field(
          lambda x: RUNNER.gen_var_seq(MAX_LEN, VOCAB_SIZE),
          field_name=index,
          new_field_name="words1",
      )
      ds.apply_field(
          lambda x: RUNNER.gen_seq(len(x), NUM_CLS),
          field_name="words1",
          new_field_name="words2",
      )
      # target1 is heads, should in range(0, len(words))
      ds.apply_field(
          lambda x: RUNNER.gen_seq(len(x), len(x)),
          field_name="words1",
          new_field_name="target1",
      )
      ds.apply_field(
          lambda x: RUNNER.gen_seq(len(x), NUM_CLS),
          field_name="words1",
          new_field_name="target2",
      )
      ds.apply_field(len, field_name="words1", new_field_name="seq_len")
      dl = TorchDataLoader(ds, batch_size=BATCH_SIZE)
      return dl
  
file_path: fastNLP/tests/models/torch/test_biaffine_parser.py
imports: import pytest
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP import Metric, seq_len_to_mask
  from .model_runner import *
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_parser_data() and returns: dl