code: def test_torchscript_e2e_text_hf_tokenizer_truncated_sequence(
      tmpdir, csv_filename
  ):
      data_csv_path = os.path.join(tmpdir, csv_filename)
      input_features = [
          text_feature(
              encoder={"vocab_size": 3, "type": "bert"},
              preprocessing={"max_sequence_length": 3},
          )
      ]
      output_features = [
          category_feature(),
      ]
      backend = LocalTestBackend()
      config = {
          "input_features": input_features,
          "output_features": output_features,
          TRAINER: {"epochs": 2, BATCH_SIZE: 128},
      }
      training_data_csv_path = generate_data(
          input_features, output_features, data_csv_path
      )
  
      validate_torchscript_outputs(
          tmpdir, config, backend, training_data_csv_path
      )
  
imports: import os
  import shutil
  from copy import deepcopy
  from typing import List
  import numpy
  import pandas
  import pytest
  import torch
  import torchtext
  from ludwig.api import LudwigModel
  from ludwig.backend import RAY
  from ludwig.constants import BATCH_SIZE, COMBINER, EVAL_BATCH_SIZE, LOGITS, NAME, PREDICTIONS, PROBABILITIES, TRAINER
  from ludwig.data.preprocessing import preprocess_for_prediction
  from ludwig.features.number_feature import numeric_transformation_registry
  from ludwig.globals import TRAIN_SET_METADATA_FILE_NAME
  from ludwig.models.inference import to_inference_module_input_from_dataframe
  from ludwig.utils import output_feature_utils
  from ludwig.utils.tokenizers import TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from tests.integration_tests import utils
  from tests.integration_tests.utils import audio_feature, bag_feature, binary_feature, category_feature, date_feature, generate_data, h3_feature, image_feature, LocalTestBackend, number_feature, sequence_feature, set_feature, text_feature, timeseries_feature, vector_feature
label_desc: Use image node_id 9 for calling a global function with example usage: test_torchscript_e2e_text_hf_tokenizer_truncated_sequence(tmpdir, csv_filename) without return types