class_docstr: Base Preparer class for nlp
class_docstr_tok: ["Base","Preparer","class","for","nlp"]
docstr: Load a raw file. Return text and label.
  For single text input, text: [sentence1, ...]
  For multiple text inputs, text: [[sentence1_1, ...], [sentence1_2, ...]]
  For single output, label: [label1, label2, ...]
  For multiple outputs, label: [[label1_1, ...], [label1_2, ...]]
docstr_tok: ["Load","a","raw","file",".","Return","text","and","label",".","For","single","text","input",",","text",":","[","sentence1",",","...","]","For","multiple","text","inputs",",","text",":","[","[","sentence1_1",",","...","]",",","[","sentence1_2",",","...","]","]","For","single","output",",","label",":","[","label1",",","label2",",","...","]","For","multiple","outputs",",","label",":","[","[","label1_1",",","...","]",",","[","label1_2",",","...","]","]"]
code: def load_a_raw_file(self, one_path, infer_without_label):
      """
      Load a raw file. Return text and label.
      For single text input, text: [sentence1, ...]
      For multiple text inputs, text: [[sentence1_1, ...], [sentence1_2, ...]]
      For single output, label: [label1, label2, ...]
      For multiple outputs, label: [[label1_1, ...], [label1_2, ...]]
      """
      raise NotImplementedError
  
imports: import os
  import math
  from pathlib import Path
  from absl import logging
  import delta.compat
  import numpy
  from delta import utils
  from delta.data.preprocess.utils import prepare_embedding
  from delta.utils.solver.utils.solver_utils import get_session_conf
  from delta.data.preprocess.utils import prepare_vocab
  from delta.data.preprocess.utils import prepare_vocab_from_config
  from delta.data.preprocess.utils import get_pre_process_text_ds_iter
  from delta.data.utils.common_utils import get_file_len
label_desc: Use image node_id 5 for calling the TextPreparer obj's underlying member method code with example usage: obj.load_a_raw_file(one_path, infer_without_label) without return types