class_docstr: Dataset as used by the SpellcheckingAsrCustomizationModel for training and validation pipelines.
  
  Args:
      input_file (str): path to tsv-file with data
      example_builder: instance of BertExampleBuilder
class_docstr_tok: ["Dataset","as","used","by","the","SpellcheckingAsrCustomizationModel","for","training","and","validation","pipelines",".","Args",":","input_file","(","str",")",":","path","to","tsv-file","with","data","example_builder",":","instance","of","BertExampleBuilder"]
code: def __init__(
      self, input_file: str, example_builder: BertExampleBuilder
  ) -> None:
      self.example_builder = example_builder
      self.examples = self.example_builder.read_input_file(
          input_file, infer=False
      )
      self.pad_token_id = self.example_builder._pad_id
  
imports: import pickle
  from io import BytesIO
  from typing import Dict, List, Optional, Tuple
  import braceexpand
  import numpy
  import torch
  import webdataset
  from nemo.collections.nlp.data.spellchecking_asr_customization.bert_example import BertExampleBuilder
  from nemo.core.classes.dataset import Dataset, IterableDataset
  from nemo.core.neural_types import ChannelType, IntType, LabelsType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 2 to create a new SpellcheckingAsrCustomizationDataset object from inherited base classes: Dataset with example: obj = SpellcheckingAsrCustomizationDataset(input_file, example_builder)