class_docstr: Dataset as used by the ThutmoseTaggerModel for training, validation, and inference
  pipelines.
  
  Args:
      input_file (str): path to tsv-file with data
      example_builder: instance of BertExampleBuilder
class_docstr_tok: ["Dataset","as","used","by","the","ThutmoseTaggerModel","for","training",",","validation",",","and","inference","pipelines",".","Args",":","input_file","(","str",")",":","path","to","tsv-file","with","data","example_builder",":","instance","of","BertExampleBuilder"]
code: def __getitem__(self, idx: int):
      input_ids = np.array(self.examples[idx].features["input_ids"])
      input_mask = np.array(self.examples[idx].features["input_mask"])
      segment_ids = np.array(self.examples[idx].features["segment_ids"])
      labels_mask = np.array(self.examples[idx].features["labels_mask"])
      tag_labels = np.array(self.examples[idx].features["tag_labels"])
      semiotic_labels = np.array(
          self.examples[idx].features["semiotic_labels"]
      )
      semiotic_spans = np.array(
          self.examples[idx].features["semiotic_spans"]
      )
      return (
          input_ids,
          input_mask,
          segment_ids,
          labels_mask,
          tag_labels,
          semiotic_labels,
          semiotic_spans,
      )
  
file_path: NeMo/nemo/collections/nlp/data/text_normalization_as_tagging/thutmose_tagger_dataset.py
imports: from typing import Dict, List, Optional
  import numpy
  from nemo.collections.nlp.data.text_normalization_as_tagging.bert_example import BertExampleBuilder, read_input_file
  from nemo.core.classes.dataset import Dataset
  from nemo.core.neural_types import ChannelType, IntType, LabelsType, MaskType, NeuralType
label_desc: Use image node_id 4 for calling the ThutmoseTaggerDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: input_ids,  input_mask,  segment_ids,  labels_mask,  tag_labels,  semiotic_labels,  semiotic_spans