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"]
docstr: Returns definitions of module output ports.
                 
docstr_tok: ["Returns","definitions","of","module","output","ports","."]
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      """Returns definitions of module output ports."""
      return {
          "input_ids": NeuralType(("B", "T"), ChannelType()),
          "input_mask": NeuralType(("B", "T"), MaskType()),
          "segment_ids": NeuralType(("B", "T"), ChannelType()),
          "labels_mask": NeuralType(("B", "T"), MaskType()),
          "tag_labels": NeuralType(("B", "T"), LabelsType()),
          "semiotic_labels": NeuralType(("B", "T"), LabelsType()),
          "semiotic_spans": NeuralType(("B", "T", "C"), IntType()),
      }
  
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 1 for calling the ThutmoseTaggerDataset obj's underlying member method code with example usage: obj.output_types() and returns: dict