class_docstr: Defines a dataset for sequence tagging. Examples in this dataset
  contain paired lists -- paired list of words and tags.
  
  For example, in the case of part-of-speech tagging, an example is of the
  form
  [I, love, PyTorch, .] paired with [PRON, VERB, PROPN, PUNCT]
  
  See torchtext/test/sequence_tagging.py on how to use this class.
code: def __init__(
      self, path, fields, encoding="utf-8", separator="\t", **kwargs
  ):
      examples = []
      columns = []
  
      with open(path, encoding=encoding) as input_file:
          for line in input_file:
              line = line.strip()
              if line == "":
                  if columns:
                      examples.append(
                          data.Example.fromlist(columns, fields)
                      )
                  columns = []
              else:
                  for i, column in enumerate(line.split(separator)):
                      if len(columns) < i + 1:
                          columns.append([])
                      columns[i].append(column)
  
          if columns:
              examples.append(data.Example.fromlist(columns, fields))
      super(SequenceTaggingDataset, self).__init__(
          examples, fields, **kwargs
      )
  
file_path: pytext/pytext/legacy/datasets/sequence_tagging.py
imports: import random
  from ..None import data
label_desc: Use image node_id 2 to create a new SequenceTaggingDataset object from inherited base classes: data with example: obj = SequenceTaggingDataset(path, fields, encoding, separator)