class_docstr: Create a ``FeatureSet`` instance from a TSV file.
  
  If example/instance IDs are included in the files, they
  must be specified in the ``id`` column.
  Also there must be a column with the name specified by ``label_col``
  if the data is labeled.
  
  Parameters
  ----------
  path_or_list : str
      The path to a comma-delimited file.
  
  replace_blanks_with : Optional[Union[Number, Dict[str, Number]]], default=None
      Specifies a new value with which to replace blank values.
      Options are:
  
          - ``Number`` : A (numeric) value with which to replace blank values.
          - ``dict`` : A dictionary specifying the replacement value for each column.
          - ``None`` : Blank values will be left as blanks, and not replaced.
  
      The replacement occurs after the data set is read into a ``pd.DataFrame``.
  
  drop_blanks : bool, default=False
      If ``True``, remove lines/rows that have any blank values. These
      lines/rows are removed after the the data set is read into a
      ``pd.DataFrame``.
  
  pandas_kwargs : Optional[Dict[str, Any]], default=None
      Arguments that will be passed directly to the ``pandas`` I/O reader.
  
  kwargs : Optional[Dict[str, Any]]
      Other arguments to the Reader object.
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docstr: Initialize TSVReader class.
docstr_tok: ["Initialize","TSVReader","class","."]
code: def __init__(
      self,
      path_or_list: Union[PathOrStr, List[Dict[str, Any]]],
      replace_blanks_with: Optional[
          Union[Number, Dict[str, Number]]
      ] = None,
      drop_blanks: bool = False,
      pandas_kwargs: Optional[Dict[str, Any]] = None,
      **kwargs,
  ):
      """Initialize TSVReader class."""
      super(TSVReader, self).__init__(
          path_or_list,
          replace_blanks_with=replace_blanks_with,
          drop_blanks=drop_blanks,
          pandas_kwargs=pandas_kwargs,
          **kwargs,
      )
      self._sep = str("\t")
  
imports: import csv
  import json
  import logging
  import re
  import sys
  from io import StringIO
  from itertools import chain
  from numbers import Number
  from pathlib import Path
  from typing import IO, Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from bs4 import UnicodeDammit
  from sklearn.feature_extraction import FeatureHasher
  from skll.data import FeatureSet
  from skll.data.dict_vectorizer import DictVectorizer
  from skll.types import ClassMap, FeatGenerator, FeatureDictList, IdType, LabelType, PathOrStr
label_desc: Use image node_id 1 to create a new TSVReader object from inherited base classes: CSVReader with example: obj = TSVReader(path_or_list, replace_blanks_with, drop_blanks, pandas_kwargs)