class_docstr: A list-like object with feature names. It allows
  feature names for unknown features to be generated using
  a provided template, and to avoid making copies of large objects
  in get_feature_names.
class_docstr_tok: ["A","list-like","object","with","feature","names",".","It","allows","feature","names","for","unknown","features","to","be","generated","using","a","provided","template",",","and","to","avoid","making","copies","of","large","objects","in","get_feature_names","."]
code: def __init__(
      self,
      feature_names=None,
      bias_name=None,  # type: str
      unkn_template=None,  # type: str
      n_features=None,  # type: int
  ):
      # type: (...) -> None
      if not (
          feature_names is not None
          or (unkn_template is not None and n_features)
      ):
          raise ValueError(
              "Pass feature_names or unkn_template and n_features"
          )
      if feature_names is not None:
          if not isinstance(feature_names, (list, dict, np.ndarray)):
              raise TypeError("Unexpected feature_names type")
          if n_features is not None and n_features != len(
              feature_names
          ):
              if not isinstance(feature_names, dict):
                  raise ValueError(
                      "n_features should match feature_names length"
                  )
              elif unkn_template is None:
                  raise ValueError(
                      "unkn_template should be set for sparse features"
                  )
      self.feature_names = feature_names
      self.unkn_template = unkn_template
      self.n_features = n_features or len(feature_names)  # type: int
      self.bias_name = bias_name
  
file_path: eli5/eli5/_feature_names.py
imports: import re
  import six
  from typing import Any, Iterable, Iterator, Tuple, Sized, List, Optional, Dict, Union, Callable, Pattern
  import numpy
  import scipy.sparse
label_desc: Use image node_id 1 to create a new FeatureNames object from inherited base classes: Sized, Iterable with example: obj = FeatureNames(feature_names, bias_name, unkn_template, n_features)