class_docstr: :class:`CandidateSelector` that selects all items, regardless of whether
  the user has rated them, as candidates.  When this selector is fit, it memorizes
  the set of items.
  
  Attributes:
      items_(numpy.ndarray): All known items.
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code: def candidates(self, user, ratings=None):
      return self.items_.copy()
  
imports: import logging
  from collections.abc import Iterable, Sequence
  import pandas
  import numpy
  from ..data import sparse_ratings
  from .None import Predictor, Recommender, CandidateSelector
  from ..util import derivable_rng
  from .bias import Bias
  from .ranking import TopN
label_desc: Use image node_id 2 for calling the AllItemsCandidateSelector obj's underlying member method code with example usage: obj.candidates(user, ratings) and returns: self