class_docstr: The Fallback algorithm predicts with its first component, uses the second to fill in
  missing values, and so forth.
class_docstr_tok: ["The","Fallback","algorithm","predicts","with","its","first","component",",","uses","the","second","to","fill","in","missing","values",",","and","so","forth","."]
docstr: Args:
      algorithms: a list of component algorithms.  Each one will be trained.
      others:
          additional algorithms, in which case ``algorithms`` is taken to be
          a single algorithm.
code: def __init__(self, algorithms, *others):
      """
      Args:
          algorithms: a list of component algorithms.  Each one will be trained.
          others:
              additional algorithms, in which case ``algorithms`` is taken to be
              a single algorithm.
      """
      if others:
          self.algorithms = [algorithms] + list(others)
      elif isinstance(algorithms, Iterable) or isinstance(
          algorithms, Sequence
      ):
          self.algorithms = algorithms
      else:
          self.algorithms = [algorithms]
  
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 1 to create a new Fallback object from inherited base classes: Predictor with example: obj = Fallback(algorithms)