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","."]
code: def fit(self, ratings, **kwargs):
      for algo in self.algorithms:
          algo.fit(ratings, **kwargs)
  
      return self
  
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 Fallback obj's underlying member method code with example usage: obj.fit(ratings) and returns: self