class_docstr: A derived class from :class:`Dataset` for which folds (for
  cross-validation) are not predefined. (Or for when there are no folds at
  all).
docstr: Do not split the dataset into folds and just return a trainset as
  is, built from the whole dataset.
  
  User can then query for predictions, as shown in the :ref:`User Guide
  <train_on_whole_trainset>`.
  
  Returns:
      The :class:`Trainset <surprise.Trainset>`.
docstr_tok: ["Do","not","split","the","dataset","into","folds","and","just","return","a","trainset","as","is",",","built","from","the","whole","dataset",".","User","can","then","query","for","predictions",",","as","shown","in","the",":","ref",":","`","User","Guide","<","train_on_whole_trainset",">","`",".","Returns",":","The",":","class",":","`","Trainset","<","surprise.Trainset",">","`","."]
code: def build_full_trainset(self):
      """Do not split the dataset into folds and just return a trainset as
      is, built from the whole dataset.
  
      User can then query for predictions, as shown in the :ref:`User Guide
      <train_on_whole_trainset>`.
  
      Returns:
          The :class:`Trainset <surprise.Trainset>`.
      """
  
      return self.construct_trainset(self.raw_ratings)
  
imports: import itertools
  import os
  import sys
  from collections import defaultdict
  from .builtin_datasets import BUILTIN_DATASETS, download_builtin_dataset
  from .reader import Reader
  from .trainset import Trainset
label_desc: Use image node_id 2 for calling the DatasetAutoFolds obj's underlying member method code with example usage: obj.build_full_trainset() and returns: self