class_docstr: Subject verb agreement as verb number predicion (binary classification).
class_docstr_tok: ["Subject","verb","agreement","as","verb","number","predicion","(","binary","classification",")","."]
docstr: Splits of data to produce and number of output shards for each.
  
  This is the setup of the main paper. 10% train/ 90% eval
  
  Returns:
    A dict containing splits information.
docstr_tok: ["Splits","of","data","to","produce","and","number","of","output","shards","for","each",".","This","is","the","setup","of","the","main","paper",".","10","%","train\/","90","%","eval","Returns",":","A","dict","containing","splits","information","."]
code: def dataset_splits(self):
      """Splits of data to produce and number of output shards for each.
  
      This is the setup of the main paper. 10% train/ 90% eval
  
      Returns:
        A dict containing splits information.
  
      """
      return [
          {
              "split": problem.DatasetSplit.TRAIN,
              "shards": 1,
          },
          {
              "split": problem.DatasetSplit.EVAL,
              "shards": 1,
          },
          {
              "split": problem.DatasetSplit.TEST,
              "shards": 10,
          },
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import csv
  import gzip
  import os
  import random
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.utils import metrics
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the SvaNumberPrediction obj's underlying member method code with example usage: obj.dataset_splits() and returns: list