docstr: Test of the parallel ccmbinator with weights and minimum.
docstr_tok: ["Test","of","the","parallel","ccmbinator","with","weights","and","minimum","."]
code: def test_parallel_with_gradual_reweighting_remainders_big(self):
      """Test of the parallel ccmbinator with weights and minimum."""
      dataset1 = lambda: (i for i in itertools.cycle(range(1)))
      dataset2 = lambda: (i for i in itertools.cycle(range(10, 30)))
      dataset3 = lambda: (i for i in itertools.cycle(range(30, 80)))
      dataset4 = lambda: (i for i in itertools.cycle(range(100, 220)))
      parallel = data.Parallel(
          [dataset2, dataset1, dataset4, dataset3],
          counters=(20, 1, 120, 50),
          gradually_reweight=True,
          use_remainders=True,
      )
      generator = parallel()
  
      for _ in range(3):
          self.assertEqual(next(generator), 0)
          for i in range(20):
              self.assertEqual(next(generator), 10 + i)
              for j in range(2):
                  self.assertEqual(next(generator), 30 + 2 * i + j)
                  for k in range(2):
                      self.assertEqual(
                          next(generator), 100 + 2 * 2 * i + 2 * j + k
                      )
          # Here we process the remainder from datasets 3 and 4:
          for i in range(10):
              self.assertEqual(next(generator), 70 + i)
          for i in range(40):
              self.assertEqual(next(generator), 180 + i)
  
imports: import itertools
  import os
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from trax import data
label_desc: Use image node_id 16 for calling the InputsTest obj's underlying member method code with example usage: obj.test_parallel_with_gradual_reweighting_remainders_big() without return types