code: def test_many_small_batches_same_as_one_big_batch(self):
      dim = 2
      num_results_per_batch = 1
      num_batches = 3
      seed = 1925
      sample1, _ = random.halton.sample(
          dim, num_results_per_batch * num_batches, seed=seed
      )
      batch_indices = (
          tf.range(
              i * num_results_per_batch, (i + 1) * num_results_per_batch
          )
          for i in range(num_batches)
      )
      sample2 = (
          random.halton.sample(
              dim, sequence_indices=sequence_indices, seed=seed
          )
          for sequence_indices in batch_indices
      )
      result_set1 = set(tuple(row) for row in self.evaluate(sample1))
      result_set2 = set()
      for batch, _ in sample2:
          result_set2.update(tuple(row) for row in self.evaluate(batch))
      self.assertEqual(result_set1, result_set2)
  
imports: import numpy
  from six.moves import range
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow.python.framework import test_util
  from tf_quant_finance.math import random
label_desc: Use image node_id 11 for calling the HaltonSequenceTest obj's underlying member method code with example usage: obj.test_many_small_batches_same_as_one_big_batch() without return types