code: def test_select_only_one_of_readers():
      num_of_reads = 1000
      np.testing.assert_array_equal(
          _count_mixed([reader0, reader1], [1.0, 0.0], num_of_reads),
          [num_of_reads, 0],
      )
      np.testing.assert_array_equal(
          _count_mixed([reader0, reader1], [0.0, 1.0], num_of_reads),
          [0, num_of_reads],
      )
  
      np.testing.assert_array_equal(
          _count_mixed(
              [reader0, reader1, reader2], [0.0, 1.0, 0.0], num_of_reads
          ),
          [0, num_of_reads, 0],
      )
      np.testing.assert_array_equal(
          _count_mixed(
              [reader0, reader1, reader2], [0.0, 0.0, 1.0], num_of_reads
          ),
          [0, 0, num_of_reads],
      )
  
imports: from __future__ import division
  import numpy
  import pytest
  import six
  import tensorflow.compat.v1
  from petastorm import make_reader
  from petastorm.ngram import NGram
  from petastorm.predicates import in_lambda
  from petastorm.pytorch import DataLoader
  from petastorm.test_util.reader_mock import ReaderMock
  from petastorm.tf_utils import tf_tensors, make_petastorm_dataset
  from petastorm.unischema import Unischema, UnischemaField
  from petastorm.weighted_sampling_reader import WeightedSamplingReader
  from petastorm.tests.test_tf_utils import create_tf_graph
label_desc: Use image node_id 2 for calling a global function with example usage: test_select_only_one_of_readers() without return types