code: def testGPUAllIndicesBig(self):
      # This test only makes sense on the GPU. There we do not check
      # for errors. In this case, we should discard all the values
      # and have an empty output.
      if not test.is_gpu_available():
          return
  
      data_list = [1.1, 2.1, 3.1, 4.1, 5.1, 6.1]
      indices_list = [90, 70, 60, 100, 110, 40]
      with self.session():
          data = constant_op.constant(data_list, dtype=dtypes.float32)
          indices = constant_op.constant(
              indices_list, dtype=dtypes.int32
          )
          partitions = data_flow_ops.dynamic_partition(
              data, indices, num_partitions=40
          )
          partition_vals = self.evaluate(partitions)
  
      self.assertEqual(40, len(partition_vals))
      for i in range(40):
          self.assertAllEqual([], partition_vals[i])
  
imports: import unittest
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.framework import config
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import data_flow_ops
  from tensorflow.python.ops import gradients_impl
  import tensorflow.python.ops.data_flow_grad
  from tensorflow.python.platform import test
label_desc: Use image node_id 13 for calling the DynamicPartitionTest obj's underlying member method code with example usage: obj.testGPUAllIndicesBig() without return types