code: def _RunAndVerifyVariable(self, dtype, large_num_splits=False):
      # Random dims of rank 5
      shape = np.random.randint(1, 5, size=5)
      split_dim = np.random.randint(-5, 5)
      if large_num_splits:
          num_split = np.random.randint(16, 25)
      else:
          num_split = np.random.randint(2, 8)
      size_splits = np.random.randint(2, 8, num_split, dtype=np.int32)
      shape[split_dim] = np.sum(size_splits)
      inp = self._makeData(shape, dtype)
      with test_util.device(use_gpu=True):
          result = self.evaluate(
              array_ops.split(inp, size_splits, split_dim)
          )
      slices = [slice(0, x) for x in shape]
      offset = 0
      for i in range(num_split):
          slices[split_dim] = slice(offset, offset + size_splits[i])
          offset += size_splits[i]
          self.assertAllEqual(result[i], inp[tuple(slices)])
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  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 gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 6 for calling the SplitOpTest obj's underlying member method code with example usage: obj._RunAndVerifyVariable(dtype, large_num_splits) without return types