code: def _testTensorArraySplitRead(self, tf_dtype):
      with self.session() as session, self.test_scope():
          convert = _make_converter(tf_dtype)
  
          def fn():
              ta = tensor_array_ops.TensorArray(
                  dtype=tf_dtype, tensor_array_name="foo", size=3
              )
  
              # Split an empty vector.
              lengths = constant_op.constant([0, 0, 0])
              w0 = ta.split(convert([]), lengths=lengths)
              r0 = w0.read(0)
              r1 = w0.read(1)
              r2 = w0.read(2)
              return [r0, r1, r2]
  
          d0, d1, d2 = self.evaluate(xla.compile(fn))
  
          self.assertAllEqual(convert([]), d0)
          self.assertAllEqual(convert([]), d1)
          self.assertAllEqual(convert([]), d2)
  
          def fn():
              # Split a vector.
              ta = tensor_array_ops.TensorArray(
                  dtype=tf_dtype, tensor_array_name="foo", size=3
              )
              lengths = constant_op.constant([1, 1, 1])
              w0 = ta.split(convert([1.0, 2.0, 3.0]), lengths=lengths)
              r0 = w0.read(0)
              r1 = w0.read(1)
              r2 = w0.read(2)
              return [r0, r1, r2]
  
          d0, d1, d2 = self.evaluate(xla.compile(fn))
  
          self.assertAllEqual(convert([1.0]), d0)
          self.assertAllEqual(convert([2.0]), d1)
          self.assertAllEqual(convert([3.0]), d2)
  
          def fn():
              # Split a matrix.
              ta = tensor_array_ops.TensorArray(
                  dtype=tf_dtype, tensor_array_name="foo", size=3
              )
              lengths = constant_op.constant([1, 1, 1])
              w0 = ta.split(
                  convert([[1.0, 101.0], [2.0, 121.0], [3.0, 127.0]]),
                  lengths=lengths,
              )
              r0 = w0.read(0)
              r1 = w0.read(1)
              r2 = w0.read(2)
              return [r0, r1, r2]
  
          d0, d1, d2 = self.evaluate(xla.compile(fn))
          self.assertAllEqual(convert([[1.0, 101.0]]), d0)
          self.assertAllEqual(convert([[2.0, 121.0]]), d1)
          self.assertAllEqual(convert([[3.0, 127.0]]), d2)
  
imports: import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.compiler.xla import xla
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  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 control_flow_util
  from tensorflow.python.ops import gen_data_flow_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import tensor_array_grad
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 10 for calling the TensorArrayTest obj's underlying member method code with example usage: obj._testTensorArraySplitRead(tf_dtype) and returns: list, list, list