code: def testDynamicSlice(self):
      start = array_ops.placeholder(np.int32, shape=(2, 3, 4))
  
      # If slice_sizes are known, the operand shape does not matter.
      # The shape of the output is equal to slice_sizes.
      slice_sizes = np.array([1, 2, 4], dtype=np.int32)
      for a_shape in [(2, 3, 4), (None, 3, 4), None]:
          a = array_ops.placeholder(np.float32, shape=a_shape)
          res = xla.dynamic_slice(a, start, slice_sizes)
          self.assertEqual(res.shape.as_list(), [1, 2, 4])
  
      # The first two dimension slice sizes are known
      slice_sizes = array_ops_stack.stack(
          [1, 2, array_ops.placeholder(np.int32, [])]
      )
      for a_shape in [(2, 3, 4), (None, 3, 4), None]:
          a = array_ops.placeholder(np.float32, shape=a_shape)
          res = xla.dynamic_slice(a, start, slice_sizes)
          self.assertEqual(res.shape.as_list(), [1, 2, None])
  
      # If slice_sizes has known rank and dimension, but is not a constant
      # then output has the same rank, but with unknown dimensions.
      slice_sizes = array_ops.placeholder(np.int32, [3])
      for a_shape in [(2, 3, 4), (None, 3, 4), None]:
          a = array_ops.placeholder(np.float32, shape=a_shape)
          res = xla.dynamic_slice(a, start, slice_sizes)
          self.assertEqual(res.shape.as_list(), [None, None, None])
  
      # slice sizes has known rank, but unknown dimensions.
      # then the output has the same rank as the operand, but with unknown
      # dimensions.
      slice_sizes = array_ops.placeholder(np.int32, [None])
      for a_shape in [(2, 3, 4), (None, 3, 4)]:
          a = array_ops.placeholder(np.float32, shape=a_shape)
          res = xla.dynamic_slice(a, start, slice_sizes)
          self.assertEqual(res.shape.as_list(), [None, None, None])
  
      a = array_ops.placeholder(np.float32, shape=None)
      slice_sizes = array_ops.placeholder(np.int32, [None])
      res = xla.dynamic_slice(a, start, slice_sizes)
      self.assertIsNone(res.shape.rank)
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.compiler.tf2xla.ops import gen_xla_ops
  from tensorflow.compiler.tf2xla.python import xla
  from local_xla.xla import xla_data_pb2
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.platform import googletest
label_desc: Use image node_id 11 for calling the XlaOpsShapeInferenceTest obj's underlying member method code with example usage: obj.testDynamicSlice() without return types