docstr: Test that the stateful initializer is not marked for compilation.
  
  XLA does not currently support seeded initialization and XLA initializers
  therefore return different values than non-XLA counterparts.  Here
  we ensure that if we can disable JIT compilation for the initializers and
  get the same variable values as if no JIT compilation happened.
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code: def testJITVariableSeed(self):
      """Test that the stateful initializer is not marked for compilation.
  
      XLA does not currently support seeded initialization and XLA initializers
      therefore return different values than non-XLA counterparts.  Here
      we ensure that if we can disable JIT compilation for the initializers and
      get the same variable values as if no JIT compilation happened.
      """
  
      def create_ops():
          with variable_scope.variable_scope(
              "root",
              initializer=init_ops.random_uniform_initializer(
                  -0.1, 0.1, seed=2
              ),
          ):
              inputs = variable_scope.get_variable("var", (1,))
              return inputs
  
      _, v_false_1 = self.compute(False, create_ops)
      _, v_false_2 = self.compute(False, create_ops)
      _, v_true_1 = self.compute(enable_jit_nonstateful, create_ops)
      _, v_true_2 = self.compute(enable_jit_nonstateful, create_ops)
      self.assertAllClose(v_false_1, v_false_2)
      self.assertAllClose(v_true_1, v_true_2)
      self.assertAllClose(v_false_1, v_true_1)
  
imports: from absl.testing import parameterized
  from tensorflow.python.compiler.xla import jit
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import function
  from tensorflow.python.framework import op_def_registry
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import gradients
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 5 for calling the JITTest obj's underlying member method code with example usage: obj.testJITVariableSeed() and returns: inputs