code: def test_no_gradient_error_xla(self):
      @tf2.function(jit_compile=True)
      def graph_fn():
          x = tf.constant(1.0)
          with tf2.GradientTape() as tape1:
              with tf2.GradientTape() as tape2:
                  tape1.watch(x)
                  tape2.watch(x)
                  self.call_histogram_op(
                      name="loss", step=0, data=x, buckets=10
                  )
  
      # Note that XLA CPU/GPU has no outside compilation support, so summaries
      # won't actually run in a jit_compiled function. TPUs do, and follow
      # some similar codepaths, so this test stops at graph building to
      # exercise those paths without a TPU available.
      writer = tf2.summary.create_file_writer(self.get_temp_dir())
      with writer.as_default():
          graph_fn.get_concrete_function()
  
imports: import glob
  import os
  import numpy
  import tensorflow
  from tensorboard.compat import tf2
  from tensorboard.compat.proto import summary_pb2
  from tensorboard.plugins.histogram import metadata
  from tensorboard.plugins.histogram import summary
  from tensorboard.util import tensor_util
label_desc: Use image node_id 2 for calling the SummaryV3OpGraphTest obj's underlying member method code with example usage: obj.test_no_gradient_error_xla() without return types