code: def assert_true_mean_equal_by_dkwm(
      samples, low, high, expected, false_fail_rate=1e-6, name=None
  ):
      """Asserts the mean of the given distribution is as expected.
  
      More precisely, fails if there is enough evidence (using the
      [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
      (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval))
      that the true mean of some distribution from which the given samples are
      drawn is _not_ the given expected mean with statistical significance
      `false_fail_rate` or stronger, otherwise passes.  If you also want to
      check that you are gathering enough evidence that a pass is not
      spurious, see `min_num_samples_for_dkwm_mean_test` and
      `min_discrepancy_of_true_means_detectable_by_dkwm`.
  
      Note that `false_fail_rate` is a total false failure rate for all
      the assertions in the batch.  As such, if the batch is nontrivial,
      the assertion will insist on stronger evidence to fail any one member.
  
      Args:
        samples: Floating-point `Tensor` of samples from the distribution(s)
          of interest.  Entries are assumed IID across the 0th dimension.
          The other dimensions must broadcast with `low` and `high`.
          The support is bounded: `low <= samples <= high`.
        low: Floating-point `Tensor` of lower bounds on the distributions'
          supports.
        high: Floating-point `Tensor` of upper bounds on the distributions'
          supports.
        expected: Floating-point `Tensor` of expected true means.
        false_fail_rate: *Scalar* floating-point `Tensor` admissible total
          rate of mistakes.
        name: A name for this operation (optional).
  
      Returns:
        check: Op that raises `InvalidArgumentError` if any expected mean is
          outside the corresponding confidence interval.
      """
      with tf.name_scope(name or "assert_true_mean_equal_by_dkwm"):
          return assert_true_mean_in_interval_by_dkwm(
              samples, low, high, expected, expected, false_fail_rate
          )
  
imports: import functools
  import itertools
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.util.seed_stream import SeedStream
label_desc: Use image node_id 19 for calling a global function with example usage: assert_true_mean_equal_by_dkwm(samples, low, high, expected, false_fail_rate, name) and returns: assert_true_mean_in_interval_by_dkwm