def assert_true_mean_equal_by_dkwm(<EOL>samples, low, high, expected, false_fail_rate=<NUM_LIT>, name=None):
with tf.compat.v1.name_scope(name, '<STR_LIT>',<EOL>[samples, low, high, expected, false_fail_rate]):<EOL><INDENT>return assert_true_mean_in_interval_by_dkwm(<EOL>samples, low, high, expected, expected, false_fail_rate)<EOL><DEDENT>
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.