def assert_true_mean_in_interval_by_dkwm(<EOL>samples, low, high, expected_low, expected_high,<EOL>false_fail_rate=<NUM_LIT>, name=None):
args_list = [samples, low, high, expected_low, expected_high, false_fail_rate]<EOL>with tf.compat.v1.name_scope(name, '<STR_LIT>',<EOL>args_list):<EOL><INDENT>dtype = dtype_util.common_dtype(args_list, tf.float32)<EOL>samples = tf.convert_to_tensor(value=samples, name='<STR_LIT>', dtype=dtype)<EOL>low = tf.convert_to_tensor(value=low, name='<STR_LIT>', dtype=dtype)<EOL>high = tf.convert_to_tensor(value=high, name='<STR_LIT>', dtype=dtype)<EOL>expected_low = tf.convert_to_tensor(<EOL>value=expected_low, name='<STR_LIT>', dtype=dtype)<EOL>expected_high = tf.convert_to_tensor(<EOL>value=expected_high, name='<STR_LIT>', dtype=dtype)<EOL>false_fail_rate = tf.convert_to_tensor(<EOL>value=false_fail_rate, name='<STR_LIT>', dtype=dtype)<EOL>samples = _check_shape_dominates(<EOL>samples, [low, high, expected_low, expected_high])<EOL>min_mean, max_mean = true_mean_confidence_interval_by_dkwm(<EOL>samples, low, high, false_fail_rate)<EOL>check_confidence_interval_can_intersect = tf.compat.v1.assert_greater_equal(<EOL>max_mean,<EOL>expected_low,<EOL>message='<STR_LIT>'<EOL>'<STR_LIT>')<EOL>with tf.control_dependencies([check_confidence_interval_can_intersect]):<EOL><INDENT>return tf.compat.v1.assert_less_equal(<EOL>min_mean,<EOL>expected_high,<EOL>message='<STR_LIT>'<EOL>'<STR_LIT>')<EOL><DEDENT><DEDENT>
Asserts the mean of the given distribution is in the given interval.

    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 mean of the distribution from which the given samples are
    drawn is _outside_ the given interval 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_low: Floating-point `Tensor` of lower bounds on the
        expected true means.
      expected_high: Floating-point `Tensor` of upper bounds on the
        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
        interval does not overlap with the corresponding confidence
        interval.