AbstractTensorHandlePtr fMinus(f_outputs[0]);

    // Take Difference of both estimates: (f(theta + eps) - f(theta - eps)).
    TF_RETURN_IF_ERROR(
        ops::Sub(ctx, fPlus.get(), fMinus.get(), f_outputs, "sub_top"));
    AbstractTensorHandlePtr fDiff(f_outputs[0]);

    // Calculate using the difference quotient definition:
    // (f(theta + eps) - f(theta - eps)) / (2 * eps).
    TF_RETURN_IF_ERROR(

            return True
        if self.security_scopes_param_name is not None:
            return True
        if self._is_security_scheme:
            return True
        for sub_dep in self.dependencies:
            if sub_dep._uses_scopes:
                return True
        return False

    @cached_property
    def _is_security_scheme(self) -> bool:
        if self.call is None:

*   `tf.data`:

    *   Moved option `warm_start` from `tf.data.experimental.OptimizationOptions` to `tf.data.Options`.

*   `tf.lite`:

    *   `sub_op` and `mul_op` support broadcasting up to 6 dimensions.