def _partition_bmus(self, X):
        """Private function used to partition bmus between jobs.

        Parameters
        ----------
        X : np.array
            List of datapoints

        Returns
        -------
        n_jobs : int
            Number of jobs
        list of int
            List of number of datapoints per job
        list of int
            List of start values for every job list

        """
        n_datapoints = len(X)
        n_jobs = min(effective_n_jobs(self.n_jobs), n_datapoints)

        n_datapoints_per_job = np.full(
            n_jobs, n_datapoints // n_jobs, dtype=np.int)

        n_datapoints_per_job[:n_datapoints % n_jobs] += 1
        starts = np.cumsum(n_datapoints_per_job)

        return n_jobs, n_datapoints_per_job.tolist(), [0] + starts.tolist() 

def effective_n_jobs_with_context(n_jobs=None):
    """Find the effective number of jobs, either specified directly, or from the joblib.parallel_backend context."""
    if n_jobs is None:
        _, n_jobs_from_context = joblib.parallel.get_active_backend()
        n_jobs = n_jobs_from_context
    return joblib.effective_n_jobs(n_jobs) 

def _partition_estimators(n_estimators, n_jobs):
    """Private function used to partition estimators between jobs."""
    # Compute the number of jobs
    n_jobs = min(effective_n_jobs(n_jobs), n_estimators)

    # Partition estimators between jobs
    n_estimators_per_job = np.full(n_jobs, n_estimators // n_jobs,
                                   dtype=np.int)
    n_estimators_per_job[:n_estimators % n_jobs] += 1
    starts = np.cumsum(n_estimators_per_job)

    xdiff = [starts[n] - starts[n - 1] for n in range(1, len(starts))]

    print("Split among workers default:", starts, xdiff)
    return n_estimators_per_job.tolist(), [0] + starts.tolist(), n_jobs 

def _partition_estimators(n_estimators, n_jobs):
    """Private function used to partition estimators between jobs."""
    # Compute the number of jobs
    n_jobs = min(effective_n_jobs(n_jobs), n_estimators)

    # Partition estimators between jobs
    n_estimators_per_job = np.full(n_jobs, n_estimators // n_jobs,
                                   dtype=np.int)
    n_estimators_per_job[:n_estimators % n_jobs] += 1
    starts = np.cumsum(n_estimators_per_job)

    xdiff = [starts[n] - starts[n - 1] for n in range(1, len(starts))]

    print("Split among workers default:", starts, xdiff)
    return n_estimators_per_job.tolist(), [0] + starts.tolist(), n_jobs 

def maybe_cythonize_extensions(top_path, config):
    """Tweaks for building extensions between release and development mode."""
    with_openmp = check_openmp_support()

    is_release = os.path.exists(os.path.join(top_path, 'PKG-INFO'))

    if is_release:
        build_from_c_and_cpp_files(config.ext_modules)
    else:
        message = ('Please install Cython with a version >= {0} in order '
                   'to build a sktime development version.').format(
            CYTHON_MIN_VERSION)
        try:
            import Cython
            if LooseVersion(Cython.__version__) < CYTHON_MIN_VERSION:
                message += ' Your version of Cython was {0}.'.format(
                    Cython.__version__)
                raise ValueError(message)
            from Cython.Build import cythonize
        except ImportError as exc:
            exc.args += (message,)
            raise

        n_jobs = 1
        with contextlib.suppress(ImportError):
            import joblib
            if LooseVersion(joblib.__version__) > LooseVersion("0.13.0"):
                # earlier joblib versions don't account for CPU affinity
                # constraints, and may over-estimate the number of available
                # CPU particularly in CI (cf loky#114)
                n_jobs = joblib.effective_n_jobs()

        config.ext_modules = cythonize(
            config.ext_modules,
            nthreads=n_jobs,
            compile_time_env={'SKTIME_OPENMP_SUPPORTED': with_openmp},
            compiler_directives={'language_level': 3}
        )