def execute(task, *args, **kwargs):
    """
    Execute ``task`` (callable or name), honoring host/role decorators, etc.

    ``task`` may be an actual callable object, or it may be a registered task
    name, which is used to look up a callable just as if the name had been
    given on the command line (including :ref:`namespaced tasks <namespaces>`,
    e.g. ``"deploy.migrate"``.

    The task will then be executed once per host in its host list, which is
    (again) assembled in the same manner as CLI-specified tasks: drawing from
    :option:`-H`, :ref:`env.hosts <hosts>`, the `~fabric.decorators.hosts` or
    `~fabric.decorators.roles` decorators, and so forth.

    ``host``, ``hosts``, ``role``, ``roles`` and ``exclude_hosts`` kwargs will
    be stripped out of the final call, and used to set the task's host list, as
    if they had been specified on the command line like e.g. ``fab
    taskname:host=hostname``.

    Any other arguments or keyword arguments will be passed verbatim into
    ``task`` (the function itself -- not the ``@task`` decorator wrapping your
    function!) when it is called, so ``execute(mytask, 'arg1',
    kwarg1='value')`` will (once per host) invoke ``mytask('arg1',
    kwarg1='value')``.

    :returns:
        a dictionary mapping host strings to the given task's return value for
        that host's execution run. For example, ``execute(foo, hosts=['a',
        'b'])`` might return ``{'a': None, 'b': 'bar'}`` if ``foo`` returned
        nothing on host `a` but returned ``'bar'`` on host `b`.

        In situations where a task execution fails for a given host but overall
        progress does not abort (such as when :ref:`env.skip_bad_hosts
        <skip-bad-hosts>` is True) the return value for that host will be the
        error object or message.

    .. seealso::
        :ref:`The execute usage docs <execute>`, for an expanded explanation
        and some examples.

    .. versionadded:: 1.3
    .. versionchanged:: 1.4
        Added the return value mapping; previously this function had no defined
        return value.
    """
    my_env = {'clean_revert': True}
    results = {}
    # Obtain task
    is_callable = callable(task)
    if not (is_callable or _is_task(task)):
        # Assume string, set env.command to it
        my_env['command'] = task
        task = crawl(task, state.commands)
        if task is None:
            msg = "%r is not callable or a valid task name" % (my_env['command'],)
            if state.env.get('skip_unknown_tasks', False):
                warn(msg)
                return
            else:
                abort(msg)
    # Set env.command if we were given a real function or callable task obj
    else:
        dunder_name = getattr(task, '__name__', None)
        my_env['command'] = getattr(task, 'name', dunder_name)
    # Normalize to Task instance if we ended up with a regular callable
    if not _is_task(task):
        task = WrappedCallableTask(task)
    # Filter out hosts/roles kwargs
    new_kwargs, hosts, roles, exclude_hosts = parse_kwargs(kwargs)
    # Set up host list
    my_env['all_hosts'], my_env['effective_roles'] = task.get_hosts_and_effective_roles(hosts, roles,
                                                                                        exclude_hosts, state.env)

    parallel = requires_parallel(task)
    if parallel:
        # Import multiprocessing if needed, erroring out usefully
        # if it can't.
        try:
            import multiprocessing
        except __HOLE__:
            import traceback
            tb = traceback.format_exc()
            abort(tb + """
    At least one task needs to be run in parallel, but the
    multiprocessing module cannot be imported (see above
    traceback.) Please make sure the module is installed
    or that the above ImportError is fixed.""")
    else:
        multiprocessing = None

    # Get pool size for this task
    pool_size = task.get_pool_size(my_env['all_hosts'], state.env.pool_size)
    # Set up job queue in case parallel is needed
    queue = multiprocessing.Queue() if parallel else None
    jobs = JobQueue(pool_size, queue)
    if state.output.debug:
        jobs._debug = True

    # Call on host list
    if my_env['all_hosts']:
        # Attempt to cycle on hosts, skipping if needed
        for host in my_env['all_hosts']:
            try:
                results[host] = _execute(
                    task, host, my_env, args, new_kwargs, jobs, queue,
                    multiprocessing
                )
            except NetworkError, e:
                results[host] = e
                # Backwards compat test re: whether to use an exception or
                # abort
                if not state.env.use_exceptions_for['network']:
                    func = warn if state.env.skip_bad_hosts else abort
                    error(e.message, func=func, exception=e.wrapped)
                else:
                    raise

            # If requested, clear out connections here and not just at the end.
            if state.env.eagerly_disconnect:
                disconnect_all()

        # If running in parallel, block until job queue is emptied
        if jobs:
            err = "One or more hosts failed while executing task '%s'" % (
                my_env['command']
            )
            jobs.close()
            # Abort if any children did not exit cleanly (fail-fast).
            # This prevents Fabric from continuing on to any other tasks.
            # Otherwise, pull in results from the child run.
            ran_jobs = jobs.run()
            for name, d in ran_jobs.iteritems():
                if d['exit_code'] != 0:
                    if isinstance(d['results'], NetworkError) and \
                            _is_network_error_ignored():
                        error(d['results'].message, func=warn, exception=d['results'].wrapped)
                    elif isinstance(d['results'], BaseException):
                        error(err, exception=d['results'])
                    else:
                        error(err)
                results[name] = d['results']

    # Or just run once for local-only
    else:
        with settings(**my_env):
            results['<local-only>'] = task.run(*args, **new_kwargs)
    # Return what we can from the inner task executions

    return results
dataset/ETHPy150Open fabric/fabric/fabric/tasks.py/execute