How to use fn method in ava

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estimator_test.py

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...63from tensorflow.python.training import training64from tensorflow.python.util import compat65_TMP_DIR = '/​tmp'66_ANOTHER_TMP_DIR = '/​another_tmp'67def dummy_model_fn(features, labels, params):68 _, _, _ = features, labels, params69def check_eventfile_for_keyword(keyword, est):70 """Checks event files for the keyword."""71 writer_cache.FileWriterCache.clear()72 # Get last Event written.73 event_paths = glob.glob(os.path.join(est.model_dir, 'events*'))74 last_event = None75 for last_event in summary_iterator.summary_iterator(event_paths[-1]):76 if last_event.summary is not None:77 if last_event.summary.value:78 if keyword in last_event.summary.value[0].tag:79 return True80 return False81class EstimatorInheritanceConstraintTest(test.TestCase):82 """Tests that sub classes cannot override methods of Estimator."""83 def test_override_a_method(self):84 class _Estimator(estimator.Estimator):85 def __init__(self):86 super(_Estimator, self).__init__(model_fn=dummy_model_fn)87 def predict(self, input_fn, predict_keys=None, hooks=None):88 pass89 with self.assertRaisesRegexp(90 ValueError, 'cannot override members of Estimator.*predict'):91 _Estimator()92 def test_override_a_method_with_tricks(self):93 class _Estimator(estimator.Estimator):94 def __init__(self):95 super(_Estimator, self).__init__(model_fn=dummy_model_fn)96 def _assert_members_are_not_overridden(self):97 pass # HAHA! I tricked you!98 def predict(self, input_fn, predict_keys=None, hooks=None):99 pass100 with self.assertRaisesRegexp(101 ValueError, 'cannot override members of Estimator.*predict'):102 _Estimator()103 def test_extension_of_api_is_ok(self):104 class _Estimator(estimator.Estimator):105 def __init__(self):106 super(_Estimator, self).__init__(model_fn=dummy_model_fn)107 def predict_proba(self, input_fn, predict_keys=None, hooks=None):108 pass109 _Estimator()110 def test_override_allowed_method(self):111 class _Estimator(estimator.Estimator):112 def __init__(self):113 super(_Estimator, self).__init__(model_fn=dummy_model_fn)114 def _call_input_fn(self, input_fn, mode):115 return input_fn()116 def _create_global_step(self, graph):117 pass118 def _convert_train_steps_to_hooks(self, steps, max_steps):119 pass120 def _convert_eval_steps_to_hooks(self, steps):121 pass122 _Estimator()123class EstimatorConstructorTest(test.TestCase):124 def test_config_must_be_a_run_config(self):125 with self.assertRaisesRegexp(ValueError, 'an instance of RunConfig'):126 estimator.Estimator(model_fn=None, config='NotARunConfig')127 def test_model_fn_must_be_provided(self):128 with self.assertRaisesRegexp(ValueError, 'model_fn.* must be'):129 estimator.Estimator(model_fn=None)130 def test_property_accessors(self):131 def model_fn(features, labels, params):132 _, _, _ = features, labels, params133 class FakeConfig(run_config.RunConfig):134 pass135 params = {'hidden_layers': [3, 4]}136 est = estimator.Estimator(137 model_fn=model_fn, model_dir='bla', config=FakeConfig(), params=params)138 self.assertTrue(isinstance(est.config, FakeConfig))139 self.assertEqual(params, est.params)140 self.assertEqual('bla', est.model_dir)141 def test_default_config(self):142 def model_fn(features, labels):143 _, _ = features, labels144 est = estimator.Estimator(model_fn=model_fn)145 self.assertTrue(isinstance(est.config, run_config.RunConfig))146 def test_default_model_dir(self):147 def model_fn(features, labels):148 _, _ = features, labels149 with test.mock.patch.object(tempfile, 'mkdtemp', return_value=_TMP_DIR):150 est = estimator.Estimator(model_fn=model_fn)151 self.assertEqual(_TMP_DIR, est.config.model_dir)152 self.assertEqual(_TMP_DIR, est.model_dir)153 def test_model_dir_in_constructor(self):154 def model_fn(features, labels):155 _, _ = features, labels156 est = estimator.Estimator(model_fn=model_fn, model_dir=_TMP_DIR)157 self.assertEqual(_TMP_DIR, est.config.model_dir)158 self.assertEqual(_TMP_DIR, est.model_dir)159 def test_model_dir_in_run_config(self):160 class FakeConfig(run_config.RunConfig):161 @property162 def model_dir(self):163 return _TMP_DIR164 def model_fn(features, labels):165 _, _ = features, labels166 est = estimator.Estimator(model_fn=model_fn, config=FakeConfig())167 self.assertEqual(_TMP_DIR, est.config.model_dir)168 self.assertEqual(_TMP_DIR, est.model_dir)169 def test_same_model_dir_in_constructor_and_run_config(self):170 class FakeConfig(run_config.RunConfig):171 @property172 def model_dir(self):173 return _TMP_DIR174 def model_fn(features, labels):175 _, _ = features, labels176 est = estimator.Estimator(177 model_fn=model_fn, config=FakeConfig(), model_dir=_TMP_DIR)178 self.assertEqual(_TMP_DIR, est.config.model_dir)179 self.assertEqual(_TMP_DIR, est.model_dir)180 def test_different_model_dir_in_constructor_and_run_config(self):181 class FakeConfig(run_config.RunConfig):182 @property183 def model_dir(self):184 return _TMP_DIR185 def model_fn(features, labels):186 _, _ = features, labels187 with self.assertRaisesRegexp(188 ValueError,189 'model_dir are set both in constructor and RunConfig, but '190 'with different values'):191 estimator.Estimator(192 model_fn=model_fn, config=FakeConfig(), model_dir=_ANOTHER_TMP_DIR)193 def test_model_fn_args_must_include_features(self):194 def model_fn(x, labels):195 _, _ = x, labels196 with self.assertRaisesRegexp(ValueError, 'features'):197 estimator.Estimator(model_fn=model_fn)198 def test_model_fn_args_labels_is_optional(self):199 def model_fn(features):200 _ = features201 estimator.Estimator(model_fn=model_fn)202 def test_if_params_provided_then_model_fn_should_accept_it(self):203 def model_fn(features, labels):204 _, _ = features, labels205 estimator.Estimator(model_fn=model_fn)206 with self.assertRaisesRegexp(ValueError, 'params'):207 estimator.Estimator(model_fn=model_fn, params={'hidden_layers': 4})208 def test_internal_params_is_a_deepcopy(self):209 def model_fn(features, labels, params):210 _, _, _ = features, labels, params211 params = {'hidden_layers': 4}212 est = estimator.Estimator(model_fn=model_fn, params=params)213 params['hidden_layers'] = 5214 self.assertEqual(4, est.params['hidden_layers'])215 def test_not_known_model_fn_args(self):216 def model_fn(features, labels, something):217 _, _, _ = features, labels, something218 with self.assertRaisesRegexp(ValueError, 'something'):219 estimator.Estimator(model_fn=model_fn)220 def test_not_known_model_fn_args_handled_by_lambda(self):221 def model_fn(features, labels, something):222 _, _, _ = features, labels, something223 new_model_fn = lambda features, labels: model_fn( # pylint: disable=g-long-lambda224 features, labels, 'something')225 estimator.Estimator(model_fn=new_model_fn)226 def test_if_model_fn_is_a_member_function_of_a_class(self):227 class ModelFnClass(object):228 def __init__(self):229 estimator.Estimator(model_fn=self.model_fn)230 def model_fn(self, features, labels, mode):231 _, _, _ = features, labels, mode232 ModelFnClass()233 def test_model_fn_property_binds_params(self):234 def model_fn(features, labels, mode, config, params):235 _, _, _, _, _ = features, labels, mode, config, params236 est = estimator.Estimator(model_fn=model_fn)237 model_fn_args = util.fn_args(est.model_fn)238 self.assertEqual(239 set(['features', 'labels', 'mode', 'config']), set(model_fn_args))240 def test_model_fn_property_returns_fixed_signature(self):241 def model_fn(features, labels):242 _, _ = features, labels243 est = estimator.Estimator(model_fn=model_fn)244 model_fn_args = util.fn_args(est.model_fn)245 self.assertEqual(246 set(['features', 'labels', 'mode', 'config']), set(model_fn_args))247def dummy_input_fn():248 return ({'x': constant_op.constant([[1], [1]])},249 constant_op.constant([[1], [1]]))250def model_fn_global_step_incrementer(features, labels, mode):251 _, _ = features, labels252 global_step = training.get_global_step()253 return model_fn_lib.EstimatorSpec(254 mode,255 loss=constant_op.constant(1.),256 train_op=state_ops.assign_add(global_step, 1))257def assert_features_op(expected_features, actual_features):258 return [259 check_ops.assert_equal(260 expected_features[k], actual_features[k], name='assert_%s' % k)261 for k in expected_features262 ]263def _estimator_spec(264 expected_features, expected_labels, actual_features, actual_labels, mode):265 assert_ops = tuple(266 assert_features_op(expected_features, actual_features) + [267 check_ops.assert_equal(268 expected_labels, actual_labels, name='assert_labels')269 ])270 global_step = training.get_global_step()271 with ops.control_dependencies(assert_ops):272 return model_fn_lib.EstimatorSpec(273 mode=mode,274 predictions=constant_op.constant(0.),275 loss=constant_op.constant(0.),276 train_op=state_ops.assign_add(global_step, 1))277def _make_input_fn(features, labels):278 def _input_fn():279 return {280 k: constant_op.constant(v)281 for k, v in six.iteritems(features)282 }, constant_op.constant(labels)283 return _input_fn284class EstimatorTrainTest(test.TestCase):285 def test_callable_model_fn(self):286 expected_features = {'x': 42., 'y': 43.}287 expected_labels = 44.288 model_fn_call_count = [0]289 test_self = self290 class ModelFn(object):291 def __call__(self, features, labels):292 model_fn_call_count[0] += 1293 test_self.assertItemsEqual(expected_features.keys(), features.keys())294 return _estimator_spec(295 expected_features, expected_labels, features, labels,296 model_fn_lib.ModeKeys.TRAIN)297 with self.assertRaisesRegexp(ValueError, 'does not include params'):298 estimator.Estimator(model_fn=ModelFn(), params={'a': 'b'})299 est = estimator.Estimator(model_fn=ModelFn(), config=run_config.RunConfig())300 self.assertEqual(0, model_fn_call_count[0])301 est.train(302 input_fn=_make_input_fn(expected_features, expected_labels), steps=1)303 self.assertEqual(1, model_fn_call_count[0])304 def test_callable_input_fn(self):305 expected_params = {'batch_size': 10}306 expected_config = run_config.RunConfig().replace(tf_random_seed=4321)307 input_fn_call_count = [0]308 def _model_fn(features, labels, mode, params, config):309 del params, config310 return model_fn_global_step_incrementer(features, labels, mode)311 test_self = self312 class InputFn(object):313 def __call__(self, params, config):314 input_fn_call_count[0] += 1315 test_self.assertEqual(expected_params, params)316 test_self.assertEqual(4321, config.tf_random_seed)317 return dummy_input_fn()318 est = estimator.Estimator(model_fn=_model_fn,319 params=expected_params,320 config=expected_config)321 self.assertEqual(0, input_fn_call_count[0])322 est.train(InputFn(), steps=1)323 self.assertEqual(1, input_fn_call_count[0])324 def test_input_fn_args(self):325 expected_params = {'batch_size': 10}326 expected_config = run_config.RunConfig().replace(tf_random_seed=4321)327 input_fn_call_count = [0]328 def _model_fn(features, labels, mode, params, config):329 del params, config330 return model_fn_global_step_incrementer(features, labels, mode)331 def _input_fn(params, config):332 input_fn_call_count[0] += 1333 self.assertEqual(expected_params, params)334 self.assertEqual(4321, config.tf_random_seed)335 return dummy_input_fn()336 est = estimator.Estimator(model_fn=_model_fn,337 params=expected_params,338 config=expected_config)339 self.assertEqual(0, input_fn_call_count[0])340 est.train(_input_fn, steps=1)341 self.assertEqual(1, input_fn_call_count[0])342 def test_minimal_model_fn_args(self):343 expected_features = {'x': 4, 'y': 5}344 def _input_fn():345 return expected_features346 model_fn_call_count = [0]347 def _model_fn(features):348 model_fn_call_count[0] += 1349 self.assertItemsEqual(expected_features.keys(), features.keys())350 with ops.control_dependencies(351 assert_features_op(expected_features, features)):352 return model_fn_lib.EstimatorSpec(353 mode=None,354 predictions=constant_op.constant(0.),355 loss=constant_op.constant(0.),356 train_op=state_ops.assign_add(training.get_global_step(), 1))357 est = estimator.Estimator(model_fn=_model_fn)358 self.assertEqual(0, model_fn_call_count[0])359 est.train(input_fn=_input_fn, steps=1)360 self.assertEqual(1, model_fn_call_count[0])361 def test_labels_should_be_none_if_model_fn_does_not_use_labels(self):362 def _input_fn_with_labels():363 return {'x': 4, 'y': 5}, [4]364 def _model_fn(features):365 _ = features366 return model_fn_lib.EstimatorSpec(367 mode=None,368 predictions=constant_op.constant(0.),369 loss=constant_op.constant(0.),370 train_op=state_ops.assign_add(training.get_global_step(), 1))371 est = estimator.Estimator(model_fn=_model_fn)372 with self.assertRaisesRegexp(ValueError, 'model_fn does not take labels'):373 est.train(input_fn=_input_fn_with_labels, steps=1)374 def test_input_fn_len_should_be_2_if_tuple_or_list(self):375 def _input_fn():376 return 4, 5, 6377 def _model_fn(features):378 _ = features379 est = estimator.Estimator(model_fn=_model_fn)380 with self.assertRaisesRegexp(ValueError, 'len 2 tuple'):381 est.train(input_fn=_input_fn, steps=1)382 def test_all_model_fn_args(self):383 expected_features = {'x': 42., 'y': 43.}384 expected_labels = 44.385 expected_params = {'some_param': 'some_value'}386 expected_config = run_config.RunConfig()387 expected_config.i_am_test = True388 # TODO(ptucker): We have to roll our own mock since Estimator._get_arguments389 # doesn't work with mock fns.390 model_fn_call_count = [0]391 # Note that args are all passed by keyword, so can be in any order.392 def _model_fn(mode, params, features, labels, config):393 model_fn_call_count[0] += 1394 self.assertItemsEqual(expected_features.keys(), features.keys())395 self.assertEqual(model_fn_lib.ModeKeys.TRAIN, mode)396 self.assertEqual(expected_params, params)397 self.assertTrue(config.i_am_test)398 return _estimator_spec(399 expected_features, expected_labels, features, labels, mode)400 est = estimator.Estimator(401 model_fn=_model_fn, params=expected_params, config=expected_config)402 self.assertEqual(0, model_fn_call_count[0])403 est.train(404 input_fn=_make_input_fn(expected_features, expected_labels), steps=1)405 self.assertEqual(1, model_fn_call_count[0])406 def test_partial_model_fn_args(self):407 expected_features = {'x': 42., 'y': 43.}408 expected_labels = 44.409 expected_params = {'some_param': 'some_value'}410 expected_config = run_config.RunConfig()411 expected_config.i_am_test = True412 expected_foo = 45.413 expected_bar = 46.414 # TODO(ptucker): We have to roll our own mock since Estimator._get_arguments415 # doesn't work with mock fns.416 model_fn_call_count = [0]417 def _model_fn(features, labels, foo, mode, params, config, bar):418 model_fn_call_count[0] += 1419 self.assertEqual(expected_foo, foo)420 self.assertEqual(expected_bar, bar)421 self.assertItemsEqual(expected_features.keys(), features.keys())422 self.assertEqual(model_fn_lib.ModeKeys.TRAIN, mode)423 self.assertEqual(expected_params, params)424 self.assertTrue(config.i_am_test)425 return _estimator_spec(426 expected_features, expected_labels, features, labels, mode)427 partial_model_fn = functools.partial(428 _model_fn, foo=expected_foo, bar=expected_bar)429 est = estimator.Estimator(430 model_fn=partial_model_fn, params=expected_params,431 config=expected_config)432 self.assertEqual(0, model_fn_call_count[0])433 est.train(434 input_fn=_make_input_fn(expected_features, expected_labels), steps=1)435 self.assertEqual(1, model_fn_call_count[0])436 def test_model_fn_must_return_estimator_spec(self):437 def model_fn(features, labels):438 _, _ = features, labels439 return 'NotGoodNotGood'440 est = estimator.Estimator(model_fn=model_fn)441 with self.assertRaisesRegexp(ValueError, 'EstimatorSpec'):442 est.train(dummy_input_fn, steps=1)443 def test_run_train_op_and_saves_at_the_end(self):444 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)445 est.train(dummy_input_fn, steps=5)446 self.assertEqual(447 5, estimator._load_global_step_from_checkpoint_dir(est.model_dir))448 def test_loss_summary(self):449 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer,450 config=run_config.RunConfig(save_summary_steps=1))451 est.train(dummy_input_fn, steps=1)452 # Make sure nothing is stuck in limbo.453 writer_cache.FileWriterCache.clear()454 if check_eventfile_for_keyword('loss', est):455 return456 self.fail('{} should be part of reported summaries.'.format('loss'))457 def test_latest_checkpoint(self):458 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)459 self.assertIsNone(est.latest_checkpoint())460 est.train(dummy_input_fn, steps=5)461 self.assertIsNotNone(est.latest_checkpoint())462 self.assertTrue(est.latest_checkpoint().startswith(est.model_dir))463 def test_steps_and_saves_reloads(self):464 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)465 est.train(dummy_input_fn, steps=5)466 self.assertEqual(467 5, estimator._load_global_step_from_checkpoint_dir(est.model_dir))468 est.train(dummy_input_fn, steps=5)469 self.assertEqual(470 10, estimator._load_global_step_from_checkpoint_dir(est.model_dir))471 def test_max_step(self):472 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)473 est.train(dummy_input_fn, max_steps=5)474 self.assertEqual(475 5, estimator._load_global_step_from_checkpoint_dir(est.model_dir))476 est.train(dummy_input_fn, max_steps=5)477 self.assertEqual(478 5, estimator._load_global_step_from_checkpoint_dir(est.model_dir))479 def test_checkpoint_contains_relative_paths(self):480 tmpdir = tempfile.mkdtemp()481 est = estimator.Estimator(482 model_dir=tmpdir,483 model_fn=model_fn_global_step_incrementer)484 est.train(dummy_input_fn, steps=5)485 checkpoint_file_content = file_io.read_file_to_string(486 os.path.join(tmpdir, 'checkpoint'))487 ckpt = checkpoint_state_pb2.CheckpointState()488 text_format.Merge(checkpoint_file_content, ckpt)489 self.assertEqual(ckpt.model_checkpoint_path, 'model.ckpt-5')490 self.assertAllEqual(491 ['model.ckpt-1', 'model.ckpt-5'], ckpt.all_model_checkpoint_paths)492 def test_train_save_copy_reload(self):493 tmpdir = tempfile.mkdtemp()494 model_dir1 = os.path.join(tmpdir, 'model_dir1')495 est1 = estimator.Estimator(496 model_dir=model_dir1,497 model_fn=model_fn_global_step_incrementer)498 est1.train(dummy_input_fn, steps=5)499 # We have to clear the cache before we can rename the directory,500 # otherwise open file handles will prevent the delete on Windows.501 writer_cache.FileWriterCache.clear()502 model_dir2 = os.path.join(tmpdir, 'model_dir2')503 os.renames(model_dir1, model_dir2)504 est2 = estimator.Estimator(505 model_dir=model_dir2,506 model_fn=model_fn_global_step_incrementer)507 self.assertEqual(508 5, estimator._load_global_step_from_checkpoint_dir(est2.model_dir))509 est2.train(dummy_input_fn, steps=5)510 self.assertEqual(511 10, estimator._load_global_step_from_checkpoint_dir(est2.model_dir))512 def test_steps0_raises_error(self):513 est = estimator.Estimator(514 model_fn=_model_fn_with_eval_metric_ops)515 with self.assertRaisesRegexp(ValueError, 'Must specify steps > 0'):516 est.train(dummy_input_fn, steps=0)517 def test_steps_negative_raises_error(self):518 est = estimator.Estimator(519 model_fn=_model_fn_with_eval_metric_ops)520 with self.assertRaisesRegexp(ValueError, 'Must specify steps > 0'):521 est.train(dummy_input_fn, steps=-1)522 def test_max_steps0_raises_error(self):523 est = estimator.Estimator(524 model_fn=_model_fn_with_eval_metric_ops)525 with self.assertRaisesRegexp(ValueError, 'Must specify max_steps > 0'):526 est.train(dummy_input_fn, max_steps=0)527 def test_max_steps_negative_raises_error(self):528 est = estimator.Estimator(529 model_fn=_model_fn_with_eval_metric_ops)530 with self.assertRaisesRegexp(ValueError, 'Must specify max_steps > 0'):531 est.train(dummy_input_fn, max_steps=-1)532 def test_scaffold_is_used(self):533 self.is_init_fn_called = False534 def _init_fn(scaffold, sess):535 _, _ = scaffold, sess536 self.is_init_fn_called = True537 def _model_fn_scaffold(features, labels, mode):538 _, _ = features, labels539 return model_fn_lib.EstimatorSpec(540 mode=mode,541 loss=constant_op.constant(0.),542 train_op=state_ops.assign_add(training.get_global_step(), 1),543 scaffold=training.Scaffold(init_fn=_init_fn))544 est = estimator.Estimator(model_fn=_model_fn_scaffold)545 est.train(dummy_input_fn, steps=1)546 self.assertTrue(self.is_init_fn_called)547 def test_hooks_should_be_session_run_hook(self):548 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)549 with self.assertRaisesRegexp(TypeError, 'must be a SessionRunHook'):550 est.train(dummy_input_fn, steps=1, hooks=['NotAHook'])551 def test_training_hooks_are_used(self):552 chief_hook = test.mock.MagicMock(553 wraps=training.SessionRunHook(), spec=training.SessionRunHook)554 hook = test.mock.MagicMock(555 wraps=training.SessionRunHook(), spec=training.SessionRunHook)556 def _model_fn_hooks(features, labels, mode):557 _, _ = features, labels558 return model_fn_lib.EstimatorSpec(559 mode=mode,560 loss=constant_op.constant(0.),561 train_op=state_ops.assign_add(training.get_global_step(), 1),562 training_chief_hooks=[chief_hook],563 training_hooks=[hook])564 est = estimator.Estimator(model_fn=_model_fn_hooks)565 self.assertFalse(chief_hook.begin.called)566 self.assertFalse(hook.begin.called)567 est.train(dummy_input_fn, steps=1)568 self.assertTrue(chief_hook.begin.called)569 self.assertTrue(hook.begin.called)570 def test_saving_listeners_are_used(self):571 listener = test.mock.Mock(spec=training.CheckpointSaverListener)572 est = estimator.Estimator(573 model_fn=model_fn_global_step_incrementer,574 config=run_config.RunConfig(save_checkpoints_steps=10))575 est.train(dummy_input_fn, steps=26, saving_listeners=[listener])576 self.assertEqual(4, listener.before_save.call_count)577 self.assertEqual(4, listener.after_save.call_count)578 def test_saver_hook_should_exist_to_use_saving_listeners(self):579 listener = test.mock.Mock(spec=training.CheckpointSaverListener)580 est = estimator.Estimator(581 model_fn=model_fn_global_step_incrementer,582 config=run_config.RunConfig(save_checkpoints_steps=None,583 save_checkpoints_secs=None))584 with self.assertRaisesRegexp(585 ValueError, 'CheckpointSaverHook to use saving_listeners'):586 est.train(dummy_input_fn, steps=1, saving_listeners=[listener])587 def test_listeners_should_be_listeners(self):588 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)589 with self.assertRaisesRegexp(590 TypeError, 'must be a list of CheckpointSaverListener'):591 est.train(dummy_input_fn, steps=1, saving_listeners=['not-a-listener'])592 def test_chief_only_hook_should_not_be_called_on_non_chief(self):593 chief_hook = test.mock.MagicMock(594 wraps=training.SessionRunHook(), spec=training.SessionRunHook)595 hook = test.mock.MagicMock(596 wraps=training.SessionRunHook(), spec=training.SessionRunHook)597 def _model_fn_hooks(features, labels, mode):598 _, _ = features, labels599 return model_fn_lib.EstimatorSpec(600 mode=mode,601 loss=constant_op.constant(0.),602 train_op=state_ops.assign_add(training.get_global_step(), 1),603 training_chief_hooks=[chief_hook],604 training_hooks=[hook])605 class NonChiefRunConfig(run_config.RunConfig):606 @property607 def is_chief(self): # pylint: disable=g-wrong-blank-lines608 return False609 # Mocking the SessionManager.wait_for_session, so that worker doesn't wait610 # for chief.611 def get_initialized_session(*args, **kwargs):612 # Session doesn't take 'max_wait_secs' argument.613 kwargs.pop('max_wait_secs', None)614 scaffold = training.Scaffold().finalize()615 sess = session.Session(*args, **kwargs)616 sess.run(scaffold.init_op)617 return sess618 with test.mock.patch.object(619 training.SessionManager,620 'wait_for_session',621 side_effect=get_initialized_session):622 est = estimator.Estimator(623 model_fn=_model_fn_hooks, config=NonChiefRunConfig())624 self.assertFalse(chief_hook.begin.called)625 self.assertFalse(hook.begin.called)626 est.train(dummy_input_fn, steps=1)627 self.assertFalse(chief_hook.begin.called)628 self.assertTrue(hook.begin.called)629 def test_features_labels_mode(self):630 given_features = {'test-features': [[1], [1]]}631 given_labels = {'test-labels': [[1], [1]]}632 def _input_fn():633 return given_features, given_labels634 def _model_fn(features, labels, mode):635 self.features, self.labels, self.mode = features, labels, mode636 return model_fn_lib.EstimatorSpec(637 mode=mode,638 loss=constant_op.constant(0.),639 train_op=state_ops.assign_add(training.get_global_step(), 1),640 predictions=constant_op.constant([[0.]]))641 est = estimator.Estimator(model_fn=_model_fn)642 est.train(_input_fn, steps=1)643 self.assertEqual(given_features, self.features)644 self.assertEqual(given_labels, self.labels)645 self.assertEqual(model_fn_lib.ModeKeys.TRAIN, self.mode)646 def test_graph_initialization_global_step_and_random_seed(self):647 expected_random_seed = run_config.RunConfig().tf_random_seed648 def _model_fn(features, labels, mode):649 _, _, _ = features, labels, mode650 self.assertIsNotNone(training.get_global_step())651 self.assertEqual(expected_random_seed, ops.get_default_graph().seed)652 return model_fn_lib.EstimatorSpec(653 mode=mode,654 loss=constant_op.constant(0.),655 train_op=state_ops.assign_add(training.get_global_step(), 1),656 predictions=constant_op.constant([[0.]]))657 est = estimator.Estimator(model_fn=_model_fn)658 est.train(dummy_input_fn, steps=1)659def _model_fn_with_eval_metric_ops(features, labels, mode, params):660 _, _ = features, labels661 metric_name = params.get('metric_name') or 'metric'662 metric_value = params.get('metric_value') or 2.663 global_step = training.get_global_step()664 loss = constant_op.constant(1.)665 metric_update_op = loss.op666 metric_tensor = control_flow_ops.with_dependencies(667 [metric_update_op], constant_op.constant(metric_value))668 return model_fn_lib.EstimatorSpec(669 mode,670 loss=loss,671 predictions={'predictions': constant_op.constant(1.)},672 train_op=state_ops.assign_add(global_step, 1),673 eval_metric_ops={metric_name: (metric_tensor, metric_update_op)})674class _StepCounterHook(session_run_hook.SessionRunHook):675 """Hooks that counts the number of times it is called."""676 def __init__(self):677 self._steps = 0678 def before_run(self, run_context):679 del run_context680 self._steps += 1681 @property682 def steps(self):683 return self._steps684class EstimatorGetVariablesTest(test.TestCase):685 def test_model_should_be_trained(self):686 def _model_fn(features, labels, mode):687 _, _ = features, labels688 variables.Variable(1., name='one')689 return model_fn_lib.EstimatorSpec(690 mode=mode,691 loss=constant_op.constant(0.),692 train_op=state_ops.assign_add(training.get_global_step(), 1))693 est = estimator.Estimator(model_fn=_model_fn)694 with self.assertRaisesRegexp(ValueError, 'not find trained model'):695 est.get_variable_names()696 with self.assertRaisesRegexp(ValueError, 'not find trained model'):697 est.get_variable_value('one')698 def test_get_variable_utils(self):699 def _model_fn(features, labels, mode):700 _, _ = features, labels701 variables.Variable(1., name='one')702 variables.Variable(3., name='three')703 return model_fn_lib.EstimatorSpec(704 mode=mode,705 loss=constant_op.constant(0.),706 train_op=state_ops.assign_add(training.get_global_step(), 1))707 est = estimator.Estimator(model_fn=_model_fn)708 est.train(input_fn=dummy_input_fn, steps=1)709 self.assertEqual(710 set(['one', 'three', 'global_step']), set(est.get_variable_names()))711 self.assertEqual(1., est.get_variable_value('one'))712 self.assertEqual(3., est.get_variable_value('three'))713class EstimatorEvaluateTest(test.TestCase):714 def test_input_fn_args(self):715 expected_params = {'batch_size': 10}716 expected_config = run_config.RunConfig().replace(tf_random_seed=4321)717 input_fn_call_count = [0]718 def _model_fn(features, labels, mode, params, config):719 del params, config720 return model_fn_global_step_incrementer(features, labels, mode)721 def _input_fn(params, config):722 input_fn_call_count[0] += 1723 self.assertEqual(expected_params, params)724 self.assertEqual(4321, config.tf_random_seed)725 return dummy_input_fn()726 est = estimator.Estimator(model_fn=_model_fn,727 params=expected_params,728 config=expected_config)729 est.train(dummy_input_fn, steps=1)730 self.assertEqual(0, input_fn_call_count[0])731 est.evaluate(_input_fn, steps=1)732 self.assertEqual(1, input_fn_call_count[0])733 def test_model_fn_must_return_estimator_spec(self):734 def _model_fn(features, labels, mode):735 _, _ = features, labels736 if mode == model_fn_lib.ModeKeys.EVAL:737 return 'NotGoodNotGood'738 return model_fn_lib.EstimatorSpec(739 mode,740 loss=constant_op.constant(1.),741 train_op=state_ops.assign_add(training.get_global_step(), 1))742 est = estimator.Estimator(model_fn=_model_fn)743 est.train(dummy_input_fn, steps=1)744 with self.assertRaisesRegexp(745 ValueError, 'model_fn should return an EstimatorSpec'):746 est.evaluate(dummy_input_fn, steps=1)747 def test_no_trained_model(self):748 est = estimator.Estimator(model_fn=_model_fn_with_eval_metric_ops)749 with self.assertRaisesRegexp(750 ValueError, 'Could not find trained model in model_dir'):751 est.evaluate(dummy_input_fn, steps=1)752 def test_scores(self):753 est = estimator.Estimator(754 model_fn=_model_fn_with_eval_metric_ops,755 params={756 'metric_name': 'metric',757 'metric_value': 2.})758 est.train(dummy_input_fn, steps=5)759 scores = est.evaluate(dummy_input_fn, steps=1)760 self.assertIn('metric', scores)761 self.assertAlmostEqual(2., scores['metric'])762 def test_tuple_metrics(self):763 def _model_fn(features, labels, mode):764 del features # unused765 del labels766 return model_fn_lib.EstimatorSpec(767 mode,768 train_op=state_ops.assign_add(training.get_global_step(), 1),769 loss=constant_op.constant(1.),770 eval_metric_ops={771 'nested_metric': (772 ((constant_op.constant(2.), constant_op.constant(1)),773 constant_op.constant(3., dtype=dtypes.float64)),774 control_flow_ops.no_op())})775 est = estimator.Estimator(model_fn=_model_fn)776 est.train(dummy_input_fn, steps=1)777 evaluation = est.evaluate(dummy_input_fn, steps=1)778 ((two_float, one_integer), three_double) = evaluation['nested_metric']779 self.assertAlmostEqual(2., two_float)780 self.assertEqual(1, one_integer)781 self.assertAlmostEqual(3., three_double)782 def test_steps0_raises_error(self):783 est = estimator.Estimator(784 model_fn=_model_fn_with_eval_metric_ops)785 est.train(dummy_input_fn, steps=5)786 with self.assertRaisesRegexp(ValueError, 'Must specify steps > 0'):787 est.evaluate(dummy_input_fn, steps=0)788 def test_steps_negative_raises_error(self):789 est = estimator.Estimator(790 model_fn=_model_fn_with_eval_metric_ops)791 est.train(dummy_input_fn, steps=5)792 with self.assertRaisesRegexp(ValueError, 'Must specify steps > 0'):793 est.evaluate(dummy_input_fn, steps=-1)794 def test_global_step_metric_raises_error(self):795 est = estimator.Estimator(796 model_fn=_model_fn_with_eval_metric_ops,797 params={798 'metric_name': 'global_step',799 'metric_value': 2.})800 est.train(dummy_input_fn, steps=5)801 with self.assertRaisesRegexp(802 ValueError, 'Metric with name `global_step` is not allowed'):803 est.evaluate(dummy_input_fn, steps=1)804 def test_global_step_is_reported(self):805 est = estimator.Estimator(806 model_fn=_model_fn_with_eval_metric_ops,807 params={'metric_name': 'metric',808 'metric_value': 2.})809 est.train(dummy_input_fn, steps=5)810 scores = est.evaluate(dummy_input_fn, steps=1)811 self.assertIn('global_step', scores)812 self.assertEqual(5, scores['global_step'])813 def test_loss_metric_is_reported(self):814 def _model_fn_with_incremental_loss(features, labels, mode):815 _, _ = features, labels816 local_weight = variables.Variable(817 0., name='local_weight', collections=[ops.GraphKeys.LOCAL_VARIABLES])818 # Loss will be 2, 4, 6, ...819 loss = 2 * state_ops.assign_add(local_weight, 1.)820 return model_fn_lib.EstimatorSpec(821 mode,822 loss=loss,823 train_op=state_ops.assign_add(training.get_global_step(), 1))824 est = estimator.Estimator(model_fn=_model_fn_with_incremental_loss)825 est.train(dummy_input_fn, steps=1)826 scores = est.evaluate(dummy_input_fn, steps=5)827 self.assertIn(model_fn_lib.LOSS_METRIC_KEY, scores)828 # Average loss will be (2 + 4 + 6 + 8 + 10)/​5=6829 self.assertAlmostEqual(6., scores[model_fn_lib.LOSS_METRIC_KEY])830 def test_hooks_should_be_session_run_hook(self):831 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)832 est.train(dummy_input_fn, steps=1)833 with self.assertRaisesRegexp(TypeError, 'must be a SessionRunHook'):834 est.evaluate(dummy_input_fn, steps=5, hooks=['NotAHook'])835 def test_hooks_are_used(self):836 step_counter_hook = _StepCounterHook()837 est = estimator.Estimator(model_fn=_model_fn_with_eval_metric_ops)838 est.train(dummy_input_fn, steps=1)839 est.evaluate(dummy_input_fn, steps=5, hooks=[step_counter_hook])840 self.assertEqual(5, step_counter_hook.steps)841 def test_evaluate_from_checkpoint(self):842 params = {843 'metric_name': 'metric',844 'metric_value': 2.}845 est1 = estimator.Estimator(846 model_fn=_model_fn_with_eval_metric_ops,847 params=params)848 est1.train(dummy_input_fn, steps=5)849 est2 = estimator.Estimator(850 model_fn=_model_fn_with_eval_metric_ops,851 params=params)852 scores = est2.evaluate(853 dummy_input_fn, steps=1, checkpoint_path=est1.latest_checkpoint())854 self.assertEqual(5, scores['global_step'])855 def test_scaffold_is_used(self):856 def _model_fn_scaffold(features, labels, mode):857 _, _ = features, labels858 variables.Variable(1., name='weight')859 real_saver = saver.Saver()860 self.mock_saver = test.mock.Mock(861 wraps=real_saver, saver_def=real_saver.saver_def)862 return model_fn_lib.EstimatorSpec(863 mode=mode,864 predictions=constant_op.constant([[1.]]),865 loss=constant_op.constant(0.),866 train_op=state_ops.assign_add(training.get_global_step(), 1),867 scaffold=training.Scaffold(saver=self.mock_saver))868 est = estimator.Estimator(model_fn=_model_fn_scaffold)869 est.train(dummy_input_fn, steps=1)870 est.evaluate(dummy_input_fn, steps=1)871 self.assertTrue(self.mock_saver.restore.called)872 def test_features_labels_mode(self):873 given_features = {'test-features': [[1], [1]]}874 given_labels = {'test-labels': [[1], [1]]}875 def _input_fn():876 return given_features, given_labels877 def _model_fn(features, labels, mode):878 self.features, self.labels, self.mode = features, labels, mode879 return model_fn_lib.EstimatorSpec(880 mode=mode,881 loss=constant_op.constant(0.),882 train_op=state_ops.assign_add(training.get_global_step(), 1),883 predictions=constant_op.constant([[0.]]))884 est = estimator.Estimator(model_fn=_model_fn)885 est.train(_input_fn, steps=1)886 est.evaluate(_input_fn, steps=1)887 self.assertEqual(given_features, self.features)888 self.assertEqual(given_labels, self.labels)889 self.assertEqual(model_fn_lib.ModeKeys.EVAL, self.mode)890 def test_graph_initialization_global_step_and_random_seed(self):891 expected_random_seed = run_config.RunConfig().tf_random_seed892 def _model_fn(features, labels, mode):893 _, _, _ = features, labels, mode894 self.assertIsNotNone(training.get_global_step())895 self.assertEqual(expected_random_seed, ops.get_default_graph().seed)896 return model_fn_lib.EstimatorSpec(897 mode=mode,898 loss=constant_op.constant(0.),899 train_op=state_ops.assign_add(training.get_global_step(), 1),900 predictions=constant_op.constant([[0.]]))901 est = estimator.Estimator(model_fn=_model_fn)902 est.train(dummy_input_fn, steps=1)903 est.evaluate(dummy_input_fn, steps=1)904 def test_evaluation_hooks_are_used(self):905 hook = test.mock.MagicMock(906 wraps=training.SessionRunHook(), spec=training.SessionRunHook)907 def _model_fn_hooks(features, labels, mode):908 _, _ = features, labels909 return model_fn_lib.EstimatorSpec(910 mode=mode,911 loss=constant_op.constant(0.),912 train_op=state_ops.assign_add(training.get_global_step(), 1),913 evaluation_hooks=[hook])914 est = estimator.Estimator(model_fn=_model_fn_hooks)915 est.train(dummy_input_fn, steps=1)916 self.assertFalse(hook.begin.called)917 est.evaluate(dummy_input_fn, steps=1)918 self.assertTrue(hook.begin.called)919 def test_summary_writing_with_summary_proto(self):920 def model_fn_global_step_incrementer_image(features, labels, mode):921 _, _ = features, labels922 global_step = training.get_global_step()923 image = array_ops.zeros([1, 3, 3, 1])924 eval_metric_ops = {925 'image': (summary.image('image', image, max_outputs=1),926 constant_op.constant(1))927 }928 return model_fn_lib.EstimatorSpec(929 mode,930 loss=constant_op.constant(1.),931 train_op=state_ops.assign_add(global_step, 1),932 eval_metric_ops=eval_metric_ops)933 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer_image,934 config=run_config.RunConfig(save_summary_steps=1))935 est.train(dummy_input_fn, steps=200)936 est.evaluate(937 input_fn=dummy_input_fn,938 steps=200,939 )940 # Make sure nothing is stuck in limbo.941 writer_cache.FileWriterCache.clear()942 # Get last Event written.943 if check_eventfile_for_keyword('image', est):944 return945 self.fail('{} should be part of reported summaries.'.format('image'))946class EstimatorPredictTest(test.TestCase):947 def test_input_fn_args(self):948 expected_params = {'batch_size': 10}949 expected_config = run_config.RunConfig().replace(tf_random_seed=4321)950 input_fn_call_count = [0]951 def _model_fn(features, labels, mode, params, config):952 del features, labels, params, config953 return model_fn_lib.EstimatorSpec(954 mode,955 loss=constant_op.constant(0.),956 train_op=state_ops.assign_add(training.get_global_step(), 1),957 predictions=constant_op.constant([[10.]]))958 def _input_fn(params, config):959 input_fn_call_count[0] += 1960 self.assertEqual(expected_params, params)961 self.assertEqual(4321, config.tf_random_seed)962 return dummy_input_fn()963 est = estimator.Estimator(model_fn=_model_fn,964 params=expected_params,965 config=expected_config)966 est.train(dummy_input_fn, steps=1)967 self.assertEqual(0, input_fn_call_count[0])968 next(est.predict(_input_fn))969 self.assertEqual(1, input_fn_call_count[0])970 def test_no_trained_model_in_model_dir(self):971 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)972 with self.assertRaisesRegexp(ValueError,973 'Could not find trained model in model_dir'):974 next(est.predict(dummy_input_fn))975 def test_no_trained_model_invalid_checkpoint_path(self):976 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)977 with self.assertRaises(ValueError):978 next(979 est.predict(980 dummy_input_fn,981 checkpoint_path=saver.latest_checkpoint('fakedir')))982 def test_tensor_predictions(self):983 def _model_fn(features, labels, mode):984 _, _ = features, labels985 return model_fn_lib.EstimatorSpec(986 mode,987 loss=constant_op.constant(0.),988 train_op=state_ops.assign_add(training.get_global_step(), 1),989 predictions=constant_op.constant([[10.]]))990 est = estimator.Estimator(model_fn=_model_fn)991 est.train(dummy_input_fn, steps=1)992 self.assertEqual(10., next(est.predict(dummy_input_fn)))993 def test_warn_if_no_queue_runner(self):994 def _model_fn(features, labels, mode):995 _, _ = features, labels996 return model_fn_lib.EstimatorSpec(997 mode,998 loss=constant_op.constant(0.),999 train_op=state_ops.assign_add(training.get_global_step(), 1),1000 predictions=constant_op.constant([[10.]]))1001 est = estimator.Estimator(model_fn=_model_fn)1002 est.train(dummy_input_fn, steps=1)1003 with test.mock.patch.object(logging, 'warning') as mock_log:1004 next(est.predict(dummy_input_fn))1005 self.assertRegexpMatches(1006 str(mock_log.call_args),1007 'Input graph does not.*contain a QueueRunner.')1008 def test_skip_warn_if_dataset_returns_features(self):1009 def _model_fn(features, labels, mode):1010 _, _ = features, labels1011 return model_fn_lib.EstimatorSpec(1012 mode,1013 loss=constant_op.constant(0.),1014 train_op=state_ops.assign_add(training.get_global_step(), 1),1015 predictions=constant_op.constant([[10.]]))1016 def _input_fn():1017 it = dataset_ops.Dataset.from_tensors([1]).make_one_shot_iterator()1018 return it.get_next()1019 est = estimator.Estimator(model_fn=_model_fn)1020 est.train(dummy_input_fn, steps=1)1021 with test.mock.patch.object(logging, 'warning') as mock_log:1022 next(est.predict(_input_fn))1023 # The warning should not have keyword QueueRunner.1024 self.assertRegexpMatches(str(mock_log.call_args), '^((?!QueueRunner).)*$')1025 def test_skip_warn_if_dataset_returns_features_dict(self):1026 def _model_fn(features, labels, mode):1027 _, _ = features, labels1028 return model_fn_lib.EstimatorSpec(1029 mode,1030 loss=constant_op.constant(0.),1031 train_op=state_ops.assign_add(training.get_global_step(), 1),1032 predictions=constant_op.constant([[10.]]))1033 def _input_fn():1034 it = dataset_ops.Dataset.from_tensors([1]).make_one_shot_iterator()1035 features = {'age': it.get_next()}1036 return features1037 est = estimator.Estimator(model_fn=_model_fn)1038 est.train(dummy_input_fn, steps=1)1039 with test.mock.patch.object(logging, 'warning') as mock_log:1040 next(est.predict(_input_fn))1041 # The warning should not have keyword QueueRunner.1042 self.assertRegexpMatches(str(mock_log.call_args), '^((?!QueueRunner).)*$')1043 def test_input_fn_can_return_just_features(self):1044 def _model_fn(features, labels, mode):1045 _, _ = features, labels1046 return model_fn_lib.EstimatorSpec(1047 mode,1048 loss=constant_op.constant(0.),1049 train_op=state_ops.assign_add(training.get_global_step(), 1),1050 predictions=constant_op.constant([[10.]]))1051 est = estimator.Estimator(model_fn=_model_fn)1052 est.train(dummy_input_fn, steps=1)1053 def _only_features():1054 return {'x': constant_op.constant([[0.]])}1055 self.assertEqual([10.], next(est.predict(_only_features)))1056 def test_batch_size_mismatch(self):1057 def _model_fn(features, labels, mode):1058 _, _ = features, labels1059 return model_fn_lib.EstimatorSpec(1060 mode,1061 loss=constant_op.constant(0.),1062 train_op=state_ops.assign_add(training.get_global_step(), 1),1063 predictions={1064 'y1': constant_op.constant([[10.]]),1065 'y2': constant_op.constant([[12.], [13]])1066 })1067 est = estimator.Estimator(model_fn=_model_fn)1068 est.train(dummy_input_fn, steps=1)1069 with self.assertRaisesRegexp(ValueError,1070 'Batch length of predictions should be same'):1071 next(est.predict(dummy_input_fn))1072 def test_predict_keys_defined_for_tensor(self):1073 def _model_fn(features, labels, mode):1074 _, _ = features, labels1075 return model_fn_lib.EstimatorSpec(1076 mode,1077 loss=constant_op.constant(0.),1078 train_op=state_ops.assign_add(training.get_global_step(), 1),1079 predictions=constant_op.constant([[10.]]))1080 est = estimator.Estimator(model_fn=_model_fn)1081 est.train(dummy_input_fn, steps=1)1082 with self.assertRaisesRegexp(1083 ValueError,1084 'predict_keys argument is not valid in case of non-dict predictions'):1085 next(est.predict(dummy_input_fn, predict_keys=['y']))1086 def test_predict_keys_does_not_exists(self):1087 def _model_fn(features, labels, mode):1088 _, _ = features, labels1089 return model_fn_lib.EstimatorSpec(1090 mode,1091 loss=constant_op.constant(0.),1092 train_op=state_ops.assign_add(training.get_global_step(), 1),1093 predictions={1094 'y1': constant_op.constant([[10.]]),1095 'y2': constant_op.constant([[12.]])1096 })1097 est = estimator.Estimator(model_fn=_model_fn)1098 est.train(dummy_input_fn, steps=1)1099 with self.assertRaisesRegexp(ValueError,1100 'Expected to run at least one output from'):1101 next(est.predict(dummy_input_fn, predict_keys=['y3']))1102 def test_return_given_predict_keys(self):1103 def _model_fn(features, labels, mode):1104 _, _ = features, labels1105 return model_fn_lib.EstimatorSpec(1106 mode,1107 loss=constant_op.constant(0.),1108 train_op=state_ops.assign_add(training.get_global_step(), 1),1109 predictions={1110 'y1': constant_op.constant([[10.]]),1111 'y2': constant_op.constant([[12.]])1112 })1113 est = estimator.Estimator(model_fn=_model_fn)1114 est.train(dummy_input_fn, steps=1)1115 results = next(est.predict(dummy_input_fn, predict_keys=['y1']))1116 self.assertIn('y1', results)1117 self.assertNotIn('y2', results)1118 def test_yield_rows_of_tensor(self):1119 def _model_fn(features, labels, mode):1120 _, _ = features, labels1121 return model_fn_lib.EstimatorSpec(1122 mode,1123 loss=constant_op.constant(0.),1124 train_op=state_ops.assign_add(training.get_global_step(), 1),1125 predictions=constant_op.constant([[10.], [12.]]))1126 est = estimator.Estimator(model_fn=_model_fn)1127 est.train(dummy_input_fn, steps=1)1128 results = est.predict(dummy_input_fn)1129 self.assertEqual([10.], next(results))1130 self.assertEqual([12.], next(results))1131 def test_yield_rows_of_dict(self):1132 def _model_fn(features, labels, mode):1133 _, _ = features, labels1134 return model_fn_lib.EstimatorSpec(1135 mode,1136 loss=constant_op.constant(0.),1137 train_op=state_ops.assign_add(training.get_global_step(), 1),1138 predictions={1139 'y1': constant_op.constant([[10.], [12]]),1140 'y2': constant_op.constant([[0.], [2.]])1141 })1142 est = estimator.Estimator(model_fn=_model_fn)1143 est.train(dummy_input_fn, steps=1)1144 results = est.predict(dummy_input_fn)1145 self.assertDictEqual({'y1': [10.], 'y2': [0.]}, next(results))1146 self.assertDictEqual({'y1': [12.], 'y2': [2.]}, next(results))1147 def test_hooks_should_be_session_run_hook(self):1148 est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)1149 est.train(dummy_input_fn, steps=1)1150 with self.assertRaisesRegexp(TypeError, 'must be a SessionRunHook'):1151 next(est.predict(dummy_input_fn, hooks=['NotAHook']))1152 def test_hooks_are_used(self):1153 def _model_fn(features, labels, mode):1154 _, _ = features, labels1155 return model_fn_lib.EstimatorSpec(1156 mode,1157 loss=constant_op.constant(0.),1158 train_op=state_ops.assign_add(training.get_global_step(), 1),1159 predictions=constant_op.constant([[10.], [12.]]))1160 step_counter_hook = _StepCounterHook()1161 est = estimator.Estimator(model_fn=_model_fn)1162 est.train(dummy_input_fn, steps=1)1163 results = est.predict(dummy_input_fn, hooks=[step_counter_hook])1164 self.assertEqual(0, step_counter_hook.steps) # not called yet1165 next(results)1166 self.assertEqual(1, step_counter_hook.steps) # first call1167 next(results)1168 self.assertEqual(1, step_counter_hook.steps) # it's in same batch1169 next(results)1170 self.assertEqual(2, step_counter_hook.steps) # next batch1171 def test_predict_from_old_model_dir(self):1172 def _model_fn(features, labels, mode):1173 _, _ = features, labels1174 v = variables.Variable([[16.]], name='weight')1175 prediction = v * 21176 return model_fn_lib.EstimatorSpec(1177 mode,1178 loss=constant_op.constant(0.),1179 train_op=state_ops.assign_add(training.get_global_step(), 1),1180 predictions=prediction)1181 est1 = estimator.Estimator(model_fn=_model_fn)1182 est1.train(dummy_input_fn, steps=1)1183 est2 = estimator.Estimator(model_fn=_model_fn, model_dir=est1.model_dir)1184 self.assertEqual([32.], next(est2.predict(dummy_input_fn)))1185 def test_predict_from_checkpoint_path(self):1186 def _model_fn(features, labels, mode):1187 _, _ = features, labels1188 v = variables.Variable([[16.]], name='weight')1189 prediction = v * 21190 return model_fn_lib.EstimatorSpec(1191 mode,1192 loss=constant_op.constant(0.),1193 train_op=state_ops.assign_add(training.get_global_step(), 1),1194 predictions=prediction)1195 est1 = estimator.Estimator(model_fn=_model_fn)1196 est1.train(dummy_input_fn, steps=1)1197 est2 = estimator.Estimator(model_fn=_model_fn, model_dir=est1.model_dir)1198 self.assertEqual([32.],1199 next(1200 est2.predict(1201 dummy_input_fn,1202 checkpoint_path=est2.latest_checkpoint())))1203 def test_scaffold_is_used(self):1204 def _model_fn_scaffold(features, labels, mode):1205 _, _ = features, labels1206 variables.Variable(1., name='weight')1207 real_saver = saver.Saver()1208 self.mock_saver = test.mock.Mock(1209 wraps=real_saver, saver_def=real_saver.saver_def)1210 return model_fn_lib.EstimatorSpec(1211 mode=mode,1212 predictions=constant_op.constant([[1.]]),1213 loss=constant_op.constant(0.),1214 train_op=state_ops.assign_add(training.get_global_step(), 1),1215 scaffold=training.Scaffold(saver=self.mock_saver))1216 est = estimator.Estimator(model_fn=_model_fn_scaffold)1217 est.train(dummy_input_fn, steps=1)1218 next(est.predict(dummy_input_fn))1219 self.assertTrue(self.mock_saver.restore.called)1220 def test_features_labels_mode(self):1221 given_features = {'test-features': [[1], [1]]}1222 given_labels = {'test-labels': [[1], [1]]}1223 def _input_fn():1224 return given_features, given_labels1225 def _model_fn(features, labels, mode):1226 self.features, self.labels, self.mode = features, labels, mode1227 return model_fn_lib.EstimatorSpec(1228 mode=mode,1229 loss=constant_op.constant(0.),1230 train_op=state_ops.assign_add(training.get_global_step(), 1),1231 predictions=constant_op.constant([[0.]]))1232 est = estimator.Estimator(model_fn=_model_fn)1233 est.train(_input_fn, steps=1)1234 next(est.predict(_input_fn))1235 self.assertEqual(given_features, self.features)1236 self.assertIsNone(self.labels)1237 self.assertEqual(model_fn_lib.ModeKeys.PREDICT, self.mode)1238 def test_graph_initialization_global_step_and_random_seed(self):1239 expected_random_seed = run_config.RunConfig().tf_random_seed1240 def _model_fn(features, labels, mode):1241 _, _, _ = features, labels, mode1242 self.assertIsNotNone(training.get_global_step())1243 self.assertEqual(expected_random_seed, ops.get_default_graph().seed)1244 return model_fn_lib.EstimatorSpec(1245 mode=mode,1246 loss=constant_op.constant(0.),1247 train_op=state_ops.assign_add(training.get_global_step(), 1),1248 predictions=constant_op.constant([[0.]]))1249 est = estimator.Estimator(model_fn=_model_fn)1250 est.train(dummy_input_fn, steps=1)1251 next(est.predict(dummy_input_fn))1252def _model_fn_for_export_tests(features, labels, mode):1253 _, _ = features, labels1254 variables.Variable(1., name='weight')1255 scores = constant_op.constant([3.])1256 classes = constant_op.constant(['wumpus'])1257 update_global_step = state_ops.assign_add(training.get_global_step(), 1)1258 with ops.control_dependencies([update_global_step]):1259 train_op = constant_op.constant(2.)1260 return model_fn_lib.EstimatorSpec(1261 mode,1262 predictions=constant_op.constant(10.),1263 loss=constant_op.constant(1.),1264 train_op=train_op,1265 export_outputs={1266 'test': export_output.ClassificationOutput(scores, classes)})1267def _model_fn_with_saveables_for_export_tests(features, labels, mode):1268 _, _ = features, labels1269 table = saver_test_utils.CheckpointedOp(name='v2')1270 update_global_step = state_ops.assign_add(training.get_global_step(), 1)1271 with ops.control_dependencies([update_global_step]):1272 train_op = table.insert('k1', 30.0)1273 prediction = table.lookup('k1', 0.0)1274 return model_fn_lib.EstimatorSpec(1275 mode,1276 predictions=prediction,1277 loss=constant_op.constant(1.),1278 train_op=train_op,1279 export_outputs={1280 'test': export_output.PredictOutput({'prediction': prediction})})1281_VOCAB_FILE_CONTENT = 'emerson\nlake\npalmer\n'1282_EXTRA_FILE_CONTENT = 'kermit\npiggy\nralph\n'1283class EstimatorExportTest(test.TestCase):1284 def test_export_savedmodel_proto_roundtrip(self):1285 tmpdir = tempfile.mkdtemp()1286 est = estimator.Estimator(model_fn=_model_fn_for_export_tests)1287 est.train(input_fn=dummy_input_fn, steps=1)1288 feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),1289 'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}1290 serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(1291 feature_spec)1292 # Perform the export.1293 export_dir_base = os.path.join(1294 compat.as_bytes(tmpdir), compat.as_bytes('export'))1295 export_dir = est.export_savedmodel(1296 export_dir_base, serving_input_receiver_fn)1297 # Check that all the files are in the right places.1298 self.assertTrue(gfile.Exists(export_dir_base))1299 self.assertTrue(gfile.Exists(export_dir))1300 self.assertTrue(gfile.Exists(os.path.join(1301 compat.as_bytes(export_dir),1302 compat.as_bytes('saved_model.pb'))))1303 self.assertTrue(gfile.Exists(os.path.join(1304 compat.as_bytes(export_dir),1305 compat.as_bytes('variables'))))1306 self.assertTrue(gfile.Exists(os.path.join(1307 compat.as_bytes(export_dir),1308 compat.as_bytes('variables/​variables.index'))))1309 self.assertTrue(gfile.Exists(os.path.join(1310 compat.as_bytes(export_dir),1311 compat.as_bytes('variables/​variables.data-00000-of-00001'))))1312 # Restore, to validate that the export was well-formed.1313 with ops.Graph().as_default() as graph:1314 with session.Session(graph=graph) as sess:1315 loader.load(sess, [tag_constants.SERVING], export_dir)1316 graph_ops = [x.name for x in graph.get_operations()]1317 self.assertTrue('input_example_tensor' in graph_ops)1318 self.assertTrue('ParseExample/​ParseExample' in graph_ops)1319 self.assertTrue('weight' in graph_ops)1320 # Clean up.1321 gfile.DeleteRecursively(tmpdir)1322 def test_export_savedmodel_with_saveables_proto_roundtrip(self):1323 tmpdir = tempfile.mkdtemp()1324 est = estimator.Estimator(1325 model_fn=_model_fn_with_saveables_for_export_tests)1326 est.train(input_fn=dummy_input_fn, steps=1)1327 feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),1328 'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}1329 serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(1330 feature_spec)1331 # Perform the export.1332 export_dir_base = os.path.join(1333 compat.as_bytes(tmpdir), compat.as_bytes('export'))1334 export_dir = est.export_savedmodel(1335 export_dir_base, serving_input_receiver_fn)1336 # Check that all the files are in the right places.1337 self.assertTrue(gfile.Exists(export_dir_base))1338 self.assertTrue(gfile.Exists(export_dir))1339 self.assertTrue(gfile.Exists(os.path.join(1340 compat.as_bytes(export_dir),1341 compat.as_bytes('saved_model.pb'))))1342 self.assertTrue(gfile.Exists(os.path.join(1343 compat.as_bytes(export_dir),1344 compat.as_bytes('variables'))))1345 self.assertTrue(gfile.Exists(os.path.join(1346 compat.as_bytes(export_dir),1347 compat.as_bytes('variables/​variables.index'))))1348 self.assertTrue(gfile.Exists(os.path.join(1349 compat.as_bytes(export_dir),1350 compat.as_bytes('variables/​variables.data-00000-of-00001'))))1351 # Restore, to validate that the export was well-formed.1352 with ops.Graph().as_default() as graph:1353 with session.Session(graph=graph) as sess:1354 loader.load(sess, [tag_constants.SERVING], export_dir)1355 graph_ops = [x.name for x in graph.get_operations()]1356 self.assertTrue('input_example_tensor' in graph_ops)1357 self.assertTrue('ParseExample/​ParseExample' in graph_ops)1358 # Note that the SavedModel builder replaced the Saver with a new one1359 self.assertTrue('save_1/​LookupTableImportV2' in graph_ops)1360 # Clean up.1361 gfile.DeleteRecursively(tmpdir)1362 def test_export_savedmodel_assets(self):1363 tmpdir = tempfile.mkdtemp()1364 est = estimator.Estimator(model_fn=_model_fn_for_export_tests)1365 est.train(input_fn=dummy_input_fn, steps=1)1366 feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),1367 'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}1368 serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(1369 feature_spec)1370 # Create a fake asset.1371 vocab_file_name = os.path.join(1372 compat.as_bytes(tmpdir), compat.as_bytes('my_vocab_file'))1373 vocab_file = gfile.GFile(vocab_file_name, mode='w')1374 vocab_file.write(_VOCAB_FILE_CONTENT)1375 vocab_file.close()1376 # hack in an op that uses the asset, in order to test asset export.1377 # this is not actually valid, of course.1378 def serving_input_receiver_with_asset_fn():1379 features, receiver_tensor, _ = serving_input_receiver_fn()1380 filename = ops.convert_to_tensor(vocab_file_name,1381 dtypes.string,1382 name='asset_filepath')1383 ops.add_to_collection(ops.GraphKeys.ASSET_FILEPATHS, filename)1384 features['bogus_filename'] = filename1385 return export.ServingInputReceiver(features, receiver_tensor)1386 # Perform the export.1387 export_dir_base = os.path.join(1388 compat.as_bytes(tmpdir), compat.as_bytes('export'))1389 export_dir = est.export_savedmodel(1390 export_dir_base, serving_input_receiver_with_asset_fn)1391 # Check that the asset files are in the right places.1392 expected_vocab_file_name = os.path.join(1393 compat.as_bytes(export_dir), compat.as_bytes('assets/​my_vocab_file'))1394 self.assertTrue(gfile.Exists(os.path.join(1395 compat.as_bytes(export_dir), compat.as_bytes('assets'))))1396 self.assertTrue(gfile.Exists(expected_vocab_file_name))1397 self.assertEqual(1398 compat.as_bytes(_VOCAB_FILE_CONTENT),1399 compat.as_bytes(gfile.GFile(expected_vocab_file_name).read()))1400 # Restore, to validate that the export was well-formed.1401 with ops.Graph().as_default() as graph:1402 with session.Session(graph=graph) as sess:1403 loader.load(sess, [tag_constants.SERVING], export_dir)1404 assets = [1405 x.eval()1406 for x in graph.get_collection(ops.GraphKeys.ASSET_FILEPATHS)1407 ]1408 self.assertItemsEqual([vocab_file_name], assets)1409 graph_ops = [x.name for x in graph.get_operations()]1410 self.assertTrue('input_example_tensor' in graph_ops)1411 self.assertTrue('ParseExample/​ParseExample' in graph_ops)1412 self.assertTrue('asset_filepath' in graph_ops)1413 self.assertTrue('weight' in graph_ops)1414 # cleanup1415 gfile.DeleteRecursively(tmpdir)1416 def test_export_savedmodel_extra_assets(self):1417 tmpdir = tempfile.mkdtemp()1418 est = estimator.Estimator(model_fn=_model_fn_for_export_tests)1419 est.train(input_fn=dummy_input_fn, steps=1)1420 feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),1421 'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}1422 serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(1423 feature_spec)1424 # Create a fake asset.1425 extra_file_name = os.path.join(1426 compat.as_bytes(tmpdir), compat.as_bytes('my_extra_file'))1427 extra_file = gfile.GFile(extra_file_name, mode='w')1428 extra_file.write(_EXTRA_FILE_CONTENT)1429 extra_file.close()1430 # Perform the export.1431 assets_extra = {'some/​sub/​directory/​my_extra_file': extra_file_name}1432 export_dir_base = os.path.join(1433 compat.as_bytes(tmpdir), compat.as_bytes('export'))1434 export_dir = est.export_savedmodel(export_dir_base,1435 serving_input_receiver_fn,1436 assets_extra=assets_extra)1437 # Check that the asset files are in the right places.1438 expected_extra_path = os.path.join(1439 compat.as_bytes(export_dir),1440 compat.as_bytes('assets.extra/​some/​sub/​directory/​my_extra_file'))1441 self.assertTrue(gfile.Exists(os.path.join(1442 compat.as_bytes(export_dir), compat.as_bytes('assets.extra'))))1443 self.assertTrue(gfile.Exists(expected_extra_path))1444 self.assertEqual(1445 compat.as_bytes(_EXTRA_FILE_CONTENT),1446 compat.as_bytes(gfile.GFile(expected_extra_path).read()))1447 # cleanup1448 gfile.DeleteRecursively(tmpdir)1449 def test_scaffold_is_used_for_saver(self):1450 tmpdir = tempfile.mkdtemp()1451 def _model_fn_scaffold(features, labels, mode):1452 _, _ = features, labels1453 variables.Variable(1., name='weight')1454 real_saver = saver.Saver()1455 self.mock_saver = test.mock.Mock(1456 wraps=real_saver, saver_def=real_saver.saver_def)1457 scores = constant_op.constant([3.])1458 return model_fn_lib.EstimatorSpec(1459 mode=mode,1460 predictions=constant_op.constant([[1.]]),1461 loss=constant_op.constant(0.),1462 train_op=state_ops.assign_add(training.get_global_step(), 1),1463 scaffold=training.Scaffold(saver=self.mock_saver),1464 export_outputs={'test': export_output.ClassificationOutput(scores)})1465 est = estimator.Estimator(model_fn=_model_fn_scaffold)1466 est.train(dummy_input_fn, steps=1)1467 feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),1468 'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}1469 serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(1470 feature_spec)1471 # Perform the export.1472 export_dir_base = os.path.join(1473 compat.as_bytes(tmpdir), compat.as_bytes('export'))1474 est.export_savedmodel(export_dir_base, serving_input_receiver_fn)1475 self.assertTrue(self.mock_saver.restore.called)1476 def test_scaffold_is_used_for_local_init(self):1477 tmpdir = tempfile.mkdtemp()1478 def _model_fn_scaffold(features, labels, mode):1479 _, _ = features, labels1480 my_int = variables.Variable(1, name='my_int',1481 collections=[ops.GraphKeys.LOCAL_VARIABLES])1482 scores = constant_op.constant([3.])1483 with ops.control_dependencies([1484 variables.local_variables_initializer(),1485 lookup_ops.tables_initializer()1486 ]):1487 assign_op = state_ops.assign(my_int, 12345)1488 # local_initSop must be an Operation, not a Tensor.1489 custom_local_init_op = control_flow_ops.group(assign_op)1490 return model_fn_lib.EstimatorSpec(1491 mode=mode,1492 predictions=constant_op.constant([[1.]]),1493 loss=constant_op.constant(0.),1494 train_op=state_ops.assign_add(training.get_global_step(), 1),1495 scaffold=training.Scaffold(local_init_op=custom_local_init_op),1496 export_outputs={'test': export_output.ClassificationOutput(scores)})1497 est = estimator.Estimator(model_fn=_model_fn_scaffold)1498 est.train(dummy_input_fn, steps=1)1499 feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),1500 'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}1501 serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(1502 feature_spec)1503 # Perform the export.1504 export_dir_base = os.path.join(1505 compat.as_bytes(tmpdir), compat.as_bytes('export'))1506 export_dir = est.export_savedmodel(export_dir_base,1507 serving_input_receiver_fn)1508 # Restore, to validate that the custom local_init_op runs.1509 with ops.Graph().as_default() as graph:1510 with session.Session(graph=graph) as sess:1511 loader.load(sess, [tag_constants.SERVING], export_dir)1512 my_int = graph.get_tensor_by_name('my_int:0')1513 my_int_value = sess.run(my_int)1514 self.assertEqual(12345, my_int_value)1515 def test_features_labels_mode(self):1516 given_features = {'test-features': constant_op.constant([[1], [1]])}1517 def serving_input_receiver_fn():1518 return export.ServingInputReceiver(1519 given_features, array_ops.placeholder(dtype=dtypes.string))1520 def _model_fn(features, labels, mode):1521 self.features, self.labels, self.mode = features, labels, mode1522 return model_fn_lib.EstimatorSpec(1523 mode=mode,1524 loss=constant_op.constant(0.),1525 train_op=state_ops.assign_add(training.get_global_step(), 1),1526 predictions=constant_op.constant([[0.]]),1527 export_outputs={1528 'test': export_output.ClassificationOutput(1529 constant_op.constant([[0.]]))1530 })1531 est = estimator.Estimator(model_fn=_model_fn)1532 est.train(dummy_input_fn, steps=1)1533 est.export_savedmodel(tempfile.mkdtemp(), serving_input_receiver_fn)1534 self.assertEqual(given_features, self.features)1535 self.assertIsNone(self.labels)1536 self.assertEqual(model_fn_lib.ModeKeys.PREDICT, self.mode)1537 def test_graph_initialization_global_step_and_random_seed(self):1538 expected_random_seed = run_config.RunConfig().tf_random_seed1539 def _model_fn(features, labels, mode):1540 _, _, _ = features, labels, mode1541 self.assertIsNotNone(training.get_global_step())1542 self.assertEqual(expected_random_seed, ops.get_default_graph().seed)1543 return model_fn_lib.EstimatorSpec(1544 mode=mode,1545 loss=constant_op.constant(0.),1546 train_op=state_ops.assign_add(training.get_global_step(), 1),1547 predictions=constant_op.constant([[0.]]),1548 export_outputs={1549 'test': export_output.ClassificationOutput(1550 constant_op.constant([[0.]]))1551 })1552 def serving_input_receiver_fn():1553 return export.ServingInputReceiver(1554 {'test-features': constant_op.constant([[1], [1]])},1555 array_ops.placeholder(dtype=dtypes.string))1556 est = estimator.Estimator(model_fn=_model_fn)1557 est.train(dummy_input_fn, steps=1)1558 est.export_savedmodel(tempfile.mkdtemp(), serving_input_receiver_fn)1559class EstimatorHookOrderingTest(test.TestCase):1560 def testCustomHooksAreCalledBeforeNanTensorHook(self):1561 def nan_making_model_fn(mode, features, labels):1562 """A graph that generates NaN's for testing."""1563 del features, labels1564 global_step = variables.Variable(1565 0, dtype=dtypes.int64, name='global_step')1566 inc_global_step = state_ops.assign_add(global_step, 1)1567 nan_const = constant_op.constant(np.nan, dtype=dtypes.float32)1568 loss = control_flow_ops.cond(1569 inc_global_step > 1, lambda: nan_const, lambda: 1.0)1570 return model_fn_lib.EstimatorSpec(1571 mode=mode,1572 predictions=global_step.read_value(),1573 loss=loss,1574 train_op=inc_global_step)1575 def empty_input_fn():1576 return dict(), None1577 class AfterRunCountingHook(session_run_hook.SessionRunHook):1578 """Hooks that counts the number of times after_run() is called."""1579 def __init__(self):1580 self.after_run_count = 01581 def after_run(self, run_context, run_values):1582 del run_context, run_values1583 self.after_run_count += 11584 test_hook = AfterRunCountingHook()1585 est = estimator.Estimator(model_fn=nan_making_model_fn)1586 with self.assertRaises(basic_session_run_hooks.NanLossDuringTrainingError):1587 est.train(input_fn=empty_input_fn, steps=2, hooks=[test_hook])1588 self.assertEqual(2, test_hook.after_run_count)1589class EstimatorIntegrationTest(test.TestCase):1590 def test_complete_flow_with_a_simple_linear_model(self):1591 def _model_fn(features, labels, mode):1592 predictions = layers.dense(1593 features['x'], 1, kernel_initializer=init_ops.zeros_initializer())1594 export_outputs = {1595 'predictions': export_output.RegressionOutput(predictions)1596 }1597 if mode == model_fn_lib.ModeKeys.PREDICT:1598 return model_fn_lib.EstimatorSpec(1599 mode, predictions=predictions, export_outputs=export_outputs)1600 loss = losses.mean_squared_error(labels, predictions)1601 train_op = training.GradientDescentOptimizer(learning_rate=0.5).minimize(1602 loss, training.get_global_step())1603 eval_metric_ops = {1604 'absolute_error': metrics_lib.mean_absolute_error(1605 labels, predictions)1606 }1607 return model_fn_lib.EstimatorSpec(1608 mode,1609 predictions=predictions,1610 loss=loss,1611 train_op=train_op,1612 eval_metric_ops=eval_metric_ops,1613 export_outputs=export_outputs)1614 est = estimator.Estimator(model_fn=_model_fn)1615 data = np.linspace(0., 1., 100, dtype=np.float32).reshape(-1, 1)1616 # TRAIN1617 # learn y = x1618 train_input_fn = numpy_io.numpy_input_fn(1619 x={'x': data}, y=data, batch_size=50, num_epochs=None, shuffle=True)1620 est.train(train_input_fn, steps=200)1621 # EVALUTE1622 eval_input_fn = numpy_io.numpy_input_fn(1623 x={'x': data}, y=data, batch_size=50, num_epochs=1, shuffle=True)1624 scores = est.evaluate(eval_input_fn)1625 self.assertEqual(200, scores['global_step'])1626 self.assertGreater(0.1, scores['absolute_error'])1627 # PREDICT1628 predict_input_fn = numpy_io.numpy_input_fn(1629 x={'x': data}, y=None, batch_size=10, num_epochs=1, shuffle=False)1630 predictions = list(est.predict(predict_input_fn))1631 self.assertAllClose(data, predictions, atol=0.01)1632 # EXPORT1633 feature_spec = {'x': parsing_ops.FixedLenFeature([1], dtypes.float32)}1634 serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(1635 feature_spec)1636 export_dir = est.export_savedmodel(tempfile.mkdtemp(),1637 serving_input_receiver_fn)1638 self.assertTrue(gfile.Exists(export_dir))1639if __name__ == '__main__':...

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1/​/​ flow-typed signature: 689311caccb0db4742bdff80086a5a092/​/​ flow-typed version: c6154227d1/​redux-saga_v1.x.x/​flow_>=v0.104.x3declare module "redux-saga" {4 /​/​ These types are copied directly from the redux libdef.5 /​/​ Importing them in this libdef causes a loss in type coverage.6 /​/​ * uncomment next line:7 /​/​ import type { Middleware} from 'redux';8 /​/​ * remove next types9 declare type DispatchAPI<A> = (action: A) => A;10 declare type Dispatch<A: { type: string, ... }> = DispatchAPI<A>;11 declare type MiddlewareAPI<S, A, D = Dispatch<A>> = {12 dispatch: D,13 getState(): S,14 ...15 };16 declare type Middleware<S, A, D = Dispatch<A>> = (api: MiddlewareAPI<S, A, D>) => (next: D) => D;17 /​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​/​18 declare export var SAGA_LOCATION: "@@redux-saga/​LOCATION";19 declare export var CANCEL: "@@redux-saga/​CANCEL_PROMISE";20 declare export type TEnd = {| +type: "@@redux-saga/​CHANNEL_END" |};21 declare export var END: TEnd;22 declare export var isEnd: {23 (input: TEnd): true,24 (input: mixed): false,25 ...26 };27 declare export type Predicate<T> = (arg: T) => boolean;28 declare export type MulticastChannel<T> = $ReadOnly<{|29 take(cb: (message: T | TEnd) => void, matcher?: Predicate<T>): void,30 put(message: T | TEnd): void,31 close(): void32 |}>;33 declare export interface Buffer<T> {34 isEmpty(): boolean;35 put(message: T): void;36 take(): T | void;37 flush(): Array<T>;38 }39 declare export interface Task<RT> {40 isRunning: () => boolean;41 isCancelled: () => boolean;42 result: () => RT | void;43 error: () => Error | void;44 cancel: () => void;45 toPromise(): Promise<RT>;46 setContext<C: {...}>(props: $Shape<C>): void;47 }48 declare export interface SagaMonitor {49 effectTriggered?: (desc: {50 +effectId: number,51 +parentEffectId: number,52 +label: string,53 +root?: boolean,54 +effect: Effect,55 ...56 }) => void;57 effectResolved?: (effectId: number, result: mixed) => void;58 effectRejected?: (effectId: number, error: any) => void;59 effectCancelled?: (effectId: number) => void;60 actionDispatched?: <A>(action: A) => void;61 }62 declare export type Saga<T> = Generator<Effect, T, any>;63 declare export type Unsubscribe = () => void;64 declare export type Subscribe<T> = (cb: (input: T | TEnd) => void) => Unsubscribe;65 declare export interface TakeableChannel<T> {66 take(cb: (message: T | TEnd) => void): void67 }68 declare export interface PuttableChannel<T> {69 put(message: T | TEnd): void70 }71 declare export interface FlushableChannel<T> {72 flush(cb: (items: Array<T> | TEnd) => void): void73 }74 declare export interface EventChannel<T> {75 take(cb: (message: T | TEnd) => void): void;76 flush(cb: (items: Array<T> | TEnd) => void): void;77 close(): void;78 }79 declare export var eventChannel: <T>(80 subscribe: Subscribe<T>,81 buffer?: Buffer<T>82 ) => EventChannel<T>;83 declare export interface Channel<T> {84 take(cb: (message: T | TEnd) => void): void;85 put(message: T | TEnd): void;86 flush(cb: (items: Array<T> | TEnd) => void): void;87 close(): void;88 }89 declare export function channel<T>(buffer?: Buffer<T>): Channel<T>;90 declare export var buffers: $ReadOnly<{|91 none: <T>() => Buffer<T>,92 fixed: <T>(limit?: number) => Buffer<T>,93 dropping: <T>(limit?: number) => Buffer<T>,94 sliding: <T>(limit?: number) => Buffer<T>,95 expanding: <T>(initialSize?: number) => Buffer<T>96 |}>;97 declare export function multicastChannel<T>(): MulticastChannel<T>;98 declare export function stdChannel<T>(): MulticastChannel<T>;99 declare export type Logger = (level: "info" | "warning" | "error", ...args: Array<any>) => void;100 declare export type EffectMiddleware = (next: (effect: mixed) => void) => (effect: mixed) => void;101 declare export interface PredicateTakeableChannel<T> {102 take(cb: (message: T | TEnd) => void, matcher?: Predicate<T>): void;103 }104 declare type RunSagaOptions<A, S> = {|105 channel?: PredicateTakeableChannel<A>,106 dispatch?: (input: A) => mixed,107 getState?: () => S,108 context?: {...},109 sagaMonitor?: SagaMonitor,110 logger?: Logger,111 effectMiddlewares?: Array<EffectMiddleware>,112 onError?: (error: Error) => void113 |};114 declare export var runSaga: {115 <A, S, R, Fn: () => Saga<R>>(options: RunSagaOptions<A, S>, saga: Fn): Task<R>,116 <A, S, R, T1, Fn: T1 => Saga<R>>(options: RunSagaOptions<A, S>, saga: Fn, T1): Task<R>,117 <A, S, R, T1, T2, Fn: (T1, T2) => Saga<R>>(118 options: RunSagaOptions<A, S>,119 saga: Fn,120 T1,121 T2122 ): Task<R>,123 <A, S, R, T1, T2, T3, Fn: (T1, T2, T3) => Saga<R>>(124 options: RunSagaOptions<A, S>,125 saga: Fn,126 T1,127 T2,128 T3129 ): Task<R>,130 <A, S, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => Saga<R>>(131 options: RunSagaOptions<A, S>,132 saga: Fn,133 T1,134 T2,135 T3,136 T4137 ): Task<R>,138 <A, S, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => Saga<R>>(139 options: RunSagaOptions<A, S>,140 saga: Fn,141 T1,142 T2,143 T3,144 T4,145 T5146 ): Task<R>,147 <A, S, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => Saga<R>>(148 options: RunSagaOptions<A, S>,149 saga: Fn,150 T1,151 T2,152 T3,153 T4,154 T5,155 T6156 ): Task<R>,157 <A, S, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => Saga<R>>(158 options: RunSagaOptions<A, S>,159 saga: Fn,160 T1,161 T2,162 T3,163 T4,164 T5,165 T6,166 T7167 ): Task<R>,168 <A, S, R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => Saga<R>>(169 options: RunSagaOptions<A, S>,170 saga: Fn,171 T1,172 T2,173 T3,174 T4,175 T5,176 T6,177 T7,178 T8179 ): Task<R>,180 ...181 };182 declare export type SagaMiddleware<C: {...}> =183 {184 <S, A, D>(api: MiddlewareAPI<S, A, D>): (next: D) => D,185 run: {186 <R, Fn: () => Saga<R>>(saga: Fn): Task<R>,187 <R, T1, Fn: T1 => Saga<R>>(saga: Fn, T1): Task<R>,188 <R, T1, T2, Fn: (T1, T2) => Saga<R>>(saga: Fn, T1, T2): Task<R>,189 <R, T1, T2, T3, Fn: (T1, T2, T3) => Saga<R>>(saga: Fn, T1, T2, T3): Task<R>,190 <R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => Saga<R>>(saga: Fn, T1, T2, T3, T4): Task<R>,191 <R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => Saga<R>>(192 saga: Fn,193 T1,194 T2,195 T3,196 T4,197 T5198 ): Task<R>,199 <R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => Saga<R>>(200 saga: Fn,201 T1,202 T2,203 T3,204 T4,205 T5,206 T6207 ): Task<R>,208 <R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => Saga<R>>(209 saga: Fn,210 T1,211 T2,212 T3,213 T4,214 T5,215 T6,216 T7217 ): Task<R>,218 <R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => Saga<R>>(219 saga: Fn,220 T1,221 T2,222 T3,223 T4,224 T5,225 T6,226 T7,227 T8228 ): Task<R>,229 ...230 },231 setContext: (props: $Shape<C>) => void,232 ...233 };234 declare export type Emit<T> = (input: T) => void;235 declare export type SagaMiddlewareOptions<C: {...}> = {|236 context?: C,237 sagaMonitor?: SagaMonitor,238 logger?: Logger,239 effectMiddlewares?: Array<EffectMiddleware>,240 emitter?: <A>(emit: Emit<A>) => Emit<any>,241 onError?: (error: Error) => void242 |};243 declare function sagaMiddlewareFactory<C>(options?: SagaMiddlewareOptions<C>): SagaMiddleware<C>;244 declare export default typeof sagaMiddlewareFactory;245 /​/​ Effect types246 declare export type SubPattern = string | (any => boolean);247 declare export type Pattern = SubPattern | Array<SubPattern>;248 declare export interface IEffect<T, P, C: boolean> {249 +type: T;250 +payload: P;251 +combinator: C;252 }253 declare export type AllTakeEffect<254 M: { maybe: true, ... } | void255 > = IEffect<256 "TAKE",257 $ReadOnly<{|258 pattern: '*',259 ...$Exact<M>260 |}>,261 false262 >;263 declare export type TakeEffect<264 P: { pattern: Pattern, ... } | void,265 C: { channel: TakeableChannel<*>, ... } | void,266 M: { maybe: true, ... } | void267 > = IEffect<268 "TAKE",269 $ReadOnly<{|270 ...$Exact<P>,271 ...$Exact<C>,272 ...$Exact<M>273 |}>,274 false275 >;276 declare export type PutEffect<277 A: {...},278 C: PuttableChannel<*> | null,279 R: { resolve: true, ... } | void280 > = IEffect<281 "PUT",282 $ReadOnly<{|283 action: A,284 channel: C,285 ...$Exact<R>286 |}>,287 false288 >;289 declare export type CallEffect<Ctx, Fn: Function | string, Args: $ReadOnlyArray<*>> = IEffect<290 "CALL",291 $ReadOnly<{|292 context: Ctx,293 fn: Fn,294 args: Args295 |}>,296 false297 >;298 declare export type ForkEffect<299 Ctx,300 Fn: (...args: Array<*>) => *,301 D: { detached: true, ... } | void,302 Args: $ReadOnlyArray<*>303 > = IEffect<304 "FORK",305 $ReadOnly<{|306 context: Ctx,307 fn: Fn,308 args: Args,309 ...$Exact<D>310 |}>,311 false312 >;313 declare export function detach<T1, T2, T3>(314 effect: ForkEffect<T1, T2, *, T3>315 ): ForkEffect<T1, T2, { detached: true, ... }, T3>;316 declare export type CpsEffect<Ctx, Fn: (...args: Array<*>) => *, Args: $ReadOnlyArray<*>> = IEffect<317 "CPS",318 $ReadOnly<{|319 context: Ctx,320 fn: Fn,321 args: Args322 |}>,323 false324 >;325 declare export type JoinEffect<T: Task<*> | Array<Task<*>>> = IEffect<"JOIN", T, false>;326 declare export type SELF_CANCELLATION = "@@redux-saga/​SELF_CANCELLATION";327 declare export type CancelEffect<328 T: Task<*> | $ReadOnlyArray<Task<*>> | SELF_CANCELLATION329 > = IEffect<"CANCEL", T, false>;330 declare export type SelectEffect<Fn: Function | void, Args: $ReadOnlyArray<*>> = IEffect<331 "SELECT",332 $ReadOnly<{|333 selector: Fn,334 args: Args335 |}>,336 false337 >;338 declare export type ActionChannelEffect<T, P: Pattern | void, B: Buffer<T> | void> = IEffect<339 "ACTION_CHANNEL",340 $ReadOnly<{|341 buffer: B,342 pattern: P343 |}>,344 false345 >;346 declare export type FlushEffect<CH: FlushableChannel<*>> = IEffect<"FLUSH", CH, false>;347 declare export type CancelledEffect = IEffect<"CANCELLED", {||}, false>;348 declare export type SetContextEffect<T: {...}> = IEffect<"SET_CONTEXT", T, false>;349 declare export type GetContextEffect<T> = IEffect<"GET_CONTEXT", T, false>;350 declare export type RaceEffect<R: { +[name: string]: Effect, ... } | $ReadOnlyArray<Effect>> = IEffect<351 "RACE",352 R,353 true354 >;355 declare export type AllEffect = IEffect<356 "ALL",357 { +[name: string]: Effect, ... } | $ReadOnlyArray<Effect>,358 true359 >;360 declare export type Effect =361 | ActionChannelEffect<*, *, *>362 | AllEffect363 | CallEffect<*, *, *>364 | CancelEffect<*>365 | CancelledEffect366 | CpsEffect<*, *, *>367 | FlushEffect<*>368 | ForkEffect<*, *, *, *>369 | GetContextEffect<*>370 | JoinEffect<*>371 | PutEffect<*, *, *>372 | RaceEffect<*>373 | SelectEffect<*, *>374 | SetContextEffect<*>375 | TakeEffect<*, *, *>376 | AllTakeEffect<*>;377}378declare module "redux-saga/​effects" {379 import type {380 ActionChannelEffect,381 AllEffect,382 Buffer,383 CallEffect,384 CancelEffect,385 CancelledEffect,386 Channel,387 CpsEffect,388 Effect,389 FlushEffect,390 ForkEffect,391 GetContextEffect,392 JoinEffect,393 Pattern,394 PutEffect,395 RaceEffect,396 Saga,397 SelectEffect,398 SetContextEffect,399 TakeEffect,400 Task,401 TakeableChannel,402 PuttableChannel,403 FlushableChannel,404 AllTakeEffect,405 } from "redux-saga";406 declare export var effectTypes: $ReadOnly<{|407 TAKE: 'TAKE',408 PUT: 'PUT',409 ALL: 'ALL',410 RACE: 'RACE',411 CALL: 'CALL',412 CPS: 'CPS',413 FORK: 'FORK',414 JOIN: 'JOIN',415 CANCEL: 'CANCEL',416 SELECT: 'SELECT',417 ACTION_CHANNEL: 'ACTION_CHANNEL',418 CANCELLED: 'CANCELLED',419 FLUSH: 'FLUSH',420 GET_CONTEXT: 'GET_CONTEXT',421 SET_CONTEXT: 'SET_CONTEXT'422 |}>;423 declare export var put: {424 <A: {...}>(action: A): PutEffect<A, null, void>,425 <A: {...}, T, CH: PuttableChannel<T>>(channel: CH, action: A): PutEffect<A, CH, void>,426 ...427 };428 declare export var putResolve: {429 <A: {...}>(action: A): PutEffect<A, null, { resolve: true, ... }>,430 <A: {...}, T, CH: PuttableChannel<T>>(channel: CH, action: A): PutEffect<A, CH, { resolve: true, ... }>,431 ...432 };433 declare export var call: {434 /​/​ call(fn, ...args)435 <R, Fn: () => R>(Fn): CallEffect<null, Fn, []>,436 <R, T1, Fn: T1 => R>(Fn, T1): CallEffect<null, Fn, [T1]>,437 <R, T1, T2, Fn: (T1, T2) => R>(Fn, T1, T2): CallEffect<null, Fn, [T1, T2]>,438 <R, T1, T2, T3, Fn: (T1, T2, T3) => R>(Fn, T1, T2, T3): CallEffect<null, Fn, [T1, T2, T3]>,439 <R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(440 Fn,441 T1,442 T2,443 T3,444 T4445 ): CallEffect<null, Fn, [T1, T2, T3, T4]>,446 <R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(447 Fn,448 T1,449 T2,450 T3,451 T4,452 T5453 ): CallEffect<null, Fn, [T1, T2, T3, T4, T5]>,454 <R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(455 Fn,456 T1,457 T2,458 T3,459 T4,460 T5,461 T6462 ): CallEffect<null, Fn, [T1, T2, T3, T4, T5, T6]>,463 <R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(464 Fn,465 T1,466 T2,467 T3,468 T4,469 T5,470 T6,471 T7472 ): CallEffect<null, Fn, [T1, T2, T3, T4, T5, T6, T7]>,473 <R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(474 Fn,475 T1,476 T2,477 T3,478 T4,479 T5,480 T6,481 T7,482 T8483 ): CallEffect<null, Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,484 /​/​ call([context, fn], ...args)485 <Ctx, R, Fn: () => R>(cfn: [Ctx, Fn]): CallEffect<Ctx, Fn, []>,486 <Ctx, R, T1, Fn: T1 => R>(cfn: [Ctx, Fn], T1): CallEffect<Ctx, Fn, [T1]>,487 <Ctx, R, T1, T2, Fn: (T1, T2) => R>(cfn: [Ctx, Fn], T1, T2): CallEffect<Ctx, Fn, [T1, T2]>,488 <Ctx, R, T1, T2, T3, Fn: (T1, T2, T3) => R>(489 cfn: [Ctx, Fn],490 T1,491 T2,492 T3493 ): CallEffect<Ctx, Fn, [T1, T2, T3]>,494 <Ctx, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(495 cfn: [Ctx, Fn],496 T1,497 T2,498 T3,499 T4500 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4]>,501 <Ctx, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(502 cfn: [Ctx, Fn],503 T1,504 T2,505 T3,506 T4,507 T5508 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5]>,509 <Ctx, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(510 cfn: [Ctx, Fn],511 T1,512 T2,513 T3,514 T4,515 T5,516 T6517 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6]>,518 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(519 cfn: [Ctx, Fn],520 T1,521 T2,522 T3,523 T4,524 T5,525 T6,526 T7527 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7]>,528 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(529 cfn: [Ctx, Fn],530 T1,531 T2,532 T3,533 T4,534 T5,535 T6,536 T7,537 T8538 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,539 /​/​ call([context, fnName], ...args)540 <R, FnName: string, Fn: () => R, Ctx: { [FnName]: Fn, ... }>(541 cfn: [Ctx, FnName]542 ): CallEffect<Ctx, Fn, []>,543 <R, FnName: string, T1, Fn: T1 => R, Ctx: { [FnName]: Fn, ... }>(544 cfn: [Ctx, FnName],545 T1546 ): CallEffect<Ctx, Fn, [T1]>,547 <R, FnName: string, T1, T2, Fn: (T1, T2) => R, Ctx: { [FnName]: Fn, ... }>(548 cfn: [Ctx, FnName],549 T1,550 T2551 ): CallEffect<Ctx, Fn, [T1, T2]>,552 <R, FnName: string, T1, T2, T3, Fn: (T1, T2, T3) => R, Ctx: { [FnName]: Fn, ... }>(553 cfn: [Ctx, FnName],554 T1,555 T2,556 T3557 ): CallEffect<Ctx, Fn, [T1, T2, T3]>,558 <R, FnName: string, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R, Ctx: { [FnName]: Fn, ... }>(559 cfn: [Ctx, FnName],560 T1,561 T2,562 T3,563 T4564 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4]>,565 <R, FnName: string, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R, Ctx: { [FnName]: Fn, ... }>(566 cfn: [Ctx, FnName],567 T1,568 T2,569 T3,570 T4,571 T5572 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5]>,573 <574 R,575 FnName: string,576 T1,577 T2,578 T3,579 T4,580 T5,581 T6,582 Fn: (T1, T2, T3, T4, T5, T6) => R,583 Ctx: { [FnName]: Fn, ... }584 >(585 cfn: [Ctx, FnName],586 T1,587 T2,588 T3,589 T4,590 T5,591 T6592 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6]>,593 <594 R,595 FnName: string,596 T1,597 T2,598 T3,599 T4,600 T5,601 T6,602 T7,603 Fn: (T1, T2, T3, T4, T5, T6, T7) => R,604 Ctx: { [FnName]: Fn, ... }605 >(606 cfn: [Ctx, FnName],607 T1,608 T2,609 T3,610 T4,611 T5,612 T6,613 T7614 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7]>,615 <616 R,617 FnName: string,618 T1,619 T2,620 T3,621 T4,622 T5,623 T6,624 T7,625 T8,626 Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R,627 Ctx: { [FnName]: Fn, ... }628 >(629 cfn: [Ctx, FnName],630 T1,631 T2,632 T3,633 T4,634 T5,635 T6,636 T7,637 T8638 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,639 /​/​ call({context, fn}, ...args)640 <Ctx, R, Fn: () => R>(cfn: {641 context: Ctx,642 fn: Fn,643 ...644 }): CallEffect<Ctx, Fn, []>,645 <Ctx, R, T1, Fn: T1 => R>(cfn: {646 context: Ctx,647 fn: Fn,648 ...649 }, T1): CallEffect<Ctx, Fn, [T1]>,650 <Ctx, R, T1, T2, Fn: (T1, T2) => R>(651 cfn: {652 context: Ctx,653 fn: Fn,654 ...655 },656 T1,657 T2658 ): CallEffect<Ctx, Fn, [T1, T2]>,659 <Ctx, R, T1, T2, T3, Fn: (T1, T2, T3) => R>(660 cfn: {661 context: Ctx,662 fn: Fn,663 ...664 },665 T1,666 T2,667 T3668 ): CallEffect<Ctx, Fn, [T1, T2, T3]>,669 <Ctx, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(670 cfn: {671 context: Ctx,672 fn: Fn,673 ...674 },675 T1,676 T2,677 T3,678 T4679 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4]>,680 <Ctx, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(681 cfn: {682 context: Ctx,683 fn: Fn,684 ...685 },686 T1,687 T2,688 T3,689 T4,690 T5691 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5]>,692 <Ctx, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(693 cfn: {694 context: Ctx,695 fn: Fn,696 ...697 },698 T1,699 T2,700 T3,701 T4,702 T5,703 T6704 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6]>,705 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(706 cfn: {707 context: Ctx,708 fn: Fn,709 ...710 },711 T1,712 T2,713 T3,714 T4,715 T5,716 T6,717 T7718 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7]>,719 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(720 cfn: {721 context: Ctx,722 fn: Fn,723 ...724 },725 T1,726 T2,727 T3,728 T4,729 T5,730 T6,731 T7,732 T8733 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,734 ...735 };736 declare export var apply: {737 /​/​ apply(context, fn, args)738 <Ctx, R, Fn: () => R>(Ctx, Fn): CallEffect<Ctx, Fn, []>,739 <Ctx, R, T1, Fn: T1 => R>(Ctx, Fn, T1): CallEffect<Ctx, Fn, [T1]>,740 <Ctx, R, T1, T2, Fn: (T1, T2) => R>(Ctx, Fn, T1, T2): CallEffect<Ctx, Fn, [T1, T2]>,741 <Ctx, R, T1, T2, T3, Fn: (T1, T2, T3) => R>(742 Ctx,743 Fn,744 T1,745 T2,746 T3747 ): CallEffect<Ctx, Fn, [T1, T2, T3]>,748 <Ctx, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(749 Ctx,750 Fn,751 T1,752 T2,753 T3,754 T4755 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4]>,756 <Ctx, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(757 Ctx,758 Fn,759 T1,760 T2,761 T3,762 T4,763 T5764 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5]>,765 <Ctx, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(766 Ctx,767 Fn,768 T1,769 T2,770 T3,771 T4,772 T5,773 T6774 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6]>,775 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(776 Ctx,777 Fn,778 T1,779 T2,780 T3,781 T4,782 T5,783 T6,784 T7785 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7]>,786 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(787 Ctx,788 Fn,789 T1,790 T2,791 T3,792 T4,793 T5,794 T6,795 T7,796 T8797 ): CallEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,798 ...799 };800 declare type NodeCallback<R> = {801 (err: Error): void,802 (err: null | void | false, result: R): void,803 ...804 };805 declare export var cps: {806 /​/​ cps(fn, ...args)807 <R, Fn: (NodeCallback<R>) => void>(Fn): CpsEffect<null, Fn, []>,808 <R, T1, Fn: (T1, NodeCallback<R>) => void>(Fn, T1): CpsEffect<null, Fn, [T1]>,809 <R, T1, T2, Fn: (T1, T2, NodeCallback<R>) => void>(Fn, T1, T2): CpsEffect<null, Fn, [T1, T2]>,810 <R, T1, T2, T3, Fn: (T1, T2, T3, NodeCallback<R>) => void>(811 Fn,812 T1,813 T2,814 T3815 ): CpsEffect<null, Fn, [T1, T2, T3]>,816 <R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4, NodeCallback<R>) => void>(817 Fn,818 T1,819 T2,820 T3,821 T4822 ): CpsEffect<null, Fn, [T1, T2, T3, T4]>,823 <R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5, NodeCallback<R>) => void>(824 Fn,825 T1,826 T2,827 T3,828 T4,829 T5830 ): CpsEffect<null, Fn, [T1, T2, T3, T4, T5]>,831 <R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6, NodeCallback<R>) => void>(832 Fn,833 T1,834 T2,835 T3,836 T4,837 T5,838 T6839 ): CpsEffect<null, Fn, [T1, T2, T3, T4, T5, T6]>,840 <R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7, NodeCallback<R>) => void>(841 Fn,842 T1,843 T2,844 T3,845 T4,846 T5,847 T6,848 T7849 ): CpsEffect<null, Fn, [T1, T2, T3, T4, T5, T6, T7]>,850 <851 R,852 T1,853 T2,854 T3,855 T4,856 T5,857 T6,858 T7,859 T8,860 Fn: (T1, T2, T3, T4, T5, T6, T7, T8, NodeCallback<R>) => void861 >(862 Fn,863 T1,864 T2,865 T3,866 T4,867 T5,868 T6,869 T7,870 T8871 ): CpsEffect<null, Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,872 /​/​ cps([context, fn], ...args)873 <Ctx, R, Fn: (NodeCallback<R>) => void>(cfn: [Ctx, Fn]): CpsEffect<Ctx, Fn, []>,874 <Ctx, R, T1, Fn: (T1, NodeCallback<R>) => void>(cfn: [Ctx, Fn], T1): CpsEffect<Ctx, Fn, [T1]>,875 <Ctx, R, T1, T2, Fn: (T1, T2, NodeCallback<R>) => void>(876 cfn: [Ctx, Fn],877 T1,878 T2879 ): CpsEffect<Ctx, Fn, [T1, T2]>,880 <Ctx, R, T1, T2, T3, Fn: (T1, T2, T3, NodeCallback<R>) => void>(881 cfn: [Ctx, Fn],882 T1,883 T2,884 T3885 ): CpsEffect<Ctx, Fn, [T1, T2, T3]>,886 <Ctx, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4, NodeCallback<R>) => void>(887 cfn: [Ctx, Fn],888 T1,889 T2,890 T3,891 T4892 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4]>,893 <Ctx, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5, NodeCallback<R>) => void>(894 cfn: [Ctx, Fn],895 T1,896 T2,897 T3,898 T4,899 T5900 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4, T5]>,901 <Ctx, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6, NodeCallback<R>) => void>(902 cfn: [Ctx, Fn],903 T1,904 T2,905 T3,906 T4,907 T5,908 T6909 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6]>,910 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7, NodeCallback<R>) => void>(911 cfn: [Ctx, Fn],912 T1,913 T2,914 T3,915 T4,916 T5,917 T6,918 T7919 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7]>,920 <921 Ctx,922 R,923 T1,924 T2,925 T3,926 T4,927 T5,928 T6,929 T7,930 T8,931 Fn: (T1, T2, T3, T4, T5, T6, T7, T8, NodeCallback<R>) => void932 >(933 cfn: [Ctx, Fn],934 T1,935 T2,936 T3,937 T4,938 T5,939 T6,940 T7,941 T8942 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,943 /​/​ cps({context, fn}, ...args)944 <Ctx, R, Fn: (NodeCallback<R>) => void>(cfn: {945 context: Ctx,946 fn: Fn,947 ...948 }): CpsEffect<Ctx, Fn, []>,949 <Ctx, R, T1, Fn: (T1, NodeCallback<R>) => void>(950 cfn: {951 context: Ctx,952 fn: Fn,953 ...954 },955 T1956 ): CpsEffect<Ctx, Fn, [T1]>,957 <Ctx, R, T1, T2, Fn: (T1, T2, NodeCallback<R>) => void>(958 cfn: {959 context: Ctx,960 fn: Fn,961 ...962 },963 T1,964 T2965 ): CpsEffect<Ctx, Fn, [T1, T2]>,966 <Ctx, R, T1, T2, T3, Fn: (T1, T2, T3, NodeCallback<R>) => void>(967 cfn: {968 context: Ctx,969 fn: Fn,970 ...971 },972 T1,973 T2,974 T3975 ): CpsEffect<Ctx, Fn, [T1, T2, T3]>,976 <Ctx, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4, NodeCallback<R>) => void>(977 cfn: {978 context: Ctx,979 fn: Fn,980 ...981 },982 T1,983 T2,984 T3,985 T4986 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4]>,987 <Ctx, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5, NodeCallback<R>) => void>(988 cfn: {989 context: Ctx,990 fn: Fn,991 ...992 },993 T1,994 T2,995 T3,996 T4,997 T5998 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4, T5]>,999 <Ctx, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6, NodeCallback<R>) => void>(1000 cfn: {1001 context: Ctx,1002 fn: Fn,1003 ...1004 },1005 T1,1006 T2,1007 T3,1008 T4,1009 T5,1010 T61011 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6]>,1012 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7, NodeCallback<R>) => void>(1013 cfn: {1014 context: Ctx,1015 fn: Fn,1016 ...1017 },1018 T1,1019 T2,1020 T3,1021 T4,1022 T5,1023 T6,1024 T71025 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7]>,1026 <1027 Ctx,1028 R,1029 T1,1030 T2,1031 T3,1032 T4,1033 T5,1034 T6,1035 T7,1036 T8,1037 Fn: (T1, T2, T3, T4, T5, T6, T7, T8, NodeCallback<R>) => void1038 >(1039 cfn: {1040 context: Ctx,1041 fn: Fn,1042 ...1043 },1044 T1,1045 T2,1046 T3,1047 T4,1048 T5,1049 T6,1050 T7,1051 T81052 ): CpsEffect<Ctx, Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,1053 ...1054 };1055 declare export var fork: {1056 /​/​ fork(fn, ...args)1057 <R, Fn: () => R>(Fn): ForkEffect<null, Fn, void, []>,1058 <R, T1, Fn: T1 => R>(Fn, T1): ForkEffect<null, Fn, void, [T1]>,1059 <R, T1, T2, Fn: (T1, T2) => R>(Fn, T1, T2): ForkEffect<null, Fn, void, [T1, T2]>,1060 <R, T1, T2, T3, Fn: (T1, T2, T3) => R>(1061 Fn,1062 T1,1063 T2,1064 T31065 ): ForkEffect<null, Fn, void, [T1, T2, T3]>,1066 <R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(1067 Fn,1068 T1,1069 T2,1070 T3,1071 T41072 ): ForkEffect<null, Fn, void, [T1, T2, T3, T4]>,1073 <R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(1074 Fn,1075 T1,1076 T2,1077 T3,1078 T4,1079 T51080 ): ForkEffect<null, Fn, void, [T1, T2, T3, T4, T5]>,1081 <R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(1082 Fn,1083 T1,1084 T2,1085 T3,1086 T4,1087 T5,1088 T61089 ): ForkEffect<null, Fn, void, [T1, T2, T3, T4, T5, T6]>,1090 <R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(1091 Fn,1092 T1,1093 T2,1094 T3,1095 T4,1096 T5,1097 T6,1098 T71099 ): ForkEffect<null, Fn, void, [T1, T2, T3, T4, T5, T6, T7]>,1100 <R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(1101 Fn,1102 T1,1103 T2,1104 T3,1105 T4,1106 T5,1107 T6,1108 T7,1109 T81110 ): ForkEffect<null, Fn, void, [T1, T2, T3, T4, T5, T6, T7, T8]>,1111 /​/​ fork([context, fn], ...args)1112 <Ctx, R, Fn: () => R>(cfn: [Ctx, Fn]): ForkEffect<Ctx, Fn, void, []>,1113 <Ctx, R, T1, Fn: T1 => R>(cfn: [Ctx, Fn], T1): ForkEffect<Ctx, Fn, void, [T1]>,1114 <Ctx, R, T1, T2, Fn: (T1, T2) => R>(1115 cfn: [Ctx, Fn],1116 T1,1117 T21118 ): ForkEffect<Ctx, Fn, void, [T1, T2]>,1119 <Ctx, R, T1, T2, T3, Fn: (T1, T2, T3) => R>(1120 cfn: [Ctx, Fn],1121 T1,1122 T2,1123 T31124 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3]>,1125 <Ctx, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(1126 cfn: [Ctx, Fn],1127 T1,1128 T2,1129 T3,1130 T41131 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4]>,1132 <Ctx, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(1133 cfn: [Ctx, Fn],1134 T1,1135 T2,1136 T3,1137 T4,1138 T51139 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4, T5]>,1140 <Ctx, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(1141 cfn: [Ctx, Fn],1142 T1,1143 T2,1144 T3,1145 T4,1146 T5,1147 T61148 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4, T5, T6]>,1149 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(1150 cfn: [Ctx, Fn],1151 T1,1152 T2,1153 T3,1154 T4,1155 T5,1156 T6,1157 T71158 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4, T5, T6, T7]>,1159 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(1160 cfn: [Ctx, Fn],1161 T1,1162 T2,1163 T3,1164 T4,1165 T5,1166 T6,1167 T7,1168 T81169 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4, T5, T6, T7, T8]>,1170 /​/​ fork({context, fn}, ...args)1171 <Ctx, R, Fn: () => R>(cfn: {1172 context: Ctx,1173 fn: Fn,1174 ...1175 }): ForkEffect<Ctx, Fn, void, []>,1176 <Ctx, R, T1, Fn: T1 => R>(cfn: {1177 context: Ctx,1178 fn: Fn,1179 ...1180 }, T1): ForkEffect<Ctx, Fn, void, [T1]>,1181 <Ctx, R, T1, T2, Fn: (T1, T2) => R>(1182 cfn: {1183 context: Ctx,1184 fn: Fn,1185 ...1186 },1187 T1,1188 T21189 ): ForkEffect<Ctx, Fn, void, [T1, T2]>,1190 <Ctx, R, T1, T2, T3, Fn: (T1, T2, T3) => R>(1191 cfn: {1192 context: Ctx,1193 fn: Fn,1194 ...1195 },1196 T1,1197 T2,1198 T31199 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3]>,1200 <Ctx, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(1201 cfn: {1202 context: Ctx,1203 fn: Fn,1204 ...1205 },1206 T1,1207 T2,1208 T3,1209 T41210 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4]>,1211 <Ctx, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(1212 cfn: {1213 context: Ctx,1214 fn: Fn,1215 ...1216 },1217 T1,1218 T2,1219 T3,1220 T4,1221 T51222 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4, T5]>,1223 <Ctx, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(1224 cfn: {1225 context: Ctx,1226 fn: Fn,1227 ...1228 },1229 T1,1230 T2,1231 T3,1232 T4,1233 T5,1234 T61235 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4, T5, T6]>,1236 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(1237 cfn: {1238 context: Ctx,1239 fn: Fn,1240 ...1241 },1242 T1,1243 T2,1244 T3,1245 T4,1246 T5,1247 T6,1248 T71249 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4, T5, T6, T7]>,1250 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(1251 cfn: {1252 context: Ctx,1253 fn: Fn,1254 ...1255 },1256 T1,1257 T2,1258 T3,1259 T4,1260 T5,1261 T6,1262 T7,1263 T81264 ): ForkEffect<Ctx, Fn, void, [T1, T2, T3, T4, T5, T6, T7, T8]>,1265 ...1266 };1267 declare export var spawn: {1268 /​/​ spawn(fn, ...args)1269 <R, Fn: () => R>(Fn): ForkEffect<null, Fn, { detached: true, ... }, []>,1270 <R, T1, Fn: T1 => R>(Fn, T1): ForkEffect<null, Fn, { detached: true, ... }, [T1]>,1271 <R, T1, T2, Fn: (T1, T2) => R>(Fn, T1, T2): ForkEffect<null, Fn, { detached: true, ... }, [T1, T2]>,1272 <R, T1, T2, T3, Fn: (T1, T2, T3) => R>(1273 Fn,1274 T1,1275 T2,1276 T31277 ): ForkEffect<null, Fn, { detached: true, ... }, [T1, T2, T3]>,1278 <R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(1279 Fn,1280 T1,1281 T2,1282 T3,1283 T41284 ): ForkEffect<null, Fn, { detached: true, ... }, [T1, T2, T3, T4]>,1285 <R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(1286 Fn,1287 T1,1288 T2,1289 T3,1290 T4,1291 T51292 ): ForkEffect<null, Fn, { detached: true, ... }, [T1, T2, T3, T4, T5]>,1293 <R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(1294 Fn,1295 T1,1296 T2,1297 T3,1298 T4,1299 T5,1300 T61301 ): ForkEffect<null, Fn, { detached: true, ... }, [T1, T2, T3, T4, T5, T6]>,1302 <R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(1303 Fn,1304 T1,1305 T2,1306 T3,1307 T4,1308 T5,1309 T6,1310 T71311 ): ForkEffect<null, Fn, { detached: true, ... }, [T1, T2, T3, T4, T5, T6, T7]>,1312 <R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(1313 Fn,1314 T1,1315 T2,1316 T3,1317 T4,1318 T5,1319 T6,1320 T7,1321 T81322 ): ForkEffect<null, Fn, { detached: true, ... }, [T1, T2, T3, T4, T5, T6, T7, T8]>,1323 /​/​ spawn([context, fn], ...args)1324 <Ctx, R, Fn: () => R>(cfn: [Ctx, Fn]): ForkEffect<Ctx, Fn, { detached: true, ... }, []>,1325 <Ctx, R, T1, Fn: T1 => R>(cfn: [Ctx, Fn], T1): ForkEffect<Ctx, Fn, { detached: true, ... }, [T1]>,1326 <Ctx, R, T1, T2, Fn: (T1, T2) => R>(1327 cfn: [Ctx, Fn],1328 T1,1329 T21330 ): ForkEffect<Ctx, Fn, { detached: true, ... }, [T1, T2]>,1331 <Ctx, R, T1, T2, T3, Fn: (T1, T2, T3) => R>(1332 cfn: [Ctx, Fn],1333 T1,1334 T2,1335 T31336 ): ForkEffect<Ctx, Fn, { detached: true, ... }, [T1, T2, T3]>,1337 <Ctx, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(1338 cfn: [Ctx, Fn],1339 T1,1340 T2,1341 T3,1342 T41343 ): ForkEffect<Ctx, Fn, { detached: true, ... }, [T1, T2, T3, T4]>,1344 <Ctx, R, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(1345 cfn: [Ctx, Fn],1346 T1,1347 T2,1348 T3,1349 T4,1350 T51351 ): ForkEffect<Ctx, Fn, { detached: true, ... }, [T1, T2, T3, T4, T5]>,1352 <Ctx, R, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(1353 cfn: [Ctx, Fn],1354 T1,1355 T2,1356 T3,1357 T4,1358 T5,1359 T61360 ): ForkEffect<Ctx, Fn, { detached: true, ... }, [T1, T2, T3, T4, T5, T6]>,1361 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(1362 cfn: [Ctx, Fn],1363 T1,1364 T2,1365 T3,1366 T4,1367 T5,1368 T6,1369 T71370 ): ForkEffect<Ctx, Fn, { detached: true, ... }, [T1, T2, T3, T4, T5, T6, T7]>,1371 <Ctx, R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R>(1372 cfn: [Ctx, Fn],1373 T1,1374 T2,1375 T3,1376 T4,1377 T5,1378 T6,1379 T7,1380 T81381 ): ForkEffect<Ctx, Fn, { detached: true, ... }, [T1, T2, T3, T4, T5, T6, T7, T8]>,1382 ...1383 };1384 declare export var join: {1385 /​/​ join(task)1386 /​/​ join([...tasks])1387 <T: Task<*>>(task: T): JoinEffect<T>,1388 <T: Array<Task<*>>>(tasks: T): JoinEffect<T>,1389 ...1390 };1391 declare export var cancel: {1392 /​/​ cancel()1393 /​/​ cancel(task)1394 /​/​ cancel([...tasks])1395 (): CancelEffect<"@@redux-saga/​SELF_CANCELLATION">,1396 <T: Task<*>>(task: T): CancelEffect<T>,1397 <T: Task<*>>(tasks: Array<T>): CancelEffect<$ReadOnlyArray<T>>,1398 ...1399 };1400 declare export var select: {1401 /​/​ select(selector, ...args)1402 (): SelectEffect<void, []>,1403 <S, R, Fn: S => R>(Fn): SelectEffect<Fn, []>,1404 <S, R, T1, Fn: (S, T1) => R>(Fn, T1): SelectEffect<Fn, [T1]>,1405 <S, R, T1, T2, Fn: (S, T1, T2) => R>(Fn, T1, T2): SelectEffect<Fn, [T1, T2]>,1406 <S, R, T1, T2, T3, Fn: (S, T1, T2, T3) => R>(Fn, T1, T2, T3): SelectEffect<Fn, [T1, T2, T3]>,1407 <S, R, T1, T2, T3, T4, Fn: (S, T1, T2, T3, T4) => R>(1408 Fn,1409 T1,1410 T2,1411 T3,1412 T41413 ): SelectEffect<Fn, [T1, T2, T3, T4]>,1414 <S, R, T1, T2, T3, T4, T5, Fn: (S, T1, T2, T3, T4, T5) => R>(1415 Fn,1416 T1,1417 T2,1418 T3,1419 T4,1420 T51421 ): SelectEffect<Fn, [T1, T2, T3, T4, T5]>,1422 <S, R, T1, T2, T3, T4, T5, T6, Fn: (S, T1, T2, T3, T4, T5, T6) => R>(1423 Fn,1424 T1,1425 T2,1426 T3,1427 T4,1428 T5,1429 T61430 ): SelectEffect<Fn, [T1, T2, T3, T4, T5, T6]>,1431 <S, R, T1, T2, T3, T4, T5, T6, T7, Fn: (S, T1, T2, T3, T4, T5, T6, T7) => R>(1432 Fn,1433 T1,1434 T2,1435 T3,1436 T4,1437 T5,1438 T6,1439 T71440 ): SelectEffect<Fn, [T1, T2, T3, T4, T5, T6, T7]>,1441 <S, R, T1, T2, T3, T4, T5, T6, T7, T8, Fn: (S, T1, T2, T3, T4, T5, T6, T7, T8) => R>(1442 Fn,1443 T1,1444 T2,1445 T3,1446 T4,1447 T5,1448 T6,1449 T7,1450 T81451 ): SelectEffect<Fn, [T1, T2, T3, T4, T5, T6, T7, T8]>,1452 ...1453 };1454 declare export var actionChannel: {1455 /​/​ actionChannel(pattern, [buffer])1456 <BT>(): ActionChannelEffect<BT, void, void>,1457 <BT, P: Pattern>(pattern: P): ActionChannelEffect<BT, P, void>,1458 <BT, T, P: Pattern, B: Buffer<T>>(pattern: P, buffer: B): ActionChannelEffect<BT, P, B>,1459 ...1460 };1461 declare export var flush: { /​/​ flush(channel)1462 <T, CH: FlushableChannel<T>>(channel: CH): FlushEffect<CH>, ... };1463 declare export var cancelled: { /​/​ cancelled()1464 (): CancelledEffect, ... };1465 declare export var setContext: { /​/​ setContext(props)1466 <T: {...}>(props: T): SetContextEffect<T>, ... };1467 declare export var getContext: { /​/​ getContext(prop)1468 <T: string>(prop: T): GetContextEffect<T>, ... };1469 declare export var race: {1470 /​/​ race(effects)1471 /​/​ race([...effects])1472 <R: { +[name: string]: Effect, ... }>(effects: R): RaceEffect<R>,1473 <R: $ReadOnlyArray<Effect>>(effects: R): RaceEffect<R>,1474 ...1475 };1476 declare export var all: {1477 /​/​ all(effects)1478 /​/​ all([...effects])1479 (effects: { +[name: string]: Effect, ... }): AllEffect,1480 (effects: $ReadOnlyArray<Effect>): AllEffect,1481 ...1482 };1483 declare export var take: {1484 /​/​ take(pattern)1485 /​/​ take(channel)1486 (): AllTakeEffect<void>,1487 <T, CH: TakeableChannel<T>>(channel: CH): TakeEffect<void, { channel: CH, ... }, void>,1488 <P: Pattern>(pattern: P): TakeEffect<{ pattern: P, ... }, void, void>,1489 ...1490 };1491 declare export var takeMaybe: {1492 /​/​ takeMaybe(pattern)1493 /​/​ takeMaybe(channel)1494 (): AllTakeEffect<{ maybe: true, ... }>,1495 <T, CH: TakeableChannel<T>>(channel: CH): TakeEffect<void, { channel: CH, ... }, { maybe: true, ... }>,1496 <P: Pattern>(pattern: P): TakeEffect<{ pattern: P, ... }, void, { maybe: true, ... }>,1497 ...1498 };1499 declare export var takeEvery: {1500 /​/​ takeEvery(pattern, saga, ...args)1501 /​/​ takeEvery(channel, saga, ...args)1502 <A, R, P: TakeableChannel<*> | Pattern, Fn: A => R>(P, Fn): ForkEffect<null, Fn, void, [P, Fn]>,1503 <A, R, P: TakeableChannel<*> | Pattern, T1, Fn: (T1, A) => R>(1504 P,1505 Fn,1506 T11507 ): ForkEffect<null, Fn, void, [P, Fn, T1]>,1508 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, Fn: (T1, T2, A) => R>(1509 P,1510 Fn,1511 T1,1512 T21513 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2]>,1514 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, Fn: (T1, T2, T3, A) => R>(1515 P,1516 Fn,1517 T1,1518 T2,1519 T31520 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3]>,1521 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, Fn: (T1, T2, T3, T4, A) => R>(1522 P,1523 Fn,1524 T1,1525 T2,1526 T3,1527 T41528 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4]>,1529 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5, A) => R>(1530 P,1531 Fn,1532 T1,1533 T2,1534 T3,1535 T4,1536 T51537 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5]>,1538 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6, A) => R>(1539 P,1540 Fn,1541 T1,1542 T2,1543 T3,1544 T4,1545 T5,1546 T61547 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6]>,1548 <1549 A,1550 R,1551 P: TakeableChannel<*> | Pattern,1552 T1,1553 T2,1554 T3,1555 T4,1556 T5,1557 T6,1558 T7,1559 Fn: (T1, T2, T3, T4, T5, T6, T7, A) => R1560 >(1561 P,1562 Fn,1563 T1,1564 T2,1565 T3,1566 T4,1567 T5,1568 T6,1569 T71570 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6, T7]>,1571 <1572 A,1573 R,1574 P: TakeableChannel<*> | Pattern,1575 T1,1576 T2,1577 T3,1578 T4,1579 T5,1580 T6,1581 T7,1582 T8,1583 Fn: (T1, T2, T3, T4, T5, T6, T7, T8, A) => R1584 >(1585 P,1586 Fn,1587 T1,1588 T2,1589 T3,1590 T4,1591 T5,1592 T6,1593 T7,1594 T81595 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6, T7, T8]>,1596 ...1597 };1598 declare export var takeLatest: {1599 /​/​ takeLatest(pattern, saga, ...args)1600 /​/​ takeLatest(channel, saga, ...args)1601 <A, R, P: TakeableChannel<*> | Pattern, Fn: A => R>(P, Fn): ForkEffect<null, Fn, void, [P, Fn]>,1602 <A, R, P: TakeableChannel<*> | Pattern, T1, Fn: (T1, A) => R>(1603 P,1604 Fn,1605 T11606 ): ForkEffect<null, Fn, void, [P, Fn, T1]>,1607 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, Fn: (T1, T2, A) => R>(1608 P,1609 Fn,1610 T1,1611 T21612 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2]>,1613 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, Fn: (T1, T2, T3, A) => R>(1614 P,1615 Fn,1616 T1,1617 T2,1618 T31619 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3]>,1620 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, Fn: (T1, T2, T3, T4, A) => R>(1621 P,1622 Fn,1623 T1,1624 T2,1625 T3,1626 T41627 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4]>,1628 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5, A) => R>(1629 P,1630 Fn,1631 T1,1632 T2,1633 T3,1634 T4,1635 T51636 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5]>,1637 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6, A) => R>(1638 P,1639 Fn,1640 T1,1641 T2,1642 T3,1643 T4,1644 T5,1645 T61646 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6]>,1647 <1648 A,1649 R,1650 P: TakeableChannel<*> | Pattern,1651 T1,1652 T2,1653 T3,1654 T4,1655 T5,1656 T6,1657 T7,1658 Fn: (T1, T2, T3, T4, T5, T6, T7, A) => R1659 >(1660 P,1661 Fn,1662 T1,1663 T2,1664 T3,1665 T4,1666 T5,1667 T6,1668 T71669 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6, T7]>,1670 <1671 A,1672 R,1673 P: TakeableChannel<*> | Pattern,1674 T1,1675 T2,1676 T3,1677 T4,1678 T5,1679 T6,1680 T7,1681 T8,1682 Fn: (T1, T2, T3, T4, T5, T6, T7, T8, A) => R1683 >(1684 P,1685 Fn,1686 T1,1687 T2,1688 T3,1689 T4,1690 T5,1691 T6,1692 T7,1693 T81694 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6, T7, T8]>,1695 ...1696 };1697 declare export var takeLeading: {1698 /​/​ takeLeading(pattern, saga, ...args)1699 /​/​ takeLeading(channel, saga, ...args)1700 <A, R, P: TakeableChannel<*> | Pattern, Fn: A => R>(P, Fn): ForkEffect<null, Fn, void, [P, Fn]>,1701 <A, R, P: TakeableChannel<*> | Pattern, T1, Fn: (T1, A) => R>(1702 P,1703 Fn,1704 T11705 ): ForkEffect<null, Fn, void, [P, Fn, T1]>,1706 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, Fn: (T1, T2, A) => R>(1707 P,1708 Fn,1709 T1,1710 T21711 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2]>,1712 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, Fn: (T1, T2, T3, A) => R>(1713 P,1714 Fn,1715 T1,1716 T2,1717 T31718 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3]>,1719 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, Fn: (T1, T2, T3, T4, A) => R>(1720 P,1721 Fn,1722 T1,1723 T2,1724 T3,1725 T41726 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4]>,1727 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5, A) => R>(1728 P,1729 Fn,1730 T1,1731 T2,1732 T3,1733 T4,1734 T51735 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5]>,1736 <A, R, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6, A) => R>(1737 P,1738 Fn,1739 T1,1740 T2,1741 T3,1742 T4,1743 T5,1744 T61745 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6]>,1746 <1747 A,1748 R,1749 P: TakeableChannel<*> | Pattern,1750 T1,1751 T2,1752 T3,1753 T4,1754 T5,1755 T6,1756 T7,1757 Fn: (T1, T2, T3, T4, T5, T6, T7, A) => R1758 >(1759 P,1760 Fn,1761 T1,1762 T2,1763 T3,1764 T4,1765 T5,1766 T6,1767 T71768 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6, T7]>,1769 <1770 A,1771 R,1772 P: TakeableChannel<*> | Pattern,1773 T1,1774 T2,1775 T3,1776 T4,1777 T5,1778 T6,1779 T7,1780 T8,1781 Fn: (T1, T2, T3, T4, T5, T6, T7, T8, A) => R1782 >(1783 P,1784 Fn,1785 T1,1786 T2,1787 T3,1788 T4,1789 T5,1790 T6,1791 T7,1792 T81793 ): ForkEffect<null, Fn, void, [P, Fn, T1, T2, T3, T4, T5, T6, T7, T8]>,1794 ...1795 };1796 declare export var delay: {1797 /​/​ delay(ms, [val])1798 <T1: number>(timeout: T1): CallEffect<null, (ms: T1) => Promise<true>, [T1]>,1799 <T1: number, T2>(timeout: T1, T2): CallEffect<null, (ms: T1) => Promise<true>, [T1, T2]>,1800 ...1801 };1802 declare export var throttle: {1803 /​/​ throttle(ms, pattern, saga, ...args)1804 /​/​ throttle(ms, channel, saga, ...args)1805 <MS: number, P: TakeableChannel<*> | Pattern, A, R, Fn: A => R>(1806 MS,1807 P,1808 Fn1809 ): ForkEffect<null, Fn, void, [MS, P, Fn]>,1810 <MS: number, P: TakeableChannel<*> | Pattern, A, R, T1, Fn: (T1, A) => R>(1811 MS,1812 P,1813 Fn,1814 T11815 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1]>,1816 <MS: number, P: TakeableChannel<*> | Pattern, A, R, T1, T2, Fn: (T1, T2, A) => R>(1817 MS,1818 P,1819 Fn,1820 T1,1821 T21822 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2]>,1823 <MS: number, P: TakeableChannel<*> | Pattern, A, R, T1, T2, T3, Fn: (T1, T2, T3, A) => R>(1824 MS,1825 P,1826 Fn,1827 T1,1828 T2,1829 T31830 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3]>,1831 <MS: number, P: TakeableChannel<*> | Pattern, A, R, T1, T2, T3, T4, Fn: (T1, T2, T3, T4, A) => R>(1832 MS,1833 P,1834 Fn,1835 T1,1836 T2,1837 T3,1838 T41839 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4]>,1840 <1841 MS: number,1842 P: TakeableChannel<*> | Pattern,1843 A,1844 R,1845 T1,1846 T2,1847 T3,1848 T4,1849 T5,1850 Fn: (T1, T2, T3, T4, T5, A) => R1851 >(1852 MS,1853 P,1854 Fn,1855 T1,1856 T2,1857 T3,1858 T4,1859 T51860 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4, T5]>,1861 <1862 MS: number,1863 P: TakeableChannel<*> | Pattern,1864 A,1865 R,1866 T1,1867 T2,1868 T3,1869 T4,1870 T5,1871 T6,1872 Fn: (T1, T2, T3, T4, T5, T6, A) => R1873 >(1874 MS,1875 P,1876 Fn,1877 T1,1878 T2,1879 T3,1880 T4,1881 T5,1882 T61883 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4, T5, T6]>,1884 <1885 MS: number,1886 P: TakeableChannel<*> | Pattern,1887 A,1888 R,1889 T1,1890 T2,1891 T3,1892 T4,1893 T5,1894 T6,1895 T7,1896 Fn: (T1, T2, T3, T4, T5, T6, T7, A) => R1897 >(1898 MS,1899 P,1900 Fn,1901 T1,1902 T2,1903 T3,1904 T4,1905 T5,1906 T6,1907 T71908 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4, T5, T6, T7]>,1909 <1910 MS: number,1911 P: TakeableChannel<*> | Pattern,1912 A,1913 R,1914 T1,1915 T2,1916 T3,1917 T4,1918 T5,1919 T6,1920 T7,1921 T8,1922 Fn: (T1, T2, T3, T4, T5, T6, T7, T8, A) => R1923 >(1924 MS,1925 P,1926 Fn,1927 T1,1928 T2,1929 T3,1930 T4,1931 T5,1932 T6,1933 T7,1934 T81935 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4, T5, T6, T7, T8]>,1936 ...1937 };1938 declare export var debounce: {1939 /​/​ debounce(ms, pattern, saga, ...args)1940 /​/​ debounce(ms, channel, saga, ...args)1941 <R, MS: number, P: TakeableChannel<*> | Pattern, Fn: () => R>(1942 MS,1943 P,1944 Fn1945 ): ForkEffect<null, Fn, void, [MS, P, Fn]>,1946 <R, MS: number, P: TakeableChannel<*> | Pattern, T1, Fn: T1 => R>(1947 MS,1948 P,1949 Fn,1950 T11951 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1]>,1952 <R, MS: number, P: TakeableChannel<*> | Pattern, T1, T2, Fn: (T1, T2) => R>(1953 MS,1954 P,1955 Fn,1956 T1,1957 T21958 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2]>,1959 <R, MS: number, P: TakeableChannel<*> | Pattern, T1, T2, T3, Fn: (T1, T2, T3) => R>(1960 MS,1961 P,1962 Fn,1963 T1,1964 T2,1965 T31966 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3]>,1967 <R, MS: number, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(1968 MS,1969 P,1970 Fn,1971 T1,1972 T2,1973 T3,1974 T41975 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4]>,1976 <R, MS: number, P: TakeableChannel<*> | Pattern, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(1977 MS,1978 P,1979 Fn,1980 T1,1981 T2,1982 T3,1983 T4,1984 T51985 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4, T5]>,1986 <1987 R,1988 MS: number,1989 P: TakeableChannel<*> | Pattern,1990 T1,1991 T2,1992 T3,1993 T4,1994 T5,1995 T6,1996 Fn: (T1, T2, T3, T4, T5, T6) => R1997 >(1998 MS,1999 P,2000 Fn,2001 T1,2002 T2,2003 T3,2004 T4,2005 T5,2006 T62007 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4, T5, T6]>,2008 <2009 R,2010 MS: number,2011 P: TakeableChannel<*> | Pattern,2012 T1,2013 T2,2014 T3,2015 T4,2016 T5,2017 T6,2018 T7,2019 Fn: (T1, T2, T3, T4, T5, T6, T7) => R2020 >(2021 MS,2022 P,2023 Fn,2024 T1,2025 T2,2026 T3,2027 T4,2028 T5,2029 T6,2030 T72031 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4, T5, T6, T7]>,2032 <2033 R,2034 MS: number,2035 P: TakeableChannel<*> | Pattern,2036 T1,2037 T2,2038 T3,2039 T4,2040 T5,2041 T6,2042 T7,2043 T8,2044 Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R2045 >(2046 MS,2047 P,2048 Fn,2049 T1,2050 T2,2051 T3,2052 T4,2053 T5,2054 T6,2055 T7,2056 T82057 ): ForkEffect<null, Fn, void, [MS, P, Fn, T1, T2, T3, T4, T5, T6, T7, T8]>,2058 ...2059 };2060 declare export var retry: {2061 /​/​ retry(maxTries, delay, fn, ...args)2062 <R, MT: number, D: number, Fn: () => R>(MT, D, Fn): CallEffect<null, Function, [MT, D, Fn]>,2063 <R, MT: number, D: number, T1, Fn: T1 => R>(2064 MT,2065 D,2066 Fn,2067 T12068 ): CallEffect<null, Function, [MT, D, Fn, T1]>,2069 <R, MT: number, D: number, T1, T2, Fn: (T1, T2) => R>(2070 MT,2071 D,2072 Fn,2073 T1,2074 T22075 ): CallEffect<null, Function, [MT, D, Fn, T1, T2]>,2076 <R, MT: number, D: number, T1, T2, T3, Fn: (T1, T2, T3) => R>(2077 MT,2078 D,2079 Fn,2080 T1,2081 T2,2082 T32083 ): CallEffect<null, Function, [MT, D, Fn, T1, T2, T3]>,2084 <R, MT: number, D: number, T1, T2, T3, T4, Fn: (T1, T2, T3, T4) => R>(2085 MT,2086 D,2087 Fn,2088 T1,2089 T2,2090 T3,2091 T42092 ): CallEffect<null, Function, [MT, D, Fn, T1, T2, T3, T4]>,2093 <R, MT: number, D: number, T1, T2, T3, T4, T5, Fn: (T1, T2, T3, T4, T5) => R>(2094 MT,2095 D,2096 Fn,2097 T1,2098 T2,2099 T3,2100 T4,2101 T52102 ): CallEffect<null, Function, [MT, D, Fn, T1, T2, T3, T4, T5]>,2103 <R, MT: number, D: number, T1, T2, T3, T4, T5, T6, Fn: (T1, T2, T3, T4, T5, T6) => R>(2104 MT,2105 D,2106 Fn,2107 T1,2108 T2,2109 T3,2110 T4,2111 T5,2112 T62113 ): CallEffect<null, Function, [MT, D, Fn, T1, T2, T3, T4, T5, T6]>,2114 <R, MT: number, D: number, T1, T2, T3, T4, T5, T6, T7, Fn: (T1, T2, T3, T4, T5, T6, T7) => R>(2115 MT,2116 D,2117 Fn,2118 T1,2119 T2,2120 T3,2121 T4,2122 T5,2123 T6,2124 T72125 ): CallEffect<null, Function, [MT, D, Fn, T1, T2, T3, T4, T5, T6, T7]>,2126 <2127 R,2128 MT: number,2129 D: number,2130 T1,2131 T2,2132 T3,2133 T4,2134 T5,2135 T6,2136 T7,2137 T8,2138 Fn: (T1, T2, T3, T4, T5, T6, T7, T8) => R2139 >(2140 MT,2141 D,2142 Fn,2143 T1,2144 T2,2145 T3,2146 T4,2147 T5,2148 T6,2149 T7,2150 T82151 ): CallEffect<null, Function, [MT, D, Fn, T1, T2, T3, T4, T5, T6, T7, T8]>,2152 ...2153 };...

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common.py

Source:common.py Github

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...221 else:222 params.append('%s p%d' % (t, i))223 call_params.append('p%d' % (i))224 if len(fn['ret']) == 1 and fn['ret'][0] == 'void':225 print(commentStr + ('inline void %s(%s) { %s_pfn(%s); }' \226 % (fn['name'], ', '.join(params), fn['name'], ', '.join(call_params))))227 else:228 print(commentStr + ('inline %s %s(%s) { return %s_pfn(%s); }' \229 % (' '.join(fn['ret']), fn['name'], ', '.join(params), fn['name'], ', '.join(call_params))))230def ProcessTemplate(inputFile, ctx, noteLine='/​/​\n/​/​ AUTOGENERATED, DO NOT EDIT\n/​/​'):231 f = open(inputFile, "r")232 if noteLine:233 print(noteLine)234 for line in f:235 if line.startswith('@'):236 assert line[-1] == '\n'237 line = line[:-1] # remove '\n'238 assert line[-1] == '@'239 name = line[1:-1]240 assert name in ctx, name241 line = ctx[name] + ('\n' if len(ctx[name]) > 0 and ctx[name][-1] != '\n' else '')242 sys.stdout.write(line)...

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_getDataFunctions.js

Source:_getDataFunctions.js Github

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...11 oTest.fnTest(12 "Single object, single property",13 function () {14 fn = table.oApi._fnGetObjectDataFn('test');15 test = fn( { "test": true } );16 },17 function () { return test }18 );19 20 oTest.fnTest(21 "Single property from object",22 function () {23 fn = table.oApi._fnGetObjectDataFn('test');24 test = fn( { "test": true, "test2": false } );25 },26 function () { return test }27 );28 29 oTest.fnTest(30 "Single property from object - different property",31 function () {32 fn = table.oApi._fnGetObjectDataFn('test2');33 test = fn( { "test": true, "test2": false } );34 },35 function () { return test===false }36 );37 38 oTest.fnTest(39 "Undefined property from object",40 function () {41 fn = table.oApi._fnGetObjectDataFn('test3');42 test = fn( { "test": true, "test2": false } );43 },44 function () { return test===undefined }45 );46 47 /​/​ Array index access48 oTest.fnTest(49 "Array access - index 0",50 function () {51 fn = table.oApi._fnGetObjectDataFn(0);52 test = fn( [true, false, false, false] );53 },54 function () { return test }55 );56 57 oTest.fnTest(58 "Array access - index 1",59 function () {60 fn = table.oApi._fnGetObjectDataFn(2);61 test = fn( [false, false, true, false] );62 },63 function () { return test }64 );65 66 oTest.fnTest(67 "Array access - undefined",68 function () {69 fn = table.oApi._fnGetObjectDataFn(7);70 test = fn( [false, false, true, false] );71 },72 function () { return test===undefined }73 );74 /​/​ null source75 oTest.fnTest(76 "null source",77 function () {78 fn = table.oApi._fnGetObjectDataFn( null );79 test = fn( [false, false, true, false] );80 },81 function () { return test===null }82 );83 /​/​ nested objects84 oTest.fnTest(85 "Nested object property",86 function () {87 fn = table.oApi._fnGetObjectDataFn( 'a.b' );88 test = fn( {89 "a":{90 "b": true,91 "c": false,92 "d": 193 }94 } );95 },96 function () { return test }97 );98 oTest.fnTest(99 "Nested object property - different prop",100 function () {101 fn = table.oApi._fnGetObjectDataFn( 'a.d' );102 test = fn( {103 "a":{104 "b": true,105 "c": false,106 "d": 1107 }108 } );109 },110 function () { return test===1 }111 );112 113 oTest.fnTest(114 "Nested object property - undefined prop",115 function () {116 fn = table.oApi._fnGetObjectDataFn( 'a.z' );117 test = fn( {118 "a":{119 "b": true,120 "c": false,121 "d": 1122 }123 } );124 },125 function () { return test===undefined }126 );127 /​/​ Nested array128 oTest.fnTest(129 "Nested array index property",130 function () {131 fn = table.oApi._fnGetObjectDataFn( 'a.0' );132 test = fn( {133 "a": [134 true,135 false,136 1137 ]138 } );139 },140 function () { return test }141 );142 oTest.fnTest(143 "Nested array index property - different index",144 function () {145 fn = table.oApi._fnGetObjectDataFn( 'a.2' );146 test = fn( {147 "a": [148 true,149 false,150 1151 ]152 } );153 },154 function () { return test===1 }155 );156 oTest.fnTest(157 "Nested array index property - undefined index",158 function () {159 fn = table.oApi._fnGetObjectDataFn( 'a.10' );160 test = fn( {161 "a": [162 true,163 false,164 1165 ]166 } );167 },168 function () { return test===undefined }169 );170 /​/​ Nested array object property171 oTest.fnTest(172 "Nested array index object property",173 function () {174 fn = table.oApi._fnGetObjectDataFn( 'a.0.m' );175 test = fn( {176 "a": [177 { "m": true, "n": 1 },178 { "m": false, "n": 2 },179 { "m": false, "n": 3 }180 ]181 } );182 },183 function () { return test }184 );185 oTest.fnTest(186 "Nested array index object property - different index",187 function () {188 fn = table.oApi._fnGetObjectDataFn( 'a.2.n' );189 test = fn( {190 "a": [191 { "m": true, "n": 1 },192 { "m": false, "n": 2 },193 { "m": false, "n": 3 }194 ]195 } );196 },197 function () { return test===3 }198 );199 oTest.fnTest(200 "Nested array index object property - undefined index",201 function () {202 fn = table.oApi._fnGetObjectDataFn( 'a.0.z' );203 test = fn( {204 "a": [205 { "m": true, "n": 1 },206 { "m": false, "n": 2 },207 { "m": false, "n": 3 }208 ]209 } );210 },211 function () { return test===undefined }212 );213 /​/​ Array notation - no join214 oTest.fnTest(215 "Array notation - no join - property",216 function () {217 fn = table.oApi._fnGetObjectDataFn( 'a[].n' );218 test = fn( {219 "a": [220 { "m": true, "n": 1 },221 { "m": false, "n": 2 },222 { "m": false, "n": 3 }223 ]224 } );225 },226 function () {227 return test.length===3 && test[0]===1228 && test[1]===2 && test[2]===3;229 }230 );231 oTest.fnTest(232 "Array notation - no join - property (2)",233 function () {234 fn = table.oApi._fnGetObjectDataFn( 'a[].m' );235 test = fn( {236 "a": [237 { "m": true, "n": 1 },238 { "m": false, "n": 2 }239 ]240 } );241 },242 function () {243 return test.length===2 && test[0]===true244 && test[1]===false;245 }246 );247 oTest.fnTest(248 "Array notation - no join - undefined property",249 function () {250 fn = table.oApi._fnGetObjectDataFn( 'a[].z' );251 test = fn( {252 "a": [253 { "m": true, "n": 1 },254 { "m": false, "n": 2 }255 ]256 } );257 },258 function () {259 return test.length===2 && test[0]===undefined260 && test[1]===undefined;261 }262 );263 /​/​ Array notation - join264 oTest.fnTest(265 "Array notation - space join - property",266 function () {267 fn = table.oApi._fnGetObjectDataFn( 'a[ ].n' );268 test = fn( {269 "a": [270 { "m": true, "n": 1 },271 { "m": false, "n": 2 },272 { "m": false, "n": 3 }273 ]274 } );275 },276 function () { return test === '1 2 3'; }277 );278 oTest.fnTest(279 "Array notation - space join - property (2)",280 function () {281 fn = table.oApi._fnGetObjectDataFn( 'a[ ].m' );282 test = fn( {283 "a": [284 { "m": true, "n": 1 },285 { "m": false, "n": 2 }286 ]287 } );288 },289 function () { return test === 'true false'; }290 );291 oTest.fnTest(292 "Array notation - sapce join - undefined property",293 function () {294 fn = table.oApi._fnGetObjectDataFn( 'a[ ].z' );295 test = fn( {296 "a": [297 { "m": true, "n": 1 },298 { "m": false, "n": 2 }299 ]300 } );301 },302 function () { return test === ' '; }303 );304 oTest.fnTest(305 "Array notation - string join - property",306 function () {307 fn = table.oApi._fnGetObjectDataFn( 'a[qwerty].n' );308 test = fn( {309 "a": [310 { "m": true, "n": 1 },311 { "m": false, "n": 2 },312 { "m": false, "n": 3 }313 ]314 } );315 },316 function () { return test === '1qwerty2qwerty3'; }317 );318 oTest.fnTest(319 "Array notation - string join - property (2)",320 function () {321 fn = table.oApi._fnGetObjectDataFn( 'a[qwerty].m' );322 test = fn( {323 "a": [324 { "m": true, "n": 1 },325 { "m": false, "n": 2 }326 ]327 } );328 },329 function () { return test === 'trueqwertyfalse'; }330 );331 332 /​/​ Array alone join333 oTest.fnTest(334 "Flat array join",335 function () {336 fn = table.oApi._fnGetObjectDataFn( 'a[ ]' );337 test = fn( {338 "a": [339 true,340 false,341 1342 ]343 } );344 },345 function () { return test==="true false 1"; }346 );347 oTest.fnTest(348 "Flat array string join",349 function () {350 fn = table.oApi._fnGetObjectDataFn( 'a[qwerty]' );351 test = fn( {352 "a": [353 true,354 false,355 1356 ]357 } );358 },359 function () { return test==="trueqwertyfalseqwerty1"; }360 );361 oTest.fnTest(362 "Flat array no join",363 function () {364 fn = table.oApi._fnGetObjectDataFn( 'a[]' );365 test = fn( {366 "a": [367 true,368 false,369 1370 ]371 } );372 },373 function () { return test.length===3 && test[0]===true &&374 test[1]===false && test[2]===1; }375 );376 377 378 379 oTest.fnComplete();...

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test_math.py

Source:test_math.py Github

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...17 cases=None,18 span=(-1., 1.), count=128,19 types=(np.float32, np.float64)):20 @hsa.jit21 def fn(dst, src):22 i = hsa.get_global_id(0)23 if i < dst.size:24 dst[i] = math_fn(src[i])25 for dtype in types:26 if cases is None:27 src = np.linspace(span[0], span[1], count).astype(dtype)28 else:29 src = np.array(cases, dtype=dtype)30 dst = np.zeros_like(src)31 fn[src.size, 1](dst, src)32 np.testing.assert_allclose(dst, npy_fn(src),33 rtol=self._get_tol(math_fn, dtype),34 err_msg='{0} ({1})'.format(35 math_fn.__name__,36 dtype.__name__))37 def _generic_test_binary(self, math_fn, npy_fn,38 cases=None,39 span=(-1., 1., 1., -1.), count=128,40 types=(np.float32, np.float64)):41 @hsa.jit42 def fn(dst, src1, src2):43 i = hsa.get_global_id(0)44 if i < dst.size:45 dst[i] = math_fn(src1[i], src2[i])46 for dtype in types:47 if cases is None:48 src1 = np.linspace(span[0], span[1], count).astype(dtype)49 src2 = np.linspace(span[2], span[3], count).astype(dtype)50 else:51 src1 = np.array(cases[0], dtype=dtype)52 src2 = np.array(cases[1], dtype=dtype)53 dst = np.zeros_like(src1)54 fn[dst.size, 1](dst, src1, src2)55 np.testing.assert_allclose(dst, npy_fn(src1, src2),56 rtol=self._get_tol(math_fn, dtype),57 err_msg='{0} ({1})'.format(58 math_fn.__name__,59 dtype.__name__))60 def test_trig(self):61 funcs = [math.sin, math.cos, math.tan]62 for fn in funcs:63 self._generic_test_unary(fn, getattr(np, fn.__name__),64 span=(-np.pi, np.pi))65 def test_trig_inv(self):66 funcs = [(math.asin, np.arcsin),67 (math.acos, np.arccos),68 (math.atan, np.arctan)]69 for fn, np_fn in funcs:...

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single_return_test.py

Source:single_return_test.py Github

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...20from tensorflow.contrib.autograph.converters import single_return21from tensorflow.python.framework.ops import name_scope22from tensorflow.python.platform import test23class SingleReturnTest(converter_test_base.TestCase):24 def compiled_fn(self, test_fn, *args):25 node = self.parse_and_analyze(test_fn, {})26 node = single_return.transform(node, self.ctx)27 module = self.compiled(node, *args)28 return module29 def test_noop(self):30 # Noop31 def test_fn(x):32 return x33 with self.compiled_fn(test_fn) as result:34 self.assertEqual(test_fn(2.0), result.test_fn(2.0))35 def test_return_expression(self):36 # ANF37 def test_fn(x):38 return x * x39 with self.compiled_fn(test_fn) as result:40 x = 241 self.assertEqual(test_fn(x), result.test_fn(x))42 def test_merge(self):43 # Simple merge44 def test_fn(x):45 if x > 0:46 return x47 else:48 return x * x49 with self.compiled_fn(test_fn) as result:50 for x in [-2, 2]:51 self.assertEqual(test_fn(x), result.test_fn(x))52 def test_orphan_branch(self):53 def test_fn(x):54 if x > 0:55 return x56 with self.assertRaises(ValueError):57 self.compiled_fn(test_fn)58 def test_lift_body_into_false_branch(self):59 def test_fn(x):60 if x > 0:61 return x62 return x * x63 with self.compiled_fn(test_fn) as result:64 for x in [-2, 2]:65 self.assertEqual(test_fn(x), result.test_fn(x))66 def test_lift_body_into_true_branch(self):67 def test_fn(x):68 if x < 0:69 x *= x70 else:71 # TODO(alexbw): linter bug here that requires us suppress this warning.72 return x # pylint: disable=undefined-loop-variable73 return x74 with self.compiled_fn(test_fn) as result:75 for x in [-2, 2]:76 self.assertEqual(test_fn(x), result.test_fn(x))77 def test_nested_if(self):78 def test_fn(x):79 if x > 0:80 if x < 5:81 return x82 else:83 return x * x84 else:85 return x * x * x86 with self.compiled_fn(test_fn) as result:87 for x in [-2, 2, 5]:88 self.assertEqual(test_fn(x), result.test_fn(x))89 def test_context_manager(self):90 def test_fn(x):91 with name_scope(''):92 return x * x93 with self.compiled_fn(test_fn) as result:94 result.name_scope = name_scope95 for x in [-2, 2]:96 self.assertEqual(test_fn(x), result.test_fn(x))97 def test_context_manager_in_conditional(self):98 def test_fn(x):99 if x > 0:100 with name_scope(''):101 return x * x102 else:103 return x104 with self.compiled_fn(test_fn, name_scope) as result:105 result.name_scope = name_scope106 for x in [-2, 2]:107 self.assertEqual(test_fn(x), result.test_fn(x))108 def text_conditional_in_context_manager(self):109 def test_fn(x):110 with name_scope(''):111 if x > 0:112 return x * x113 else:114 return x115 with self.compiled_fn(test_fn) as result:116 result.name_scope = name_scope117 for x in [-2, 2]:118 self.assertEqual(test_fn(x), result.test_fn(x))119 def test_no_return(self):120 def test_fn(x):121 x *= x122 with self.compiled_fn(test_fn) as result:123 self.assertEqual(test_fn(2), result.test_fn(2))124 def test_nested_functiondefs(self):125 def test_fn(x):126 def inner_fn(y):127 if y > 0:128 return y * y129 else:130 return y131 return inner_fn(x)132 with self.compiled_fn(test_fn) as result:133 for x in [-2, 2]:134 self.assertEqual(test_fn(x), result.test_fn(x))135 def test_loop(self):136 def test_fn(x):137 for _ in range(10):138 return x139 return x140 with self.assertRaises(ValueError):141 self.compiled_fn(test_fn)142if __name__ == '__main__':...

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api.internal.js

Source:api.internal.js Github

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1/​*2 * This is really a good bit rubbish this method of exposing the internal methods3 * publicly... - To be fixed in 2.0 using methods on the prototype4 */​5/​**6 * Create a wrapper function for exporting an internal functions to an external API.7 * @param {string} sFunc API function name8 * @returns {function} wrapped function9 * @memberof DataTable#oApi10 */​11function _fnExternApiFunc (sFunc)12{13 return function() {14 var aArgs = [_fnSettingsFromNode(this[DataTable.ext.iApiIndex])].concat( 15 Array.prototype.slice.call(arguments) );16 return DataTable.ext.oApi[sFunc].apply( this, aArgs );17 };18}19/​**20 * Reference to internal functions for use by plug-in developers. Note that these21 * methods are references to internal functions and are considered to be private.22 * If you use these methods, be aware that they are liable to change between versions23 * (check the upgrade notes).24 * @namespace25 */​26this.oApi = {27 "_fnExternApiFunc": _fnExternApiFunc,28 "_fnInitialise": _fnInitialise,29 "_fnInitComplete": _fnInitComplete,30 "_fnLanguageCompat": _fnLanguageCompat,31 "_fnAddColumn": _fnAddColumn,32 "_fnColumnOptions": _fnColumnOptions,33 "_fnAddData": _fnAddData,34 "_fnCreateTr": _fnCreateTr,35 "_fnGatherData": _fnGatherData,36 "_fnBuildHead": _fnBuildHead,37 "_fnDrawHead": _fnDrawHead,38 "_fnDraw": _fnDraw,39 "_fnReDraw": _fnReDraw,40 "_fnAjaxUpdate": _fnAjaxUpdate,41 "_fnAjaxParameters": _fnAjaxParameters,42 "_fnAjaxUpdateDraw": _fnAjaxUpdateDraw,43 "_fnServerParams": _fnServerParams,44 "_fnAddOptionsHtml": _fnAddOptionsHtml,45 "_fnFeatureHtmlTable": _fnFeatureHtmlTable,46 "_fnScrollDraw": _fnScrollDraw,47 "_fnAdjustColumnSizing": _fnAdjustColumnSizing,48 "_fnFeatureHtmlFilter": _fnFeatureHtmlFilter,49 "_fnFilterComplete": _fnFilterComplete,50 "_fnFilterCustom": _fnFilterCustom,51 "_fnFilterColumn": _fnFilterColumn,52 "_fnFilter": _fnFilter,53 "_fnBuildSearchArray": _fnBuildSearchArray,54 "_fnBuildSearchRow": _fnBuildSearchRow,55 "_fnFilterCreateSearch": _fnFilterCreateSearch,56 "_fnDataToSearch": _fnDataToSearch,57 "_fnSort": _fnSort,58 "_fnSortAttachListener": _fnSortAttachListener,59 "_fnSortingClasses": _fnSortingClasses,60 "_fnFeatureHtmlPaginate": _fnFeatureHtmlPaginate,61 "_fnPageChange": _fnPageChange,62 "_fnFeatureHtmlInfo": _fnFeatureHtmlInfo,63 "_fnUpdateInfo": _fnUpdateInfo,64 "_fnFeatureHtmlLength": _fnFeatureHtmlLength,65 "_fnFeatureHtmlProcessing": _fnFeatureHtmlProcessing,66 "_fnProcessingDisplay": _fnProcessingDisplay,67 "_fnVisibleToColumnIndex": _fnVisibleToColumnIndex,68 "_fnColumnIndexToVisible": _fnColumnIndexToVisible,69 "_fnNodeToDataIndex": _fnNodeToDataIndex,70 "_fnVisbleColumns": _fnVisbleColumns,71 "_fnCalculateEnd": _fnCalculateEnd,72 "_fnConvertToWidth": _fnConvertToWidth,73 "_fnCalculateColumnWidths": _fnCalculateColumnWidths,74 "_fnScrollingWidthAdjust": _fnScrollingWidthAdjust,75 "_fnGetWidestNode": _fnGetWidestNode,76 "_fnGetMaxLenString": _fnGetMaxLenString,77 "_fnStringToCss": _fnStringToCss,78 "_fnDetectType": _fnDetectType,79 "_fnSettingsFromNode": _fnSettingsFromNode,80 "_fnGetDataMaster": _fnGetDataMaster,81 "_fnGetTrNodes": _fnGetTrNodes,82 "_fnGetTdNodes": _fnGetTdNodes,83 "_fnEscapeRegex": _fnEscapeRegex,84 "_fnDeleteIndex": _fnDeleteIndex,85 "_fnReOrderIndex": _fnReOrderIndex,86 "_fnColumnOrdering": _fnColumnOrdering,87 "_fnLog": _fnLog,88 "_fnClearTable": _fnClearTable,89 "_fnSaveState": _fnSaveState,90 "_fnLoadState": _fnLoadState,91 "_fnCreateCookie": _fnCreateCookie,92 "_fnReadCookie": _fnReadCookie,93 "_fnDetectHeader": _fnDetectHeader,94 "_fnGetUniqueThs": _fnGetUniqueThs,95 "_fnScrollBarWidth": _fnScrollBarWidth,96 "_fnApplyToChildren": _fnApplyToChildren,97 "_fnMap": _fnMap,98 "_fnGetRowData": _fnGetRowData,99 "_fnGetCellData": _fnGetCellData,100 "_fnSetCellData": _fnSetCellData,101 "_fnGetObjectDataFn": _fnGetObjectDataFn,102 "_fnSetObjectDataFn": _fnSetObjectDataFn,103 "_fnApplyColumnDefs": _fnApplyColumnDefs,104 "_fnBindAction": _fnBindAction,105 "_fnExtend": _fnExtend,106 "_fnCallbackReg": _fnCallbackReg,107 "_fnCallbackFire": _fnCallbackFire,108 "_fnJsonString": _fnJsonString,109 "_fnRender": _fnRender,110 "_fnNodeToColumnIndex": _fnNodeToColumnIndex,111 "_fnInfoMacros": _fnInfoMacros,112 "_fnBrowserDetect": _fnBrowserDetect,113 "_fnGetColumns": _fnGetColumns114};115$.extend( DataTable.ext.oApi, this.oApi );116for ( var sFunc in DataTable.ext.oApi )117{118 if ( sFunc )119 {120 this[sFunc] = _fnExternApiFunc(sFunc);121 }...

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_setDataFunctions.js

Source:_setDataFunctions.js Github

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...12 "Create property",13 function () {14 fn = table.oApi._fnSetObjectDataFn('test');15 o = {};16 fn( o, true );17 },18 function () { return o.test }19 );20 21 oTest.fnTest(22 "Single property doesn't kill other properties",23 function () {24 fn = table.oApi._fnSetObjectDataFn('test');25 o = {26 "test2": false27 };28 fn( o, true );29 },30 function () { return o.test && o.test2===false; }31 );32 33 oTest.fnTest(34 "Single property overwrite old property",35 function () {36 fn = table.oApi._fnSetObjectDataFn('test');37 o = {38 "test": false,39 "test2": false40 };41 fn( o, true );42 },43 function () { return o.test && o.test2===false; }44 );45 /​/​ Nested46 oTest.fnTest(47 "Create nested property",48 function () {49 fn = table.oApi._fnSetObjectDataFn('test.inner');50 o = {51 "test": {}52 };53 fn( o, true );54 },55 function () { return o.test.inner }56 );57 oTest.fnTest(58 "Deep create nested property",59 function () {60 fn = table.oApi._fnSetObjectDataFn('test.inner');61 o = {};62 fn( o, true );63 },64 function () { return o.test.inner }65 );66 67 oTest.fnTest(68 "Nested property doesn't kill other properties",69 function () {70 fn = table.oApi._fnSetObjectDataFn('test.inner');71 o = {72 "test": {73 "test2": false74 }75 };76 fn( o, true );77 },78 function () { return o.test.inner && o.test.test2===false; }79 );80 81 oTest.fnTest(82 "Single property overwrite old property",83 function () {84 fn = table.oApi._fnSetObjectDataFn('nested.test');85 o = {86 "nested": {87 "test": false,88 "test2": false89 }90 };91 fn( o, true );92 },93 function () { return o.nested.test && o.nested.test2===false; }94 );95 /​/​ Set arrays /​ objects96 oTest.fnTest(97 "Create object",98 function () {99 fn = table.oApi._fnSetObjectDataFn('test');100 o = {};101 fn( o, {"a":true, "b":false} );102 },103 function () { return o.test.a && o.test.b===false }104 );105 oTest.fnTest(106 "Create nested object",107 function () {108 fn = table.oApi._fnSetObjectDataFn('nested.test');109 o = {};110 fn( o, {"a":true, "b":false} );111 },112 function () { return o.nested.test.a && o.nested.test.b===false }113 );114 oTest.fnTest(115 "Create array",116 function () {117 fn = table.oApi._fnSetObjectDataFn('test');118 o = {};119 fn( o, [1,2,3] );120 },121 function () { return o.test[0]===1 && o.test[2]===3 }122 );123 oTest.fnTest(124 "Create nested array",125 function () {126 fn = table.oApi._fnSetObjectDataFn('nested.test');127 o = {};128 fn( o, [1,2,3] );129 },130 function () { return o.nested.test[0]===1 && o.nested.test[2]===3 }131 );132 /​/​ Array notation133 oTest.fnTest(134 "Create array of objects",135 function () {136 fn = table.oApi._fnSetObjectDataFn('test[].a');137 o = {};138 fn( o, [1,2,3] );139 },140 function () { return o.test.length===3 && o.test[0].a===1 && o.test[1].a===2; }141 );142 oTest.fnTest(143 "Create array of nested objects",144 function () {145 fn = table.oApi._fnSetObjectDataFn('test[].a.b');146 o = {};147 fn( o, [1,2,3] );148 },149 function () { return o.test.length===3 && o.test[0].a.b===1 && o.test[1].a.b===2; }150 );151 oTest.fnTest(152 "Create array",153 function () {154 fn = table.oApi._fnSetObjectDataFn('test[]');155 o = {};156 fn( o, [1,2,3] );157 },158 function () { return o.test.length===3 && o.test[0]===1 && o.test[1]===2; }159 );160 161 oTest.fnComplete();...

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Using AI Code Generation

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1var fn = require('./​fn');2fn();3var fn2 = require('./​fn2');4fn2();5var fn3 = require('./​fn3');6fn3();7var fn4 = require('./​fn4');8fn4();9var fn5 = require('./​fn5');10fn5();11var fn6 = require('./​fn6');12fn6();13var fn7 = require('./​fn7');14fn7();15var fn8 = require('./​fn8');16fn8();17var fn9 = require('./​fn9');18fn9();19var fn10 = require('./​fn10');20fn10();21var fn11 = require('./​fn11');22fn11();23var fn12 = require('./​fn12');24fn12();25var fn13 = require('./​fn13');26fn13();27var fn14 = require('./​fn14');28fn14();29var fn15 = require('./​fn15');30fn15();31var fn16 = require('./​fn16');32fn16();33var fn17 = require('./​fn17');34fn17();35var fn18 = require('./​fn18');36fn18();37var fn19 = require('./​fn19');38fn19();39var fn20 = require('./​fn20');40fn20();41var fn21 = require('./​fn21');42fn21();43var fn22 = require('./​fn22');44fn22();

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Using AI Code Generation

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1var myModule = require('./​module');2myModule.fn();3module.exports.fn = function () {4 console.log('Hello World');5}6module.exports = function () {7 console.log('Hello World');8}9module.exports = {10 fn: function () {11 console.log('Hello World');12 }13}14module.exports = class {15 constructor() {16 console.log('Hello World');17 }18}19module.exports = {20 fn: function () {21 console.log('Hello World');22 },23 class: class {24 constructor() {25 console.log('Hello World');26 }27 }28}

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Using AI Code Generation

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1var module = require('./​module');2module.fn();3module.exports = {4 fn: function() {5 console.log('fn called');6 }7};8var module = require('./​module');9module.fn();10console.log(module.property);11module.exports = {12 fn: function() {13 console.log('fn called');14 },15};16module.exports = function() {17 console.log('fn called');18};

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Using AI Code Generation

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1var myModule = require('./​myModule');2myModule.fn();3exports.fn = function(){4 console.log('fn method called');5}6import {fn} from './​myModule';7fn();8export function fn(){9 console.log('fn method called');10}11var fs = require('fs');12fs.appendFile()13fs.open()14fs.writeFile()15fs.appendFile()16fs.writeFile()17fs.unlink()18fs.rename()19fs.mkdir()

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Using AI Code Generation

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1var mod = require('./​mod');2mod.fn();3module.exports = {4 fn: function(){5 console.log('fn method of module');6 }7};8var fs = require('fs');

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Using AI Code Generation

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1var myModule = require('./​myModule.js');2myModule.fn();3module.exports.fn = function() {4 console.log('Hello World');5};6module.exports = function() {7 console.log('Hello World');8};9module.exports = {10 fn: function() {11 console.log('Hello World');12 }13};14module.exports = class {15 fn() {16 console.log('Hello World');17 }18};19module.exports = function() {20 this.fn = function() {21 console.log('Hello World');22 }23};24module.exports = function() {

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