Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
test_as_utc_with_local_object | () | Test the UTC time with local object. | Test the UTC time with local object. | def test_as_utc_with_local_object():
"""Test the UTC time with local object."""
dt_util.set_default_time_zone(dt_util.get_time_zone(TEST_TIME_ZONE))
localnow = dt_util.now()
utcnow = dt_util.as_utc(localnow)
assert localnow == utcnow
assert localnow.tzinfo != utcnow.tzinfo | [
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71,
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78,
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test_as_local_with_naive_object | () | Test local time with native object. | Test local time with native object. | def test_as_local_with_naive_object():
"""Test local time with native object."""
now = dt_util.now()
assert abs(now - dt_util.as_local(datetime.utcnow())) < timedelta(seconds=1) | [
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84,
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test_as_local_with_local_object | () | Test local with local object. | Test local with local object. | def test_as_local_with_local_object():
"""Test local with local object."""
now = dt_util.now()
assert now == now | [
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test_as_local_with_utc_object | () | Test local time with UTC object. | Test local time with UTC object. | def test_as_local_with_utc_object():
"""Test local time with UTC object."""
dt_util.set_default_time_zone(dt_util.get_time_zone(TEST_TIME_ZONE))
utcnow = dt_util.utcnow()
localnow = dt_util.as_local(utcnow)
assert localnow == utcnow
assert localnow.tzinfo != utcnow.tzinfo | [
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101,
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test_utc_from_timestamp | () | Test utc_from_timestamp method. | Test utc_from_timestamp method. | def test_utc_from_timestamp():
"""Test utc_from_timestamp method."""
assert datetime(1986, 7, 9, tzinfo=dt_util.UTC) == dt_util.utc_from_timestamp(
521251200
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test_as_timestamp | () | Test as_timestamp method. | Test as_timestamp method. | def test_as_timestamp():
"""Test as_timestamp method."""
ts = 1462401234
utc_dt = dt_util.utc_from_timestamp(ts)
assert ts == dt_util.as_timestamp(utc_dt)
utc_iso = utc_dt.isoformat()
assert ts == dt_util.as_timestamp(utc_iso)
# confirm the ability to handle a string passed in
delta = d... | [
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test_parse_datetime_converts_correctly | () | Test parse_datetime converts strings. | Test parse_datetime converts strings. | def test_parse_datetime_converts_correctly():
"""Test parse_datetime converts strings."""
assert datetime(1986, 7, 9, 12, 0, 0, tzinfo=dt_util.UTC) == dt_util.parse_datetime(
"1986-07-09T12:00:00Z"
)
utcnow = dt_util.utcnow()
assert utcnow == dt_util.parse_datetime(utcnow.isoformat()) | [
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test_parse_datetime_returns_none_for_incorrect_format | () | Test parse_datetime returns None if incorrect format. | Test parse_datetime returns None if incorrect format. | def test_parse_datetime_returns_none_for_incorrect_format():
"""Test parse_datetime returns None if incorrect format."""
assert dt_util.parse_datetime("not a datetime string") is None | [
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test_get_age | () | Test get_age. | Test get_age. | def test_get_age():
"""Test get_age."""
diff = dt_util.now() - timedelta(seconds=0)
assert dt_util.get_age(diff) == "0 seconds"
diff = dt_util.now() - timedelta(seconds=1)
assert dt_util.get_age(diff) == "1 second"
diff = dt_util.now() - timedelta(seconds=30)
assert dt_util.get_age(diff) =... | [
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test_parse_time_expression | () | Test parse_time_expression. | Test parse_time_expression. | def test_parse_time_expression():
"""Test parse_time_expression."""
assert [x for x in range(60)] == dt_util.parse_time_expression("*", 0, 59)
assert [x for x in range(60)] == dt_util.parse_time_expression(None, 0, 59)
assert [x for x in range(0, 60, 5)] == dt_util.parse_time_expression("/5", 0, 59)
... | [
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test_find_next_time_expression_time_basic | () | Test basic stuff for find_next_time_expression_time. | Test basic stuff for find_next_time_expression_time. | def test_find_next_time_expression_time_basic():
"""Test basic stuff for find_next_time_expression_time."""
def find(dt, hour, minute, second):
"""Call test_find_next_time_expression_time."""
seconds = dt_util.parse_time_expression(second, 0, 59)
minutes = dt_util.parse_time_expression(... | [
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test_find_next_time_expression_time_dst | () | Test daylight saving time for find_next_time_expression_time. | Test daylight saving time for find_next_time_expression_time. | def test_find_next_time_expression_time_dst():
"""Test daylight saving time for find_next_time_expression_time."""
tz = dt_util.get_time_zone("Europe/Vienna")
dt_util.set_default_time_zone(tz)
def find(dt, hour, minute, second):
"""Call test_find_next_time_expression_time."""
seconds = ... | [
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N_A2C.update_search_space | (self, search_space) | Update the self.bounds and self.types by the search_space.json file.
Override of the abstract method in :class:`~nni.tuner.Tuner`.
| Update the self.bounds and self.types by the search_space.json file. | def update_search_space(self, search_space):
"""Update the self.bounds and self.types by the search_space.json file.
Override of the abstract method in :class:`~nni.tuner.Tuner`.
"""
if not isinstance(search_space, dict):
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N_A2C.generate_multiple_parameters | (self, parameter_id_list, **kwargs) | Returns multiple sets of trial (hyper-)parameters,
as iterable of serializable objects.
| Returns multiple sets of trial (hyper-)parameters,
as iterable of serializable objects.
| def generate_multiple_parameters(self, parameter_id_list, **kwargs):
"""Returns multiple sets of trial (hyper-)parameters,
as iterable of serializable objects.
"""
result = []
self.send_trial_callback = kwargs['st_callback']
for parameter_id in parameter_id_list:
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N_A2C.generate_parameters | (self, parameter_id, **kwargs) | Method which provides one set of hyper-parameters.
Override of the abstract method in :class:`~nni.tuner.Tuner`.
| Method which provides one set of hyper-parameters. | def generate_parameters(self, parameter_id, **kwargs):
"""Method which provides one set of hyper-parameters.
Override of the abstract method in :class:`~nni.tuner.Tuner`.
"""
if self.serve_list:
self.wait_dict[parameter_id] = self.serve_list.pop()
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N_A2C.receive_trial_result | (self, parameter_id, parameters, value, **kwargs) | Method invoked when a trial reports its final result.
Override of the abstract method in :class:`~nni.tuner.Tuner`.
| Method invoked when a trial reports its final result. | def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
"""Method invoked when a trial reports its final result.
Override of the abstract method in :class:`~nni.tuner.Tuner`.
"""
if isinstance(value, dict):
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N_A2C.trial_end | (self, parameter_id, success, **kwargs) | Method invoked when a trial is completed or terminated.
Override of the abstract method in :class:`~nni.tuner.Tuner`.
| Method invoked when a trial is completed or terminated. | def trial_end(self, parameter_id, success, **kwargs):
"""Method invoked when a trial is completed or terminated.
Override of the abstract method in :class:`~nni.tuner.Tuner`.
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if not success:
self.population.append(self.wait_dict[parameter_id], 0.0)
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async_setup_entry | (hass, config_entry, async_add_entities) | Perform the setup for Xiaomi devices. | Perform the setup for Xiaomi devices. | async def async_setup_entry(hass, config_entry, async_add_entities):
"""Perform the setup for Xiaomi devices."""
entities = []
gateway = hass.data[DOMAIN][GATEWAYS_KEY][config_entry.entry_id]
for device in gateway.devices["lock"]:
model = device["model"]
if model == "lock.aq1":
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XiaomiAqaraLock.__init__ | (self, device, name, xiaomi_hub, config_entry) | Initialize the XiaomiAqaraLock. | Initialize the XiaomiAqaraLock. | def __init__(self, device, name, xiaomi_hub, config_entry):
"""Initialize the XiaomiAqaraLock."""
self._changed_by = 0
self._verified_wrong_times = 0
super().__init__(device, name, xiaomi_hub, config_entry) | [
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XiaomiAqaraLock.is_locked | (self) | Return true if lock is locked. | Return true if lock is locked. | def is_locked(self) -> bool:
"""Return true if lock is locked."""
if self._state is not None:
return self._state == STATE_LOCKED | [
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XiaomiAqaraLock.changed_by | (self) | Last change triggered by. | Last change triggered by. | def changed_by(self) -> int:
"""Last change triggered by."""
return self._changed_by | [
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XiaomiAqaraLock.device_state_attributes | (self) | Return the state attributes. | Return the state attributes. | def device_state_attributes(self) -> dict:
"""Return the state attributes."""
attributes = {ATTR_VERIFIED_WRONG_TIMES: self._verified_wrong_times}
return attributes | [
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XiaomiAqaraLock.clear_unlock_state | (self, _) | Clear unlock state automatically. | Clear unlock state automatically. | def clear_unlock_state(self, _):
"""Clear unlock state automatically."""
self._state = STATE_LOCKED
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XiaomiAqaraLock.parse_data | (self, data, raw_data) | Parse data sent by gateway. | Parse data sent by gateway. | def parse_data(self, data, raw_data):
"""Parse data sent by gateway."""
value = data.get(VERIFIED_WRONG_KEY)
if value is not None:
self._verified_wrong_times = int(value)
return True
for key in (FINGER_KEY, PASSWORD_KEY, CARD_KEY):
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setup_decrypt | () | Return decryption function and length of key.
Async friendly.
| Return decryption function and length of key. | def setup_decrypt() -> Tuple[int, Callable]:
"""Return decryption function and length of key.
Async friendly.
"""
def decrypt(ciphertext, key):
"""Decrypt ciphertext using key."""
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setup_encrypt | () | Return encryption function and length of key.
Async friendly.
| Return encryption function and length of key. | def setup_encrypt() -> Tuple[int, Callable]:
"""Return encryption function and length of key.
Async friendly.
"""
def encrypt(ciphertext, key):
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_decrypt_payload | (key: str, ciphertext: str) | Decrypt encrypted payload. | Decrypt encrypted payload. | def _decrypt_payload(key: str, ciphertext: str) -> Dict[str, str]:
"""Decrypt encrypted payload."""
try:
keylen, decrypt = setup_decrypt()
except OSError:
_LOGGER.warning("Ignoring encrypted payload because libsodium not installed")
return None
if key is None:
_LOGGER.wa... | [
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registration_context | (registration: Dict) | Generate a context from a request. | Generate a context from a request. | def registration_context(registration: Dict) -> Context:
"""Generate a context from a request."""
return Context(user_id=registration[CONF_USER_ID]) | [
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empty_okay_response | (headers: Dict = None, status: int = HTTP_OK) | Return a Response with empty JSON object and a 200. | Return a Response with empty JSON object and a 200. | def empty_okay_response(headers: Dict = None, status: int = HTTP_OK) -> Response:
"""Return a Response with empty JSON object and a 200."""
return Response(
text="{}", status=status, content_type=CONTENT_TYPE_JSON, headers=headers
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error_response | (
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code: str, message: str, status: int = HTTP_BAD_REQUEST, headers: dict = None
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supports_encryption | () | Test if we support encryption. | Test if we support encryption. | def supports_encryption() -> bool:
"""Test if we support encryption."""
try:
import nacl # noqa: F401 pylint: disable=unused-import, import-outside-toplevel
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safe_registration | (registration: Dict) | Return a registration without sensitive values. | Return a registration without sensitive values. | def safe_registration(registration: Dict) -> Dict:
"""Return a registration without sensitive values."""
# Sensitive values: webhook_id, secret, cloudhook_url
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savable_state | (hass: HomeAssistantType) | Return a clean object containing things that should be saved. | Return a clean object containing things that should be saved. | def savable_state(hass: HomeAssistantType) -> Dict:
"""Return a clean object containing things that should be saved."""
return {
DATA_BINARY_SENSOR: hass.data[DOMAIN][DATA_BINARY_SENSOR],
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webhook_response | (
data, *, registration: Dict, status: int = HTTP_OK, headers: Dict = None
) | Return a encrypted response if registration supports it. | Return a encrypted response if registration supports it. | def webhook_response(
data, *, registration: Dict, status: int = HTTP_OK, headers: Dict = None
) -> Response:
"""Return a encrypted response if registration supports it."""
data = json.dumps(data, cls=JSONEncoder)
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device_info | (registration: Dict) | Return the device info for this registration. | Return the device info for this registration. | def device_info(registration: Dict) -> Dict:
"""Return the device info for this registration."""
return {
"identifiers": {(DOMAIN, registration[ATTR_DEVICE_ID])},
"manufacturer": registration[ATTR_MANUFACTURER],
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setup_platform | (hass, config, add_devices, discovery_info=None) | Set up the cartridge sensor. | Set up the cartridge sensor. | def setup_platform(hass, config, add_devices, discovery_info=None):
"""Set up the cartridge sensor."""
host = config.get(CONF_HOST)
api = EpsonPrinterAPI(host)
if not api.available:
raise PlatformNotReady()
sensors = [
EpsonPrinterCartridge(api, condition)
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EpsonPrinterCartridge.__init__ | (self, api, cartridgeidx) | Initialize a cartridge sensor. | Initialize a cartridge sensor. | def __init__(self, api, cartridgeidx):
"""Initialize a cartridge sensor."""
self._api = api
self._id = cartridgeidx
self._name = MONITORED_CONDITIONS[self._id][0]
self._unit = MONITORED_CONDITIONS[self._id][1]
self._icon = MONITORED_CONDITIONS[self._id][2] | [
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EpsonPrinterCartridge.name | (self) | Return the name of the sensor. | Return the name of the sensor. | def name(self):
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EpsonPrinterCartridge.icon | (self) | Icon to use in the frontend, if any. | Icon to use in the frontend, if any. | def icon(self):
"""Icon to use in the frontend, if any."""
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EpsonPrinterCartridge.unit_of_measurement | (self) | Return the unit the value is expressed in. | Return the unit the value is expressed in. | def unit_of_measurement(self):
"""Return the unit the value is expressed in."""
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EpsonPrinterCartridge.state | (self) | Return the state of the device. | Return the state of the device. | def state(self):
"""Return the state of the device."""
return self._api.getSensorValue(self._id) | [
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EpsonPrinterCartridge.available | (self) | Could the device be accessed during the last update call. | Could the device be accessed during the last update call. | def available(self):
"""Could the device be accessed during the last update call."""
return self._api.available | [
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EpsonPrinterCartridge.update | (self) | Get the latest data from the Epson printer. | Get the latest data from the Epson printer. | def update(self):
"""Get the latest data from the Epson printer."""
self._api.update() | [
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async_setup | (hass, config) | Set up the Twilio component. | Set up the Twilio component. | async def async_setup(hass, config):
"""Set up the Twilio component."""
if DOMAIN not in config:
return True
conf = config[DOMAIN]
hass.data[DATA_TWILIO] = Client(
conf.get(CONF_ACCOUNT_SID), conf.get(CONF_AUTH_TOKEN)
)
return True | [
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handle_webhook | (hass, webhook_id, request) | Handle incoming webhook from Twilio for inbound messages and calls. | Handle incoming webhook from Twilio for inbound messages and calls. | async def handle_webhook(hass, webhook_id, request):
"""Handle incoming webhook from Twilio for inbound messages and calls."""
data = dict(await request.post())
data["webhook_id"] = webhook_id
hass.bus.async_fire(RECEIVED_DATA, dict(data))
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async_setup_entry | (hass, entry) | Configure based on config entry. | Configure based on config entry. | async def async_setup_entry(hass, entry):
"""Configure based on config entry."""
hass.components.webhook.async_register(
DOMAIN, "Twilio", entry.data[CONF_WEBHOOK_ID], handle_webhook
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async_unload_entry | (hass, entry) | Unload a config entry. | Unload a config entry. | async def async_unload_entry(hass, entry):
"""Unload a config entry."""
hass.components.webhook.async_unregister(entry.data[CONF_WEBHOOK_ID])
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masked_softmax | (vector: torch.Tensor, mask: torch.Tensor, dim: int = -1) |
``torch.nn.functional.softmax(vector)`` does not work if some elements of ``vector`` should be
masked. This performs a softmax on just the non-masked portions of ``vector``. Passing
``None`` in for the mask is also acceptable; you'll just get a regular softmax.
``vector`` can have an arbitrary numbe... |
``torch.nn.functional.softmax(vector)`` does not work if some elements of ``vector`` should be
masked. This performs a softmax on just the non-masked portions of ``vector``. Passing
``None`` in for the mask is also acceptable; you'll just get a regular softmax. | def masked_softmax(vector: torch.Tensor, mask: torch.Tensor, dim: int = -1) -> torch.Tensor:
"""
``torch.nn.functional.softmax(vector)`` does not work if some elements of ``vector`` should be
masked. This performs a softmax on just the non-masked portions of ``vector``. Passing
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test_services | (hass, light_data, sent_messages) | Test services on lock. | Test services on lock. | async def test_services(hass, light_data, sent_messages):
"""Test services on lock."""
await setup_ozw(hass, fixture=light_data)
# Test set_config_parameter list by label
await hass.services.async_call(
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114,
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] | python | en | ['en', 'en', 'en'] | True |
test_adam_climate_entity_attributes | (hass, mock_smile_adam) | Test creation of adam climate device environment. | Test creation of adam climate device environment. | async def test_adam_climate_entity_attributes(hass, mock_smile_adam):
"""Test creation of adam climate device environment."""
entry = await async_init_integration(hass, mock_smile_adam)
assert entry.state == ENTRY_STATE_LOADED
state = hass.states.get("climate.zone_lisa_wk")
attrs = state.attributes... | [
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test_adam_climate_entity_climate_changes | (hass, mock_smile_adam) | Test handling of user requests in adam climate device environment. | Test handling of user requests in adam climate device environment. | async def test_adam_climate_entity_climate_changes(hass, mock_smile_adam):
"""Test handling of user requests in adam climate device environment."""
entry = await async_init_integration(hass, mock_smile_adam)
assert entry.state == ENTRY_STATE_LOADED
await hass.services.async_call(
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test_anna_climate_entity_attributes | (hass, mock_smile_anna) | Test creation of anna climate device environment. | Test creation of anna climate device environment. | async def test_anna_climate_entity_attributes(hass, mock_smile_anna):
"""Test creation of anna climate device environment."""
entry = await async_init_integration(hass, mock_smile_anna)
assert entry.state == ENTRY_STATE_LOADED
state = hass.states.get("climate.anna")
attrs = state.attributes
as... | [
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test_anna_climate_entity_climate_changes | (hass, mock_smile_anna) | Test handling of user requests in anna climate device environment. | Test handling of user requests in anna climate device environment. | async def test_anna_climate_entity_climate_changes(hass, mock_smile_anna):
"""Test handling of user requests in anna climate device environment."""
entry = await async_init_integration(hass, mock_smile_anna)
assert entry.state == ENTRY_STATE_LOADED
await hass.services.async_call(
"climate",
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add_on_off_event_device | (hass, device) | Register an Insteon device as an on/off event device. | Register an Insteon device as an on/off event device. | def add_on_off_event_device(hass, device):
"""Register an Insteon device as an on/off event device."""
@callback
def async_fire_group_on_off_event(name, address, group, button):
# Firing an event when a button is pressed.
if button and button[-2] == "_":
button_id = button[-1].l... | [
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register_new_device_callback | (hass) | Register callback for new Insteon device. | Register callback for new Insteon device. | def register_new_device_callback(hass):
"""Register callback for new Insteon device."""
@callback
def async_new_insteon_device(address=None):
"""Detect device from transport to be delegated to platform."""
hass.async_create_task(async_create_new_entities(address))
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async_register_services | (hass) | Register services used by insteon component. | Register services used by insteon component. | def async_register_services(hass):
"""Register services used by insteon component."""
save_lock = asyncio.Lock()
async def async_srv_add_all_link(service):
"""Add an INSTEON All-Link between two devices."""
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print_aldb_to_log | (aldb) | Print the All-Link Database to the log file. | Print the All-Link Database to the log file. | def print_aldb_to_log(aldb):
"""Print the All-Link Database to the log file."""
logger = logging.getLogger(f"{__name__}.links")
logger.info("%s ALDB load status is %s", aldb.address, aldb.status.name)
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async_add_insteon_entities | (
hass, platform, entity_type, async_add_entities, discovery_info
) | Add Insteon devices to a platform. | Add Insteon devices to a platform. | def async_add_insteon_entities(
hass, platform, entity_type, async_add_entities, discovery_info
):
"""Add Insteon devices to a platform."""
new_entities = []
device_list = [discovery_info.get("address")] if discovery_info else devices
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device = devices[address]
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test_blueprint_schema | (blueprint) | Test different schemas. | Test different schemas. | def test_blueprint_schema(blueprint):
"""Test different schemas."""
try:
schemas.BLUEPRINT_SCHEMA(blueprint)
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_LOGGER.exception("%s", blueprint)
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test_blueprint_schema_invalid | (blueprint) | Test different schemas. | Test different schemas. | def test_blueprint_schema_invalid(blueprint):
"""Test different schemas."""
with pytest.raises(vol.Invalid):
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test_blueprint_instance_fields | (bp_instance) | Test blueprint instance fields. | Test blueprint instance fields. | def test_blueprint_instance_fields(bp_instance):
"""Test blueprint instance fields."""
schemas.BLUEPRINT_INSTANCE_FIELDS({"use_blueprint": bp_instance}) | [
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CoolmasterConfigFlow.async_step_user | (self, user_input=None) | Handle a flow initialized by the user. | Handle a flow initialized by the user. | async def async_step_user(self, user_input=None):
"""Handle a flow initialized by the user."""
if user_input is None:
return self.async_show_form(step_id="user", data_schema=DATA_SCHEMA)
errors = {}
host = user_input[CONF_HOST]
try:
result = await _vali... | [
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setup | (hass, config) | Set up the StatsD component. | Set up the StatsD component. | def setup(hass, config):
"""Set up the StatsD component."""
conf = config[DOMAIN]
host = conf.get(CONF_HOST)
port = conf.get(CONF_PORT)
sample_rate = conf.get(CONF_RATE)
prefix = conf.get(CONF_PREFIX)
value_mapping = conf.get(CONF_VALUE_MAP)
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export_model | (sym, params, input_shape, input_type=np.float32,
onnx_file_path='model.onnx', verbose=False) | Exports the MXNet model file, passed as a parameter, into ONNX model.
Accepts both symbol,parameter objects as well as json and params filepaths as input.
Operator support and coverage -
https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage
Parameters
----------
sym : str or ... | Exports the MXNet model file, passed as a parameter, into ONNX model.
Accepts both symbol,parameter objects as well as json and params filepaths as input.
Operator support and coverage -
https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage | def export_model(sym, params, input_shape, input_type=np.float32,
onnx_file_path='model.onnx', verbose=False):
"""Exports the MXNet model file, passed as a parameter, into ONNX model.
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async_setup_entry | (hass, config_entry, async_add_entities) | Set up the VeSync fan platform. | Set up the VeSync fan platform. | async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the VeSync fan platform."""
async def async_discover(devices):
"""Add new devices to platform."""
_async_setup_entities(devices, async_add_entities)
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_async_setup_entities | (devices, async_add_entities) | Check if device is online and add entity. | Check if device is online and add entity. | def _async_setup_entities(devices, async_add_entities):
"""Check if device is online and add entity."""
dev_list = []
for dev in devices:
if DEV_TYPE_TO_HA.get(dev.device_type) == "fan":
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VeSyncFanHA.__init__ | (self, fan) | Initialize the VeSync fan device. | Initialize the VeSync fan device. | def __init__(self, fan):
"""Initialize the VeSync fan device."""
super().__init__(fan)
self.smartfan = fan | [
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VeSyncFanHA.supported_features | (self) | Flag supported features. | Flag supported features. | def supported_features(self):
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VeSyncFanHA.speed | (self) | Return the current speed. | Return the current speed. | def speed(self):
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VeSyncFanHA.speed_list | (self) | Get the list of available speeds. | Get the list of available speeds. | def speed_list(self):
"""Get the list of available speeds."""
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VeSyncFanHA.unique_info | (self) | Return the ID of this fan. | Return the ID of this fan. | def unique_info(self):
"""Return the ID of this fan."""
return self.smartfan.uuid | [
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VeSyncFanHA.device_state_attributes | (self) | Return the state attributes of the fan. | Return the state attributes of the fan. | def device_state_attributes(self):
"""Return the state attributes of the fan."""
return {
"mode": self.smartfan.mode,
"active_time": self.smartfan.active_time,
"filter_life": self.smartfan.filter_life,
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VeSyncFanHA.set_speed | (self, speed) | Set the speed of the device. | Set the speed of the device. | def set_speed(self, speed):
"""Set the speed of the device."""
if not self.smartfan.is_on:
self.smartfan.turn_on()
self.smartfan.manual_mode()
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VeSyncFanHA.turn_on | (self, speed: str = None, **kwargs) | Turn the device on. | Turn the device on. | def turn_on(self, speed: str = None, **kwargs) -> None:
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self.smartfan.turn_on()
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async_setup_platform | (hass, config, async_add_entities, discovery_info=None) | Set up the Random binary sensor. | Set up the Random binary sensor. | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Set up the Random binary sensor."""
name = config.get(CONF_NAME)
device_class = config.get(CONF_DEVICE_CLASS)
async_add_entities([RandomSensor(name, device_class)], True) | [
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RandomSensor.__init__ | (self, name, device_class) | Initialize the Random binary sensor. | Initialize the Random binary sensor. | def __init__(self, name, device_class):
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RandomSensor.name | (self) | Return the name of the sensor. | Return the name of the sensor. | def name(self):
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RandomSensor.is_on | (self) | Return true if sensor is on. | Return true if sensor is on. | def is_on(self):
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RandomSensor.device_class | (self) | Return the sensor class of the sensor. | Return the sensor class of the sensor. | def device_class(self):
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RandomSensor.async_update | (self) | Get new state and update the sensor's state. | Get new state and update the sensor's state. | async def async_update(self):
"""Get new state and update the sensor's state."""
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Assessor.assess_trial | (self, trial_job_id, trial_history) |
Abstract method for determining whether a trial should be killed. Must override.
The NNI framework has little guarantee on ``trial_history``.
This method is not guaranteed to be invoked for each time ``trial_history`` get updated.
It is also possible that a trial's history keeps updati... |
Abstract method for determining whether a trial should be killed. Must override. | def assess_trial(self, trial_job_id, trial_history):
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Abstract method for determining whether a trial should be killed. Must override.
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Assessor.trial_end | (self, trial_job_id, success) |
Abstract method invoked when a trial is completed or terminated. Do nothing by default.
Parameters
----------
trial_job_id : str
Unique identifier of the trial.
success : bool
True if the trial successfully completed; False if failed or terminated.
... |
Abstract method invoked when a trial is completed or terminated. Do nothing by default. | def trial_end(self, trial_job_id, success):
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Abstract method invoked when a trial is completed or terminated. Do nothing by default.
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Unique identifier of the trial.
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Assessor.load_checkpoint | (self) |
Internal API under revising, not recommended for end users.
|
Internal API under revising, not recommended for end users.
| def load_checkpoint(self):
"""
Internal API under revising, not recommended for end users.
"""
checkpoin_path = self.get_checkpoint_path()
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")"
] | [
105,
4
] | [
110,
96
] | python | en | ['en', 'error', 'th'] | False |
Assessor.save_checkpoint | (self) |
Internal API under revising, not recommended for end users.
|
Internal API under revising, not recommended for end users.
| def save_checkpoint(self):
"""
Internal API under revising, not recommended for end users.
"""
checkpoin_path = self.get_checkpoint_path()
_logger.info('Save checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path) | [
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117,
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set_conv_prune_dim | (dim) |
Parameters:
dim: int
0: filter pruning
1: channel pruning
|
Parameters:
dim: int
0: filter pruning
1: channel pruning
| def set_conv_prune_dim(dim):
"""
Parameters:
dim: int
0: filter pruning
1: channel pruning
"""
global conv_prune_dim
conv_prune_dim = dim | [
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24,
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cat_inshape | (module_masks, mask, cat_info, last_visited) |
Inference the output mask of the cat operation from the
input mask.
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the Conv2d
mask : CoarseMask
The mask of its input tensor
cat_info: dict
Dict object that records the necessary informati... |
Inference the output mask of the cat operation from the
input mask. | def cat_inshape(module_masks, mask, cat_info, last_visited):
"""
Inference the output mask of the cat operation from the
input mask.
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the Conv2d
mask : CoarseMask
The mask of its input tensor
cat... | [
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347,
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424,
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add_inshape | (module_masks, mask) |
Inference the output mask of the add operation from the
input mask.
|
Inference the output mask of the add operation from the
input mask.
| def add_inshape(module_masks, mask):
"""
Inference the output mask of the add operation from the
input mask.
"""
assert isinstance(mask, CoarseMask)
if module_masks.input_mask is None:
module_masks.set_input_mask(mask)
module_masks.set_output_mask(mask)
# module_masks.inp... | [
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add_outshape | (module_masks, mask) |
Inference the input mask of the add operation from the
output mask.
|
Inference the input mask of the add operation from the
output mask.
| def add_outshape(module_masks, mask):
"""
Inference the input mask of the add operation from the
output mask.
"""
assert isinstance(mask, CoarseMask)
if module_masks.output_mask is None:
module_masks.set_output_mask(mask)
module_masks.set_input_mask(mask)
return mask
... | [
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batchnorm2d_inshape | (module_masks, mask) |
We assume only the second dimension has coarse grained mask
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the batchnorm2d
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its output tensor
|
We assume only the second dimension has coarse grained mask | def batchnorm2d_inshape(module_masks, mask):
"""
We assume only the second dimension has coarse grained mask
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the batchnorm2d
mask : CoarseMask
The mask of its input tensor
Returns
-------
C... | [
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batchnorm2d_outshape | (module_masks, mask) |
We assume only the second dimension has coarse grained mask
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the batchnorm2d
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its output tensor
|
We assume only the second dimension has coarse grained mask | def batchnorm2d_outshape(module_masks, mask):
"""
We assume only the second dimension has coarse grained mask
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the batchnorm2d
mask : CoarseMask
The mask of its input tensor
Returns
-------
... | [
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linear_inshape | (module_masks, mask) |
Coarse grained input mask does not change the shape of weights and output tensor
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the linear
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its ou... |
Coarse grained input mask does not change the shape of weights and output tensor | def linear_inshape(module_masks, mask):
"""
Coarse grained input mask does not change the shape of weights and output tensor
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the linear
mask : CoarseMask
The mask of its input tensor
Returns
--... | [
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543,
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] | python | en | ['en', 'error', 'th'] | False |
view_inshape | (module_masks, mask, shape) |
This is a limited support
TODO: consider replace tensor.view with nn.Flatten, because tensor.view is not
included in module, thus, cannot be replaced by our framework.
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the ```view``` op
mask : CoarseMask
... |
This is a limited support | def view_inshape(module_masks, mask, shape):
"""
This is a limited support
TODO: consider replace tensor.view with nn.Flatten, because tensor.view is not
included in module, thus, cannot be replaced by our framework.
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks ... | [
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591,
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view_outshape | (module_masks, mask, shape) |
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the ```view``` op
mask : CoarseMask
The mask of its output tensor
shape : dict
Original shape of its input and output tensors
Returns
-------
CoarseMask
The mask of its input ten... |
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the ```view``` op
mask : CoarseMask
The mask of its output tensor
shape : dict
Original shape of its input and output tensors
Returns
-------
CoarseMask
The mask of its input ten... | def view_outshape(module_masks, mask, shape):
"""
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the ```view``` op
mask : CoarseMask
The mask of its output tensor
shape : dict
Original shape of its input and output tensors
Returns
---... | [
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594,
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] | [
630,
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size_inshape | (module_masks, mask) |
No need to do anything for this ```size``` op
|
No need to do anything for this ```size``` op
| def size_inshape(module_masks, mask):
"""
No need to do anything for this ```size``` op
"""
return None | [
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] | [
633,
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] | [
637,
15
] | python | en | ['en', 'error', 'th'] | False |
mean_inshape | (module_masks, mask, shape) |
Similar to view operation, currently mask inference only supports
the mean operation on the 3rd and 4th dimensions.
|
Similar to view operation, currently mask inference only supports
the mean operation on the 3rd and 4th dimensions.
| def mean_inshape(module_masks, mask, shape):
"""
Similar to view operation, currently mask inference only supports
the mean operation on the 3rd and 4th dimensions.
"""
assert shape['in_shape'][0] == shape['out_shape'][0]
assert shape['out_shape'][1] == shape['in_shape'][1]
assert len(shape[... | [
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"]... | [
640,
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] | [
660,
23
] | python | en | ['en', 'error', 'th'] | False |
mean_outshape | (module_masks, mask, shape) |
Similar to view operation, currently mask inference only supports
the mean operation on the 3rd and 4th dimensions.
|
Similar to view operation, currently mask inference only supports
the mean operation on the 3rd and 4th dimensions.
| def mean_outshape(module_masks, mask, shape):
"""
Similar to view operation, currently mask inference only supports
the mean operation on the 3rd and 4th dimensions.
"""
assert shape['in_shape'][0] == shape['out_shape'][0]
assert shape['out_shape'][1] == shape['in_shape'][1]
assert len(shape... | [
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"... | [
663,
0
] | [
681,
22
] | python | en | ['en', 'error', 'th'] | False |
maxpool2d_inshape | (module_masks, mask) |
Assume only the second dimension is masked
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the maxpool2d
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its output tensor
|
Assume only the second dimension is masked | def maxpool2d_inshape(module_masks, mask):
"""
Assume only the second dimension is masked
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the maxpool2d
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The... | [
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"... | [
684,
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] | [
710,
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maxpool2d_outshape | (module_masks, mask) |
Assume only the second dimension is masked
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the maxpool2d
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its output tensor
|
Assume only the second dimension is masked | def maxpool2d_outshape(module_masks, mask):
"""
Assume only the second dimension is masked
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the maxpool2d
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
Th... | [
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... | [
713,
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735,
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relu_inshape | (module_masks, mask) |
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the relu
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its output tensor
|
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the relu
mask : CoarseMask
The mask of its input tensor | def relu_inshape(module_masks, mask):
"""
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the relu
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its output tensor
"""
assert isinstance(m... | [
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"module_ma... | [
738,
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relu_outshape | (module_masks, mask) |
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the relu
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its output tensor
|
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the relu
mask : CoarseMask
The mask of its input tensor | def relu_outshape(module_masks, mask):
"""
Parameters
----------
module_masks : ModuleMasks
The ModuleMasks instance of the relu
mask : CoarseMask
The mask of its input tensor
Returns
-------
CoarseMask
The mask of its output tensor
"""
assert isinstance(... | [
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... | [
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