body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def __init__(__self__, *, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None):
'\n Input properties used for looking up and filtering GroupMemberships resources.\n :param pulumi.Input[str] group_id: ID of a Okta group.\n :param pulumi.Inp... | -1,124,800,230,950,680,700 | Input properties used for looking up and filtering GroupMemberships resources.
:param pulumi.Input[str] group_id: ID of a Okta group.
:param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for. | sdk/python/pulumi_okta/group_memberships.py | __init__ | pulumi/pulumi-okta | python | def __init__(__self__, *, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None):
'\n Input properties used for looking up and filtering GroupMemberships resources.\n :param pulumi.Input[str] group_id: ID of a Okta group.\n :param pulumi.Inp... |
@property
@pulumi.getter(name='groupId')
def group_id(self) -> Optional[pulumi.Input[str]]:
'\n ID of a Okta group.\n '
return pulumi.get(self, 'group_id') | -3,177,421,181,971,841,500 | ID of a Okta group. | sdk/python/pulumi_okta/group_memberships.py | group_id | pulumi/pulumi-okta | python | @property
@pulumi.getter(name='groupId')
def group_id(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'group_id') |
@property
@pulumi.getter
def users(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
'\n The list of Okta user IDs which the group should have membership managed for.\n '
return pulumi.get(self, 'users') | 99,600,952,663,328,530 | The list of Okta user IDs which the group should have membership managed for. | sdk/python/pulumi_okta/group_memberships.py | users | pulumi/pulumi-okta | python | @property
@pulumi.getter
def users(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
'\n \n '
return pulumi.get(self, 'users') |
@overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, __props__=None):
'\n Resource to manage a set of memberships for a specific group.\n\n This res... | -5,755,180,085,078,006,000 | Resource to manage a set of memberships for a specific group.
This resource will allow you to bulk manage group membership in Okta for a given group. This offers an interface to pass multiple users into a single resource call, for better API resource usage. Effectively this is the same as using the `group.Membership` ... | sdk/python/pulumi_okta/group_memberships.py | __init__ | pulumi/pulumi-okta | python | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, __props__=None):
'\n Resource to manage a set of memberships for a specific group.\n\n This res... |
@overload
def __init__(__self__, resource_name: str, args: GroupMembershipsArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Resource to manage a set of memberships for a specific group.\n\n This resource will allow you to bulk manage group membership in Okta for a given group. This offers an in... | -7,360,302,168,923,495,000 | Resource to manage a set of memberships for a specific group.
This resource will allow you to bulk manage group membership in Okta for a given group. This offers an interface to pass multiple users into a single resource call, for better API resource usage. Effectively this is the same as using the `group.Membership` ... | sdk/python/pulumi_okta/group_memberships.py | __init__ | pulumi/pulumi-okta | python | @overload
def __init__(__self__, resource_name: str, args: GroupMembershipsArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Resource to manage a set of memberships for a specific group.\n\n This resource will allow you to bulk manage group membership in Okta for a given group. This offers an in... |
@staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None) -> 'GroupMemberships':
"\n Get an existing GroupMemberships resource's state with the give... | -1,442,683,605,509,781,800 | Get an existing GroupMemberships resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Op... | sdk/python/pulumi_okta/group_memberships.py | get | pulumi/pulumi-okta | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None) -> 'GroupMemberships':
"\n Get an existing GroupMemberships resource's state with the give... |
@property
@pulumi.getter(name='groupId')
def group_id(self) -> pulumi.Output[str]:
'\n ID of a Okta group.\n '
return pulumi.get(self, 'group_id') | -3,855,980,625,715,913,000 | ID of a Okta group. | sdk/python/pulumi_okta/group_memberships.py | group_id | pulumi/pulumi-okta | python | @property
@pulumi.getter(name='groupId')
def group_id(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'group_id') |
@property
@pulumi.getter
def users(self) -> pulumi.Output[Sequence[str]]:
'\n The list of Okta user IDs which the group should have membership managed for.\n '
return pulumi.get(self, 'users') | -9,122,935,689,198,096,000 | The list of Okta user IDs which the group should have membership managed for. | sdk/python/pulumi_okta/group_memberships.py | users | pulumi/pulumi-okta | python | @property
@pulumi.getter
def users(self) -> pulumi.Output[Sequence[str]]:
'\n \n '
return pulumi.get(self, 'users') |
def build_grid(hyperparameters):
'Build a grid represented as a list of parameter dictionaries.'
parameter_dicts = []
for parameters in product(*hyperparameters.values()):
parameter_tuples = zip(hyperparameters.keys(), parameters)
parameter_dict = dict(parameter_tuples)
parameter_dic... | 5,178,821,570,779,621,000 | Build a grid represented as a list of parameter dictionaries. | autotabular/metalearning/optimizers/optimizer_base.py | build_grid | Fanxingye/Autotabular | python | def build_grid(hyperparameters):
parameter_dicts = []
for parameters in product(*hyperparameters.values()):
parameter_tuples = zip(hyperparameters.keys(), parameters)
parameter_dict = dict(parameter_tuples)
parameter_dicts.append(parameter_dict)
return parameter_dicts |
def test_invalid_unicode_in_build_file(self):
'Demonstrate that unicode characters causing parse errors raise real parse errors.'
self.add_to_build_file('BUILD', dedent("\n jvm_binary(name = ‘hello’, # Parse error due to smart quotes (non ascii characters)\n source = 'HelloWorld.java'\n main... | -6,823,476,443,962,574,000 | Demonstrate that unicode characters causing parse errors raise real parse errors. | tests/python/pants_test/build_graph/test_build_file_parser.py | test_invalid_unicode_in_build_file | omerzach/pants | python | def test_invalid_unicode_in_build_file(self):
self.add_to_build_file('BUILD', dedent("\n jvm_binary(name = ‘hello’, # Parse error due to smart quotes (non ascii characters)\n source = 'HelloWorld.java'\n main = 'foo.HelloWorld',\n )\n "))
build_file = self.create_buildfile('BU... |
def test_unicode_string_in_build_file(self):
'Demonstrates that a string containing unicode should work in a BUILD file.'
self.add_to_build_file('BUILD', dedent("\n java_library(\n name='foo',\n sources=['א.java']\n )\n "))
build_file = self.create_buildfile('BUILD')
... | -2,613,146,341,876,287,000 | Demonstrates that a string containing unicode should work in a BUILD file. | tests/python/pants_test/build_graph/test_build_file_parser.py | test_unicode_string_in_build_file | omerzach/pants | python | def test_unicode_string_in_build_file(self):
self.add_to_build_file('BUILD', dedent("\n java_library(\n name='foo',\n sources=['א.java']\n )\n "))
build_file = self.create_buildfile('BUILD')
self.build_file_parser.parse_build_file(build_file) |
def test_build_file_parser_error_hierarcy(self):
'Exception handling code depends on the fact that all explicit exceptions from BuildFileParser\n are subclassed from the BuildFileParserError base class.\n '
def assert_build_file_parser_error(e):
self.assertIsInstance(e, BuildFileParser.BuildFileP... | 1,864,469,725,580,416,000 | Exception handling code depends on the fact that all explicit exceptions from BuildFileParser
are subclassed from the BuildFileParserError base class. | tests/python/pants_test/build_graph/test_build_file_parser.py | test_build_file_parser_error_hierarcy | omerzach/pants | python | def test_build_file_parser_error_hierarcy(self):
'Exception handling code depends on the fact that all explicit exceptions from BuildFileParser\n are subclassed from the BuildFileParserError base class.\n '
def assert_build_file_parser_error(e):
self.assertIsInstance(e, BuildFileParser.BuildFileP... |
def gen_attributes(rp_attributes):
"Generate list of attributes for the API request.\n\n Example of input list:\n ['tag_name:tag_value1', 'tag_value2']\n Output of the function for the given input list:\n [{'key': 'tag_name', 'value': 'tag_value1'}, {'value': 'tag_value2'}]\n\n :param rp_attributes: ... | -4,259,652,237,405,757,000 | Generate list of attributes for the API request.
Example of input list:
['tag_name:tag_value1', 'tag_value2']
Output of the function for the given input list:
[{'key': 'tag_name', 'value': 'tag_value1'}, {'value': 'tag_value2'}]
:param rp_attributes: List of attributes(tags)
:return: Correctly created li... | reportportal_client/helpers.py | gen_attributes | jyejare/client-Python | python | def gen_attributes(rp_attributes):
"Generate list of attributes for the API request.\n\n Example of input list:\n ['tag_name:tag_value1', 'tag_value2']\n Output of the function for the given input list:\n [{'key': 'tag_name', 'value': 'tag_value1'}, {'value': 'tag_value2'}]\n\n :param rp_attributes: ... |
def get_launch_sys_attrs():
"Generate attributes for the launch containing system information.\n\n :return: dict {'os': 'Windows',\n 'cpu': 'AMD',\n 'machine': 'Windows10_pc'}\n "
return {'os': system(), 'cpu': (processor() or 'unknown'), 'machine': machine(), 'system':... | 8,550,479,848,873,520,000 | Generate attributes for the launch containing system information.
:return: dict {'os': 'Windows',
'cpu': 'AMD',
'machine': 'Windows10_pc'} | reportportal_client/helpers.py | get_launch_sys_attrs | jyejare/client-Python | python | def get_launch_sys_attrs():
"Generate attributes for the launch containing system information.\n\n :return: dict {'os': 'Windows',\n 'cpu': 'AMD',\n 'machine': 'Windows10_pc'}\n "
return {'os': system(), 'cpu': (processor() or 'unknown'), 'machine': machine(), 'system':... |
def get_package_version(package_name):
'Get version of the given package.\n\n :param package_name: Name of the package\n :return: Version of the package\n '
try:
package_version = get_distribution(package_name).version
except DistributionNotFound:
package_version = 'not ... | 1,597,655,982,996,706,800 | Get version of the given package.
:param package_name: Name of the package
:return: Version of the package | reportportal_client/helpers.py | get_package_version | jyejare/client-Python | python | def get_package_version(package_name):
'Get version of the given package.\n\n :param package_name: Name of the package\n :return: Version of the package\n '
try:
package_version = get_distribution(package_name).version
except DistributionNotFound:
package_version = 'not ... |
def neighbord_analysis(x_as, column=0):
'\n\tGiven an array xas this function compute the distance between the elements the mean distance and the variance\n\t\n\tAuthor: Michele Monti\n\t\n\tArgs:\n\t\t\tx_as: the name of the list or data set that you want:\n\t\n\tKwargs:\n\t\tcolumn: is the column of the data set ... | 3,318,488,411,711,542,300 | Given an array xas this function compute the distance between the elements the mean distance and the variance
Author: Michele Monti
Args:
x_as: the name of the list or data set that you want:
Kwargs:
column: is the column of the data set that you need to analyze
Returns:
mean_distanc... | amolf/numerical_data_analysis/NeighbourAnalysis.py | neighbord_analysis | Repythory/Libraries | python | def neighbord_analysis(x_as, column=0):
'\n\tGiven an array xas this function compute the distance between the elements the mean distance and the variance\n\t\n\tAuthor: Michele Monti\n\t\n\tArgs:\n\t\t\tx_as: the name of the list or data set that you want:\n\t\n\tKwargs:\n\t\tcolumn: is the column of the data set ... |
def _setup(self):
'Sets up and resets flags before each test.'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
if (KerasBenchmark.local_flags is None):
for flag_method in self.flag_methods:
flag_method()
flags.FLAGS(['foo'])
for (k, v) in self.default_flags... | 2,129,715,044,460,102,000 | Sets up and resets flags before each test. | official/resnet/keras/keras_benchmark.py | _setup | LinMiaoShuSheng/models | python | def _setup(self):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
if (KerasBenchmark.local_flags is None):
for flag_method in self.flag_methods:
flag_method()
flags.FLAGS(['foo'])
for (k, v) in self.default_flags.items():
setattr(FLAGS, k, v)
... |
def _report_benchmark(self, stats, wall_time_sec, top_1_max=None, top_1_min=None, log_steps=None, total_batch_size=None, warmup=1):
"Report benchmark results by writing to local protobuf file.\n\n Args:\n stats: dict returned from keras models with known entries.\n wall_time_sec: the during of the benc... | 1,068,746,809,112,859,500 | Report benchmark results by writing to local protobuf file.
Args:
stats: dict returned from keras models with known entries.
wall_time_sec: the during of the benchmark execution in seconds
top_1_max: highest passing level for top_1 accuracy.
top_1_min: lowest passing level for top_1 accuracy.
log_steps: How ... | official/resnet/keras/keras_benchmark.py | _report_benchmark | LinMiaoShuSheng/models | python | def _report_benchmark(self, stats, wall_time_sec, top_1_max=None, top_1_min=None, log_steps=None, total_batch_size=None, warmup=1):
"Report benchmark results by writing to local protobuf file.\n\n Args:\n stats: dict returned from keras models with known entries.\n wall_time_sec: the during of the benc... |
def setup_package():
'Run on testing package.'
oauth2client.util.positional_parameters_enforcement = 'EXCEPTION' | 7,842,196,517,937,913,000 | Run on testing package. | tests/__init__.py | setup_package | 1ap/google-api-python-client | python | def setup_package():
oauth2client.util.positional_parameters_enforcement = 'EXCEPTION' |
def initialize_flow(self, img):
' Flow is represented as difference between two coordinate grids flow = coords1 - coords0'
(N, C, H, W) = img.shape
coords0 = coords_grid(N, (H // 8), (W // 8)).to(img.device)
coords1 = coords_grid(N, (H // 8), (W // 8)).to(img.device)
return (coords0, coords1) | 5,953,690,645,597,147,000 | Flow is represented as difference between two coordinate grids flow = coords1 - coords0 | nets/raft_core/backraft.py | initialize_flow | aharley/track_check_repeat | python | def initialize_flow(self, img):
' '
(N, C, H, W) = img.shape
coords0 = coords_grid(N, (H // 8), (W // 8)).to(img.device)
coords1 = coords_grid(N, (H // 8), (W // 8)).to(img.device)
return (coords0, coords1) |
def upsample_flow(self, flow, mask):
' Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination '
(N, _, H, W) = flow.shape
mask = mask.view(N, 1, 9, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold((8 * flow), [3, 3], padding=1)
up_flow = up_flow.view(N, 2, 9, 1, ... | 5,758,413,022,218,375,000 | Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination | nets/raft_core/backraft.py | upsample_flow | aharley/track_check_repeat | python | def upsample_flow(self, flow, mask):
' '
(N, _, H, W) = flow.shape
mask = mask.view(N, 1, 9, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold((8 * flow), [3, 3], padding=1)
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
up_flow = torch.sum((mask * up_flow), dim=2)
up_flow =... |
def forward(self, image1):
' get featmap for one frame '
image1 = ((2 * (image1 / 255.0)) - 1.0)
image1 = image1.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
with autocast(enabled=self.args.mixed_precision):
fmap1 = self.fnet(image1)
fmap1 = fmap1.float()
return fm... | 9,102,215,985,324,929,000 | get featmap for one frame | nets/raft_core/backraft.py | forward | aharley/track_check_repeat | python | def forward(self, image1):
' '
image1 = ((2 * (image1 / 255.0)) - 1.0)
image1 = image1.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
with autocast(enabled=self.args.mixed_precision):
fmap1 = self.fnet(image1)
fmap1 = fmap1.float()
return fmap1 |
def old_forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
' Estimate optical flow between pair of frames '
image1 = ((2 * (image1 / 255.0)) - 1.0)
image2 = ((2 * (image2 / 255.0)) - 1.0)
image1 = image1.contiguous()
image2 = image2.contiguous()
hdim = self.... | -4,838,898,532,510,094,000 | Estimate optical flow between pair of frames | nets/raft_core/backraft.py | old_forward | aharley/track_check_repeat | python | def old_forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
' '
image1 = ((2 * (image1 / 255.0)) - 1.0)
image2 = ((2 * (image2 / 255.0)) - 1.0)
image1 = image1.contiguous()
image2 = image2.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
w... |
def start(self, initialization_hook: Optional[Callable[([], None)]]=None):
'Starts the worker group.'
self.worker_group = WorkerGroup(self._num_workers, self._num_cpus_per_worker, self._num_gpus_per_worker)
if initialization_hook:
self.worker_group.execute(initialization_hook)
self._backend.on_s... | 4,632,527,791,434,311,000 | Starts the worker group. | python/ray/util/sgd/v2/backends/backend.py | start | cuongnvan/ray | python | def start(self, initialization_hook: Optional[Callable[([], None)]]=None):
self.worker_group = WorkerGroup(self._num_workers, self._num_cpus_per_worker, self._num_gpus_per_worker)
if initialization_hook:
self.worker_group.execute(initialization_hook)
self._backend.on_start(self.worker_group, se... |
def start_training(self, train_func: Callable[([], T)]) -> None:
'Executes a training function on all workers in a separate thread.\n\n ``finish_training`` should be called after this.\n\n Args:\n train_func (Callable): The training function to run on each worker.\n '
def initia... | -328,435,917,548,396,860 | Executes a training function on all workers in a separate thread.
``finish_training`` should be called after this.
Args:
train_func (Callable): The training function to run on each worker. | python/ray/util/sgd/v2/backends/backend.py | start_training | cuongnvan/ray | python | def start_training(self, train_func: Callable[([], T)]) -> None:
'Executes a training function on all workers in a separate thread.\n\n ``finish_training`` should be called after this.\n\n Args:\n train_func (Callable): The training function to run on each worker.\n '
def initia... |
def fetch_next_result(self) -> Optional[List[Dict]]:
'Fetch next results produced by ``sgd.report()`` from each worker.\n\n Assumes ``start_training`` has already been called.\n\n Returns:\n A list of dictionaries of values passed to ``sgd.report()`` from\n each worker. Each ... | 9,198,031,502,204,319,000 | Fetch next results produced by ``sgd.report()`` from each worker.
Assumes ``start_training`` has already been called.
Returns:
A list of dictionaries of values passed to ``sgd.report()`` from
each worker. Each item corresponds to an intermediate result
a single worker. If there are no more items t... | python/ray/util/sgd/v2/backends/backend.py | fetch_next_result | cuongnvan/ray | python | def fetch_next_result(self) -> Optional[List[Dict]]:
'Fetch next results produced by ``sgd.report()`` from each worker.\n\n Assumes ``start_training`` has already been called.\n\n Returns:\n A list of dictionaries of values passed to ``sgd.report()`` from\n each worker. Each ... |
def finish_training(self) -> List[T]:
'Finish training and return final results. Propagate any exceptions.\n\n Blocks until training is finished on all workers.\n\n Assumes `start_training` has already been called.\n\n Returns:\n A list of return values from calling ``train_func`` on... | -105,494,462,415,802,850 | Finish training and return final results. Propagate any exceptions.
Blocks until training is finished on all workers.
Assumes `start_training` has already been called.
Returns:
A list of return values from calling ``train_func`` on each worker.
Each item corresponds to the return value from a single work... | python/ray/util/sgd/v2/backends/backend.py | finish_training | cuongnvan/ray | python | def finish_training(self) -> List[T]:
'Finish training and return final results. Propagate any exceptions.\n\n Blocks until training is finished on all workers.\n\n Assumes `start_training` has already been called.\n\n Returns:\n A list of return values from calling ``train_func`` on... |
def get_with_failure_handling(self, remote_values):
'Gets the remote values while handling for worker failures.\n\n Args:\n remote_values (list): List of object refs representing functions\n that may fail in the middle of execution. For example, running\n a SGD traini... | -3,861,662,312,918,846,500 | Gets the remote values while handling for worker failures.
Args:
remote_values (list): List of object refs representing functions
that may fail in the middle of execution. For example, running
a SGD training loop in multiple parallel actor calls.
Returns:
The resolved objects represented by th... | python/ray/util/sgd/v2/backends/backend.py | get_with_failure_handling | cuongnvan/ray | python | def get_with_failure_handling(self, remote_values):
'Gets the remote values while handling for worker failures.\n\n Args:\n remote_values (list): List of object refs representing functions\n that may fail in the middle of execution. For example, running\n a SGD traini... |
def shutdown(self):
'Shuts down the workers in the worker group.'
try:
self._backend.on_shutdown(self.worker_group, self._backend_config)
except RayActorError:
logger.warning('Graceful shutdown of backend failed. This is expected if one of the workers has crashed.')
self.worker_group.shu... | 7,046,521,673,996,014,000 | Shuts down the workers in the worker group. | python/ray/util/sgd/v2/backends/backend.py | shutdown | cuongnvan/ray | python | def shutdown(self):
try:
self._backend.on_shutdown(self.worker_group, self._backend_config)
except RayActorError:
logger.warning('Graceful shutdown of backend failed. This is expected if one of the workers has crashed.')
self.worker_group.shutdown()
self.worker_group = InactiveWorke... |
def test_create_user(self):
'\n Test User model can create a user successfully\n '
self.assertIsInstance(User.objects.create_user(username='username', email='example@example.com', password='password'), User) | -8,015,587,724,313,469,000 | Test User model can create a user successfully | authors/apps/authentication/tests/test_create_user.py | test_create_user | andela/ah-bird-box | python | def test_create_user(self):
'\n \n '
self.assertIsInstance(User.objects.create_user(username='username', email='example@example.com', password='password'), User) |
def test_random_color_py(degrees=(0.1, 1.9), plot=False):
'\n Test Python RandomColor\n '
logger.info('Test RandomColor')
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.... | -2,311,255,942,487,829,500 | Test Python RandomColor | tests/ut/python/dataset/test_random_color.py | test_random_color_py | king4arabs/mindspore | python | def test_random_color_py(degrees=(0.1, 1.9), plot=False):
'\n \n '
logger.info('Test RandomColor')
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.ToTensor()])
ds_ori... |
def test_random_color_c(degrees=(0.1, 1.9), plot=False, run_golden=True):
'\n Test Cpp RandomColor\n '
logger.info('test_random_color_op')
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCH... | 8,684,165,931,818,608,000 | Test Cpp RandomColor | tests/ut/python/dataset/test_random_color.py | test_random_color_c | king4arabs/mindspore | python | def test_random_color_c(degrees=(0.1, 1.9), plot=False, run_golden=True):
'\n \n '
logger.info('test_random_color_op')
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_lis... |
def test_random_color_py_md5():
'\n Test Python RandomColor with md5 check\n '
logger.info('Test RandomColor with md5 check')
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffl... | 8,487,769,337,350,655,000 | Test Python RandomColor with md5 check | tests/ut/python/dataset/test_random_color.py | test_random_color_py_md5 | king4arabs/mindspore | python | def test_random_color_py_md5():
'\n \n '
logger.info('Test RandomColor with md5 check')
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms = mindspore.da... |
def test_compare_random_color_op(degrees=None, plot=False):
'\n Compare Random Color op in Python and Cpp\n '
logger.info('test_random_color_op')
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data1 = ds.TFRecordDataset(C_DATA_DIR,... | -6,266,641,206,013,360,000 | Compare Random Color op in Python and Cpp | tests/ut/python/dataset/test_random_color.py | test_compare_random_color_op | king4arabs/mindspore | python | def test_compare_random_color_op(degrees=None, plot=False):
'\n \n '
logger.info('test_random_color_op')
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], sh... |
def test_random_color_c_errors():
'\n Test that Cpp RandomColor errors with bad input\n '
with pytest.raises(TypeError) as error_info:
vision.RandomColor(12)
assert ('degrees must be either a tuple or a list.' in str(error_info.value))
with pytest.raises(TypeError) as error_info:
v... | -8,695,921,805,919,751,000 | Test that Cpp RandomColor errors with bad input | tests/ut/python/dataset/test_random_color.py | test_random_color_c_errors | king4arabs/mindspore | python | def test_random_color_c_errors():
'\n \n '
with pytest.raises(TypeError) as error_info:
vision.RandomColor(12)
assert ('degrees must be either a tuple or a list.' in str(error_info.value))
with pytest.raises(TypeError) as error_info:
vision.RandomColor(('col', 3))
assert ("Argu... |
def __init__(self, resource_handle, create_op, name):
'Creates a _TreeEnsembleSavable object.\n\n Args:\n resource_handle: handle to the decision tree ensemble variable.\n create_op: the op to initialize the variable.\n name: the name to save the tree ensemble variable under.\n '
(stamp_tok... | -7,466,408,169,068,972,000 | Creates a _TreeEnsembleSavable object.
Args:
resource_handle: handle to the decision tree ensemble variable.
create_op: the op to initialize the variable.
name: the name to save the tree ensemble variable under. | tensorflow/python/ops/boosted_trees_ops.py | __init__ | AnyaTracy/tensorflow | python | def __init__(self, resource_handle, create_op, name):
'Creates a _TreeEnsembleSavable object.\n\n Args:\n resource_handle: handle to the decision tree ensemble variable.\n create_op: the op to initialize the variable.\n name: the name to save the tree ensemble variable under.\n '
(stamp_tok... |
def restore(self, restored_tensors, unused_restored_shapes):
"Restores the associated tree ensemble from 'restored_tensors'.\n\n Args:\n restored_tensors: the tensors that were loaded from a checkpoint.\n unused_restored_shapes: the shapes this object should conform to after\n restore. Not meani... | 4,755,641,117,312,512,000 | Restores the associated tree ensemble from 'restored_tensors'.
Args:
restored_tensors: the tensors that were loaded from a checkpoint.
unused_restored_shapes: the shapes this object should conform to after
restore. Not meaningful for trees.
Returns:
The operation that restores the state of the tree ensemble... | tensorflow/python/ops/boosted_trees_ops.py | restore | AnyaTracy/tensorflow | python | def restore(self, restored_tensors, unused_restored_shapes):
"Restores the associated tree ensemble from 'restored_tensors'.\n\n Args:\n restored_tensors: the tensors that were loaded from a checkpoint.\n unused_restored_shapes: the shapes this object should conform to after\n restore. Not meani... |
def get_stamp_token(self):
'Returns the current stamp token of the resource.'
(stamp_token, _, _, _, _) = gen_boosted_trees_ops.boosted_trees_get_ensemble_states(self.resource_handle)
return stamp_token | -9,195,282,269,080,987,000 | Returns the current stamp token of the resource. | tensorflow/python/ops/boosted_trees_ops.py | get_stamp_token | AnyaTracy/tensorflow | python | def get_stamp_token(self):
(stamp_token, _, _, _, _) = gen_boosted_trees_ops.boosted_trees_get_ensemble_states(self.resource_handle)
return stamp_token |
def get_states(self):
'Returns states of the tree ensemble.\n\n Returns:\n stamp_token, num_trees, num_finalized_trees, num_attempted_layers and\n range of the nodes in the latest layer.\n '
(stamp_token, num_trees, num_finalized_trees, num_attempted_layers, nodes_range) = gen_boosted_trees_ops.... | 4,252,967,200,812,865,500 | Returns states of the tree ensemble.
Returns:
stamp_token, num_trees, num_finalized_trees, num_attempted_layers and
range of the nodes in the latest layer. | tensorflow/python/ops/boosted_trees_ops.py | get_states | AnyaTracy/tensorflow | python | def get_states(self):
'Returns states of the tree ensemble.\n\n Returns:\n stamp_token, num_trees, num_finalized_trees, num_attempted_layers and\n range of the nodes in the latest layer.\n '
(stamp_token, num_trees, num_finalized_trees, num_attempted_layers, nodes_range) = gen_boosted_trees_ops.... |
def serialize(self):
'Serializes the ensemble into proto and returns the serialized proto.\n\n Returns:\n stamp_token: int64 scalar Tensor to denote the stamp of the resource.\n serialized_proto: string scalar Tensor of the serialized proto.\n '
return gen_boosted_trees_ops.boosted_trees_seriali... | 1,912,311,882,728,369 | Serializes the ensemble into proto and returns the serialized proto.
Returns:
stamp_token: int64 scalar Tensor to denote the stamp of the resource.
serialized_proto: string scalar Tensor of the serialized proto. | tensorflow/python/ops/boosted_trees_ops.py | serialize | AnyaTracy/tensorflow | python | def serialize(self):
'Serializes the ensemble into proto and returns the serialized proto.\n\n Returns:\n stamp_token: int64 scalar Tensor to denote the stamp of the resource.\n serialized_proto: string scalar Tensor of the serialized proto.\n '
return gen_boosted_trees_ops.boosted_trees_seriali... |
def deserialize(self, stamp_token, serialized_proto):
'Deserialize the input proto and resets the ensemble from it.\n\n Args:\n stamp_token: int64 scalar Tensor to denote the stamp of the resource.\n serialized_proto: string scalar Tensor of the serialized proto.\n\n Returns:\n Operation (for d... | 660,778,015,599,044,900 | Deserialize the input proto and resets the ensemble from it.
Args:
stamp_token: int64 scalar Tensor to denote the stamp of the resource.
serialized_proto: string scalar Tensor of the serialized proto.
Returns:
Operation (for dependencies). | tensorflow/python/ops/boosted_trees_ops.py | deserialize | AnyaTracy/tensorflow | python | def deserialize(self, stamp_token, serialized_proto):
'Deserialize the input proto and resets the ensemble from it.\n\n Args:\n stamp_token: int64 scalar Tensor to denote the stamp of the resource.\n serialized_proto: string scalar Tensor of the serialized proto.\n\n Returns:\n Operation (for d... |
def ZZZ(self):
'hardcoded/mock instance of the class'
return CookieContainer() | 7,398,721,178,004,803,000 | hardcoded/mock instance of the class | release/stubs.min/System/Net/__init___parts/CookieContainer.py | ZZZ | tranconbv/ironpython-stubs | python | def ZZZ(self):
return CookieContainer() |
def Add(self, *__args):
'\n Add(self: CookieContainer,cookie: Cookie)\n\n Adds a System.Net.Cookie to a System.Net.CookieContainer. This method uses the domain from the System.Net.Cookie to determine which domain collection to associate the \n\n System.Net.Cookie with.\n\n \n\n \n\n cookie: The System.Net... | -8,225,875,563,953,717,000 | Add(self: CookieContainer,cookie: Cookie)
Adds a System.Net.Cookie to a System.Net.CookieContainer. This method uses the domain from the System.Net.Cookie to determine which domain collection to associate the
System.Net.Cookie with.
cookie: The System.Net.Cookie to be added to the System.Net.CookieContainer... | release/stubs.min/System/Net/__init___parts/CookieContainer.py | Add | tranconbv/ironpython-stubs | python | def Add(self, *__args):
'\n Add(self: CookieContainer,cookie: Cookie)\n\n Adds a System.Net.Cookie to a System.Net.CookieContainer. This method uses the domain from the System.Net.Cookie to determine which domain collection to associate the \n\n System.Net.Cookie with.\n\n \n\n \n\n cookie: The System.Net... |
def GetCookieHeader(self, uri):
'\n GetCookieHeader(self: CookieContainer,uri: Uri) -> str\n\n \n\n Gets the HTTP cookie header that contains the HTTP cookies that represent the System.Net.Cookie instances that are associated with a specific URI.\n\n \n\n uri: The URI of the System.Net.Cookie instances desir... | 5,028,364,629,411,337,000 | GetCookieHeader(self: CookieContainer,uri: Uri) -> str
Gets the HTTP cookie header that contains the HTTP cookies that represent the System.Net.Cookie instances that are associated with a specific URI.
uri: The URI of the System.Net.Cookie instances desired.
Returns: An HTTP cookie header,with strings represe... | release/stubs.min/System/Net/__init___parts/CookieContainer.py | GetCookieHeader | tranconbv/ironpython-stubs | python | def GetCookieHeader(self, uri):
'\n GetCookieHeader(self: CookieContainer,uri: Uri) -> str\n\n \n\n Gets the HTTP cookie header that contains the HTTP cookies that represent the System.Net.Cookie instances that are associated with a specific URI.\n\n \n\n uri: The URI of the System.Net.Cookie instances desir... |
def GetCookies(self, uri):
'\n GetCookies(self: CookieContainer,uri: Uri) -> CookieCollection\n\n \n\n Gets a System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI.\n\n \n\n uri: The URI of the System.Net.Cookie instances desired.\n\n Returns: A S... | -6,849,928,071,652,846,000 | GetCookies(self: CookieContainer,uri: Uri) -> CookieCollection
Gets a System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI.
uri: The URI of the System.Net.Cookie instances desired.
Returns: A System.Net.CookieCollection that contains the System.Net.... | release/stubs.min/System/Net/__init___parts/CookieContainer.py | GetCookies | tranconbv/ironpython-stubs | python | def GetCookies(self, uri):
'\n GetCookies(self: CookieContainer,uri: Uri) -> CookieCollection\n\n \n\n Gets a System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI.\n\n \n\n uri: The URI of the System.Net.Cookie instances desired.\n\n Returns: A S... |
def SetCookies(self, uri, cookieHeader):
'\n SetCookies(self: CookieContainer,uri: Uri,cookieHeader: str)\n\n Adds System.Net.Cookie instances for one or more cookies from an HTTP cookie header to the System.Net.CookieContainer for a specific URI.\n\n \n\n uri: The URI of the System.Net.CookieCollection.\n\n ... | 767,938,559,518,995,000 | SetCookies(self: CookieContainer,uri: Uri,cookieHeader: str)
Adds System.Net.Cookie instances for one or more cookies from an HTTP cookie header to the System.Net.CookieContainer for a specific URI.
uri: The URI of the System.Net.CookieCollection.
cookieHeader: The contents of an HTTP set-cookie header as retur... | release/stubs.min/System/Net/__init___parts/CookieContainer.py | SetCookies | tranconbv/ironpython-stubs | python | def SetCookies(self, uri, cookieHeader):
'\n SetCookies(self: CookieContainer,uri: Uri,cookieHeader: str)\n\n Adds System.Net.Cookie instances for one or more cookies from an HTTP cookie header to the System.Net.CookieContainer for a specific URI.\n\n \n\n uri: The URI of the System.Net.CookieCollection.\n\n ... |
def __add__(self, *args):
' x.__add__(y) <==> x+yx.__add__(y) <==> x+yx.__add__(y) <==> x+yx.__add__(y) <==> x+y '
pass | -6,471,137,132,733,238,000 | x.__add__(y) <==> x+yx.__add__(y) <==> x+yx.__add__(y) <==> x+yx.__add__(y) <==> x+y | release/stubs.min/System/Net/__init___parts/CookieContainer.py | __add__ | tranconbv/ironpython-stubs | python | def __add__(self, *args):
' '
pass |
@staticmethod
def __new__(self, capacity=None, perDomainCapacity=None, maxCookieSize=None):
'\n __new__(cls: type)\n\n __new__(cls: type,capacity: int)\n\n __new__(cls: type,capacity: int,perDomainCapacity: int,maxCookieSize: int)\n '
pass | 2,610,757,587,463,797,000 | __new__(cls: type)
__new__(cls: type,capacity: int)
__new__(cls: type,capacity: int,perDomainCapacity: int,maxCookieSize: int) | release/stubs.min/System/Net/__init___parts/CookieContainer.py | __new__ | tranconbv/ironpython-stubs | python | @staticmethod
def __new__(self, capacity=None, perDomainCapacity=None, maxCookieSize=None):
'\n __new__(cls: type)\n\n __new__(cls: type,capacity: int)\n\n __new__(cls: type,capacity: int,perDomainCapacity: int,maxCookieSize: int)\n '
pass |
@cache_json('cbsa_lookup.json')
def cbsa_lookup():
'\n Construct a County->CBSA Lookup table from NBER data\n Returns: dict\n each key is a (State Code, County FIPS code) tuple\n each value is a (CBSA FIPS code, CBSA Name) tuple\n '
logging.info('Beginning CBSA lookup')
cbsa_lookup = ... | -8,489,966,963,965,884,000 | Construct a County->CBSA Lookup table from NBER data
Returns: dict
each key is a (State Code, County FIPS code) tuple
each value is a (CBSA FIPS code, CBSA Name) tuple | datasets/nber_county_cbsa.py | cbsa_lookup | squatter1/skills-ml | python | @cache_json('cbsa_lookup.json')
def cbsa_lookup():
'\n Construct a County->CBSA Lookup table from NBER data\n Returns: dict\n each key is a (State Code, County FIPS code) tuple\n each value is a (CBSA FIPS code, CBSA Name) tuple\n '
logging.info('Beginning CBSA lookup')
cbsa_lookup = ... |
def gelu(x):
"Implementation of the gelu activation function.\n For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):\n 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\n Also see https://arxiv.org/abs/1606.08415\n ... | 1,420,372,343,885,535,700 | Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415 | models/SketchTransformer/models/networks.py | gelu | avalonstrel/SketchBERT | python | def gelu(x):
"Implementation of the gelu activation function.\n For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):\n 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\n Also see https://arxiv.org/abs/1606.08415\n ... |
def __init__(self, hidden_size, eps=1e-12):
'\n Construct a layernorm module in the TF style (epsilon inside the square root).\n '
super(SketchLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.varianc... | -8,357,680,159,207,132,000 | Construct a layernorm module in the TF style (epsilon inside the square root). | models/SketchTransformer/models/networks.py | __init__ | avalonstrel/SketchBERT | python | def __init__(self, hidden_size, eps=1e-12):
'\n \n '
super(SketchLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps |
def transpose_(self, x):
'\n Transpose Function for simplicity.\n '
new_x_shape = (x.size()[:(- 1)] + (self.num_heads, self.head_dim))
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3) | -6,336,780,184,453,579,000 | Transpose Function for simplicity. | models/SketchTransformer/models/networks.py | transpose_ | avalonstrel/SketchBERT | python | def transpose_(self, x):
'\n \n '
new_x_shape = (x.size()[:(- 1)] + (self.num_heads, self.head_dim))
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3) |
def forward(self, hidden_states, attention_mask, head_mask=None, output_attentions=False, keep_multihead_output=False):
'\n Input:\n hidden_states[batch, seq_len, hidden_dim]\n attention_mask[batch, 1, 1, seq_len]\n Output:\n context_states[batch, seq_len, hidden_dim]... | -3,967,524,633,107,120,000 | Input:
hidden_states[batch, seq_len, hidden_dim]
attention_mask[batch, 1, 1, seq_len]
Output:
context_states[batch, seq_len, hidden_dim]
attention_probs[seq_len, hidden_dim] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, hidden_states, attention_mask, head_mask=None, output_attentions=False, keep_multihead_output=False):
'\n Input:\n hidden_states[batch, seq_len, hidden_dim]\n attention_mask[batch, 1, 1, seq_len]\n Output:\n context_states[batch, seq_len, hidden_dim]... |
def get_seg_states(self, hidden_states, segment_index):
'\n Input:\n hidden_states[batch, seq_len, hidden_dim]\n segment_index[batch, seq_len]\n '
seg_states = torch.zeros(hidden_states.size(0), self.max_segment, hidden_states.size(2)).to(hidden_states.device)
length = (s... | -7,235,732,249,509,121,000 | Input:
hidden_states[batch, seq_len, hidden_dim]
segment_index[batch, seq_len] | models/SketchTransformer/models/networks.py | get_seg_states | avalonstrel/SketchBERT | python | def get_seg_states(self, hidden_states, segment_index):
'\n Input:\n hidden_states[batch, seq_len, hidden_dim]\n segment_index[batch, seq_len]\n '
seg_states = torch.zeros(hidden_states.size(0), self.max_segment, hidden_states.size(2)).to(hidden_states.device)
length = (s... |
def forward(self, hidden_states, attention_mask, segments, segment_index, head_mask=None, output_attentions=False):
'\n Input:\n hidden_states[batch, seg_len, hidden_dim]:\n attention_mask[batch, seg_len](segment-based)\n segments[batch, seg_len]:\n segment_index[b... | 6,916,960,397,091,090,000 | Input:
hidden_states[batch, seg_len, hidden_dim]:
attention_mask[batch, seg_len](segment-based)
segments[batch, seg_len]:
segment_index[batch, seq_len] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, hidden_states, attention_mask, segments, segment_index, head_mask=None, output_attentions=False):
'\n Input:\n hidden_states[batch, seg_len, hidden_dim]:\n attention_mask[batch, seg_len](segment-based)\n segments[batch, seg_len]:\n segment_index[b... |
def forward(self, input_states, attention_mask, targets=None, segments=None, head_mask=None, output_all_states=False, output_attentions=False, keep_multihead_output=False):
'\n Input:\n input_states[batch, seq_len, 5],\n zs[batch, latent_dim]\n '
if (attention_mask is None):
... | -1,672,195,904,779,026,700 | Input:
input_states[batch, seq_len, 5],
zs[batch, latent_dim] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, input_states, attention_mask, targets=None, segments=None, head_mask=None, output_all_states=False, output_attentions=False, keep_multihead_output=False):
'\n Input:\n input_states[batch, seq_len, 5],\n zs[batch, latent_dim]\n '
if (attention_mask is None):
... |
def forward(self, input_states, zs, attention_mask, targets=None, segments=None, head_mask=None, output_all_states=False, output_attentions=False, keep_multihead_output=False):
'\n Input:\n input_states[batch, seq_len, 5],\n zs[batch, latent_dim]\n '
if (attention_mask is Non... | -4,880,509,655,281,427,000 | Input:
input_states[batch, seq_len, 5],
zs[batch, latent_dim] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, input_states, zs, attention_mask, targets=None, segments=None, head_mask=None, output_all_states=False, output_attentions=False, keep_multihead_output=False):
'\n Input:\n input_states[batch, seq_len, 5],\n zs[batch, latent_dim]\n '
if (attention_mask is Non... |
def forward(self, hidden_states):
'\n Input:\n hidden_states[batch, seq_len+cls_dim, hidden_dim](0 dim is cls)\n Output:\n x_pred[batch, seq_len+cls_dim, 2*max_size[0]+1]\n y_pred[batch, seq_len+cls_dim, 2*max_size[1]+1]\n type_pred[batch, seq_len+cls_dim, t... | 5,573,380,538,648,980,000 | Input:
hidden_states[batch, seq_len+cls_dim, hidden_dim](0 dim is cls)
Output:
x_pred[batch, seq_len+cls_dim, 2*max_size[0]+1]
y_pred[batch, seq_len+cls_dim, 2*max_size[1]+1]
type_pred[batch, seq_len+cls_dim, type_size] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, hidden_states):
'\n Input:\n hidden_states[batch, seq_len+cls_dim, hidden_dim](0 dim is cls)\n Output:\n x_pred[batch, seq_len+cls_dim, 2*max_size[0]+1]\n y_pred[batch, seq_len+cls_dim, 2*max_size[1]+1]\n type_pred[batch, seq_len+cls_dim, t... |
def forward(self, hidden_states, segment_index):
'\n Input:\n hidden_states[batch, seg_len, hidden_dim]\n segment_index[batch, seq_len]\n '
seg_states = hidden_states[:, (self.cls_in_input + self.rel_in_input):, :][(segment_index == 0), :]
return self.sg_fc(seg_states) | 1,118,064,636,496,338,400 | Input:
hidden_states[batch, seg_len, hidden_dim]
segment_index[batch, seq_len] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, hidden_states, segment_index):
'\n Input:\n hidden_states[batch, seg_len, hidden_dim]\n segment_index[batch, seq_len]\n '
seg_states = hidden_states[:, (self.cls_in_input + self.rel_in_input):, :][(segment_index == 0), :]
return self.sg_fc(seg_states) |
@property
def function_object(self):
'get the generated function object'
return self._function | -4,883,460,870,703,305,000 | get the generated function object | mlrun/runtimes/function_reference.py | function_object | AlonMaor14/mlrun | python | @property
def function_object(self):
return self._function |
def to_function(self, default_kind=None):
'generate a function object from the ref definitions'
if (self.url and ('://' not in self.url)):
if (not os.path.isfile(self.url)):
raise OSError(f'{self.url} not found')
kind = (self.kind or default_kind)
if self.url:
if (self.url.en... | -1,610,808,312,545,578,200 | generate a function object from the ref definitions | mlrun/runtimes/function_reference.py | to_function | AlonMaor14/mlrun | python | def to_function(self, default_kind=None):
if (self.url and ('://' not in self.url)):
if (not os.path.isfile(self.url)):
raise OSError(f'{self.url} not found')
kind = (self.kind or default_kind)
if self.url:
if (self.url.endswith('.yaml') or self.url.startswith('db://') or se... |
def deploy(self, **kwargs):
'deploy the function'
self._address = self._function.deploy(**kwargs)
return self._address | 3,107,861,624,235,756,500 | deploy the function | mlrun/runtimes/function_reference.py | deploy | AlonMaor14/mlrun | python | def deploy(self, **kwargs):
self._address = self._function.deploy(**kwargs)
return self._address |
def __init__(self, allow_crash_consistent_snapshot=None):
'Constructor for the HypervBackupEnvParams class'
self.allow_crash_consistent_snapshot = allow_crash_consistent_snapshot | 2,498,918,717,880,499,700 | Constructor for the HypervBackupEnvParams class | cohesity_management_sdk/models/hyperv_backup_env_params.py | __init__ | anoopbhat/management-sdk-python | python | def __init__(self, allow_crash_consistent_snapshot=None):
self.allow_crash_consistent_snapshot = allow_crash_consistent_snapshot |
@classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match proper... | -3,474,178,223,966,733,300 | Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class. | cohesity_management_sdk/models/hyperv_backup_env_params.py | from_dictionary | anoopbhat/management-sdk-python | python | @classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match proper... |
@staticmethod
def promote_ase_atoms(obj, symmetry=None):
' Convert ASE Atoms object to the one usable by SPRKKR.\n For the case of the usability it is a bit ugly hack: The __class__ attribute\n is replaced so the extra methods and properties of the objects will\n be available.\n\n ... | -8,266,258,733,905,201,000 | Convert ASE Atoms object to the one usable by SPRKKR.
For the case of the usability it is a bit ugly hack: The __class__ attribute
is replaced so the extra methods and properties of the objects will
be available.
Parameters
----------
obj: ase.Atoms
The atoms object to be promoted to be used for SPRKKR calculations
... | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | promote_ase_atoms | ase2sprkkr/ase2sprkkr | python | @staticmethod
def promote_ase_atoms(obj, symmetry=None):
' Convert ASE Atoms object to the one usable by SPRKKR.\n For the case of the usability it is a bit ugly hack: The __class__ attribute\n is replaced so the extra methods and properties of the objects will\n be available.\n\n ... |
def __init__(self, *args, symmetry=True, potential=None, **kwargs):
'\n Creates SPRKKRAtoms atoms\n\n Parameters\n ----------\n *args: list\n The positionals arguments of ase.Atoms.__init__\n symmetry: boolean\n The symmetry will be computed when the sites property wi... | 5,208,751,560,967,905,000 | Creates SPRKKRAtoms atoms
Parameters
----------
*args: list
The positionals arguments of ase.Atoms.__init__
symmetry: boolean
The symmetry will be computed when the sites property will be initialized.
I.e., the by-symmetry-equal atomic sites will share the same sites object.
**kwargs: dict
The named argume... | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | __init__ | ase2sprkkr/ase2sprkkr | python | def __init__(self, *args, symmetry=True, potential=None, **kwargs):
'\n Creates SPRKKRAtoms atoms\n\n Parameters\n ----------\n *args: list\n The positionals arguments of ase.Atoms.__init__\n symmetry: boolean\n The symmetry will be computed when the sites property wi... |
def _init(self, symmetry=True, potential=None):
' The initialization of the additional (not-in-ASE) properties. To be used\n by constructor and by promote_ase_atoms'
self._unique_sites = None
self._potential = potential
self._symmetry = symmetry | 7,673,537,083,973,724,000 | The initialization of the additional (not-in-ASE) properties. To be used
by constructor and by promote_ase_atoms | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | _init | ase2sprkkr/ase2sprkkr | python | def _init(self, symmetry=True, potential=None):
' The initialization of the additional (not-in-ASE) properties. To be used\n by constructor and by promote_ase_atoms'
self._unique_sites = None
self._potential = potential
self._symmetry = symmetry |
@property
def symmetry(self):
'\n Whether the sites property is/will be generated using symmetry, i.e.\n whether the Sites objects in the sites property will be shared among\n symmetric atomic sites.\n '
return self._symmetry | -7,475,728,776,709,522,000 | Whether the sites property is/will be generated using symmetry, i.e.
whether the Sites objects in the sites property will be shared among
symmetric atomic sites. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | symmetry | ase2sprkkr/ase2sprkkr | python | @property
def symmetry(self):
'\n Whether the sites property is/will be generated using symmetry, i.e.\n whether the Sites objects in the sites property will be shared among\n symmetric atomic sites.\n '
return self._symmetry |
@symmetry.setter
def symmetry(self, value):
'\n Recomputes the sites with enabled/disabled symmetry if the value of the property\n has changed.\n '
if (self._symmetry == value):
return
self._symmetry = value
if (self._unique_sites is not None):
if value:
sel... | 4,876,355,944,345,147,000 | Recomputes the sites with enabled/disabled symmetry if the value of the property
has changed. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | symmetry | ase2sprkkr/ase2sprkkr | python | @symmetry.setter
def symmetry(self, value):
'\n Recomputes the sites with enabled/disabled symmetry if the value of the property\n has changed.\n '
if (self._symmetry == value):
return
self._symmetry = value
if (self._unique_sites is not None):
if value:
sel... |
def compute_spacegroup_for_atomic_numbers(self, atomic_numbers=None, symprec=1e-05):
" Return spacegroup that suits to the atoms' cell structure and to the given\n atomic_numbers (not necessary the real ones, they can be just ''labels'').\n "
atomic_numbers = (atomic_numbers if (atomic_numbers i... | 254,699,370,758,858,100 | Return spacegroup that suits to the atoms' cell structure and to the given
atomic_numbers (not necessary the real ones, they can be just ''labels''). | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | compute_spacegroup_for_atomic_numbers | ase2sprkkr/ase2sprkkr | python | def compute_spacegroup_for_atomic_numbers(self, atomic_numbers=None, symprec=1e-05):
" Return spacegroup that suits to the atoms' cell structure and to the given\n atomic_numbers (not necessary the real ones, they can be just labels).\n "
atomic_numbers = (atomic_numbers if (atomic_numbers is no... |
def compute_sites_symmetry(self, spacegroup=None, atomic_numbers=None, consider_old=False, symprec=1e-05):
' SPRKKR has some properties shared by all by-symmetry-equal sites.\n This method initializes _sites property, that holds these properties:\n makes identical all the atoms on the "symmetry ... | 8,060,052,139,602,305,000 | SPRKKR has some properties shared by all by-symmetry-equal sites.
This method initializes _sites property, that holds these properties:
makes identical all the atoms on the "symmetry identical positions" with
the same atomic number.
The method is called automatically when the sites property is firstly accessed.
The ef... | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | compute_sites_symmetry | ase2sprkkr/ase2sprkkr | python | def compute_sites_symmetry(self, spacegroup=None, atomic_numbers=None, consider_old=False, symprec=1e-05):
' SPRKKR has some properties shared by all by-symmetry-equal sites.\n This method initializes _sites property, that holds these properties:\n makes identical all the atoms on the "symmetry ... |
def _compute_sites_symmetry(self, spacegroup=None, atomic_numbers=None, consider_old=False, symprec=1e-05):
' See compute_sites_symmetry - this metod does just the same, but it does not set the symmetry property.'
occupation = self.info.get('occupancy', {})
if ((not spacegroup) and self._symmetry):
... | -7,394,917,638,340,702,000 | See compute_sites_symmetry - this metod does just the same, but it does not set the symmetry property. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | _compute_sites_symmetry | ase2sprkkr/ase2sprkkr | python | def _compute_sites_symmetry(self, spacegroup=None, atomic_numbers=None, consider_old=False, symprec=1e-05):
' '
occupation = self.info.get('occupancy', {})
if ((not spacegroup) and self._symmetry):
if atomic_numbers:
mapping = UniqueValuesMapping(atomic_numbers)
else:
... |
def cancel_sites_symmetry(self):
' Cancel the use of symmetry in the structure, i.e., makes the Site object\n uniqe (not shared) for each atomic site.\n\n Calling this method is nearly equivalent to the setting the symmetry property\n to False, however, this method always recompute the sites ob... | -1,303,938,581,556,577,000 | Cancel the use of symmetry in the structure, i.e., makes the Site object
uniqe (not shared) for each atomic site.
Calling this method is nearly equivalent to the setting the symmetry property
to False, however, this method always recompute the sites object, while
setting symmetry=False recomputes the sites property on... | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | cancel_sites_symmetry | ase2sprkkr/ase2sprkkr | python | def cancel_sites_symmetry(self):
' Cancel the use of symmetry in the structure, i.e., makes the Site object\n uniqe (not shared) for each atomic site.\n\n Calling this method is nearly equivalent to the setting the symmetry property\n to False, however, this method always recompute the sites ob... |
def _cancel_sites_symmetry(self):
' See cancel_sites_symmetry - this metod does just the same, but it does not set the symmetry property.'
sites = np.empty(len(self), dtype=object)
used = set()
occupation = self.info.get('occupancy', {})
for i in range(len(self)):
if (self._unique_sites is n... | 7,062,645,137,594,079,000 | See cancel_sites_symmetry - this metod does just the same, but it does not set the symmetry property. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | _cancel_sites_symmetry | ase2sprkkr/ase2sprkkr | python | def _cancel_sites_symmetry(self):
' '
sites = np.empty(len(self), dtype=object)
used = set()
occupation = self.info.get('occupancy', {})
for i in range(len(self)):
if (self._unique_sites is not None):
site = self._unique_sites[i]
if (site in used):
sit... |
@property
def sites(self):
' The sites property holds all the information for the SPR-KKR package:\n atomic types (including number of semicore and valence electrons),\n occupancy, symmetries, meshes...\n Some of the properties are stored in the ASE atoms properties\n (e.g. o... | -5,191,896,333,846,247,000 | The sites property holds all the information for the SPR-KKR package:
atomic types (including number of semicore and valence electrons),
occupancy, symmetries, meshes...
Some of the properties are stored in the ASE atoms properties
(e.g. occupancy, atomic symbol), however, ASE is not able to hold them
all and/or to des... | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | sites | ase2sprkkr/ase2sprkkr | python | @property
def sites(self):
' The sites property holds all the information for the SPR-KKR package:\n atomic types (including number of semicore and valence electrons),\n occupancy, symmetries, meshes...\n Some of the properties are stored in the ASE atoms properties\n (e.g. o... |
@sites.setter
def sites(self, v):
' Set the sites property and update all other dependent\n properties (symbols, occupancy) according to the sites '
an = np.zeros(len(v), dtype=int)
occ = {}
for (i, j) in enumerate(v):
occ[i] = j.occupation.as_dict
an[i] = j.occupation.primary_atom... | 2,869,206,069,894,840,000 | Set the sites property and update all other dependent
properties (symbols, occupancy) according to the sites | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | sites | ase2sprkkr/ase2sprkkr | python | @sites.setter
def sites(self, v):
' Set the sites property and update all other dependent\n properties (symbols, occupancy) according to the sites '
an = np.zeros(len(v), dtype=int)
occ = {}
for (i, j) in enumerate(v):
occ[i] = j.occupation.as_dict
an[i] = j.occupation.primary_atom... |
def upload_file_to_shock(self, file_path, token):
'\n Use HTTP multi-part POST to save a file to a SHOCK instance.\n '
if (token is None):
raise Exception('Authentication token required!')
header = {'Authorization': 'Oauth {0}'.format(token)}
if (file_path is None):
raise E... | -7,824,875,898,772,669,000 | Use HTTP multi-part POST to save a file to a SHOCK instance. | lib/kb_SPAdes/kb_SPAdesImpl.py | upload_file_to_shock | mclark58/kb_SPAdes | python | def upload_file_to_shock(self, file_path, token):
'\n \n '
if (token is None):
raise Exception('Authentication token required!')
header = {'Authorization': 'Oauth {0}'.format(token)}
if (file_path is None):
raise Exception('No file given for upload to SHOCK!')
with open... |
def run_SPAdes(self, ctx, params):
'\n Run SPAdes on paired end libraries\n :param params: instance of type "SPAdesParams" (Input parameters for\n running SPAdes. workspace_name - the name of the workspace from\n which to take input and store output. output_contigset_name - the\n ... | -7,648,011,864,929,356,000 | Run SPAdes on paired end libraries
:param params: instance of type "SPAdesParams" (Input parameters for
running SPAdes. workspace_name - the name of the workspace from
which to take input and store output. output_contigset_name - the
name of the output contigset read_libraries - a list of Illumina
PairedEnd... | lib/kb_SPAdes/kb_SPAdesImpl.py | run_SPAdes | mclark58/kb_SPAdes | python | def run_SPAdes(self, ctx, params):
'\n Run SPAdes on paired end libraries\n :param params: instance of type "SPAdesParams" (Input parameters for\n running SPAdes. workspace_name - the name of the workspace from\n which to take input and store output. output_contigset_name - the\n ... |
def run_HybridSPAdes(self, ctx, params):
'\n Run HybridSPAdes on paired end libraries with PacBio CLR and Oxford Nanopore reads\n :param params: instance of type "HybridSPAdesParams" (------To run\n HybridSPAdes 3.13.0 you need at least one library of the following\n types:------ 1... | -3,089,188,004,854,902,000 | Run HybridSPAdes on paired end libraries with PacBio CLR and Oxford Nanopore reads
:param params: instance of type "HybridSPAdesParams" (------To run
HybridSPAdes 3.13.0 you need at least one library of the following
types:------ 1) Illumina paired-end/high-quality
mate-pairs/unpaired reads 2) IonTorrent paire... | lib/kb_SPAdes/kb_SPAdesImpl.py | run_HybridSPAdes | mclark58/kb_SPAdes | python | def run_HybridSPAdes(self, ctx, params):
'\n Run HybridSPAdes on paired end libraries with PacBio CLR and Oxford Nanopore reads\n :param params: instance of type "HybridSPAdesParams" (------To run\n HybridSPAdes 3.13.0 you need at least one library of the following\n types:------ 1... |
def run_metaSPAdes(self, ctx, params):
'\n Run SPAdes on paired end libraries for metagenomes\n :param params: instance of type "SPAdesParams" (Input parameters for\n running SPAdes. workspace_name - the name of the workspace from\n which to take input and store output. output_cont... | 1,842,031,156,406,198,000 | Run SPAdes on paired end libraries for metagenomes
:param params: instance of type "SPAdesParams" (Input parameters for
running SPAdes. workspace_name - the name of the workspace from
which to take input and store output. output_contigset_name - the
name of the output contigset read_libraries - a list of Illum... | lib/kb_SPAdes/kb_SPAdesImpl.py | run_metaSPAdes | mclark58/kb_SPAdes | python | def run_metaSPAdes(self, ctx, params):
'\n Run SPAdes on paired end libraries for metagenomes\n :param params: instance of type "SPAdesParams" (Input parameters for\n running SPAdes. workspace_name - the name of the workspace from\n which to take input and store output. output_cont... |
def init(self, var_list=None, ckpt_dir=None, ckpt_file=None, optimistic=False):
'\n :param var_list: vars for restore\n :param ckpt_dir: prefix of model files.\n :param ckpt_file: exact name of model file, priority is higher than `ckpt_dir`\n :param optimistic: only restore weig... | 7,315,703,408,418,164,000 | :param var_list: vars for restore
:param ckpt_dir: prefix of model files.
:param ckpt_file: exact name of model file, priority is higher than `ckpt_dir`
:param optimistic: only restore weights of same names with model.
:return: | tensorkit/restore.py | init | nonu116/HDR-GAN | python | def init(self, var_list=None, ckpt_dir=None, ckpt_file=None, optimistic=False):
'\n :param var_list: vars for restore\n :param ckpt_dir: prefix of model files.\n :param ckpt_file: exact name of model file, priority is higher than `ckpt_dir`\n :param optimistic: only restore weig... |
def _restore_vars(self, sess):
'\n :param sess:\n :return: boolean for successful or not\n '
if (not self._restore_optimistic):
if (self.restore_ckpt_file is None):
logger.warn(Color.yellow('No checkpoint file for restore vars, checkpoint file is None', bold=True))
... | -6,708,701,434,879,647,000 | :param sess:
:return: boolean for successful or not | tensorkit/restore.py | _restore_vars | nonu116/HDR-GAN | python | def _restore_vars(self, sess):
'\n :param sess:\n :return: boolean for successful or not\n '
if (not self._restore_optimistic):
if (self.restore_ckpt_file is None):
logger.warn(Color.yellow('No checkpoint file for restore vars, checkpoint file is None', bold=True))
... |
def _optimistic_restore_model(self, sess):
'\n restore weights of same names with model.\n :param sess:\n :return:\n '
if (self.restore_ckpt_file is None):
logger.warn(Color.yellow('No ckpt file for restore vars, ckpt file is None'))
return False
reader = tf.train... | -3,370,335,051,060,885,500 | restore weights of same names with model.
:param sess:
:return: | tensorkit/restore.py | _optimistic_restore_model | nonu116/HDR-GAN | python | def _optimistic_restore_model(self, sess):
'\n restore weights of same names with model.\n :param sess:\n :return:\n '
if (self.restore_ckpt_file is None):
logger.warn(Color.yellow('No ckpt file for restore vars, ckpt file is None'))
return False
reader = tf.train... |
def _type_repr(obj):
'Return the repr() of an object, special-casing types (internal helper).\n If obj is a type, we return a shorter version than the default\n type.__repr__, based on the module and qualified name, which is\n typically enough to uniquely identify a type. For everything\n else, we fall... | 7,618,330,322,038,824,000 | Return the repr() of an object, special-casing types (internal helper).
If obj is a type, we return a shorter version than the default
type.__repr__, based on the module and qualified name, which is
typically enough to uniquely identify a type. For everything
else, we fall back on repr(obj). | venv/Lib/site-packages/torch/fx/node.py | _type_repr | Westlanderz/AI-Plat1 | python | def _type_repr(obj):
'Return the repr() of an object, special-casing types (internal helper).\n If obj is a type, we return a shorter version than the default\n type.__repr__, based on the module and qualified name, which is\n typically enough to uniquely identify a type. For everything\n else, we fall... |
@compatibility(is_backward_compatible=True)
def map_arg(a: Argument, fn: Callable[([Node], Argument)]) -> Argument:
'\n Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys.\n '
assert callable(fn), 'torch.fx.map_arg(a, fn): fn must be a callable'
return map_a... | -5,129,645,273,626,015,000 | Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys. | venv/Lib/site-packages/torch/fx/node.py | map_arg | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def map_arg(a: Argument, fn: Callable[([Node], Argument)]) -> Argument:
'\n \n '
assert callable(fn), 'torch.fx.map_arg(a, fn): fn must be a callable'
return map_aggregate(a, (lambda x: (fn(x) if isinstance(x, Node) else x))) |
@compatibility(is_backward_compatible=True)
def map_aggregate(a: Argument, fn: Callable[([Argument], Argument)]) -> Argument:
'\n Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys.\n '
if isinstance(a, tuple):
return tuple((map_aggregate(elem, fn) for e... | -8,735,130,783,526,883,000 | Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys. | venv/Lib/site-packages/torch/fx/node.py | map_aggregate | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def map_aggregate(a: Argument, fn: Callable[([Argument], Argument)]) -> Argument:
'\n \n '
if isinstance(a, tuple):
return tuple((map_aggregate(elem, fn) for elem in a))
elif isinstance(a, list):
return immutable_list((map_aggregate(elem, fn)... |
@compatibility(is_backward_compatible=True)
def __init__(self, graph: 'Graph', name: str, op: str, target: 'Target', args: Tuple[('Argument', ...)], kwargs: Dict[(str, 'Argument')], return_type: Optional[Any]=None) -> None:
"\n Instantiate an instance of ``Node``. Note: most often, you want to use the\n ... | -269,260,086,734,934,900 | Instantiate an instance of ``Node``. Note: most often, you want to use the
Graph APIs, i.e. ``Graph.call_module``, ``Graph.call_method``, etc. rather
than instantiating a ``Node`` directly.
Args:
graph (Graph): The ``Graph`` to which this ``Node`` should belong.
name (str): The name to which the output of thi... | venv/Lib/site-packages/torch/fx/node.py | __init__ | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def __init__(self, graph: 'Graph', name: str, op: str, target: 'Target', args: Tuple[('Argument', ...)], kwargs: Dict[(str, 'Argument')], return_type: Optional[Any]=None) -> None:
"\n Instantiate an instance of ``Node``. Note: most often, you want to use the\n ... |
@property
def next(self) -> 'Node':
'\n Returns the next ``Node`` in the linked list of Nodes.\n\n Returns:\n\n The next ``Node`` in the linked list of Nodes.\n '
return self._next | -112,273,230,731,126,510 | Returns the next ``Node`` in the linked list of Nodes.
Returns:
The next ``Node`` in the linked list of Nodes. | venv/Lib/site-packages/torch/fx/node.py | next | Westlanderz/AI-Plat1 | python | @property
def next(self) -> 'Node':
'\n Returns the next ``Node`` in the linked list of Nodes.\n\n Returns:\n\n The next ``Node`` in the linked list of Nodes.\n '
return self._next |
@property
def prev(self) -> 'Node':
'\n Returns the previous ``Node`` in the linked list of Nodes.\n\n Returns:\n\n The previous ``Node`` in the linked list of Nodes.\n '
return self._prev | -7,637,238,228,281,718,000 | Returns the previous ``Node`` in the linked list of Nodes.
Returns:
The previous ``Node`` in the linked list of Nodes. | venv/Lib/site-packages/torch/fx/node.py | prev | Westlanderz/AI-Plat1 | python | @property
def prev(self) -> 'Node':
'\n Returns the previous ``Node`` in the linked list of Nodes.\n\n Returns:\n\n The previous ``Node`` in the linked list of Nodes.\n '
return self._prev |
@compatibility(is_backward_compatible=True)
def prepend(self, x: 'Node') -> None:
'\n Insert x before this node in the list of nodes in the graph. Example::\n\n Before: p -> self\n bx -> x -> ax\n After: p -> x -> self\n bx -> ax\n\n Args:\n... | -7,424,264,496,378,813,000 | Insert x before this node in the list of nodes in the graph. Example::
Before: p -> self
bx -> x -> ax
After: p -> x -> self
bx -> ax
Args:
x (Node): The node to put before this node. Must be a member of the same graph. | venv/Lib/site-packages/torch/fx/node.py | prepend | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def prepend(self, x: 'Node') -> None:
'\n Insert x before this node in the list of nodes in the graph. Example::\n\n Before: p -> self\n bx -> x -> ax\n After: p -> x -> self\n bx -> ax\n\n Args:\n... |
@compatibility(is_backward_compatible=True)
def append(self, x: 'Node') -> None:
'\n Insert x after this node in the list of nodes in the graph.\n Equvalent to ``self.next.prepend(x)``\n\n Args:\n x (Node): The node to put after this node. Must be a member of the same graph.\n ... | -1,150,084,651,612,744,200 | Insert x after this node in the list of nodes in the graph.
Equvalent to ``self.next.prepend(x)``
Args:
x (Node): The node to put after this node. Must be a member of the same graph. | venv/Lib/site-packages/torch/fx/node.py | append | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def append(self, x: 'Node') -> None:
'\n Insert x after this node in the list of nodes in the graph.\n Equvalent to ``self.next.prepend(x)``\n\n Args:\n x (Node): The node to put after this node. Must be a member of the same graph.\n ... |
@property
def args(self) -> Tuple[(Argument, ...)]:
"\n The tuple of arguments to this ``Node``. The interpretation of arguments\n depends on the node's opcode. See the :class:`Node` docstring for more\n information.\n\n Assignment to this property is allowed. All accounting of uses and ... | 3,899,425,412,167,228,400 | The tuple of arguments to this ``Node``. The interpretation of arguments
depends on the node's opcode. See the :class:`Node` docstring for more
information.
Assignment to this property is allowed. All accounting of uses and users
is updated automatically on assignment. | venv/Lib/site-packages/torch/fx/node.py | args | Westlanderz/AI-Plat1 | python | @property
def args(self) -> Tuple[(Argument, ...)]:
"\n The tuple of arguments to this ``Node``. The interpretation of arguments\n depends on the node's opcode. See the :class:`Node` docstring for more\n information.\n\n Assignment to this property is allowed. All accounting of uses and ... |
@args.setter
def args(self, a: Tuple[(Argument, ...)]):
"\n Set the tuple of arguments to this Node. The interpretation of arguments\n depends on the node's opcode. See the ``fx.Graph`` docstring for more\n information.\n "
self.__update_args_kwargs(map_arg(a, (lambda x: x)), self._k... | 6,250,060,837,152,039,000 | Set the tuple of arguments to this Node. The interpretation of arguments
depends on the node's opcode. See the ``fx.Graph`` docstring for more
information. | venv/Lib/site-packages/torch/fx/node.py | args | Westlanderz/AI-Plat1 | python | @args.setter
def args(self, a: Tuple[(Argument, ...)]):
"\n Set the tuple of arguments to this Node. The interpretation of arguments\n depends on the node's opcode. See the ``fx.Graph`` docstring for more\n information.\n "
self.__update_args_kwargs(map_arg(a, (lambda x: x)), self._k... |
@property
def kwargs(self) -> Dict[(str, Argument)]:
"\n The dict of keyword arguments to this ``Node``. The interpretation of arguments\n depends on the node's opcode. See the :class:`Node` docstring for more\n information.\n\n Assignment to this property is allowed. All accounting of u... | -5,066,895,608,468,566,000 | The dict of keyword arguments to this ``Node``. The interpretation of arguments
depends on the node's opcode. See the :class:`Node` docstring for more
information.
Assignment to this property is allowed. All accounting of uses and users
is updated automatically on assignment. | venv/Lib/site-packages/torch/fx/node.py | kwargs | Westlanderz/AI-Plat1 | python | @property
def kwargs(self) -> Dict[(str, Argument)]:
"\n The dict of keyword arguments to this ``Node``. The interpretation of arguments\n depends on the node's opcode. See the :class:`Node` docstring for more\n information.\n\n Assignment to this property is allowed. All accounting of u... |
@kwargs.setter
def kwargs(self, k: Dict[(str, Argument)]):
"\n Set the dict of kwargs to this Node. The interpretation of arguments\n depends on the node's opcode. See the ``fx.Graph`` docstring for more\n information.\n "
self.__update_args_kwargs(self._args, map_arg(k, (lambda x: x... | -835,094,360,972,673,800 | Set the dict of kwargs to this Node. The interpretation of arguments
depends on the node's opcode. See the ``fx.Graph`` docstring for more
information. | venv/Lib/site-packages/torch/fx/node.py | kwargs | Westlanderz/AI-Plat1 | python | @kwargs.setter
def kwargs(self, k: Dict[(str, Argument)]):
"\n Set the dict of kwargs to this Node. The interpretation of arguments\n depends on the node's opcode. See the ``fx.Graph`` docstring for more\n information.\n "
self.__update_args_kwargs(self._args, map_arg(k, (lambda x: x... |
@property
def all_input_nodes(self) -> List['Node']:
'\n Return all Nodes that are inputs to this Node. This is equivalent to\n iterating over ``args`` and ``kwargs`` and only collecting the values that\n are Nodes.\n\n Returns:\n\n List of ``Nodes`` that appear in the ``args`... | -7,689,755,375,074,671,000 | Return all Nodes that are inputs to this Node. This is equivalent to
iterating over ``args`` and ``kwargs`` and only collecting the values that
are Nodes.
Returns:
List of ``Nodes`` that appear in the ``args`` and ``kwargs`` of this
``Node``, in that order. | venv/Lib/site-packages/torch/fx/node.py | all_input_nodes | Westlanderz/AI-Plat1 | python | @property
def all_input_nodes(self) -> List['Node']:
'\n Return all Nodes that are inputs to this Node. This is equivalent to\n iterating over ``args`` and ``kwargs`` and only collecting the values that\n are Nodes.\n\n Returns:\n\n List of ``Nodes`` that appear in the ``args`... |
@compatibility(is_backward_compatible=True)
def update_arg(self, idx: int, arg: Argument) -> None:
'\n Update an existing positional argument to contain the new value\n ``arg``. After calling, ``self.args[idx] == arg``.\n\n Args:\n\n idx (int): The index into ``self.args`` of the ele... | -6,276,467,114,808,523,000 | Update an existing positional argument to contain the new value
``arg``. After calling, ``self.args[idx] == arg``.
Args:
idx (int): The index into ``self.args`` of the element to update
arg (Argument): The new argument value to write into ``args`` | venv/Lib/site-packages/torch/fx/node.py | update_arg | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def update_arg(self, idx: int, arg: Argument) -> None:
'\n Update an existing positional argument to contain the new value\n ``arg``. After calling, ``self.args[idx] == arg``.\n\n Args:\n\n idx (int): The index into ``self.args`` of the ele... |
@compatibility(is_backward_compatible=True)
def update_kwarg(self, key: str, arg: Argument) -> None:
'\n Update an existing keyword argument to contain the new value\n ``arg``. After calling, ``self.kwargs[key] == arg``.\n\n Args:\n\n key (str): The key in ``self.kwargs`` of the elem... | 265,324,208,968,271,550 | Update an existing keyword argument to contain the new value
``arg``. After calling, ``self.kwargs[key] == arg``.
Args:
key (str): The key in ``self.kwargs`` of the element to update
arg (Argument): The new argument value to write into ``kwargs`` | venv/Lib/site-packages/torch/fx/node.py | update_kwarg | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def update_kwarg(self, key: str, arg: Argument) -> None:
'\n Update an existing keyword argument to contain the new value\n ``arg``. After calling, ``self.kwargs[key] == arg``.\n\n Args:\n\n key (str): The key in ``self.kwargs`` of the elem... |
@property
def stack_trace(self) -> Optional[str]:
'\n Return the Python stack trace that was recorded during tracing, if any.\n This property is usually populated by `Tracer.create_proxy`. To record\n stack traces during tracing for debug purposes, set\n `record_stack_traces = True` on t... | -4,988,679,728,696,897,000 | Return the Python stack trace that was recorded during tracing, if any.
This property is usually populated by `Tracer.create_proxy`. To record
stack traces during tracing for debug purposes, set
`record_stack_traces = True` on the `Tracer` instance. | venv/Lib/site-packages/torch/fx/node.py | stack_trace | Westlanderz/AI-Plat1 | python | @property
def stack_trace(self) -> Optional[str]:
'\n Return the Python stack trace that was recorded during tracing, if any.\n This property is usually populated by `Tracer.create_proxy`. To record\n stack traces during tracing for debug purposes, set\n `record_stack_traces = True` on t... |
def __update_args_kwargs(self, new_args: Tuple[('Argument', ...)], new_kwargs: Dict[(str, 'Argument')]):
'\n This API is internal. Do *not* call it directly.\n '
self._args = new_args
self._kwargs = new_kwargs
for old_use in self._input_nodes.keys():
old_use.users.pop(self)
sel... | -4,245,011,480,387,905,500 | This API is internal. Do *not* call it directly. | venv/Lib/site-packages/torch/fx/node.py | __update_args_kwargs | Westlanderz/AI-Plat1 | python | def __update_args_kwargs(self, new_args: Tuple[('Argument', ...)], new_kwargs: Dict[(str, 'Argument')]):
'\n \n '
self._args = new_args
self._kwargs = new_kwargs
for old_use in self._input_nodes.keys():
old_use.users.pop(self)
self._input_nodes = {}
map_arg(self._args, (lam... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.