blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e0e6e8bcdf473841fcc19a9e49ba8bce02bd9c23 | [
"pseqs = []\nfor i, ind in enumerate(indexes):\n pseqs.append(MySeq(self.seqs[i][ind:ind + self.motif_size], self.seqs[i].get_seq_biotype()))\nreturn MyMotifs(pseqs)",
"from random import randint\ns = [0] * len(self.seqs)\nfor k in range(len(s)):\n s[k] = randint(0, self.seq_size(k) - self.motif_size)\nmoti... | <|body_start_0|>
pseqs = []
for i, ind in enumerate(indexes):
pseqs.append(MySeq(self.seqs[i][ind:ind + self.motif_size], self.seqs[i].get_seq_biotype()))
return MyMotifs(pseqs)
<|end_body_0|>
<|body_start_1|>
from random import randint
s = [0] * len(self.seqs)
... | Inherited from parent class: ``DeterministicMotifFinding`` Add new function: * ``new_create_motif_from_indexes()`` * ``heuristic_stochastic()`` | MotifFinding | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MotifFinding:
"""Inherited from parent class: ``DeterministicMotifFinding`` Add new function: * ``new_create_motif_from_indexes()`` * ``heuristic_stochastic()``"""
def new_create_motif_from_indexes(self, indexes):
"""Description:: * A matrix is constructed from a set of motifs. * The... | stack_v2_sparse_classes_36k_train_025000 | 6,636 | no_license | [
{
"docstring": "Description:: * A matrix is constructed from a set of motifs. * The number of columns is equal to the length of motif and the number of rows is equal to the size of alphabet. * Each cell of the matrix contains the frequency of the symbol in a given location",
"name": "new_create_motif_from_i... | 2 | stack_v2_sparse_classes_30k_test_001020 | Implement the Python class `MotifFinding` described below.
Class description:
Inherited from parent class: ``DeterministicMotifFinding`` Add new function: * ``new_create_motif_from_indexes()`` * ``heuristic_stochastic()``
Method signatures and docstrings:
- def new_create_motif_from_indexes(self, indexes): Descriptio... | Implement the Python class `MotifFinding` described below.
Class description:
Inherited from parent class: ``DeterministicMotifFinding`` Add new function: * ``new_create_motif_from_indexes()`` * ``heuristic_stochastic()``
Method signatures and docstrings:
- def new_create_motif_from_indexes(self, indexes): Descriptio... | a9448241b629681dfba44ce69d0b28c0d7717735 | <|skeleton|>
class MotifFinding:
"""Inherited from parent class: ``DeterministicMotifFinding`` Add new function: * ``new_create_motif_from_indexes()`` * ``heuristic_stochastic()``"""
def new_create_motif_from_indexes(self, indexes):
"""Description:: * A matrix is constructed from a set of motifs. * The... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MotifFinding:
"""Inherited from parent class: ``DeterministicMotifFinding`` Add new function: * ``new_create_motif_from_indexes()`` * ``heuristic_stochastic()``"""
def new_create_motif_from_indexes(self, indexes):
"""Description:: * A matrix is constructed from a set of motifs. * The number of co... | the_stack_v2_python_sparse | M_BioPy/BioAlgo_1028_1730416009.py | NatureGeorge/ProBioinformatics | train | 1 |
d0dc872a10714073dd4989a83ec6f50474bde170 | [
"logger.info('Overriding class: Discriminator -> LSTMDiscriminator.')\nsuper(LSTMDiscriminator, self).__init__(name='D_lstm')\nself.embedding = Dense(embedding_size, name='embedding')\nself.cell = LSTMCell(hidden_size, name='lstm_cell')\nself.rnn = RNN(self.cell, name='rnn_layer', return_sequences=True, stateful=Tr... | <|body_start_0|>
logger.info('Overriding class: Discriminator -> LSTMDiscriminator.')
super(LSTMDiscriminator, self).__init__(name='D_lstm')
self.embedding = Dense(embedding_size, name='embedding')
self.cell = LSTMCell(hidden_size, name='lstm_cell')
self.rnn = RNN(self.cell, name... | A LSTMDiscriminator class is the one in charge of a discriminative Long Short-Term Memory implementation. References: S. Hochreiter, Jürgen Schmidhuber. Long short-term memory. Neural computation 9.8 (1997). | LSTMDiscriminator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LSTMDiscriminator:
"""A LSTMDiscriminator class is the one in charge of a discriminative Long Short-Term Memory implementation. References: S. Hochreiter, Jürgen Schmidhuber. Long short-term memory. Neural computation 9.8 (1997)."""
def __init__(self, embedding_size: Optional[int]=32, hidden... | stack_v2_sparse_classes_36k_train_025001 | 1,714 | permissive | [
{
"docstring": "Initialization method. Args: embedding_size: The size of the embedding layer. hidden_size: The amount of hidden neurons.",
"name": "__init__",
"signature": "def __init__(self, embedding_size: Optional[int]=32, hidden_size: Optional[int]=64) -> None"
},
{
"docstring": "Method that... | 2 | stack_v2_sparse_classes_30k_train_007593 | Implement the Python class `LSTMDiscriminator` described below.
Class description:
A LSTMDiscriminator class is the one in charge of a discriminative Long Short-Term Memory implementation. References: S. Hochreiter, Jürgen Schmidhuber. Long short-term memory. Neural computation 9.8 (1997).
Method signatures and docst... | Implement the Python class `LSTMDiscriminator` described below.
Class description:
A LSTMDiscriminator class is the one in charge of a discriminative Long Short-Term Memory implementation. References: S. Hochreiter, Jürgen Schmidhuber. Long short-term memory. Neural computation 9.8 (1997).
Method signatures and docst... | 4b7e7c1b1a304a5b37b21a972c50668e60b7bd7f | <|skeleton|>
class LSTMDiscriminator:
"""A LSTMDiscriminator class is the one in charge of a discriminative Long Short-Term Memory implementation. References: S. Hochreiter, Jürgen Schmidhuber. Long short-term memory. Neural computation 9.8 (1997)."""
def __init__(self, embedding_size: Optional[int]=32, hidden... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LSTMDiscriminator:
"""A LSTMDiscriminator class is the one in charge of a discriminative Long Short-Term Memory implementation. References: S. Hochreiter, Jürgen Schmidhuber. Long short-term memory. Neural computation 9.8 (1997)."""
def __init__(self, embedding_size: Optional[int]=32, hidden_size: Option... | the_stack_v2_python_sparse | nalp/models/discriminators/lstm.py | gugarosa/nalp | train | 25 |
90e08401cf50ef80d42fcfa6c388650e5fc09d54 | [
"target_user = auth.User.filter(id=assignee_id)\nif not target_user:\n return jsonify_response({'status': f\"User with provided ID doesn't exist\"}, 404)\ntarget_user = target_user[0]\ndata = json.loads(request.data)\nif 'role' not in data:\n return jsonify_response({'status': f'The `role` parameter must be p... | <|body_start_0|>
target_user = auth.User.filter(id=assignee_id)
if not target_user:
return jsonify_response({'status': f"User with provided ID doesn't exist"}, 404)
target_user = target_user[0]
data = json.loads(request.data)
if 'role' not in data:
return ... | Contains the PUT and DELETE method for assigned users for a given node | UpdateDeleteNodesAssignedUsers | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpdateDeleteNodesAssignedUsers:
"""Contains the PUT and DELETE method for assigned users for a given node"""
def put(self, vertex=None, vertex_type=None, vertex_id=None, assignee_id=None):
"""Endpoint for updating a user's role for a given node"""
<|body_0|>
def delete(s... | stack_v2_sparse_classes_36k_train_025002 | 44,865 | no_license | [
{
"docstring": "Endpoint for updating a user's role for a given node",
"name": "put",
"signature": "def put(self, vertex=None, vertex_type=None, vertex_id=None, assignee_id=None)"
},
{
"docstring": "Endpoint for removing an assigned user edge for a given node/user pair",
"name": "delete",
... | 2 | stack_v2_sparse_classes_30k_train_014616 | Implement the Python class `UpdateDeleteNodesAssignedUsers` described below.
Class description:
Contains the PUT and DELETE method for assigned users for a given node
Method signatures and docstrings:
- def put(self, vertex=None, vertex_type=None, vertex_id=None, assignee_id=None): Endpoint for updating a user's role... | Implement the Python class `UpdateDeleteNodesAssignedUsers` described below.
Class description:
Contains the PUT and DELETE method for assigned users for a given node
Method signatures and docstrings:
- def put(self, vertex=None, vertex_type=None, vertex_id=None, assignee_id=None): Endpoint for updating a user's role... | 00434985013b65fe45b0a8c8a7f0b50bb727087a | <|skeleton|>
class UpdateDeleteNodesAssignedUsers:
"""Contains the PUT and DELETE method for assigned users for a given node"""
def put(self, vertex=None, vertex_type=None, vertex_id=None, assignee_id=None):
"""Endpoint for updating a user's role for a given node"""
<|body_0|>
def delete(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UpdateDeleteNodesAssignedUsers:
"""Contains the PUT and DELETE method for assigned users for a given node"""
def put(self, vertex=None, vertex_type=None, vertex_id=None, assignee_id=None):
"""Endpoint for updating a user's role for a given node"""
target_user = auth.User.filter(id=assigne... | the_stack_v2_python_sparse | core/views.py | gingerComms/gingerCommsAPIs | train | 0 |
14e085bc601432516c434b14f6d448d16a463172 | [
"self.degree = degree\nself.n_vars = n_vars\nself.coefs = coefs",
"coefs = [2 * (random.random() - 0.5) for i in Polynomial.terms(degree, n_vars)]\np = Polynomial(degree, n_vars, coefs)\nreturn p",
"for pows in itertools.product(range(degree + 1), repeat=n_vars):\n if sum(pows) <= degree:\n yield pows... | <|body_start_0|>
self.degree = degree
self.n_vars = n_vars
self.coefs = coefs
<|end_body_0|>
<|body_start_1|>
coefs = [2 * (random.random() - 0.5) for i in Polynomial.terms(degree, n_vars)]
p = Polynomial(degree, n_vars, coefs)
return p
<|end_body_1|>
<|body_start_2|>
... | A polynomial of a given degree and a given number of variables, with one coefficient for each term. | Polynomial | [
"MIT",
"GPL-3.0-only",
"GPL-1.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Polynomial:
"""A polynomial of a given degree and a given number of variables, with one coefficient for each term."""
def __init__(self, degree, n_vars, coefs):
"""Constructor for the case where we already have coefficients."""
<|body_0|>
def from_random(cls, degree, n_v... | stack_v2_sparse_classes_36k_train_025003 | 4,452 | permissive | [
{
"docstring": "Constructor for the case where we already have coefficients.",
"name": "__init__",
"signature": "def __init__(self, degree, n_vars, coefs)"
},
{
"docstring": "Constructor for the case where we want random coefficients.",
"name": "from_random",
"signature": "def from_rando... | 5 | stack_v2_sparse_classes_30k_train_005100 | Implement the Python class `Polynomial` described below.
Class description:
A polynomial of a given degree and a given number of variables, with one coefficient for each term.
Method signatures and docstrings:
- def __init__(self, degree, n_vars, coefs): Constructor for the case where we already have coefficients.
- ... | Implement the Python class `Polynomial` described below.
Class description:
A polynomial of a given degree and a given number of variables, with one coefficient for each term.
Method signatures and docstrings:
- def __init__(self, degree, n_vars, coefs): Constructor for the case where we already have coefficients.
- ... | 27f19678d69e2c6c7038514e26638a9962229118 | <|skeleton|>
class Polynomial:
"""A polynomial of a given degree and a given number of variables, with one coefficient for each term."""
def __init__(self, degree, n_vars, coefs):
"""Constructor for the case where we already have coefficients."""
<|body_0|>
def from_random(cls, degree, n_v... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Polynomial:
"""A polynomial of a given degree and a given number of variables, with one coefficient for each term."""
def __init__(self, degree, n_vars, coefs):
"""Constructor for the case where we already have coefficients."""
self.degree = degree
self.n_vars = n_vars
sel... | the_stack_v2_python_sparse | src/PonyGE2/src/fitness/supervised_learning/regression_random_polynomial.py | flexgp/novelty-prog-sys | train | 1 |
374b0a5a5544f3a94498ded7e45e8bda524cb17f | [
"if address is None:\n return\ntry:\n if network:\n ipaddress.ip_network(address, strict=True)\n else:\n ipaddress.ip_address(address)\nexcept ValueError as exc:\n msg = \"The value '{}={}' is invalid: {}\".format(field_name, address, str(exc))\n raise BaseHttpError(code=400, msg=msg)",... | <|body_start_0|>
if address is None:
return
try:
if network:
ipaddress.ip_network(address, strict=True)
else:
ipaddress.ip_address(address)
except ValueError as exc:
msg = "The value '{}={}' is invalid: {}".format(fi... | Resource for subnets | SubnetResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubnetResource:
"""Resource for subnets"""
def _assert_address(address, field_name, network=False):
"""Assert that address is a valid ip address. Args: address (str): network or ip address field_name (str): field name to report in case of error network (bool): whether it is a network... | stack_v2_sparse_classes_36k_train_025004 | 8,007 | permissive | [
{
"docstring": "Assert that address is a valid ip address. Args: address (str): network or ip address field_name (str): field name to report in case of error network (bool): whether it is a network address Raises: BaseHttpError: in case provided address is invalid",
"name": "_assert_address",
"signature... | 4 | stack_v2_sparse_classes_30k_train_012958 | Implement the Python class `SubnetResource` described below.
Class description:
Resource for subnets
Method signatures and docstrings:
- def _assert_address(address, field_name, network=False): Assert that address is a valid ip address. Args: address (str): network or ip address field_name (str): field name to report... | Implement the Python class `SubnetResource` described below.
Class description:
Resource for subnets
Method signatures and docstrings:
- def _assert_address(address, field_name, network=False): Assert that address is a valid ip address. Args: address (str): network or ip address field_name (str): field name to report... | 9c9040f6a173af5c495f5447889e9349fa56f234 | <|skeleton|>
class SubnetResource:
"""Resource for subnets"""
def _assert_address(address, field_name, network=False):
"""Assert that address is a valid ip address. Args: address (str): network or ip address field_name (str): field name to report in case of error network (bool): whether it is a network... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubnetResource:
"""Resource for subnets"""
def _assert_address(address, field_name, network=False):
"""Assert that address is a valid ip address. Args: address (str): network or ip address field_name (str): field name to report in case of error network (bool): whether it is a network address Rais... | the_stack_v2_python_sparse | tessia/server/api/resources/subnets.py | tessia-project/tessia | train | 10 |
30e95d22f0c92190090e870f6152c549a5e30e74 | [
"super(ImageEncoding, self).__init__()\nself.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1)\nself.batchNorm1 = nn.BatchNorm2d(32)\nself.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1)\nself.batchNorm2 = nn.BatchNorm2d(64)\nself.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2)\nself.batchNorm3 = nn.BatchNor... | <|body_start_0|>
super(ImageEncoding, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1)
self.batchNorm1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1)
self.batchNorm2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(64, 128, ... | Image encoding using 4 convolutional layers with batch normalization, it was designed specifically for sort of clevr https://arxiv.org/abs/1706.01427. | ImageEncoding | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageEncoding:
"""Image encoding using 4 convolutional layers with batch normalization, it was designed specifically for sort of clevr https://arxiv.org/abs/1706.01427."""
def __init__(self):
"""Constructor of the ImageEncoding class."""
<|body_0|>
def forward(self, img)... | stack_v2_sparse_classes_36k_train_025005 | 4,377 | permissive | [
{
"docstring": "Constructor of the ImageEncoding class.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Apply 4 convolutional layers over the image. :param img: input image [batch_size, num_channels, height, width] :return x: feature map with flattening the width and h... | 2 | null | Implement the Python class `ImageEncoding` described below.
Class description:
Image encoding using 4 convolutional layers with batch normalization, it was designed specifically for sort of clevr https://arxiv.org/abs/1706.01427.
Method signatures and docstrings:
- def __init__(self): Constructor of the ImageEncoding... | Implement the Python class `ImageEncoding` described below.
Class description:
Image encoding using 4 convolutional layers with batch normalization, it was designed specifically for sort of clevr https://arxiv.org/abs/1706.01427.
Method signatures and docstrings:
- def __init__(self): Constructor of the ImageEncoding... | c655c88cc6aec4d0724c19ea95209f1c2dd6770d | <|skeleton|>
class ImageEncoding:
"""Image encoding using 4 convolutional layers with batch normalization, it was designed specifically for sort of clevr https://arxiv.org/abs/1706.01427."""
def __init__(self):
"""Constructor of the ImageEncoding class."""
<|body_0|>
def forward(self, img)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageEncoding:
"""Image encoding using 4 convolutional layers with batch normalization, it was designed specifically for sort of clevr https://arxiv.org/abs/1706.01427."""
def __init__(self):
"""Constructor of the ImageEncoding class."""
super(ImageEncoding, self).__init__()
self.... | the_stack_v2_python_sparse | models/stacked_attention_vqa/image_encoding.py | aasseman/mi-prometheus | train | 0 |
f02f997b82ae619a6edbefdf26afa4ea95d0d2f6 | [
"super().__init__(env)\nself.env = env\nself.env_category = env_config.env_name.split(':')[0]\nself.max_videos = env_config.video.max_videos\nself.capture_interval = env_config.video.record_every\nself.frame_interval = frame_interval\nself.fps = fps\nself.ext = '.gif' if use_gif else '.mp4'\nself.save_folder = env_... | <|body_start_0|>
super().__init__(env)
self.env = env
self.env_category = env_config.env_name.split(':')[0]
self.max_videos = env_config.video.max_videos
self.capture_interval = env_config.video.record_every
self.frame_interval = frame_interval
self.fps = fps
... | Environment Wrappers for automatically rendering and saving test runs. Attributes: public attributes env (Env): environment to be wrapped capture_interval (int): number of episodes between captures default to 10 frame_interval (int): number of frames between each recorded frame default to 10 fps (int): frame rate defau... | VideoWrapper | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VideoWrapper:
"""Environment Wrappers for automatically rendering and saving test runs. Attributes: public attributes env (Env): environment to be wrapped capture_interval (int): number of episodes between captures default to 10 frame_interval (int): number of frames between each recorded frame d... | stack_v2_sparse_classes_36k_train_025006 | 6,239 | permissive | [
{
"docstring": "Constructor for VideoWrapper. also creates the save directory if not present Args: env (Env): environment to be wrapped capture_interval (int): number of episodes between captures frame_interval (int): number of frames between each recorded frame fps (int): frame rate save_folder (str): director... | 4 | stack_v2_sparse_classes_30k_train_020637 | Implement the Python class `VideoWrapper` described below.
Class description:
Environment Wrappers for automatically rendering and saving test runs. Attributes: public attributes env (Env): environment to be wrapped capture_interval (int): number of episodes between captures default to 10 frame_interval (int): number ... | Implement the Python class `VideoWrapper` described below.
Class description:
Environment Wrappers for automatically rendering and saving test runs. Attributes: public attributes env (Env): environment to be wrapped capture_interval (int): number of episodes between captures default to 10 frame_interval (int): number ... | 2556bd9c362a53e0a94da914ba59b5d4621c4081 | <|skeleton|>
class VideoWrapper:
"""Environment Wrappers for automatically rendering and saving test runs. Attributes: public attributes env (Env): environment to be wrapped capture_interval (int): number of episodes between captures default to 10 frame_interval (int): number of frames between each recorded frame d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VideoWrapper:
"""Environment Wrappers for automatically rendering and saving test runs. Attributes: public attributes env (Env): environment to be wrapped capture_interval (int): number of episodes between captures default to 10 frame_interval (int): number of frames between each recorded frame default to 10 ... | the_stack_v2_python_sparse | surreal/env/video_env.py | PeihongYu/surreal | train | 0 |
aeac0fed66c294dac95010b19e1cc1ca2568eb08 | [
"actual_dict = linear.csv_to_dict('data/products.csv')\nexpected_dict_1 = {'product_id': 'prd001', 'description': '700-W microwave', 'product_type': 'kitchen', 'quantity_available': '5'}\nself.assertEqual(actual_dict[0], expected_dict_1)",
"linear.drop_db()\nactual_1_raw = linear.import_data('', 'data/products.cs... | <|body_start_0|>
actual_dict = linear.csv_to_dict('data/products.csv')
expected_dict_1 = {'product_id': 'prd001', 'description': '700-W microwave', 'product_type': 'kitchen', 'quantity_available': '5'}
self.assertEqual(actual_dict[0], expected_dict_1)
<|end_body_0|>
<|body_start_1|>
lin... | Contains test functions to evaluate basic_operations | DatabaseTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatabaseTests:
"""Contains test functions to evaluate basic_operations"""
def test_csv_to_dict(self):
"""Test csv_to_dict function"""
<|body_0|>
def test_import_data(self):
"""Test import_data function"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_025007 | 1,089 | no_license | [
{
"docstring": "Test csv_to_dict function",
"name": "test_csv_to_dict",
"signature": "def test_csv_to_dict(self)"
},
{
"docstring": "Test import_data function",
"name": "test_import_data",
"signature": "def test_import_data(self)"
}
] | 2 | null | Implement the Python class `DatabaseTests` described below.
Class description:
Contains test functions to evaluate basic_operations
Method signatures and docstrings:
- def test_csv_to_dict(self): Test csv_to_dict function
- def test_import_data(self): Test import_data function | Implement the Python class `DatabaseTests` described below.
Class description:
Contains test functions to evaluate basic_operations
Method signatures and docstrings:
- def test_csv_to_dict(self): Test csv_to_dict function
- def test_import_data(self): Test import_data function
<|skeleton|>
class DatabaseTests:
"... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class DatabaseTests:
"""Contains test functions to evaluate basic_operations"""
def test_csv_to_dict(self):
"""Test csv_to_dict function"""
<|body_0|>
def test_import_data(self):
"""Test import_data function"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatabaseTests:
"""Contains test functions to evaluate basic_operations"""
def test_csv_to_dict(self):
"""Test csv_to_dict function"""
actual_dict = linear.csv_to_dict('data/products.csv')
expected_dict_1 = {'product_id': 'prd001', 'description': '700-W microwave', 'product_type': ... | the_stack_v2_python_sparse | students/will_chang/lesson07/test_linear.py | JavaRod/SP_Python220B_2019 | train | 1 |
91591795c31dc82345f24568d9d0dc053db5f715 | [
"try:\n self.email = email\n self.password = password\nexcept Exception as e:\n config.logger.log('ERROR', str(e))",
"try:\n if self.email is not None and self.password is not None:\n self.email = self.email.lower()\n config.logger.log('INFO', 'Searching for email in database...')\n ... | <|body_start_0|>
try:
self.email = email
self.password = password
except Exception as e:
config.logger.log('ERROR', str(e))
<|end_body_0|>
<|body_start_1|>
try:
if self.email is not None and self.password is not None:
self.email = ... | Validation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Validation:
def __init__(self, email, password):
""":param email: email of the user :param password: password of the user"""
<|body_0|>
def check(self):
""":return: whether valid or not along with a new jwt."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_025008 | 1,885 | no_license | [
{
"docstring": ":param email: email of the user :param password: password of the user",
"name": "__init__",
"signature": "def __init__(self, email, password)"
},
{
"docstring": ":return: whether valid or not along with a new jwt.",
"name": "check",
"signature": "def check(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006682 | Implement the Python class `Validation` described below.
Class description:
Implement the Validation class.
Method signatures and docstrings:
- def __init__(self, email, password): :param email: email of the user :param password: password of the user
- def check(self): :return: whether valid or not along with a new j... | Implement the Python class `Validation` described below.
Class description:
Implement the Validation class.
Method signatures and docstrings:
- def __init__(self, email, password): :param email: email of the user :param password: password of the user
- def check(self): :return: whether valid or not along with a new j... | b7444684784d8320c833bf9f7a5ee15d983e474b | <|skeleton|>
class Validation:
def __init__(self, email, password):
""":param email: email of the user :param password: password of the user"""
<|body_0|>
def check(self):
""":return: whether valid or not along with a new jwt."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Validation:
def __init__(self, email, password):
""":param email: email of the user :param password: password of the user"""
try:
self.email = email
self.password = password
except Exception as e:
config.logger.log('ERROR', str(e))
def check(sel... | the_stack_v2_python_sparse | py_backend/login/login_user.py | shubhamgantayat/MedHub | train | 2 | |
37ba07b527f3cf7c38faf219ea0cf7ba220da65a | [
"s = cb.ConvergenceEvaluationSpecification()\ns.add_sampled_input(name='Inlet_Flowrate', pyomo_path='fs.pc.control_volume.properties_in[0].flow_mol', lower=1, upper=1000000.0, mean=5000.0, std=5000.0)\ns.add_sampled_input(name='Inlet_Pressure', pyomo_path='fs.pc.control_volume.properties_in[0].pressure', lower=1000... | <|body_start_0|>
s = cb.ConvergenceEvaluationSpecification()
s.add_sampled_input(name='Inlet_Flowrate', pyomo_path='fs.pc.control_volume.properties_in[0].flow_mol', lower=1, upper=1000000.0, mean=5000.0, std=5000.0)
s.add_sampled_input(name='Inlet_Pressure', pyomo_path='fs.pc.control_volume.prop... | PressureChangerConvergenceEvaluation | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PressureChangerConvergenceEvaluation:
def get_specification(self):
"""Returns the convergence evaluation specification for the PressureChanger unit model Returns ------- ConvergenceEvaluationSpecification"""
<|body_0|>
def get_initialized_model(self):
"""Returns an i... | stack_v2_sparse_classes_36k_train_025009 | 3,539 | permissive | [
{
"docstring": "Returns the convergence evaluation specification for the PressureChanger unit model Returns ------- ConvergenceEvaluationSpecification",
"name": "get_specification",
"signature": "def get_specification(self)"
},
{
"docstring": "Returns an initialized model for the PressureChanger... | 3 | null | Implement the Python class `PressureChangerConvergenceEvaluation` described below.
Class description:
Implement the PressureChangerConvergenceEvaluation class.
Method signatures and docstrings:
- def get_specification(self): Returns the convergence evaluation specification for the PressureChanger unit model Returns -... | Implement the Python class `PressureChangerConvergenceEvaluation` described below.
Class description:
Implement the PressureChangerConvergenceEvaluation class.
Method signatures and docstrings:
- def get_specification(self): Returns the convergence evaluation specification for the PressureChanger unit model Returns -... | afec89b8273fcc17a0b5f08ea9b97b5fc98d2a31 | <|skeleton|>
class PressureChangerConvergenceEvaluation:
def get_specification(self):
"""Returns the convergence evaluation specification for the PressureChanger unit model Returns ------- ConvergenceEvaluationSpecification"""
<|body_0|>
def get_initialized_model(self):
"""Returns an i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PressureChangerConvergenceEvaluation:
def get_specification(self):
"""Returns the convergence evaluation specification for the PressureChanger unit model Returns ------- ConvergenceEvaluationSpecification"""
s = cb.ConvergenceEvaluationSpecification()
s.add_sampled_input(name='Inlet_Fl... | the_stack_v2_python_sparse | idaes/convergence/generic_models/pressure_changer.py | JackaChou/idaes-pse | train | 1 | |
eb167cd1538afe2f068c909707ebad2089f9937e | [
"super().__init__()\nself._model = deepcopy(model)\nseed = torch.tensor(seed if seed is not None else torch.randint(0, 1000000, (1,)).item())\nself.register_buffer('_seed', seed)",
"try:\n return self._Xs\nexcept AttributeError:\n return None",
"try:\n return self._Ys\nexcept AttributeError:\n retur... | <|body_start_0|>
super().__init__()
self._model = deepcopy(model)
seed = torch.tensor(seed if seed is not None else torch.randint(0, 1000000, (1,)).item())
self.register_buffer('_seed', seed)
<|end_body_0|>
<|body_start_1|>
try:
return self._Xs
except Attribu... | Convenience wrapper for sampling a function from a GP prior. This wrapper implicitly defines the GP sample as a self-updating function by keeping track of the evaluated points and respective base samples used during the evaluation. This does not yet support multi-output models. | GPDraw | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GPDraw:
"""Convenience wrapper for sampling a function from a GP prior. This wrapper implicitly defines the GP sample as a self-updating function by keeping track of the evaluated points and respective base samples used during the evaluation. This does not yet support multi-output models."""
... | stack_v2_sparse_classes_36k_train_025010 | 15,960 | permissive | [
{
"docstring": "Construct a GP function sampler. Args: model: The Model defining the GP prior.",
"name": "__init__",
"signature": "def __init__(self, model: Model, seed: Optional[int]=None) -> None"
},
{
"docstring": "A `(batch_shape) x n_eval x d`-dim tensor of locations at which the GP was eva... | 4 | stack_v2_sparse_classes_30k_train_010625 | Implement the Python class `GPDraw` described below.
Class description:
Convenience wrapper for sampling a function from a GP prior. This wrapper implicitly defines the GP sample as a self-updating function by keeping track of the evaluated points and respective base samples used during the evaluation. This does not y... | Implement the Python class `GPDraw` described below.
Class description:
Convenience wrapper for sampling a function from a GP prior. This wrapper implicitly defines the GP sample as a self-updating function by keeping track of the evaluated points and respective base samples used during the evaluation. This does not y... | 52c611cb716856777af87763a98c141507b019b3 | <|skeleton|>
class GPDraw:
"""Convenience wrapper for sampling a function from a GP prior. This wrapper implicitly defines the GP sample as a self-updating function by keeping track of the evaluated points and respective base samples used during the evaluation. This does not yet support multi-output models."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GPDraw:
"""Convenience wrapper for sampling a function from a GP prior. This wrapper implicitly defines the GP sample as a self-updating function by keeping track of the evaluated points and respective base samples used during the evaluation. This does not yet support multi-output models."""
def __init__... | the_stack_v2_python_sparse | botorch/utils/gp_sampling.py | utkarshiam/botorch | train | 0 |
735bdcd3cefd3a12af3f8cd5f5c4c27c11e9cd57 | [
"self.inline = inline\nself.escape = escape\nsuper().__init__(data=data)",
"if self.inline:\n return '$' + self.dumps_content() + '$'\nreturn '\\\\[%\\n' + self.dumps_content() + '%\\n\\\\]'"
] | <|body_start_0|>
self.inline = inline
self.escape = escape
super().__init__(data=data)
<|end_body_0|>
<|body_start_1|>
if self.inline:
return '$' + self.dumps_content() + '$'
return '\\[%\n' + self.dumps_content() + '%\n\\]'
<|end_body_1|>
| A class representing a math environment. | Math | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Math:
"""A class representing a math environment."""
def __init__(self, *, inline=False, data=None, escape=None):
"""Args ---- data: list Content of the math container. inline: bool If the math should be displayed inline or not. escape : bool if True, will escape strings"""
<... | stack_v2_sparse_classes_36k_train_025011 | 3,905 | permissive | [
{
"docstring": "Args ---- data: list Content of the math container. inline: bool If the math should be displayed inline or not. escape : bool if True, will escape strings",
"name": "__init__",
"signature": "def __init__(self, *, inline=False, data=None, escape=None)"
},
{
"docstring": "Return a ... | 2 | stack_v2_sparse_classes_30k_train_007229 | Implement the Python class `Math` described below.
Class description:
A class representing a math environment.
Method signatures and docstrings:
- def __init__(self, *, inline=False, data=None, escape=None): Args ---- data: list Content of the math container. inline: bool If the math should be displayed inline or not... | Implement the Python class `Math` described below.
Class description:
A class representing a math environment.
Method signatures and docstrings:
- def __init__(self, *, inline=False, data=None, escape=None): Args ---- data: list Content of the math container. inline: bool If the math should be displayed inline or not... | 2050b14d8d7ed4fe788c769afec6816e2b703355 | <|skeleton|>
class Math:
"""A class representing a math environment."""
def __init__(self, *, inline=False, data=None, escape=None):
"""Args ---- data: list Content of the math container. inline: bool If the math should be displayed inline or not. escape : bool if True, will escape strings"""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Math:
"""A class representing a math environment."""
def __init__(self, *, inline=False, data=None, escape=None):
"""Args ---- data: list Content of the math container. inline: bool If the math should be displayed inline or not. escape : bool if True, will escape strings"""
self.inline = ... | the_stack_v2_python_sparse | pylatex/math.py | JelteF/PyLaTeX | train | 2,104 |
feda5f5ab6d8f5e0aca8fcecf68979188a8fb768 | [
"engine = db_connect()\ncreate_table(engine)\nself.Session = sessionmaker(bind=engine)",
"session = self.Session()\npractice_id = uuid.uuid4()\ndb1 = PhysicianDB()\ndb1.id = practice_id\ndb1.license = item['license']\ndb1.license_type = item['license_type']\ndb1.physician_name = item['name'].title()\ndb1.address ... | <|body_start_0|>
engine = db_connect()
create_table(engine)
self.Session = sessionmaker(bind=engine)
<|end_body_0|>
<|body_start_1|>
session = self.Session()
practice_id = uuid.uuid4()
db1 = PhysicianDB()
db1.id = practice_id
db1.license = item['license']... | ScrapyDcaPipeline | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScrapyDcaPipeline:
def __init__(self):
"""Initializes database connection and sessionmaker. Creates deals table."""
<|body_0|>
def process_item(self, item, spider):
"""This method is called for every item pipeline component."""
<|body_1|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_36k_train_025012 | 1,473 | no_license | [
{
"docstring": "Initializes database connection and sessionmaker. Creates deals table.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "This method is called for every item pipeline component.",
"name": "process_item",
"signature": "def process_item(self, item, ... | 2 | stack_v2_sparse_classes_30k_train_014489 | Implement the Python class `ScrapyDcaPipeline` described below.
Class description:
Implement the ScrapyDcaPipeline class.
Method signatures and docstrings:
- def __init__(self): Initializes database connection and sessionmaker. Creates deals table.
- def process_item(self, item, spider): This method is called for eve... | Implement the Python class `ScrapyDcaPipeline` described below.
Class description:
Implement the ScrapyDcaPipeline class.
Method signatures and docstrings:
- def __init__(self): Initializes database connection and sessionmaker. Creates deals table.
- def process_item(self, item, spider): This method is called for eve... | 13bf1f02bc6c6817badf7d162c55169e276725b8 | <|skeleton|>
class ScrapyDcaPipeline:
def __init__(self):
"""Initializes database connection and sessionmaker. Creates deals table."""
<|body_0|>
def process_item(self, item, spider):
"""This method is called for every item pipeline component."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScrapyDcaPipeline:
def __init__(self):
"""Initializes database connection and sessionmaker. Creates deals table."""
engine = db_connect()
create_table(engine)
self.Session = sessionmaker(bind=engine)
def process_item(self, item, spider):
"""This method is called fo... | the_stack_v2_python_sparse | scrapy/scrapy-webdriver/scrapy_dca/pipelines.py | bidcms/crawler | train | 0 | |
7957f484cd7f8831bcc656d7cae5ec2c058e93db | [
"less = []\nfor pair in zip(words, words[1:]):\n for a, b in zip(*pair):\n if a != b:\n less += (a + b,)\n break\nchars = set(''.join(words))\norder = []\nwhile less:\n free = chars - set(zip(*less)[1])\n if not free:\n return ''\n order += free\n less = filter(fre... | <|body_start_0|>
less = []
for pair in zip(words, words[1:]):
for a, b in zip(*pair):
if a != b:
less += (a + b,)
break
chars = set(''.join(words))
order = []
while less:
free = chars - set(zip(*less)... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def alienOrder(self, words):
""":type words: List[str] :rtype: str"""
<|body_0|>
def rewrite(self, words):
""":type words: List[str] :rtype: str 注意 data structure 的 type [ "wrt", "wrf", "er", "ett", "rftt" ]"""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_36k_train_025013 | 2,995 | no_license | [
{
"docstring": ":type words: List[str] :rtype: str",
"name": "alienOrder",
"signature": "def alienOrder(self, words)"
},
{
"docstring": ":type words: List[str] :rtype: str 注意 data structure 的 type [ \"wrt\", \"wrf\", \"er\", \"ett\", \"rftt\" ]",
"name": "rewrite",
"signature": "def rewr... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def alienOrder(self, words): :type words: List[str] :rtype: str
- def rewrite(self, words): :type words: List[str] :rtype: str 注意 data structure 的 type [ "wrt", "wrf", "er", "ett... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def alienOrder(self, words): :type words: List[str] :rtype: str
- def rewrite(self, words): :type words: List[str] :rtype: str 注意 data structure 的 type [ "wrt", "wrf", "er", "ett... | 6350568d16b0f8c49a020f055bb6d72e2705ea56 | <|skeleton|>
class Solution:
def alienOrder(self, words):
""":type words: List[str] :rtype: str"""
<|body_0|>
def rewrite(self, words):
""":type words: List[str] :rtype: str 注意 data structure 的 type [ "wrt", "wrf", "er", "ett", "rftt" ]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def alienOrder(self, words):
""":type words: List[str] :rtype: str"""
less = []
for pair in zip(words, words[1:]):
for a, b in zip(*pair):
if a != b:
less += (a + b,)
break
chars = set(''.join(words))... | the_stack_v2_python_sparse | co_fb/269_Alien_Dictionary.py | vsdrun/lc_public | train | 6 | |
1aaee45ad67cba7f3f8226db140a91c7e75a86c5 | [
"super().__init__()\nself.root_dir = root_dir\nself.dir_filter = dir_filter\nself.file_keys = file_keys\nself.file_path_generator = file_path_generator\nself.file_extension = file_extension if file_extension.startswith('.') else '.' + file_extension\nself.data = {}\ndata_dir = self._crawl_directories()\nself._crawl... | <|body_start_0|>
super().__init__()
self.root_dir = root_dir
self.dir_filter = dir_filter
self.file_keys = file_keys
self.file_path_generator = file_path_generator
self.file_extension = file_extension if file_extension.startswith('.') else '.' + file_extension
sel... | Represents a file system data crawler. Examples: Suppose we have the following directory structure:: /path/to/root_dir ./Patient1 ./Image.mha ./GroundTruth.mha ./some_text_file.txt ./Patient2 ./Image.mha ./GroundTruth.mha ./GroundTruthRater2.mha ./Atlas ./Atlas.mha We can use the following code to load the images `Imag... | FileSystemDataCrawler | [
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileSystemDataCrawler:
"""Represents a file system data crawler. Examples: Suppose we have the following directory structure:: /path/to/root_dir ./Patient1 ./Image.mha ./GroundTruth.mha ./some_text_file.txt ./Patient2 ./Image.mha ./GroundTruth.mha ./GroundTruthRater2.mha ./Atlas ./Atlas.mha We ca... | stack_v2_sparse_classes_36k_train_025014 | 6,815 | permissive | [
{
"docstring": "Initializes a new instance of the FileSystemDataCrawler class. Args: root_dir (str): The path to the root directory, which contains subdirectories with the data. file_keys (list): A list of objects, which represent human readable data identifiers (one identifier for each data file to crawl). fil... | 3 | stack_v2_sparse_classes_30k_train_018051 | Implement the Python class `FileSystemDataCrawler` described below.
Class description:
Represents a file system data crawler. Examples: Suppose we have the following directory structure:: /path/to/root_dir ./Patient1 ./Image.mha ./GroundTruth.mha ./some_text_file.txt ./Patient2 ./Image.mha ./GroundTruth.mha ./GroundTr... | Implement the Python class `FileSystemDataCrawler` described below.
Class description:
Represents a file system data crawler. Examples: Suppose we have the following directory structure:: /path/to/root_dir ./Patient1 ./Image.mha ./GroundTruth.mha ./some_text_file.txt ./Patient2 ./Image.mha ./GroundTruth.mha ./GroundTr... | 7917c6a6c4e3728db17ec762c63f8253392e6c04 | <|skeleton|>
class FileSystemDataCrawler:
"""Represents a file system data crawler. Examples: Suppose we have the following directory structure:: /path/to/root_dir ./Patient1 ./Image.mha ./GroundTruth.mha ./some_text_file.txt ./Patient2 ./Image.mha ./GroundTruth.mha ./GroundTruthRater2.mha ./Atlas ./Atlas.mha We ca... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileSystemDataCrawler:
"""Represents a file system data crawler. Examples: Suppose we have the following directory structure:: /path/to/root_dir ./Patient1 ./Image.mha ./GroundTruth.mha ./some_text_file.txt ./Patient2 ./Image.mha ./GroundTruth.mha ./GroundTruthRater2.mha ./Atlas ./Atlas.mha We can use the fol... | the_stack_v2_python_sparse | miapy/miapy/data/loading.py | SCAN-NRAD/BrainRegressorCNN | train | 3 |
dcb3401a9110b7c3383f2bb9d596bd8f0e54e97d | [
"outputs = sorted(StreamAlertOutput.get_all_outputs().keys())\nget_parser = generate_subparser(subparser, 'get', description=cls.description, help=cls.description, subcommand=True)\nget_parser.add_argument('service', choices=outputs, metavar='SERVICE', help='Service to pull configured outputs and their secrets, sel... | <|body_start_0|>
outputs = sorted(StreamAlertOutput.get_all_outputs().keys())
get_parser = generate_subparser(subparser, 'get', description=cls.description, help=cls.description, subcommand=True)
get_parser.add_argument('service', choices=outputs, metavar='SERVICE', help='Service to pull configu... | OutputGetSubCommand | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OutputGetSubCommand:
def setup_subparser(cls, subparser):
"""Add the output get subparser: manage.py output get [options]"""
<|body_0|>
def handler(cls, options, config):
"""Fetches the configuration for a service Args: options (argparse.Namespace): Basically a named... | stack_v2_sparse_classes_36k_train_025015 | 19,044 | permissive | [
{
"docstring": "Add the output get subparser: manage.py output get [options]",
"name": "setup_subparser",
"signature": "def setup_subparser(cls, subparser)"
},
{
"docstring": "Fetches the configuration for a service Args: options (argparse.Namespace): Basically a namedtuple with the service sett... | 2 | stack_v2_sparse_classes_30k_train_019803 | Implement the Python class `OutputGetSubCommand` described below.
Class description:
Implement the OutputGetSubCommand class.
Method signatures and docstrings:
- def setup_subparser(cls, subparser): Add the output get subparser: manage.py output get [options]
- def handler(cls, options, config): Fetches the configura... | Implement the Python class `OutputGetSubCommand` described below.
Class description:
Implement the OutputGetSubCommand class.
Method signatures and docstrings:
- def setup_subparser(cls, subparser): Add the output get subparser: manage.py output get [options]
- def handler(cls, options, config): Fetches the configura... | 75ba140d2e1aa6e903313d88326920adcb8bff45 | <|skeleton|>
class OutputGetSubCommand:
def setup_subparser(cls, subparser):
"""Add the output get subparser: manage.py output get [options]"""
<|body_0|>
def handler(cls, options, config):
"""Fetches the configuration for a service Args: options (argparse.Namespace): Basically a named... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OutputGetSubCommand:
def setup_subparser(cls, subparser):
"""Add the output get subparser: manage.py output get [options]"""
outputs = sorted(StreamAlertOutput.get_all_outputs().keys())
get_parser = generate_subparser(subparser, 'get', description=cls.description, help=cls.description,... | the_stack_v2_python_sparse | streamalert_cli/outputs/handler.py | avmi/streamalert | train | 0 | |
3aedabadd9652e25b37713f09f9db2f95aab1c83 | [
"Inter.__init__(self, slab=slab)\nself['B'] = []\nself['L'] = []\nself[''] = []\nself['T'] = []\nself['D'] = []\nself.evaluated = False\nself.nimax = 0",
"for i in li:\n if i.idx != []:\n self.nimax = max(self.nimax, max(i.idx)) + 1\nfor i in li:\n self.addi(i)",
"if not isinstance(self.typ, np.nda... | <|body_start_0|>
Inter.__init__(self, slab=slab)
self['B'] = []
self['L'] = []
self[''] = []
self['T'] = []
self['D'] = []
self.evaluated = False
self.nimax = 0
<|end_body_0|>
<|body_start_1|>
for i in li:
if i.idx != []:
... | Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or self.addi and create the self.I which gather all thoses interactions 5 followin... | Interactions | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Interactions:
"""Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or self.addi and create the self.I which g... | stack_v2_sparse_classes_36k_train_025016 | 25,448 | permissive | [
{
"docstring": "object constructor",
"name": "__init__",
"signature": "def __init__(self, slab={})"
},
{
"docstring": "add a list of interactions Parameters ---------- li : list list of interactions",
"name": "add",
"signature": "def add(self, li)"
},
{
"docstring": "add interact... | 4 | stack_v2_sparse_classes_30k_train_020784 | Implement the Python class `Interactions` described below.
Class description:
Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or ... | Implement the Python class `Interactions` described below.
Class description:
Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or ... | a3a5973a0cb549d0a16f17b96a9c78c200cf0c7e | <|skeleton|>
class Interactions:
"""Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or self.addi and create the self.I which g... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Interactions:
"""Interaction parameters gather all type of interactions (IntB/L/R/T) Methods ------- add(self,li): add a list of basis interactions addi(self,i): add a single interaction eval(self) : evaluate all the interactions added thanks to self.add or self.addi and create the self.I which gather all tho... | the_stack_v2_python_sparse | pylayers/antprop/interactions.py | sahibdhanjal/DeepLocNet | train | 40 |
f80028f432062fd25b47322822d8adbb095ae004 | [
"try:\n project_obj = self.get_object()\n part_id = request.query_params.get('part_id') or None\n part_name = request.query_params.get('part_name') or None\n if part_id is not None:\n project_obj.delete_tag_by_id(tag_id=part_id)\n return Response({'status': 'ok'})\n if part_name is not ... | <|body_start_0|>
try:
project_obj = self.get_object()
part_id = request.query_params.get('part_id') or None
part_name = request.query_params.get('part_name') or None
if part_id is not None:
project_obj.delete_tag_by_id(tag_id=part_id)
... | Project ModelViewSet Filters: is_demo | ProjectViewSet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProjectViewSet:
"""Project ModelViewSet Filters: is_demo"""
def delete_tag(self, request, pk=None):
"""delete tag"""
<|body_0|>
def relabel_keep_alive(self, request, pk=None) -> Response:
"""relabel_keep_alive. Args: request: kwargs: Returns: Response: Return pro... | stack_v2_sparse_classes_36k_train_025017 | 30,302 | permissive | [
{
"docstring": "delete tag",
"name": "delete_tag",
"signature": "def delete_tag(self, request, pk=None)"
},
{
"docstring": "relabel_keep_alive. Args: request: kwargs: Returns: Response: Return project with updated timestamp",
"name": "relabel_keep_alive",
"signature": "def relabel_keep_a... | 2 | stack_v2_sparse_classes_30k_train_020520 | Implement the Python class `ProjectViewSet` described below.
Class description:
Project ModelViewSet Filters: is_demo
Method signatures and docstrings:
- def delete_tag(self, request, pk=None): delete tag
- def relabel_keep_alive(self, request, pk=None) -> Response: relabel_keep_alive. Args: request: kwargs: Returns:... | Implement the Python class `ProjectViewSet` described below.
Class description:
Project ModelViewSet Filters: is_demo
Method signatures and docstrings:
- def delete_tag(self, request, pk=None): delete tag
- def relabel_keep_alive(self, request, pk=None) -> Response: relabel_keep_alive. Args: request: kwargs: Returns:... | d72c3c1c2d56a762b74a72cd3befd076dc77b8ac | <|skeleton|>
class ProjectViewSet:
"""Project ModelViewSet Filters: is_demo"""
def delete_tag(self, request, pk=None):
"""delete tag"""
<|body_0|>
def relabel_keep_alive(self, request, pk=None) -> Response:
"""relabel_keep_alive. Args: request: kwargs: Returns: Response: Return pro... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProjectViewSet:
"""Project ModelViewSet Filters: is_demo"""
def delete_tag(self, request, pk=None):
"""delete tag"""
try:
project_obj = self.get_object()
part_id = request.query_params.get('part_id') or None
part_name = request.query_params.get('part_na... | the_stack_v2_python_sparse | factory-ai-vision/EdgeSolution/modules/WebModule/backend/vision_on_edge/azure_training/api/views.py | timlawless/azure-intelligent-edge-patterns | train | 0 |
79db637826dc4d3c063b33d40e609d8327f02874 | [
"super(JointTorqueController, self).__init__()\nself.input_max = input_max\nself.input_min = input_min\nself.output_max = output_max\nself.output_min = output_min\nself.joint_dim = robot_model.joint_dim\nself.control_freq = control_freq\nself.model = robot_model\nself.interpolator = interpolator\nself.goal_torque =... | <|body_start_0|>
super(JointTorqueController, self).__init__()
self.input_max = input_max
self.input_min = input_min
self.output_max = output_max
self.output_min = output_min
self.joint_dim = robot_model.joint_dim
self.control_freq = control_freq
self.mode... | Controller for joint torque Attributes: robot_model (Model): model of robot containing state and parameters. input_max (float or list of floats): Maximum above which an inputted action will be clipped. input_min (float or list of floats): Minimum below which an inputted action will be clipped. output_max (float or list... | JointTorqueController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JointTorqueController:
"""Controller for joint torque Attributes: robot_model (Model): model of robot containing state and parameters. input_max (float or list of floats): Maximum above which an inputted action will be clipped. input_min (float or list of floats): Minimum below which an inputted ... | stack_v2_sparse_classes_36k_train_025018 | 4,761 | no_license | [
{
"docstring": "Initialize Joint Torque Controller. Args: robot_model (Model): model of robot containing state and parameters. input_max (float or list of floats): Maximum above which an inputted action will be clipped. input_min (float or list of floats): Minimum below which an inputted action will be clipped.... | 3 | null | Implement the Python class `JointTorqueController` described below.
Class description:
Controller for joint torque Attributes: robot_model (Model): model of robot containing state and parameters. input_max (float or list of floats): Maximum above which an inputted action will be clipped. input_min (float or list of fl... | Implement the Python class `JointTorqueController` described below.
Class description:
Controller for joint torque Attributes: robot_model (Model): model of robot containing state and parameters. input_max (float or list of floats): Maximum above which an inputted action will be clipped. input_min (float or list of fl... | d15791905abf8ff5def7fd0d3e303e619fc150d1 | <|skeleton|>
class JointTorqueController:
"""Controller for joint torque Attributes: robot_model (Model): model of robot containing state and parameters. input_max (float or list of floats): Maximum above which an inputted action will be clipped. input_min (float or list of floats): Minimum below which an inputted ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JointTorqueController:
"""Controller for joint torque Attributes: robot_model (Model): model of robot containing state and parameters. input_max (float or list of floats): Maximum above which an inputted action will be clipped. input_min (float or list of floats): Minimum below which an inputted action will b... | the_stack_v2_python_sparse | perls2/controllers/joint_torque.py | kayburns/perls2 | train | 0 |
6b531ca890793a4312b2a8a52d0256eff09a2f20 | [
"super(InceptionV3, self).__init__()\nself.resize_input = resize_input\nself.normalize_input = normalize_input\nself.output_blocks = sorted(output_blocks)\nself.last_needed_block = max(output_blocks)\nassert self.last_needed_block <= 3, 'Last possible output block index is 3'\nself.blocks = nn.ModuleList()\nif use_... | <|body_start_0|>
super(InceptionV3, self).__init__()
self.resize_input = resize_input
self.normalize_input = normalize_input
self.output_blocks = sorted(output_blocks)
self.last_needed_block = max(output_blocks)
assert self.last_needed_block <= 3, 'Last possible output bl... | Pretrained InceptionV3 network returning feature maps | InceptionV3 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InceptionV3:
"""Pretrained InceptionV3 network returning feature maps"""
def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=False, normalize_input=True, requires_grad=False, use_fid_inception=True):
"""Build pretrained InceptionV3 Parameters ---------- output_blocks... | stack_v2_sparse_classes_36k_train_025019 | 40,832 | no_license | [
{
"docstring": "Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier ... | 2 | null | Implement the Python class `InceptionV3` described below.
Class description:
Pretrained InceptionV3 network returning feature maps
Method signatures and docstrings:
- def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=False, normalize_input=True, requires_grad=False, use_fid_inception=True): Build p... | Implement the Python class `InceptionV3` described below.
Class description:
Pretrained InceptionV3 network returning feature maps
Method signatures and docstrings:
- def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=False, normalize_input=True, requires_grad=False, use_fid_inception=True): Build p... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class InceptionV3:
"""Pretrained InceptionV3 network returning feature maps"""
def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=False, normalize_input=True, requires_grad=False, use_fid_inception=True):
"""Build pretrained InceptionV3 Parameters ---------- output_blocks... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InceptionV3:
"""Pretrained InceptionV3 network returning feature maps"""
def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=False, normalize_input=True, requires_grad=False, use_fid_inception=True):
"""Build pretrained InceptionV3 Parameters ---------- output_blocks : list of in... | the_stack_v2_python_sparse | generated/test_mit_han_lab_anycost_gan.py | jansel/pytorch-jit-paritybench | train | 35 |
959063d06591719ff7e20c65efa9fa544953cfc7 | [
"if post_id:\n post = Post.query.filter_by(id=post_id).first()\n if not post:\n abort(404)\n return post\nelse:\n args = parsers.post_get_parser.parse_args()\n page = args['page'] or 1\n if args['user']:\n user = User.query.filter_by(username=args['user']).first()\n if not use... | <|body_start_0|>
if post_id:
post = Post.query.filter_by(id=post_id).first()
if not post:
abort(404)
return post
else:
args = parsers.post_get_parser.parse_args()
page = args['page'] or 1
if args['user']:
... | Restful API of posts resource. | PostApi | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PostApi:
"""Restful API of posts resource."""
def get(self, post_id=None):
"""Can be execute when receive HTTP Method `GET`. Will be return the Dict object as post_fields."""
<|body_0|>
def post(self, post_id=None):
"""Can be execute when receive HTTP Method `POS... | stack_v2_sparse_classes_36k_train_025020 | 4,835 | permissive | [
{
"docstring": "Can be execute when receive HTTP Method `GET`. Will be return the Dict object as post_fields.",
"name": "get",
"signature": "def get(self, post_id=None)"
},
{
"docstring": "Can be execute when receive HTTP Method `POST`.",
"name": "post",
"signature": "def post(self, post... | 4 | stack_v2_sparse_classes_30k_train_012516 | Implement the Python class `PostApi` described below.
Class description:
Restful API of posts resource.
Method signatures and docstrings:
- def get(self, post_id=None): Can be execute when receive HTTP Method `GET`. Will be return the Dict object as post_fields.
- def post(self, post_id=None): Can be execute when rec... | Implement the Python class `PostApi` described below.
Class description:
Restful API of posts resource.
Method signatures and docstrings:
- def get(self, post_id=None): Can be execute when receive HTTP Method `GET`. Will be return the Dict object as post_fields.
- def post(self, post_id=None): Can be execute when rec... | b05ed73210ab7ebdaa39d8ebcf9687d4ef16125c | <|skeleton|>
class PostApi:
"""Restful API of posts resource."""
def get(self, post_id=None):
"""Can be execute when receive HTTP Method `GET`. Will be return the Dict object as post_fields."""
<|body_0|>
def post(self, post_id=None):
"""Can be execute when receive HTTP Method `POS... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PostApi:
"""Restful API of posts resource."""
def get(self, post_id=None):
"""Can be execute when receive HTTP Method `GET`. Will be return the Dict object as post_fields."""
if post_id:
post = Post.query.filter_by(id=post_id).first()
if not post:
a... | the_stack_v2_python_sparse | flask/JmilkFan-s-Blog-master/jmilkfansblog/controllers/flask_restful/posts.py | chenzhenpin/py_test | train | 0 |
73e8babfd5e0e672e15a5b6968724ece0cf6a44c | [
"players = get_object_or_404(player, pk=pk)\nuser = request.user\nplayers.voters.remove(user)\nplayers.save()\nserializer_context = {'request': request}\nserializer = self.serializer_class(players, context=serializer_context)\nreturn Response(serializer.data, status=status.HTTP_200_OK)",
"players = get_object_or_... | <|body_start_0|>
players = get_object_or_404(player, pk=pk)
user = request.user
players.voters.remove(user)
players.save()
serializer_context = {'request': request}
serializer = self.serializer_class(players, context=serializer_context)
return Response(serializer.... | Allow users to add/remove a like to/from an answer instance. | PlayerLikeAPIView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlayerLikeAPIView:
"""Allow users to add/remove a like to/from an answer instance."""
def delete(self, request, pk):
"""Remove request.user from the voters queryset of an answer instance."""
<|body_0|>
def post(self, request, pk):
"""Add request.user to the voter... | stack_v2_sparse_classes_36k_train_025021 | 10,756 | no_license | [
{
"docstring": "Remove request.user from the voters queryset of an answer instance.",
"name": "delete",
"signature": "def delete(self, request, pk)"
},
{
"docstring": "Add request.user to the voters queryset of an answer instance.",
"name": "post",
"signature": "def post(self, request, p... | 2 | stack_v2_sparse_classes_30k_val_000859 | Implement the Python class `PlayerLikeAPIView` described below.
Class description:
Allow users to add/remove a like to/from an answer instance.
Method signatures and docstrings:
- def delete(self, request, pk): Remove request.user from the voters queryset of an answer instance.
- def post(self, request, pk): Add requ... | Implement the Python class `PlayerLikeAPIView` described below.
Class description:
Allow users to add/remove a like to/from an answer instance.
Method signatures and docstrings:
- def delete(self, request, pk): Remove request.user from the voters queryset of an answer instance.
- def post(self, request, pk): Add requ... | e74237fd26226afa108d981c95e962c72ab4b11a | <|skeleton|>
class PlayerLikeAPIView:
"""Allow users to add/remove a like to/from an answer instance."""
def delete(self, request, pk):
"""Remove request.user from the voters queryset of an answer instance."""
<|body_0|>
def post(self, request, pk):
"""Add request.user to the voter... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlayerLikeAPIView:
"""Allow users to add/remove a like to/from an answer instance."""
def delete(self, request, pk):
"""Remove request.user from the voters queryset of an answer instance."""
players = get_object_or_404(player, pk=pk)
user = request.user
players.voters.remo... | the_stack_v2_python_sparse | battlesoccer/app/api/views.py | battlesocce/tournament | train | 0 |
d769422300e8714038fd3f4a8d75586abd1eade8 | [
"Source.__init__(self)\nself.note_handles = []\nself.repositories = []",
"if 'batch_id' in kwargs:\n batch_id = kwargs['batch_id']\nelse:\n raise RuntimeError(f'Source_gramps.save needs batch_id for {self.id}')\nself.uuid = self.newUuid()\ns_attr = {}\ntry:\n s_attr = {'uuid': self.uuid, 'handle': self.h... | <|body_start_0|>
Source.__init__(self)
self.note_handles = []
self.repositories = []
<|end_body_0|>
<|body_start_1|>
if 'batch_id' in kwargs:
batch_id = kwargs['batch_id']
else:
raise RuntimeError(f'Source_gramps.save needs batch_id for {self.id}')
... | Genealogical data source from Gramps xml file. Properties: handle change id esim. "S0001" stitle str lähteen otsikko sauthor str lähteen tekijä spubinfo str lähteen julkaisutiedot note_handles[] str list note handles (ent. noteref_hlink) repositories[] Repository object containing prev. reporef_hlink and reporef_medium | Source_gramps | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Source_gramps:
"""Genealogical data source from Gramps xml file. Properties: handle change id esim. "S0001" stitle str lähteen otsikko sauthor str lähteen tekijä spubinfo str lähteen julkaisutiedot note_handles[] str list note handles (ent. noteref_hlink) repositories[] Repository object containi... | stack_v2_sparse_classes_36k_train_025022 | 4,580 | no_license | [
{
"docstring": "Creates a Source instance for Gramps xml upload.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Saves this Source and connect it to Notes and Repositories.",
"name": "save",
"signature": "def save(self, tx, **kwargs)"
}
] | 2 | null | Implement the Python class `Source_gramps` described below.
Class description:
Genealogical data source from Gramps xml file. Properties: handle change id esim. "S0001" stitle str lähteen otsikko sauthor str lähteen tekijä spubinfo str lähteen julkaisutiedot note_handles[] str list note handles (ent. noteref_hlink) re... | Implement the Python class `Source_gramps` described below.
Class description:
Genealogical data source from Gramps xml file. Properties: handle change id esim. "S0001" stitle str lähteen otsikko sauthor str lähteen tekijä spubinfo str lähteen julkaisutiedot note_handles[] str list note handles (ent. noteref_hlink) re... | 0f8d6ba035e3cca8dc756531b7cc51029a549a4f | <|skeleton|>
class Source_gramps:
"""Genealogical data source from Gramps xml file. Properties: handle change id esim. "S0001" stitle str lähteen otsikko sauthor str lähteen tekijä spubinfo str lähteen julkaisutiedot note_handles[] str list note handles (ent. noteref_hlink) repositories[] Repository object containi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Source_gramps:
"""Genealogical data source from Gramps xml file. Properties: handle change id esim. "S0001" stitle str lähteen otsikko sauthor str lähteen tekijä spubinfo str lähteen julkaisutiedot note_handles[] str list note handles (ent. noteref_hlink) repositories[] Repository object containing prev. repo... | the_stack_v2_python_sparse | bp/gramps/models/source_gramps.py | kkujansuu/stk | train | 0 |
ce24a91ca8b71e0eb47145a13fcb9a5e8b16dc81 | [
"if digits == '':\n return []\nself.letters = dict()\nself.letters['2'] = ['a', 'b', 'c']\nself.letters['3'] = ['d', 'e', 'f']\nself.letters['4'] = ['g', 'h', 'i']\nself.letters['5'] = ['j', 'k', 'l']\nself.letters['6'] = ['m', 'n', 'o']\nself.letters['7'] = ['p', 'q', 'r', 's']\nself.letters['8'] = ['t', 'u', '... | <|body_start_0|>
if digits == '':
return []
self.letters = dict()
self.letters['2'] = ['a', 'b', 'c']
self.letters['3'] = ['d', 'e', 'f']
self.letters['4'] = ['g', 'h', 'i']
self.letters['5'] = ['j', 'k', 'l']
self.letters['6'] = ['m', 'n', 'o']
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def letterCombinations(self, digits):
""":type digits: str :rtype: List[str]"""
<|body_0|>
def _letterCombinations(self, curr_str, digits):
"""Helper method to compute letter combinations of a digit"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_025023 | 1,142 | no_license | [
{
"docstring": ":type digits: str :rtype: List[str]",
"name": "letterCombinations",
"signature": "def letterCombinations(self, digits)"
},
{
"docstring": "Helper method to compute letter combinations of a digit",
"name": "_letterCombinations",
"signature": "def _letterCombinations(self, ... | 2 | stack_v2_sparse_classes_30k_test_000784 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def letterCombinations(self, digits): :type digits: str :rtype: List[str]
- def _letterCombinations(self, curr_str, digits): Helper method to compute letter combinations of a dig... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def letterCombinations(self, digits): :type digits: str :rtype: List[str]
- def _letterCombinations(self, curr_str, digits): Helper method to compute letter combinations of a dig... | 43dbcc129de3092d1ef24b95eaf35e20363cbd93 | <|skeleton|>
class Solution:
def letterCombinations(self, digits):
""":type digits: str :rtype: List[str]"""
<|body_0|>
def _letterCombinations(self, curr_str, digits):
"""Helper method to compute letter combinations of a digit"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def letterCombinations(self, digits):
""":type digits: str :rtype: List[str]"""
if digits == '':
return []
self.letters = dict()
self.letters['2'] = ['a', 'b', 'c']
self.letters['3'] = ['d', 'e', 'f']
self.letters['4'] = ['g', 'h', 'i']
... | the_stack_v2_python_sparse | letter-combinations-of-a-phone-number.py | iyyuan/leetcode-practice | train | 0 | |
692d9d81bd566d70c7fad026a9fb881f60bf92ef | [
"update_interval = REST_SENSORS_UPDATE_INTERVAL\nif device.settings['device']['type'] in BATTERY_DEVICES_WITH_PERMANENT_CONNECTION:\n update_interval = SLEEP_PERIOD_MULTIPLIER * device.settings['coiot']['update_period']\nsuper().__init__(hass, entry, device, update_interval)",
"LOGGER.debug('REST update for %s... | <|body_start_0|>
update_interval = REST_SENSORS_UPDATE_INTERVAL
if device.settings['device']['type'] in BATTERY_DEVICES_WITH_PERMANENT_CONNECTION:
update_interval = SLEEP_PERIOD_MULTIPLIER * device.settings['coiot']['update_period']
super().__init__(hass, entry, device, update_interv... | Coordinator for a Shelly REST device. | ShellyRestCoordinator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShellyRestCoordinator:
"""Coordinator for a Shelly REST device."""
def __init__(self, hass: HomeAssistant, device: BlockDevice, entry: ConfigEntry) -> None:
"""Initialize the Shelly REST device coordinator."""
<|body_0|>
async def _async_update_data(self) -> None:
... | stack_v2_sparse_classes_36k_train_025024 | 24,604 | permissive | [
{
"docstring": "Initialize the Shelly REST device coordinator.",
"name": "__init__",
"signature": "def __init__(self, hass: HomeAssistant, device: BlockDevice, entry: ConfigEntry) -> None"
},
{
"docstring": "Fetch data.",
"name": "_async_update_data",
"signature": "async def _async_updat... | 2 | stack_v2_sparse_classes_30k_train_003487 | Implement the Python class `ShellyRestCoordinator` described below.
Class description:
Coordinator for a Shelly REST device.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, device: BlockDevice, entry: ConfigEntry) -> None: Initialize the Shelly REST device coordinator.
- async def _async_u... | Implement the Python class `ShellyRestCoordinator` described below.
Class description:
Coordinator for a Shelly REST device.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, device: BlockDevice, entry: ConfigEntry) -> None: Initialize the Shelly REST device coordinator.
- async def _async_u... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class ShellyRestCoordinator:
"""Coordinator for a Shelly REST device."""
def __init__(self, hass: HomeAssistant, device: BlockDevice, entry: ConfigEntry) -> None:
"""Initialize the Shelly REST device coordinator."""
<|body_0|>
async def _async_update_data(self) -> None:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ShellyRestCoordinator:
"""Coordinator for a Shelly REST device."""
def __init__(self, hass: HomeAssistant, device: BlockDevice, entry: ConfigEntry) -> None:
"""Initialize the Shelly REST device coordinator."""
update_interval = REST_SENSORS_UPDATE_INTERVAL
if device.settings['devi... | the_stack_v2_python_sparse | homeassistant/components/shelly/coordinator.py | home-assistant/core | train | 35,501 |
591fefc123a82c64cb171ed030cfb6cb4e42aefc | [
"if n_qubits <= 0:\n raise ValueError('Operator must operate on at least 1 qubit!')\nself.n_qubits = n_qubits\nself.size = 2 ** n_qubits\nif self.size < len(base):\n raise ValueError('Operator cannot act on the specified number' + 'of qubits.')\nact_qubits = int(np.log2(len(base)))\nbase_matrix = SparseMatrix... | <|body_start_0|>
if n_qubits <= 0:
raise ValueError('Operator must operate on at least 1 qubit!')
self.n_qubits = n_qubits
self.size = 2 ** n_qubits
if self.size < len(base):
raise ValueError('Operator cannot act on the specified number' + 'of qubits.')
ac... | Operator class inherits from SparceMatrix | Operator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Operator:
"""Operator class inherits from SparceMatrix"""
def __init__(self, n_qubits: int=1, base=np.zeros((2, 2))):
"""Class constructor Inputs: n_qubits <int>: Number of qubits the oparator acts on base <np.array>: Matrix representation for the operators"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_025025 | 3,117 | no_license | [
{
"docstring": "Class constructor Inputs: n_qubits <int>: Number of qubits the oparator acts on base <np.array>: Matrix representation for the operators",
"name": "__init__",
"signature": "def __init__(self, n_qubits: int=1, base=np.zeros((2, 2)))"
},
{
"docstring": ":return: (QuantumRegister / ... | 3 | stack_v2_sparse_classes_30k_train_019112 | Implement the Python class `Operator` described below.
Class description:
Operator class inherits from SparceMatrix
Method signatures and docstrings:
- def __init__(self, n_qubits: int=1, base=np.zeros((2, 2))): Class constructor Inputs: n_qubits <int>: Number of qubits the oparator acts on base <np.array>: Matrix re... | Implement the Python class `Operator` described below.
Class description:
Operator class inherits from SparceMatrix
Method signatures and docstrings:
- def __init__(self, n_qubits: int=1, base=np.zeros((2, 2))): Class constructor Inputs: n_qubits <int>: Number of qubits the oparator acts on base <np.array>: Matrix re... | 488aeaacf84b9019c967a1f118818a96445b9eb5 | <|skeleton|>
class Operator:
"""Operator class inherits from SparceMatrix"""
def __init__(self, n_qubits: int=1, base=np.zeros((2, 2))):
"""Class constructor Inputs: n_qubits <int>: Number of qubits the oparator acts on base <np.array>: Matrix representation for the operators"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Operator:
"""Operator class inherits from SparceMatrix"""
def __init__(self, n_qubits: int=1, base=np.zeros((2, 2))):
"""Class constructor Inputs: n_qubits <int>: Number of qubits the oparator acts on base <np.array>: Matrix representation for the operators"""
if n_qubits <= 0:
... | the_stack_v2_python_sparse | QCP-Group-1-2019/src/quantum_operator.py | IainMcl/Quantum-Computing-Project | train | 0 |
ef28dba2899646192d7caf2ae8c731db744294aa | [
"specs = super().getInputSpecification()\nspecs.name = 'logtransformer'\nspecs.description = 'applies the natural logarithm to the data and inverts by applying the\\n exponential function.'\nreturn specs",
"for t, (target, data) in enumerate(params.items()):\n if np.any(initial[:, t] <= ... | <|body_start_0|>
specs = super().getInputSpecification()
specs.name = 'logtransformer'
specs.description = 'applies the natural logarithm to the data and inverts by applying the\n exponential function.'
return specs
<|end_body_0|>
<|body_start_1|>
for t, (... | Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp | LogTransformer | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer",
"BSD-2-Clause",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LogTransformer:
"""Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp"""
def getInputSpecification(cls):
"""Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs, InputData.ParameterInput, class to use for specifying in... | stack_v2_sparse_classes_36k_train_025026 | 6,301 | permissive | [
{
"docstring": "Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs, InputData.ParameterInput, class to use for specifying input of cls.",
"name": "getInputSpecification",
"signature": "def getInputSpecification(cls)"
},
{
"docstring": "Remov... | 2 | null | Implement the Python class `LogTransformer` described below.
Class description:
Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp
Method signatures and docstrings:
- def getInputSpecification(cls): Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs,... | Implement the Python class `LogTransformer` described below.
Class description:
Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp
Method signatures and docstrings:
- def getInputSpecification(cls): Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs,... | 2b16e7aa3325fe84cab2477947a951414c635381 | <|skeleton|>
class LogTransformer:
"""Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp"""
def getInputSpecification(cls):
"""Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs, InputData.ParameterInput, class to use for specifying in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LogTransformer:
"""Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp"""
def getInputSpecification(cls):
"""Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs, InputData.ParameterInput, class to use for specifying input of cls.""... | the_stack_v2_python_sparse | ravenframework/TSA/Transformers/FunctionTransformers.py | idaholab/raven | train | 201 |
2df5df756ce58a338d527cf8296f173414b6e2d5 | [
"if tokenizer_args is None:\n tokenizer_args = {}\ntokenizer_options = []\nif arg_separator != ' ':\n tokenizer_options = [option + arg_separator + str(tokenizer_args[option]) for option in tokenizer_args]\nelse:\n for option in tokenizer_args:\n tokenizer_options.extend([option, str(tokenizer_args[... | <|body_start_0|>
if tokenizer_args is None:
tokenizer_args = {}
tokenizer_options = []
if arg_separator != ' ':
tokenizer_options = [option + arg_separator + str(tokenizer_args[option]) for option in tokenizer_args]
else:
for option in tokenizer_args:
... | Class for arbitrary external tokenizer that accepts untokenized text to stdin and emits tokenized tezt to stdout, with passable parameters. It is assumed that in general, external tokenizers will be more efficient when run once per file, so are run as such (instead of one-execution-per-line.) Args: path tokenizer_args ... | ExternalTokenizer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExternalTokenizer:
"""Class for arbitrary external tokenizer that accepts untokenized text to stdin and emits tokenized tezt to stdout, with passable parameters. It is assumed that in general, external tokenizers will be more efficient when run once per file, so are run as such (instead of one-ex... | stack_v2_sparse_classes_36k_train_025027 | 32,168 | permissive | [
{
"docstring": "Initialize the wrapper around the external tokenizer.",
"name": "__init__",
"signature": "def __init__(self, path: str, tokenizer_args: Optional[Sequence[str]]=None, arg_separator: str=' ') -> None"
},
{
"docstring": "Pass the sentence through the external tokenizer. Args: sent: ... | 2 | stack_v2_sparse_classes_30k_train_017609 | Implement the Python class `ExternalTokenizer` described below.
Class description:
Class for arbitrary external tokenizer that accepts untokenized text to stdin and emits tokenized tezt to stdout, with passable parameters. It is assumed that in general, external tokenizers will be more efficient when run once per file... | Implement the Python class `ExternalTokenizer` described below.
Class description:
Class for arbitrary external tokenizer that accepts untokenized text to stdin and emits tokenized tezt to stdout, with passable parameters. It is assumed that in general, external tokenizers will be more efficient when run once per file... | b5e6985d3bedfac102312cab030a60594bc17baf | <|skeleton|>
class ExternalTokenizer:
"""Class for arbitrary external tokenizer that accepts untokenized text to stdin and emits tokenized tezt to stdout, with passable parameters. It is assumed that in general, external tokenizers will be more efficient when run once per file, so are run as such (instead of one-ex... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExternalTokenizer:
"""Class for arbitrary external tokenizer that accepts untokenized text to stdin and emits tokenized tezt to stdout, with passable parameters. It is assumed that in general, external tokenizers will be more efficient when run once per file, so are run as such (instead of one-execution-per-l... | the_stack_v2_python_sparse | xnmt/preproc.py | philip30/xnmt | train | 0 |
15300c101c7ff9642e06e2278c5842b425ccddc0 | [
"Report.__init__(self, database, options, user)\nself._user = user\nself.set_locale(options.menu.get_option_by_name('trans').get_value())",
"mark = IndexMark(self._('Table Of Contents'), INDEX_TYPE_TOC, 1)\nself.doc.start_paragraph('TOC-Title')\nself.doc.write_text('', mark)\nself.doc.end_paragraph()\nself.doc.to... | <|body_start_0|>
Report.__init__(self, database, options, user)
self._user = user
self.set_locale(options.menu.get_option_by_name('trans').get_value())
<|end_body_0|>
<|body_start_1|>
mark = IndexMark(self._('Table Of Contents'), INDEX_TYPE_TOC, 1)
self.doc.start_paragraph('TOC-... | This report class generates a table of contents for a book. | TableOfContents | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TableOfContents:
"""This report class generates a table of contents for a book."""
def __init__(self, database, options, user):
"""Create TableOfContents object that produces the report. The arguments are: database - the GRAMPS database instance options - instance of the Options clas... | stack_v2_sparse_classes_36k_train_025028 | 4,800 | no_license | [
{
"docstring": "Create TableOfContents object that produces the report. The arguments are: database - the GRAMPS database instance options - instance of the Options class for this report user - a gen.user.User() instance",
"name": "__init__",
"signature": "def __init__(self, database, options, user)"
... | 2 | null | Implement the Python class `TableOfContents` described below.
Class description:
This report class generates a table of contents for a book.
Method signatures and docstrings:
- def __init__(self, database, options, user): Create TableOfContents object that produces the report. The arguments are: database - the GRAMPS... | Implement the Python class `TableOfContents` described below.
Class description:
This report class generates a table of contents for a book.
Method signatures and docstrings:
- def __init__(self, database, options, user): Create TableOfContents object that produces the report. The arguments are: database - the GRAMPS... | 0c79561bed7ff42c88714edbc85197fa9235e188 | <|skeleton|>
class TableOfContents:
"""This report class generates a table of contents for a book."""
def __init__(self, database, options, user):
"""Create TableOfContents object that produces the report. The arguments are: database - the GRAMPS database instance options - instance of the Options clas... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TableOfContents:
"""This report class generates a table of contents for a book."""
def __init__(self, database, options, user):
"""Create TableOfContents object that produces the report. The arguments are: database - the GRAMPS database instance options - instance of the Options class for this re... | the_stack_v2_python_sparse | plugins/textreport/tableofcontents.py | balrok/gramps_addon | train | 2 |
04f2e2a2b2c56f38744dda340891b0fbcc2f646f | [
"super(CustomRNN, self).__init__()\nself.hidden_size = hidden_size\nself.rnn_type = rnn_type\nself.vocab_size = vocab_size\nself.num_layers = num_layers\nself.net = nn.ModuleList()\nfor i in range(num_layers):\n if rnn_type == 'basic_rnn':\n if i == 0:\n self.net.append(BasicRNNCell(vocab_size,... | <|body_start_0|>
super(CustomRNN, self).__init__()
self.hidden_size = hidden_size
self.rnn_type = rnn_type
self.vocab_size = vocab_size
self.num_layers = num_layers
self.net = nn.ModuleList()
for i in range(num_layers):
if rnn_type == 'basic_rnn':
... | CustomRNN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomRNN:
def __init__(self, vocab_size, hidden_size, num_layers=1, rnn_type='basic_rnn'):
"""Creates an recurrent neural network of type {basic_rnn, lstm_rnn} basic_rnn is an rnn whose layers implement a tanH activation function lstm_rnn is ann rnn whose layers implement an LSTM cell A... | stack_v2_sparse_classes_36k_train_025029 | 4,622 | no_license | [
{
"docstring": "Creates an recurrent neural network of type {basic_rnn, lstm_rnn} basic_rnn is an rnn whose layers implement a tanH activation function lstm_rnn is ann rnn whose layers implement an LSTM cell Arguments --------- vocab_size: (int), the number of unique characters in the corpus. This is the number... | 2 | stack_v2_sparse_classes_30k_train_008388 | Implement the Python class `CustomRNN` described below.
Class description:
Implement the CustomRNN class.
Method signatures and docstrings:
- def __init__(self, vocab_size, hidden_size, num_layers=1, rnn_type='basic_rnn'): Creates an recurrent neural network of type {basic_rnn, lstm_rnn} basic_rnn is an rnn whose lay... | Implement the Python class `CustomRNN` described below.
Class description:
Implement the CustomRNN class.
Method signatures and docstrings:
- def __init__(self, vocab_size, hidden_size, num_layers=1, rnn_type='basic_rnn'): Creates an recurrent neural network of type {basic_rnn, lstm_rnn} basic_rnn is an rnn whose lay... | 07703e76f49d9be86b39d7c2af1ee1e0efe07031 | <|skeleton|>
class CustomRNN:
def __init__(self, vocab_size, hidden_size, num_layers=1, rnn_type='basic_rnn'):
"""Creates an recurrent neural network of type {basic_rnn, lstm_rnn} basic_rnn is an rnn whose layers implement a tanH activation function lstm_rnn is ann rnn whose layers implement an LSTM cell A... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomRNN:
def __init__(self, vocab_size, hidden_size, num_layers=1, rnn_type='basic_rnn'):
"""Creates an recurrent neural network of type {basic_rnn, lstm_rnn} basic_rnn is an rnn whose layers implement a tanH activation function lstm_rnn is ann rnn whose layers implement an LSTM cell Arguments -----... | the_stack_v2_python_sparse | DL_HW/Jialing_Wu_HW4/create_rnn.py | muzhiBryn/ML_DL_at-Dartmouth | train | 0 | |
39132d568ce373d0516bb6a7afa7b2ee5455bc6c | [
"super(LandsatBandstack, self).__init__(dataset.metadata_dict)\nself.dataset = dataset\nself.band_dict = OrderedDict(sorted(band_dict.items(), key=lambda t: t[0]))\nself.source_file_list = None\nself.nodata_list = None\nself.vrt_name = None\nself.vrt_band_stack = None",
"self.source_file_list, self.nodata_list = ... | <|body_start_0|>
super(LandsatBandstack, self).__init__(dataset.metadata_dict)
self.dataset = dataset
self.band_dict = OrderedDict(sorted(band_dict.items(), key=lambda t: t[0]))
self.source_file_list = None
self.nodata_list = None
self.vrt_name = None
self.vrt_ban... | Landsat subclass of AbstractBandstack class | LandsatBandstack | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LandsatBandstack:
"""Landsat subclass of AbstractBandstack class"""
def __init__(self, dataset, band_dict):
"""The bandstack allows for the construction of a list, or stack, of bands from the given dataset."""
<|body_0|>
def buildvrt(self, temp_dir):
"""Given a d... | stack_v2_sparse_classes_36k_train_025030 | 6,670 | permissive | [
{
"docstring": "The bandstack allows for the construction of a list, or stack, of bands from the given dataset.",
"name": "__init__",
"signature": "def __init__(self, dataset, band_dict)"
},
{
"docstring": "Given a dataset_record and corresponding dataset, build the vrt that will be used to repr... | 5 | stack_v2_sparse_classes_30k_train_005791 | Implement the Python class `LandsatBandstack` described below.
Class description:
Landsat subclass of AbstractBandstack class
Method signatures and docstrings:
- def __init__(self, dataset, band_dict): The bandstack allows for the construction of a list, or stack, of bands from the given dataset.
- def buildvrt(self,... | Implement the Python class `LandsatBandstack` described below.
Class description:
Landsat subclass of AbstractBandstack class
Method signatures and docstrings:
- def __init__(self, dataset, band_dict): The bandstack allows for the construction of a list, or stack, of bands from the given dataset.
- def buildvrt(self,... | 05fdfdad0100875ffa72ebdd41d32ad3a7a213bc | <|skeleton|>
class LandsatBandstack:
"""Landsat subclass of AbstractBandstack class"""
def __init__(self, dataset, band_dict):
"""The bandstack allows for the construction of a list, or stack, of bands from the given dataset."""
<|body_0|>
def buildvrt(self, temp_dir):
"""Given a d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LandsatBandstack:
"""Landsat subclass of AbstractBandstack class"""
def __init__(self, dataset, band_dict):
"""The bandstack allows for the construction of a list, or stack, of bands from the given dataset."""
super(LandsatBandstack, self).__init__(dataset.metadata_dict)
self.data... | the_stack_v2_python_sparse | agdc/agdc/landsat_ingester/landsat_bandstack.py | jharrison902/Data-Cube | train | 0 |
a6060d8832484d2996c8dfa132d32b9d5b986de1 | [
"if map_protocol.lower() == 'wms':\n return MapService.proxy_request_to_geoserver(map_protocol, query_string)\nelif map_protocol.lower() == 'geojson':\n return MapService.handle_geojson_request(query_string)\nelse:\n raise MapServiceClientError(f'Unknown map protocol: {map_protocol}')",
"layer_source = M... | <|body_start_0|>
if map_protocol.lower() == 'wms':
return MapService.proxy_request_to_geoserver(map_protocol, query_string)
elif map_protocol.lower() == 'geojson':
return MapService.handle_geojson_request(query_string)
else:
raise MapServiceClientError(f'Unkno... | MapService | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MapService:
def handle_map_request(map_protocol: str, query_string: str) -> Response:
"""Handler looks at request protocol and then determines how to process the request"""
<|body_0|>
def handle_geojson_request(query_string: str) -> Response:
"""Validate that request... | stack_v2_sparse_classes_36k_train_025031 | 4,038 | no_license | [
{
"docstring": "Handler looks at request protocol and then determines how to process the request",
"name": "handle_map_request",
"signature": "def handle_map_request(map_protocol: str, query_string: str) -> Response"
},
{
"docstring": "Validate that request if for a known geojson datasource, the... | 5 | stack_v2_sparse_classes_30k_train_009164 | Implement the Python class `MapService` described below.
Class description:
Implement the MapService class.
Method signatures and docstrings:
- def handle_map_request(map_protocol: str, query_string: str) -> Response: Handler looks at request protocol and then determines how to process the request
- def handle_geojso... | Implement the Python class `MapService` described below.
Class description:
Implement the MapService class.
Method signatures and docstrings:
- def handle_map_request(map_protocol: str, query_string: str) -> Response: Handler looks at request protocol and then determines how to process the request
- def handle_geojso... | 8c851d2f740100c43f7b033f64adfa5a0d563f39 | <|skeleton|>
class MapService:
def handle_map_request(map_protocol: str, query_string: str) -> Response:
"""Handler looks at request protocol and then determines how to process the request"""
<|body_0|>
def handle_geojson_request(query_string: str) -> Response:
"""Validate that request... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MapService:
def handle_map_request(map_protocol: str, query_string: str) -> Response:
"""Handler looks at request protocol and then determines how to process the request"""
if map_protocol.lower() == 'wms':
return MapService.proxy_request_to_geoserver(map_protocol, query_string)
... | the_stack_v2_python_sparse | server/services/mapping/map_service.py | thinkWhere/DMIS | train | 4 | |
33684d71c7d1159b255c60d12c74aa03f8e028b0 | [
"self.s3_output_path = s3_output_path\nself.container_local_output_path = container_local_output_path\nself.hook_parameters = hook_parameters\nself.collection_configs = collection_configs",
"debugger_hook_config_request = {'S3OutputPath': self.s3_output_path}\nif self.container_local_output_path is not None:\n ... | <|body_start_0|>
self.s3_output_path = s3_output_path
self.container_local_output_path = container_local_output_path
self.hook_parameters = hook_parameters
self.collection_configs = collection_configs
<|end_body_0|>
<|body_start_1|>
debugger_hook_config_request = {'S3OutputPath'... | Create a Debugger hook configuration object to save the tensor for debugging. DebuggerHookConfig provides options to customize how debugging information is emitted and saved. This high-level DebuggerHookConfig class runs based on the `smdebug.SaveConfig <https://github.com/awslabs/sagemaker-debugger/blob/master/docs/ a... | DebuggerHookConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DebuggerHookConfig:
"""Create a Debugger hook configuration object to save the tensor for debugging. DebuggerHookConfig provides options to customize how debugging information is emitted and saved. This high-level DebuggerHookConfig class runs based on the `smdebug.SaveConfig <https://github.com/... | stack_v2_sparse_classes_36k_train_025032 | 42,015 | permissive | [
{
"docstring": "Initialize the DebuggerHookConfig instance. Args: s3_output_path (str or PipelineVariable): Optional. The location in Amazon S3 to store the output tensors. The default Debugger output path is created under the default output path of the :class:`~sagemaker.estimator.Estimator` class. For example... | 2 | stack_v2_sparse_classes_30k_test_001042 | Implement the Python class `DebuggerHookConfig` described below.
Class description:
Create a Debugger hook configuration object to save the tensor for debugging. DebuggerHookConfig provides options to customize how debugging information is emitted and saved. This high-level DebuggerHookConfig class runs based on the `... | Implement the Python class `DebuggerHookConfig` described below.
Class description:
Create a Debugger hook configuration object to save the tensor for debugging. DebuggerHookConfig provides options to customize how debugging information is emitted and saved. This high-level DebuggerHookConfig class runs based on the `... | 8d5d7fd8ae1a917ed3e2b988d5e533bce244fd85 | <|skeleton|>
class DebuggerHookConfig:
"""Create a Debugger hook configuration object to save the tensor for debugging. DebuggerHookConfig provides options to customize how debugging information is emitted and saved. This high-level DebuggerHookConfig class runs based on the `smdebug.SaveConfig <https://github.com/... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DebuggerHookConfig:
"""Create a Debugger hook configuration object to save the tensor for debugging. DebuggerHookConfig provides options to customize how debugging information is emitted and saved. This high-level DebuggerHookConfig class runs based on the `smdebug.SaveConfig <https://github.com/awslabs/sagem... | the_stack_v2_python_sparse | src/sagemaker/debugger/debugger.py | aws/sagemaker-python-sdk | train | 2,050 |
48b7448c62d20140413ba9b699395f4e552895ce | [
"gray = []\nfor num in range(1 << n):\n gray.append(num ^ num >> 1)\nreturn gray",
"gray = [0]\nfor i in range(1, 2 ** n):\n gray.append(gray[-1] ^ i & -i)\nreturn gray"
] | <|body_start_0|>
gray = []
for num in range(1 << n):
gray.append(num ^ num >> 1)
return gray
<|end_body_0|>
<|body_start_1|>
gray = [0]
for i in range(1, 2 ** n):
gray.append(gray[-1] ^ i & -i)
return gray
<|end_body_1|>
| GrayCode | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GrayCode:
def generates(self, n: int) -> List[int]:
"""Approach: Bit Manipulation Time Complexity: O(N) Space Complexity: O(N) Formulae: i xor (i >> 1) :param n: :return:"""
<|body_0|>
def generate(self, n: int) -> List[int]:
"""Approach: Bit Manipulation Formulae: r... | stack_v2_sparse_classes_36k_train_025033 | 1,190 | no_license | [
{
"docstring": "Approach: Bit Manipulation Time Complexity: O(N) Space Complexity: O(N) Formulae: i xor (i >> 1) :param n: :return:",
"name": "generates",
"signature": "def generates(self, n: int) -> List[int]"
},
{
"docstring": "Approach: Bit Manipulation Formulae: res[-1] xor (Y & - Y) Time Co... | 2 | null | Implement the Python class `GrayCode` described below.
Class description:
Implement the GrayCode class.
Method signatures and docstrings:
- def generates(self, n: int) -> List[int]: Approach: Bit Manipulation Time Complexity: O(N) Space Complexity: O(N) Formulae: i xor (i >> 1) :param n: :return:
- def generate(self,... | Implement the Python class `GrayCode` described below.
Class description:
Implement the GrayCode class.
Method signatures and docstrings:
- def generates(self, n: int) -> List[int]: Approach: Bit Manipulation Time Complexity: O(N) Space Complexity: O(N) Formulae: i xor (i >> 1) :param n: :return:
- def generate(self,... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class GrayCode:
def generates(self, n: int) -> List[int]:
"""Approach: Bit Manipulation Time Complexity: O(N) Space Complexity: O(N) Formulae: i xor (i >> 1) :param n: :return:"""
<|body_0|>
def generate(self, n: int) -> List[int]:
"""Approach: Bit Manipulation Formulae: r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GrayCode:
def generates(self, n: int) -> List[int]:
"""Approach: Bit Manipulation Time Complexity: O(N) Space Complexity: O(N) Formulae: i xor (i >> 1) :param n: :return:"""
gray = []
for num in range(1 << n):
gray.append(num ^ num >> 1)
return gray
def generat... | the_stack_v2_python_sparse | revisited/math_and_strings/bitwise_operator/gray_code.py | Shiv2157k/leet_code | train | 1 | |
76f32816b81a2645b48c5f143d13198f86ec11e7 | [
"if isinstance(value, unicode):\n return value.encode('utf-8')\nelif isinstance(value, field.SEQUENCE_TYPES):\n if value and len(value) == 1:\n value = value[0]\n elif not value:\n value = ''\n else:\n value = ''.join(value)\nif not isinstance(value, str):\n value = str(value)\nr... | <|body_start_0|>
if isinstance(value, unicode):
return value.encode('utf-8')
elif isinstance(value, field.SEQUENCE_TYPES):
if value and len(value) == 1:
value = value[0]
elif not value:
value = ''
else:
value... | SFString field/event type base-class | _SFString | [
"GPL-1.0-or-later",
"MIT",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-other-copyleft",
"LGPL-2.1-or-later",
"GPL-3.0-only",
"LGPL-2.0-or-later",
"GPL-3.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _SFString:
"""SFString field/event type base-class"""
def coerce(self, value):
"""Coerce the given value to our type Allowable types: simple string -> unchanged unicode string -> utf-8 encoded sequence of length == 1 where first element is a string -> returns first element sequence o... | stack_v2_sparse_classes_36k_train_025034 | 34,853 | permissive | [
{
"docstring": "Coerce the given value to our type Allowable types: simple string -> unchanged unicode string -> utf-8 encoded sequence of length == 1 where first element is a string -> returns first element sequence of length > 1 where all elements are strings -> returns string.join( value, '')",
"name": "... | 2 | stack_v2_sparse_classes_30k_train_011648 | Implement the Python class `_SFString` described below.
Class description:
SFString field/event type base-class
Method signatures and docstrings:
- def coerce(self, value): Coerce the given value to our type Allowable types: simple string -> unchanged unicode string -> utf-8 encoded sequence of length == 1 where firs... | Implement the Python class `_SFString` described below.
Class description:
SFString field/event type base-class
Method signatures and docstrings:
- def coerce(self, value): Coerce the given value to our type Allowable types: simple string -> unchanged unicode string -> utf-8 encoded sequence of length == 1 where firs... | 7f600ad153270feff12aa7aa86d7ed0a49ebc71c | <|skeleton|>
class _SFString:
"""SFString field/event type base-class"""
def coerce(self, value):
"""Coerce the given value to our type Allowable types: simple string -> unchanged unicode string -> utf-8 encoded sequence of length == 1 where first element is a string -> returns first element sequence o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _SFString:
"""SFString field/event type base-class"""
def coerce(self, value):
"""Coerce the given value to our type Allowable types: simple string -> unchanged unicode string -> utf-8 encoded sequence of length == 1 where first element is a string -> returns first element sequence of length > 1 ... | the_stack_v2_python_sparse | pythonAnimations/pyOpenGLChess/engineDirectory/oglc-env/lib/python2.7/site-packages/vrml/fieldtypes.py | alexus37/AugmentedRealityChess | train | 1 |
b6d751bee3e871bce59453d32b8c4bb19b1aa645 | [
"self.parser = reqparse.RequestParser()\nself.parser.add_argument('vtSymbol')\nsuper(Tick, self).__init__()",
"args = self.parser.parse_args()\nvtSymbol = args['vtSymbol']\ncontract = me.getContract(vtSymbol)\nif not contract:\n return {'result_code': 'error', 'message': 'contract error'}\nreq = VtSubscribeReq... | <|body_start_0|>
self.parser = reqparse.RequestParser()
self.parser.add_argument('vtSymbol')
super(Tick, self).__init__()
<|end_body_0|>
<|body_start_1|>
args = self.parser.parse_args()
vtSymbol = args['vtSymbol']
contract = me.getContract(vtSymbol)
if not contra... | 行情 | Tick | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Tick:
"""行情"""
def __init__(self):
"""初始化"""
<|body_0|>
def post(self):
"""订阅"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.parser = reqparse.RequestParser()
self.parser.add_argument('vtSymbol')
super(Tick, self).__init__(... | stack_v2_sparse_classes_36k_train_025035 | 24,002 | permissive | [
{
"docstring": "初始化",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "订阅",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000341 | Implement the Python class `Tick` described below.
Class description:
行情
Method signatures and docstrings:
- def __init__(self): 初始化
- def post(self): 订阅 | Implement the Python class `Tick` described below.
Class description:
行情
Method signatures and docstrings:
- def __init__(self): 初始化
- def post(self): 订阅
<|skeleton|>
class Tick:
"""行情"""
def __init__(self):
"""初始化"""
<|body_0|>
def post(self):
"""订阅"""
<|body_1|>
<|end... | c316649161086da2543d39bf0455d0f793cdd08f | <|skeleton|>
class Tick:
"""行情"""
def __init__(self):
"""初始化"""
<|body_0|>
def post(self):
"""订阅"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Tick:
"""行情"""
def __init__(self):
"""初始化"""
self.parser = reqparse.RequestParser()
self.parser.add_argument('vtSymbol')
super(Tick, self).__init__()
def post(self):
"""订阅"""
args = self.parser.parse_args()
vtSymbol = args['vtSymbol']
c... | the_stack_v2_python_sparse | WebTrader/webServer.py | webclinic017/riskBacktestingPlatform | train | 0 |
4c7151c381244afa91b1678239a2103fb64725fa | [
"super().__init__()\nself.cnn_layers = nn.Sequential()\nself.fc_layers = nn.Sequential()\nself.loss_criterion = None\nself.cnn_layers = nn.Sequential(nn.Conv2d(1, 10, kernel_size=5), nn.MaxPool2d(kernel_size=3), nn.ReLU(), nn.Conv2d(10, 20, kernel_size=5), nn.MaxPool2d(kernel_size=3), nn.ReLU())\nself.fc_layers = n... | <|body_start_0|>
super().__init__()
self.cnn_layers = nn.Sequential()
self.fc_layers = nn.Sequential()
self.loss_criterion = None
self.cnn_layers = nn.Sequential(nn.Conv2d(1, 10, kernel_size=5), nn.MaxPool2d(kernel_size=3), nn.ReLU(), nn.Conv2d(10, 20, kernel_size=5), nn.MaxPool2... | SimpleNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleNet:
def __init__(self):
"""Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means"""
<|body_0|>
def forward(self, x: torch.tensor) -> torch.tensor:
"""Perform t... | stack_v2_sparse_classes_36k_train_025036 | 2,085 | no_license | [
{
"docstring": "Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Perform the forward pass with the net Args: - x: t... | 2 | stack_v2_sparse_classes_30k_train_020511 | Implement the Python class `SimpleNet` described below.
Class description:
Implement the SimpleNet class.
Method signatures and docstrings:
- def __init__(self): Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means
-... | Implement the Python class `SimpleNet` described below.
Class description:
Implement the SimpleNet class.
Method signatures and docstrings:
- def __init__(self): Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means
-... | 79efc17404225c9d5f9845d6e7d8beea6a714a57 | <|skeleton|>
class SimpleNet:
def __init__(self):
"""Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means"""
<|body_0|>
def forward(self, x: torch.tensor) -> torch.tensor:
"""Perform t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleNet:
def __init__(self):
"""Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means"""
super().__init__()
self.cnn_layers = nn.Sequential()
self.fc_layers = nn.Sequential()
... | the_stack_v2_python_sparse | Project6/simple_net.py | echen67/Computer-Vision | train | 1 | |
029f59df7a4007938fefed469693a8f5cedc34e5 | [
"if gateway == 'admin_pay':\n request = Request(HttpRequest)\n try:\n setattr(request.user, 'id', orders.user_id)\n except Exception as e:\n return e\nresult = Wallet.update_balance(request=request, orders=orders, method=WALLET_ACTION_METHOD[0])\nif isinstance(result, Exception):\n return ... | <|body_start_0|>
if gateway == 'admin_pay':
request = Request(HttpRequest)
try:
setattr(request.user, 'id', orders.user_id)
except Exception as e:
return e
result = Wallet.update_balance(request=request, orders=orders, method=WALLET_ACT... | 钱包相关功能 | WalletAction | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WalletAction:
"""钱包相关功能"""
def recharge(self, request, orders, gateway='auth', does_give_coupons=False):
"""充值"""
<|body_0|>
def orders_refund(self, request, orders, gateway='auth'):
"""订单退款（从订单的应付款中退款到钱包中） 适用场景：1.管理员取消用户的未核销订单（此时需要把订单的应付款返回到用户钱包中）"""
<|b... | stack_v2_sparse_classes_36k_train_025037 | 14,801 | no_license | [
{
"docstring": "充值",
"name": "recharge",
"signature": "def recharge(self, request, orders, gateway='auth', does_give_coupons=False)"
},
{
"docstring": "订单退款（从订单的应付款中退款到钱包中） 适用场景：1.管理员取消用户的未核销订单（此时需要把订单的应付款返回到用户钱包中）",
"name": "orders_refund",
"signature": "def orders_refund(self, request,... | 2 | stack_v2_sparse_classes_30k_train_001876 | Implement the Python class `WalletAction` described below.
Class description:
钱包相关功能
Method signatures and docstrings:
- def recharge(self, request, orders, gateway='auth', does_give_coupons=False): 充值
- def orders_refund(self, request, orders, gateway='auth'): 订单退款（从订单的应付款中退款到钱包中） 适用场景：1.管理员取消用户的未核销订单（此时需要把订单的应付款返回到... | Implement the Python class `WalletAction` described below.
Class description:
钱包相关功能
Method signatures and docstrings:
- def recharge(self, request, orders, gateway='auth', does_give_coupons=False): 充值
- def orders_refund(self, request, orders, gateway='auth'): 订单退款（从订单的应付款中退款到钱包中） 适用场景：1.管理员取消用户的未核销订单（此时需要把订单的应付款返回到... | 5a0d42cf794b5e0cd127e625d36e6f8e96812ae5 | <|skeleton|>
class WalletAction:
"""钱包相关功能"""
def recharge(self, request, orders, gateway='auth', does_give_coupons=False):
"""充值"""
<|body_0|>
def orders_refund(self, request, orders, gateway='auth'):
"""订单退款（从订单的应付款中退款到钱包中） 适用场景：1.管理员取消用户的未核销订单（此时需要把订单的应付款返回到用户钱包中）"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WalletAction:
"""钱包相关功能"""
def recharge(self, request, orders, gateway='auth', does_give_coupons=False):
"""充值"""
if gateway == 'admin_pay':
request = Request(HttpRequest)
try:
setattr(request.user, 'id', orders.user_id)
except Exception... | the_stack_v2_python_sparse | Consumer_App/cs_wallet/models.py | dennis1984/YSAdminApp | train | 0 |
9309cdca58cb9dfcd11b68aeda042d44441594ef | [
"super(KeystoneSession, self).__init__()\nself.session = session\nself.endpoint = endpoint",
"if not session:\n session = self.session\nif not session:\n session = ks_session.Session()\nif self.endpoint:\n if url:\n url = '/'.join([self.endpoint.rstrip('/'), url.lstrip('/')])\n else:\n u... | <|body_start_0|>
super(KeystoneSession, self).__init__()
self.session = session
self.endpoint = endpoint
<|end_body_0|>
<|body_start_1|>
if not session:
session = self.session
if not session:
session = ks_session.Session()
if self.endpoint:
... | Wrapper for the Keystone Session Restore some requests.session.Session compatibility; keystoneauth1.session.Session.request() has the method and url arguments swapped from the rest of the requests-using world. | KeystoneSession | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KeystoneSession:
"""Wrapper for the Keystone Session Restore some requests.session.Session compatibility; keystoneauth1.session.Session.request() has the method and url arguments swapped from the rest of the requests-using world."""
def __init__(self, session=None, endpoint=None, **kwargs):
... | stack_v2_sparse_classes_36k_train_025038 | 9,762 | permissive | [
{
"docstring": "Base object that contains some common API objects and methods :param Session session: The default session to be used for making the HTTP API calls. :param string endpoint: The URL from the Service Catalog to be used as the base for API requests on this API.",
"name": "__init__",
"signatu... | 2 | stack_v2_sparse_classes_30k_train_010637 | Implement the Python class `KeystoneSession` described below.
Class description:
Wrapper for the Keystone Session Restore some requests.session.Session compatibility; keystoneauth1.session.Session.request() has the method and url arguments swapped from the rest of the requests-using world.
Method signatures and docst... | Implement the Python class `KeystoneSession` described below.
Class description:
Wrapper for the Keystone Session Restore some requests.session.Session compatibility; keystoneauth1.session.Session.request() has the method and url arguments swapped from the rest of the requests-using world.
Method signatures and docst... | 78988d1786c0634ee055714910d1e6187f941673 | <|skeleton|>
class KeystoneSession:
"""Wrapper for the Keystone Session Restore some requests.session.Session compatibility; keystoneauth1.session.Session.request() has the method and url arguments swapped from the rest of the requests-using world."""
def __init__(self, session=None, endpoint=None, **kwargs):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KeystoneSession:
"""Wrapper for the Keystone Session Restore some requests.session.Session compatibility; keystoneauth1.session.Session.request() has the method and url arguments swapped from the rest of the requests-using world."""
def __init__(self, session=None, endpoint=None, **kwargs):
"""Ba... | the_stack_v2_python_sparse | openstackclient/api/api.py | openstack/python-openstackclient | train | 286 |
ab7d94ff6509aac101925b1ab246dbb74581f0b3 | [
"ret = 0\nsign = 1 if x > 0 else -1\nx = abs(x)\nwhile x:\n x, y = divmod(x, 10)\n ret = ret * 10 + y\nreturn ret * sign if -2 ** 31 <= ret <= 2 ** 31 - 1 else 0",
"\"\"\"\n >>> s = Solution()\n >>> s.reverse(123)\n 321\n >>> s.reverse(-123)\n -321\n >>> s.reverse(1... | <|body_start_0|>
ret = 0
sign = 1 if x > 0 else -1
x = abs(x)
while x:
x, y = divmod(x, 10)
ret = ret * 10 + y
return ret * sign if -2 ** 31 <= ret <= 2 ** 31 - 1 else 0
<|end_body_0|>
<|body_start_1|>
"""
>>> s = Solution()
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverse(self, x):
""">>> s = Solution() >>> s.reverse(123) 321 >>> s.reverse(-123) -321 >>> s.reverse(120) 21"""
<|body_0|>
def reverse2(self, x):
"""使用中间字符串"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ret = 0
sign = 1 if x... | stack_v2_sparse_classes_36k_train_025039 | 1,360 | no_license | [
{
"docstring": ">>> s = Solution() >>> s.reverse(123) 321 >>> s.reverse(-123) -321 >>> s.reverse(120) 21",
"name": "reverse",
"signature": "def reverse(self, x)"
},
{
"docstring": "使用中间字符串",
"name": "reverse2",
"signature": "def reverse2(self, x)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverse(self, x): >>> s = Solution() >>> s.reverse(123) 321 >>> s.reverse(-123) -321 >>> s.reverse(120) 21
- def reverse2(self, x): 使用中间字符串 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverse(self, x): >>> s = Solution() >>> s.reverse(123) 321 >>> s.reverse(-123) -321 >>> s.reverse(120) 21
- def reverse2(self, x): 使用中间字符串
<|skeleton|>
class Solution:
... | 166d97f36bbeea74c84ec57466bd0a65b608ed09 | <|skeleton|>
class Solution:
def reverse(self, x):
""">>> s = Solution() >>> s.reverse(123) 321 >>> s.reverse(-123) -321 >>> s.reverse(120) 21"""
<|body_0|>
def reverse2(self, x):
"""使用中间字符串"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def reverse(self, x):
""">>> s = Solution() >>> s.reverse(123) 321 >>> s.reverse(-123) -321 >>> s.reverse(120) 21"""
ret = 0
sign = 1 if x > 0 else -1
x = abs(x)
while x:
x, y = divmod(x, 10)
ret = ret * 10 + y
return ret * sign... | the_stack_v2_python_sparse | leetcode/reverse_interger.py | Activity00/Python | train | 0 | |
f3eda8bf55090abd0a7a46b70178b3a5cfef4317 | [
"Utils.validate_uuid(id_body_zone)\nif not BodyZone.objects.filter(id=id_body_zone).exists():\n raise ValidationError('The body zone ' + id_body_zone + ' is not valid!')",
"body_zones_list = QueryService.get_trigram_similarity_results(BodyZone, query)\npaginator = Paginator(body_zones_list, page_size)\nPaginat... | <|body_start_0|>
Utils.validate_uuid(id_body_zone)
if not BodyZone.objects.filter(id=id_body_zone).exists():
raise ValidationError('The body zone ' + id_body_zone + ' is not valid!')
<|end_body_0|>
<|body_start_1|>
body_zones_list = QueryService.get_trigram_similarity_results(BodyZo... | Service class for body zone related operations | BodyZoneService | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BodyZoneService:
"""Service class for body zone related operations"""
def is_valid_body_zone(id_body_zone):
"""Checks if the specified body zone exists :param id_body_zone: ID of body zone to be checked"""
<|body_0|>
def list_body_zones(page_num, page_size, path, query='... | stack_v2_sparse_classes_36k_train_025040 | 1,706 | no_license | [
{
"docstring": "Checks if the specified body zone exists :param id_body_zone: ID of body zone to be checked",
"name": "is_valid_body_zone",
"signature": "def is_valid_body_zone(id_body_zone)"
},
{
"docstring": "Method to list body zones on the system :param page_num: Specified page number :param... | 2 | null | Implement the Python class `BodyZoneService` described below.
Class description:
Service class for body zone related operations
Method signatures and docstrings:
- def is_valid_body_zone(id_body_zone): Checks if the specified body zone exists :param id_body_zone: ID of body zone to be checked
- def list_body_zones(pa... | Implement the Python class `BodyZoneService` described below.
Class description:
Service class for body zone related operations
Method signatures and docstrings:
- def is_valid_body_zone(id_body_zone): Checks if the specified body zone exists :param id_body_zone: ID of body zone to be checked
- def list_body_zones(pa... | 941e8b2870f8724db3d5103dda5157fd597cfcc7 | <|skeleton|>
class BodyZoneService:
"""Service class for body zone related operations"""
def is_valid_body_zone(id_body_zone):
"""Checks if the specified body zone exists :param id_body_zone: ID of body zone to be checked"""
<|body_0|>
def list_body_zones(page_num, page_size, path, query='... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BodyZoneService:
"""Service class for body zone related operations"""
def is_valid_body_zone(id_body_zone):
"""Checks if the specified body zone exists :param id_body_zone: ID of body zone to be checked"""
Utils.validate_uuid(id_body_zone)
if not BodyZone.objects.filter(id=id_body... | the_stack_v2_python_sparse | backend/martin_helder/services/body_zone_service.py | JoaoAlvaroFerreira/FEUP-LGP | train | 1 |
9f80b8fce268c87e43adbe8d3abc995e502b2d84 | [
"main.clear_collections()\nresult = main.import_data('./data/', 'products', 'customers', 'rentals')\nself.assertEqual(result[1], 1000)\nself.assertEqual(result[2], 0)\nself.assertEqual(result[3], 1000)\nself.assertGreater(result[4], 0)",
"function = main.show_available_products()\nproduct_1 = function['prd0018'][... | <|body_start_0|>
main.clear_collections()
result = main.import_data('./data/', 'products', 'customers', 'rentals')
self.assertEqual(result[1], 1000)
self.assertEqual(result[2], 0)
self.assertEqual(result[3], 1000)
self.assertGreater(result[4], 0)
<|end_body_0|>
<|body_st... | Class for testing HP Norton database | ModuleTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModuleTests:
"""Class for testing HP Norton database"""
def test_import_data(self):
"""Test CSV import and correct database insertion functionality"""
<|body_0|>
def test_show_available_products(self):
"""Test DB to show all available products as a Python diction... | stack_v2_sparse_classes_36k_train_025041 | 2,469 | no_license | [
{
"docstring": "Test CSV import and correct database insertion functionality",
"name": "test_import_data",
"signature": "def test_import_data(self)"
},
{
"docstring": "Test DB to show all available products as a Python dictionary",
"name": "test_show_available_products",
"signature": "de... | 3 | stack_v2_sparse_classes_30k_train_013380 | Implement the Python class `ModuleTests` described below.
Class description:
Class for testing HP Norton database
Method signatures and docstrings:
- def test_import_data(self): Test CSV import and correct database insertion functionality
- def test_show_available_products(self): Test DB to show all available product... | Implement the Python class `ModuleTests` described below.
Class description:
Class for testing HP Norton database
Method signatures and docstrings:
- def test_import_data(self): Test CSV import and correct database insertion functionality
- def test_show_available_products(self): Test DB to show all available product... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class ModuleTests:
"""Class for testing HP Norton database"""
def test_import_data(self):
"""Test CSV import and correct database insertion functionality"""
<|body_0|>
def test_show_available_products(self):
"""Test DB to show all available products as a Python diction... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModuleTests:
"""Class for testing HP Norton database"""
def test_import_data(self):
"""Test CSV import and correct database insertion functionality"""
main.clear_collections()
result = main.import_data('./data/', 'products', 'customers', 'rentals')
self.assertEqual(result[... | the_stack_v2_python_sparse | students/stellie/lesson07/assignment/test_parallel.py | JavaRod/SP_Python220B_2019 | train | 1 |
694945488381dbff153b8598b937ef45f9115d6a | [
"if self.is_api_request(request):\n try:\n parsed_session_uri = parse_session_id(request)\n if parsed_session_uri is not None:\n domain = get_domain(request)\n if parsed_session_uri['realm'] != domain:\n raise exceptions.PermissionDenied(_('Can not accept cookie... | <|body_start_0|>
if self.is_api_request(request):
try:
parsed_session_uri = parse_session_id(request)
if parsed_session_uri is not None:
domain = get_domain(request)
if parsed_session_uri['realm'] != domain:
... | Implement session through headers: http://www.w3.org/TR/WD-session-id TODO: Implement gateway protection, with permission options for usage of header sessions. With that in place the api can be used for both trusted and non trusted clients, see README.rst. | HeaderSessionMiddleware | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HeaderSessionMiddleware:
"""Implement session through headers: http://www.w3.org/TR/WD-session-id TODO: Implement gateway protection, with permission options for usage of header sessions. With that in place the api can be used for both trusted and non trusted clients, see README.rst."""
def ... | stack_v2_sparse_classes_36k_train_025042 | 8,688 | permissive | [
{
"docstring": "Parse the session id from the 'Session-Id: ' header when using the api.",
"name": "process_request",
"signature": "def process_request(self, request)"
},
{
"docstring": "Add the 'Session-Id: ' header when using the api.",
"name": "process_response",
"signature": "def proc... | 2 | stack_v2_sparse_classes_30k_train_004378 | Implement the Python class `HeaderSessionMiddleware` described below.
Class description:
Implement session through headers: http://www.w3.org/TR/WD-session-id TODO: Implement gateway protection, with permission options for usage of header sessions. With that in place the api can be used for both trusted and non truste... | Implement the Python class `HeaderSessionMiddleware` described below.
Class description:
Implement session through headers: http://www.w3.org/TR/WD-session-id TODO: Implement gateway protection, with permission options for usage of header sessions. With that in place the api can be used for both trusted and non truste... | 79cecbc136cdf75c5f47beaf68fa7002e3c431ce | <|skeleton|>
class HeaderSessionMiddleware:
"""Implement session through headers: http://www.w3.org/TR/WD-session-id TODO: Implement gateway protection, with permission options for usage of header sessions. With that in place the api can be used for both trusted and non trusted clients, see README.rst."""
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HeaderSessionMiddleware:
"""Implement session through headers: http://www.w3.org/TR/WD-session-id TODO: Implement gateway protection, with permission options for usage of header sessions. With that in place the api can be used for both trusted and non trusted clients, see README.rst."""
def process_reque... | the_stack_v2_python_sparse | oscarapi/middleware.py | django-oscar/django-oscar-api | train | 354 |
972e4fde2fa6cc544ab77f7b486b983b4782cda0 | [
"self.gpus = gpus\ntransformer_primitive = []\ntransformer_primitive.append(T.Resize(size=i_shape))\nif h_flip > 0:\n transformer_primitive.append(T.RandomHorizontalFlip(p=h_flip))\nif t_crop:\n transformer_primitive.append(T.RandomCrop(size=i_shape))\ntransformer_primitive.append(T.ToTensor())\nif rea:\n ... | <|body_start_0|>
self.gpus = gpus
transformer_primitive = []
transformer_primitive.append(T.Resize(size=i_shape))
if h_flip > 0:
transformer_primitive.append(T.RandomHorizontalFlip(p=h_flip))
if t_crop:
transformer_primitive.append(T.RandomCrop(size=i_shap... | ClassedGenerator returns images only for the specified classes | ClassedGenerator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassedGenerator:
"""ClassedGenerator returns images only for the specified classes"""
def __init__(self, preload='', gpus=1, i_shape=(128, 128), normalization_mean=0.5, normalization_std=0.5, normalization_scale=1.0 / 255.0, h_flip=0.0, t_crop=False, rea=False, **kwargs):
"""Data ge... | stack_v2_sparse_classes_36k_train_025043 | 4,424 | no_license | [
{
"docstring": "Data generator for training and testing. Args: gpus (int): Number of GPUs i_shape (int, int): 2D Image shape normalization_mean (float): Value to pass as mean normalization parameter to pytorch Normalization normalization_std (float): Value to pass as std normalization parameter to pytorch Norma... | 2 | stack_v2_sparse_classes_30k_test_000767 | Implement the Python class `ClassedGenerator` described below.
Class description:
ClassedGenerator returns images only for the specified classes
Method signatures and docstrings:
- def __init__(self, preload='', gpus=1, i_shape=(128, 128), normalization_mean=0.5, normalization_std=0.5, normalization_scale=1.0 / 255.0... | Implement the Python class `ClassedGenerator` described below.
Class description:
ClassedGenerator returns images only for the specified classes
Method signatures and docstrings:
- def __init__(self, preload='', gpus=1, i_shape=(128, 128), normalization_mean=0.5, normalization_std=0.5, normalization_scale=1.0 / 255.0... | 4938936dbf08b5331275d4413dbad51acbaf7da9 | <|skeleton|>
class ClassedGenerator:
"""ClassedGenerator returns images only for the specified classes"""
def __init__(self, preload='', gpus=1, i_shape=(128, 128), normalization_mean=0.5, normalization_std=0.5, normalization_scale=1.0 / 255.0, h_flip=0.0, t_crop=False, rea=False, **kwargs):
"""Data ge... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassedGenerator:
"""ClassedGenerator returns images only for the specified classes"""
def __init__(self, preload='', gpus=1, i_shape=(128, 128), normalization_mean=0.5, normalization_std=0.5, normalization_scale=1.0 / 255.0, h_flip=0.0, t_crop=False, rea=False, **kwargs):
"""Data generator for t... | the_stack_v2_python_sparse | generators/ClassedGenerator.py | asuprem/ODIN | train | 7 |
6751dc18eb80c087c4d0fd7d2d111944670d0505 | [
"ruleTableName = 'Rule %s %s Cross %s %s' % (crosserTable, crosserColumn, crosseeTable, crosseeColumn)\nRule.__init__(self, ruleTableName)\nif crosserTable == crosseeTable:\n self._selectQuery = 'select r.Date, r.Code, %s as Crosser, %s as Crossee from %s r' % (crosserColumn, crosseeColumn, crosserTable)\nelse:\... | <|body_start_0|>
ruleTableName = 'Rule %s %s Cross %s %s' % (crosserTable, crosserColumn, crosseeTable, crosseeColumn)
Rule.__init__(self, ruleTableName)
if crosserTable == crosseeTable:
self._selectQuery = 'select r.Date, r.Code, %s as Crosser, %s as Crossee from %s r' % (crosserCol... | Crossing Rule Class. | CrossingRule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CrossingRule:
"""Crossing Rule Class."""
def __init__(self, crosserTable, crosserColumn, crosseeTable, crosseeColumn):
"""Class Constructor. :param crosserTable: :param crosserColumn: :param crosseeTable: :param crosseeColumn:"""
<|body_0|>
def evaluateRule(self, tickerC... | stack_v2_sparse_classes_36k_train_025044 | 3,295 | no_license | [
{
"docstring": "Class Constructor. :param crosserTable: :param crosserColumn: :param crosseeTable: :param crosseeColumn:",
"name": "__init__",
"signature": "def __init__(self, crosserTable, crosserColumn, crosseeTable, crosseeColumn)"
},
{
"docstring": "? :param tickerCode:",
"name": "evalua... | 2 | stack_v2_sparse_classes_30k_train_008652 | Implement the Python class `CrossingRule` described below.
Class description:
Crossing Rule Class.
Method signatures and docstrings:
- def __init__(self, crosserTable, crosserColumn, crosseeTable, crosseeColumn): Class Constructor. :param crosserTable: :param crosserColumn: :param crosseeTable: :param crosseeColumn:
... | Implement the Python class `CrossingRule` described below.
Class description:
Crossing Rule Class.
Method signatures and docstrings:
- def __init__(self, crosserTable, crosserColumn, crosseeTable, crosseeColumn): Class Constructor. :param crosserTable: :param crosserColumn: :param crosseeTable: :param crosseeColumn:
... | 08b07b50ead69decd381cf5f866f4d8ffb80fa54 | <|skeleton|>
class CrossingRule:
"""Crossing Rule Class."""
def __init__(self, crosserTable, crosserColumn, crosseeTable, crosseeColumn):
"""Class Constructor. :param crosserTable: :param crosserColumn: :param crosseeTable: :param crosseeColumn:"""
<|body_0|>
def evaluateRule(self, tickerC... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CrossingRule:
"""Crossing Rule Class."""
def __init__(self, crosserTable, crosserColumn, crosseeTable, crosseeColumn):
"""Class Constructor. :param crosserTable: :param crosserColumn: :param crosseeTable: :param crosseeColumn:"""
ruleTableName = 'Rule %s %s Cross %s %s' % (crosserTable, c... | the_stack_v2_python_sparse | pyswing/objects/rules/crossingRule.py | garyjoy/pyswing | train | 1 |
346a239096bad2bf87dd82454cc9178ca5cacf84 | [
"self.enabled = enabled\nself.spare_serial = spare_serial\nself.uplink_mode = uplink_mode\nself.virtual_ip_1 = virtual_ip_1\nself.virtual_ip_2 = virtual_ip_2",
"if dictionary is None:\n return None\nenabled = dictionary.get('enabled')\nspare_serial = dictionary.get('spareSerial')\nuplink_mode = dictionary.get(... | <|body_start_0|>
self.enabled = enabled
self.spare_serial = spare_serial
self.uplink_mode = uplink_mode
self.virtual_ip_1 = virtual_ip_1
self.virtual_ip_2 = virtual_ip_2
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
enabled = dict... | Implementation of the 'updateNetworkWarmSpareSettings' model. TODO: type model description here. Attributes: enabled (bool): Enable warm spare spare_serial (string): Serial number of the warm spare appliance uplink_mode (string): Uplink mode, either virtual or public virtual_ip_1 (string): The WAN 1 shared IP virtual_i... | UpdateNetworkWarmSpareSettingsModel | [
"MIT",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpdateNetworkWarmSpareSettingsModel:
"""Implementation of the 'updateNetworkWarmSpareSettings' model. TODO: type model description here. Attributes: enabled (bool): Enable warm spare spare_serial (string): Serial number of the warm spare appliance uplink_mode (string): Uplink mode, either virtual... | stack_v2_sparse_classes_36k_train_025045 | 2,514 | permissive | [
{
"docstring": "Constructor for the UpdateNetworkWarmSpareSettingsModel class",
"name": "__init__",
"signature": "def __init__(self, enabled=None, spare_serial=None, uplink_mode=None, virtual_ip_1=None, virtual_ip_2=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Ar... | 2 | stack_v2_sparse_classes_30k_train_017998 | Implement the Python class `UpdateNetworkWarmSpareSettingsModel` described below.
Class description:
Implementation of the 'updateNetworkWarmSpareSettings' model. TODO: type model description here. Attributes: enabled (bool): Enable warm spare spare_serial (string): Serial number of the warm spare appliance uplink_mod... | Implement the Python class `UpdateNetworkWarmSpareSettingsModel` described below.
Class description:
Implementation of the 'updateNetworkWarmSpareSettings' model. TODO: type model description here. Attributes: enabled (bool): Enable warm spare spare_serial (string): Serial number of the warm spare appliance uplink_mod... | 9894089eb013318243ae48869cc5130eb37f80c0 | <|skeleton|>
class UpdateNetworkWarmSpareSettingsModel:
"""Implementation of the 'updateNetworkWarmSpareSettings' model. TODO: type model description here. Attributes: enabled (bool): Enable warm spare spare_serial (string): Serial number of the warm spare appliance uplink_mode (string): Uplink mode, either virtual... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UpdateNetworkWarmSpareSettingsModel:
"""Implementation of the 'updateNetworkWarmSpareSettings' model. TODO: type model description here. Attributes: enabled (bool): Enable warm spare spare_serial (string): Serial number of the warm spare appliance uplink_mode (string): Uplink mode, either virtual or public vi... | the_stack_v2_python_sparse | meraki_sdk/models/update_network_warm_spare_settings_model.py | RaulCatalano/meraki-python-sdk | train | 1 |
7e093e74310f5655cc8a651585324eae618aa8c2 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn UserLastSignInRecommendationInsightSetting()",
"from .access_review_recommendation_insight_setting import AccessReviewRecommendationInsightSetting\nfrom .user_sign_in_recommendation_scope import UserSignInRecommendationScope\nfrom .acc... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return UserLastSignInRecommendationInsightSetting()
<|end_body_0|>
<|body_start_1|>
from .access_review_recommendation_insight_setting import AccessReviewRecommendationInsightSetting
from .user... | UserLastSignInRecommendationInsightSetting | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserLastSignInRecommendationInsightSetting:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserLastSignInRecommendationInsightSetting:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to ... | stack_v2_sparse_classes_36k_train_025046 | 3,774 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: UserLastSignInRecommendationInsightSetting",
"name": "create_from_discriminator_value",
"signature": "def cr... | 3 | null | Implement the Python class `UserLastSignInRecommendationInsightSetting` described below.
Class description:
Implement the UserLastSignInRecommendationInsightSetting class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserLastSignInRecommendationInsig... | Implement the Python class `UserLastSignInRecommendationInsightSetting` described below.
Class description:
Implement the UserLastSignInRecommendationInsightSetting class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserLastSignInRecommendationInsig... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class UserLastSignInRecommendationInsightSetting:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserLastSignInRecommendationInsightSetting:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserLastSignInRecommendationInsightSetting:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserLastSignInRecommendationInsightSetting:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discr... | the_stack_v2_python_sparse | msgraph/generated/models/user_last_sign_in_recommendation_insight_setting.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
bed7f4fbb45a777fab13dbf5b3ecc15b5f78a2a7 | [
"left, right = (0, len(nums) - 1)\nwhile left < right:\n mid = left + (right - left) // 2\n if nums[mid] > nums[right]:\n left = mid + 1\n else:\n right = mid\nreturn nums[left]",
"if len(nums) <= 2:\n return min(nums)\nleft, right = (0, len(nums) - 1)\nmid = left + (right - left) // 2\n... | <|body_start_0|>
left, right = (0, len(nums) - 1)
while left < right:
mid = left + (right - left) // 2
if nums[mid] > nums[right]:
left = mid + 1
else:
right = mid
return nums[left]
<|end_body_0|>
<|body_start_1|>
if le... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMin(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findMin_verbose(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
left, right = (0, len(nums) - 1)
whil... | stack_v2_sparse_classes_36k_train_025047 | 1,622 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findMin",
"signature": "def findMin(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findMin_verbose",
"signature": "def findMin_verbose(self, nums)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMin(self, nums): :type nums: List[int] :rtype: int
- def findMin_verbose(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMin(self, nums): :type nums: List[int] :rtype: int
- def findMin_verbose(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def findMin(sel... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def findMin(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findMin_verbose(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findMin(self, nums):
""":type nums: List[int] :rtype: int"""
left, right = (0, len(nums) - 1)
while left < right:
mid = left + (right - left) // 2
if nums[mid] > nums[right]:
left = mid + 1
else:
right = ... | the_stack_v2_python_sparse | src/lt_153.py | oxhead/CodingYourWay | train | 0 | |
421631a880fc00fba21195c44ad4f7274292df31 | [
"attrs = super().validate(attrs)\nerrors = {}\ncombiner = DataCombiner(self.instance, attrs)\nvalidators = (self._validate_related_trade_agreements,)\nfor validator in validators:\n errors.update(validator(combiner))\nif errors:\n raise serializers.ValidationError(errors)\nreturn attrs",
"errors = {}\nrelat... | <|body_start_0|>
attrs = super().validate(attrs)
errors = {}
combiner = DataCombiner(self.instance, attrs)
validators = (self._validate_related_trade_agreements,)
for validator in validators:
errors.update(validator(combiner))
if errors:
raise seri... | Event serialiser for V4 endpoint. | EventSerializerV4 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventSerializerV4:
"""Event serialiser for V4 endpoint."""
def validate(self, attrs):
"""Performs cross-field validation."""
<|body_0|>
def _validate_related_trade_agreements(self, combiner):
"""Validates trade agreement state for consistency with has_related_tra... | stack_v2_sparse_classes_36k_train_025048 | 7,153 | permissive | [
{
"docstring": "Performs cross-field validation.",
"name": "validate",
"signature": "def validate(self, attrs)"
},
{
"docstring": "Validates trade agreement state for consistency with has_related_trade_agreements",
"name": "_validate_related_trade_agreements",
"signature": "def _validate... | 2 | stack_v2_sparse_classes_30k_train_010635 | Implement the Python class `EventSerializerV4` described below.
Class description:
Event serialiser for V4 endpoint.
Method signatures and docstrings:
- def validate(self, attrs): Performs cross-field validation.
- def _validate_related_trade_agreements(self, combiner): Validates trade agreement state for consistency... | Implement the Python class `EventSerializerV4` described below.
Class description:
Event serialiser for V4 endpoint.
Method signatures and docstrings:
- def validate(self, attrs): Performs cross-field validation.
- def _validate_related_trade_agreements(self, combiner): Validates trade agreement state for consistency... | a92faabf73fb93b5bfd94fd465eafc3e29aa6d8e | <|skeleton|>
class EventSerializerV4:
"""Event serialiser for V4 endpoint."""
def validate(self, attrs):
"""Performs cross-field validation."""
<|body_0|>
def _validate_related_trade_agreements(self, combiner):
"""Validates trade agreement state for consistency with has_related_tra... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventSerializerV4:
"""Event serialiser for V4 endpoint."""
def validate(self, attrs):
"""Performs cross-field validation."""
attrs = super().validate(attrs)
errors = {}
combiner = DataCombiner(self.instance, attrs)
validators = (self._validate_related_trade_agreeme... | the_stack_v2_python_sparse | datahub/event/serializers.py | cgsunkel/data-hub-api | train | 0 |
be83dd97a38b8f2d477bc564c8631c612d1eb4fe | [
"self.env.revert_snapshot('deploy_ha_toolchain')\nself.helpers.emulate_whole_network_disaster(delay_before_recover=7 * 60)\nself.INFLUXDB_GRAFANA.wait_plugin_online()\nself.ELASTICSEARCH_KIBANA.wait_plugin_online()\nself.LMA_INFRASTRUCTURE_ALERTING.wait_plugin_online()\nself.helpers.run_ostf()",
"self.env.revert_... | <|body_start_0|>
self.env.revert_snapshot('deploy_ha_toolchain')
self.helpers.emulate_whole_network_disaster(delay_before_recover=7 * 60)
self.INFLUXDB_GRAFANA.wait_plugin_online()
self.ELASTICSEARCH_KIBANA.wait_plugin_online()
self.LMA_INFRASTRUCTURE_ALERTING.wait_plugin_online(... | Class for testing plugin failover after network disaster. | TestDestructiveToolchainPlugin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDestructiveToolchainPlugin:
"""Class for testing plugin failover after network disaster."""
def check_cluster_outage_toolchain(self):
"""Verify that the backends and dashboards recover after a network outage of the whole cluster with plugins toolchain. Scenario: 1. Revert the sna... | stack_v2_sparse_classes_36k_train_025049 | 3,423 | no_license | [
{
"docstring": "Verify that the backends and dashboards recover after a network outage of the whole cluster with plugins toolchain. Scenario: 1. Revert the snapshot with 9 deployed nodes in HA configuration 2. Simulate a network outage of the whole cluster with plugins toolchain 3. Wait for at least 7 minutes b... | 2 | stack_v2_sparse_classes_30k_train_012095 | Implement the Python class `TestDestructiveToolchainPlugin` described below.
Class description:
Class for testing plugin failover after network disaster.
Method signatures and docstrings:
- def check_cluster_outage_toolchain(self): Verify that the backends and dashboards recover after a network outage of the whole cl... | Implement the Python class `TestDestructiveToolchainPlugin` described below.
Class description:
Class for testing plugin failover after network disaster.
Method signatures and docstrings:
- def check_cluster_outage_toolchain(self): Verify that the backends and dashboards recover after a network outage of the whole cl... | 179249df2d206eeabb3955c9dc8cb78cac3c36c6 | <|skeleton|>
class TestDestructiveToolchainPlugin:
"""Class for testing plugin failover after network disaster."""
def check_cluster_outage_toolchain(self):
"""Verify that the backends and dashboards recover after a network outage of the whole cluster with plugins toolchain. Scenario: 1. Revert the sna... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestDestructiveToolchainPlugin:
"""Class for testing plugin failover after network disaster."""
def check_cluster_outage_toolchain(self):
"""Verify that the backends and dashboards recover after a network outage of the whole cluster with plugins toolchain. Scenario: 1. Revert the snapshot with 9 ... | the_stack_v2_python_sparse | stacklight_tests/toolchain/test_destructive.py | rkhozinov/stacklight-integration-tests | train | 1 |
c79cf61afcfd06cb3761980662f5a548fa9b1760 | [
"if not root:\n return ''\nleft_str = self.serialize(root.left)\nright_str = self.serialize(root.right)\nreturn str(root.val) + ' ' + left_str + ' ' + right_str",
"q = [int(x) for x in data.split(' ') if x]\n\ndef dfs(q, min_, max_):\n if not q:\n return None\n if q[0] > min_ and q[0] < max_:\n ... | <|body_start_0|>
if not root:
return ''
left_str = self.serialize(root.left)
right_str = self.serialize(root.right)
return str(root.val) + ' ' + left_str + ' ' + right_str
<|end_body_0|>
<|body_start_1|>
q = [int(x) for x in data.split(' ') if x]
def dfs(q, ... | Codec | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
... | stack_v2_sparse_classes_36k_train_025050 | 2,234 | permissive | [
{
"docstring": "Encodes a tree to a single string.",
"name": "serialize",
"signature": "def serialize(self, root: TreeNode) -> str"
},
{
"docstring": "Decodes your encoded data to tree.",
"name": "deserialize",
"signature": "def deserialize(self, data: str) -> TreeNode"
}
] | 2 | stack_v2_sparse_classes_30k_train_019829 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree. | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree.
<|skeleton|>
class Co... | 26fa64525c92e01dfbcdd7851f5b3a91f6ec203b | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
if not root:
return ''
left_str = self.serialize(root.left)
right_str = self.serialize(root.right)
return str(root.val) + ' ' + left_str + ' ' + right_str
def de... | the_stack_v2_python_sparse | daily_coding_challenge/october_2020/serialize_and_deserialize_BST_449.py | anjaligopi/leetcode | train | 0 | |
1a32627732d35ab00ee3dfa587cfb8ff55feb0a3 | [
"if not p:\n return not s\nfirst_match = bool(s) and p[0] in {s[0], '.'}\nif len(p) >= 2 and p[1] == '*':\n return self.isMatch_recursive(s, p[2:]) or (first_match and self.isMatch_recursive(s[1:], p))\nelse:\n return first_match and self.isMatch_recursive(s[1:], p[1:])",
"memo = {}\n\ndef dp(i, j):\n ... | <|body_start_0|>
if not p:
return not s
first_match = bool(s) and p[0] in {s[0], '.'}
if len(p) >= 2 and p[1] == '*':
return self.isMatch_recursive(s, p[2:]) or (first_match and self.isMatch_recursive(s[1:], p))
else:
return first_match and self.isMatc... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isMatch_recursive(self, s, p):
""":type s: str :type p: str :rtype: bool This is the recursive version, while it will TLE in leetcode"""
<|body_0|>
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool revise the recursive version into DP t... | stack_v2_sparse_classes_36k_train_025051 | 2,313 | no_license | [
{
"docstring": ":type s: str :type p: str :rtype: bool This is the recursive version, while it will TLE in leetcode",
"name": "isMatch_recursive",
"signature": "def isMatch_recursive(self, s, p)"
},
{
"docstring": ":type s: str :type p: str :rtype: bool revise the recursive version into DP top-d... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isMatch_recursive(self, s, p): :type s: str :type p: str :rtype: bool This is the recursive version, while it will TLE in leetcode
- def isMatch(self, s, p): :type s: str :ty... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isMatch_recursive(self, s, p): :type s: str :type p: str :rtype: bool This is the recursive version, while it will TLE in leetcode
- def isMatch(self, s, p): :type s: str :ty... | 4d340a45fb2e9459d47cbe179ebfa7a82e5f1b8c | <|skeleton|>
class Solution:
def isMatch_recursive(self, s, p):
""":type s: str :type p: str :rtype: bool This is the recursive version, while it will TLE in leetcode"""
<|body_0|>
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool revise the recursive version into DP t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isMatch_recursive(self, s, p):
""":type s: str :type p: str :rtype: bool This is the recursive version, while it will TLE in leetcode"""
if not p:
return not s
first_match = bool(s) and p[0] in {s[0], '.'}
if len(p) >= 2 and p[1] == '*':
re... | the_stack_v2_python_sparse | 10_RegularExpressionMatching/solution.py | llgeek/leetcode | train | 1 | |
65d59a3d6ed579420ad3853458a05cb231ef9b92 | [
"m, n = (len(matrix), len(matrix[0]))\ncount = 0\ndp = [[0] * n for _ in range(m)]\nfor i in range(m):\n for j in range(n):\n if matrix[i][j] == 1:\n if not i or not j:\n dp[i][j] = 1\n else:\n dp[i][j] = min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])... | <|body_start_0|>
m, n = (len(matrix), len(matrix[0]))
count = 0
dp = [[0] * n for _ in range(m)]
for i in range(m):
for j in range(n):
if matrix[i][j] == 1:
if not i or not j:
dp[i][j] = 1
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def countSquares(self, matrix: List[List[int]]) -> int:
"""思路：动态规划 dp[i][j] 表示，以 [i][j] 为右下角的 正方的边长， dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1 边界： 0< =i < M, 0<= j < N 优化：直接修改原始矩阵，不需要额外空间 1 1 1 0 1 1 0 1 1 [2] 1 [1] 0 [1] 1 [1] min(1, 1, 1) + 1 = 2 min(1, 0, 1) +... | stack_v2_sparse_classes_36k_train_025052 | 2,669 | no_license | [
{
"docstring": "思路：动态规划 dp[i][j] 表示，以 [i][j] 为右下角的 正方的边长， dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1 边界： 0< =i < M, 0<= j < N 优化：直接修改原始矩阵，不需要额外空间 1 1 1 0 1 1 0 1 1 [2] 1 [1] 0 [1] 1 [1] min(1, 1, 1) + 1 = 2 min(1, 0, 1) + 1 = 1 时间复杂度：O(mn) 空间复杂度：O(mn)",
"name": "countSquares",
"signature":... | 2 | stack_v2_sparse_classes_30k_train_011385 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countSquares(self, matrix: List[List[int]]) -> int: 思路：动态规划 dp[i][j] 表示，以 [i][j] 为右下角的 正方的边长， dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1 边界： 0< =i < M, 0<= j < ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countSquares(self, matrix: List[List[int]]) -> int: 思路：动态规划 dp[i][j] 表示，以 [i][j] 为右下角的 正方的边长， dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1 边界： 0< =i < M, 0<= j < ... | 4994b8b19abcdbcc0bda2944350e325242fadfd1 | <|skeleton|>
class Solution:
def countSquares(self, matrix: List[List[int]]) -> int:
"""思路：动态规划 dp[i][j] 表示，以 [i][j] 为右下角的 正方的边长， dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1 边界： 0< =i < M, 0<= j < N 优化：直接修改原始矩阵，不需要额外空间 1 1 1 0 1 1 0 1 1 [2] 1 [1] 0 [1] 1 [1] min(1, 1, 1) + 1 = 2 min(1, 0, 1) +... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def countSquares(self, matrix: List[List[int]]) -> int:
"""思路：动态规划 dp[i][j] 表示，以 [i][j] 为右下角的 正方的边长， dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1 边界： 0< =i < M, 0<= j < N 优化：直接修改原始矩阵，不需要额外空间 1 1 1 0 1 1 0 1 1 [2] 1 [1] 0 [1] 1 [1] min(1, 1, 1) + 1 = 2 min(1, 0, 1) + 1 = 1 时间复杂度：O... | the_stack_v2_python_sparse | Week_04/countSquares.py | NanZhang715/AlgorithmCHUNZHAO | train | 0 | |
c0dd8063454ef6019609759601ca151b04c4be1d | [
"i = 1\nwhile i <= x / 2:\n if i * i > x:\n return i - 1\n elif i * i == x:\n return i\n i += 1",
"if x == 1:\n return 1\ni = 0\nwhile i <= x / 2:\n if i * i > x:\n return i - 1\n elif i * i == x:\n return i\n i += 1\nreturn i - 1"
] | <|body_start_0|>
i = 1
while i <= x / 2:
if i * i > x:
return i - 1
elif i * i == x:
return i
i += 1
<|end_body_0|>
<|body_start_1|>
if x == 1:
return 1
i = 0
while i <= x / 2:
if i * i >... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mySqrt(self, x):
""":type x: int :rtype: int"""
<|body_0|>
def mySqrtTimeLimit(self, x):
""":type x: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
i = 1
while i <= x / 2:
if i * i > x:
... | stack_v2_sparse_classes_36k_train_025053 | 658 | no_license | [
{
"docstring": ":type x: int :rtype: int",
"name": "mySqrt",
"signature": "def mySqrt(self, x)"
},
{
"docstring": ":type x: int :rtype: int",
"name": "mySqrtTimeLimit",
"signature": "def mySqrtTimeLimit(self, x)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mySqrt(self, x): :type x: int :rtype: int
- def mySqrtTimeLimit(self, x): :type x: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mySqrt(self, x): :type x: int :rtype: int
- def mySqrtTimeLimit(self, x): :type x: int :rtype: int
<|skeleton|>
class Solution:
def mySqrt(self, x):
""":type x:... | b925bb22d1daa4a56c5a238a5758a926905559b4 | <|skeleton|>
class Solution:
def mySqrt(self, x):
""":type x: int :rtype: int"""
<|body_0|>
def mySqrtTimeLimit(self, x):
""":type x: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def mySqrt(self, x):
""":type x: int :rtype: int"""
i = 1
while i <= x / 2:
if i * i > x:
return i - 1
elif i * i == x:
return i
i += 1
def mySqrtTimeLimit(self, x):
""":type x: int :rtype: int""... | the_stack_v2_python_sparse | 69. Sqrt(x).py | beninghton/notGivenUpToG | train | 0 | |
c87b5f435ace6e515cb65a064ee8eecbaad7fad9 | [
"yield (None, 'port grouping is determined by the global default.')\nyield (False, 'ports are not grouped in an additional record.')\nyield (re.compile('[a-zA-Z][a-zA-Z0-9_]*'), 'ports are grouped in a record with the specified name.')",
"yield (None, 'port flattening is determined by the global default.')\nyield... | <|body_start_0|>
yield (None, 'port grouping is determined by the global default.')
yield (False, 'ports are not grouped in an additional record.')
yield (re.compile('[a-zA-Z][a-zA-Z0-9_]*'), 'ports are grouped in a record with the specified name.')
<|end_body_0|>
<|body_start_1|>
yield... | Each field and interrupt in `vhdmmio` can register scalar and vector inputs and outputs, as well as generics. This configuration structure determines how these interfaces are exposed in the entity. By default, the ports are grouped by field/interrupt into records while generics are flattened, but either can be overridd... | InterfaceConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InterfaceConfig:
"""Each field and interrupt in `vhdmmio` can register scalar and vector inputs and outputs, as well as generics. This configuration structure determines how these interfaces are exposed in the entity. By default, the ports are grouped by field/interrupt into records while generic... | stack_v2_sparse_classes_36k_train_025054 | 3,434 | permissive | [
{
"docstring": "Name of the group record used for ports, if any. The ports for any objects that share the same non-null `group` tag are combined into a single record pair (`in` and `out`).",
"name": "group",
"signature": "def group()"
},
{
"docstring": "Whether the ports for this object should b... | 4 | stack_v2_sparse_classes_30k_train_000045 | Implement the Python class `InterfaceConfig` described below.
Class description:
Each field and interrupt in `vhdmmio` can register scalar and vector inputs and outputs, as well as generics. This configuration structure determines how these interfaces are exposed in the entity. By default, the ports are grouped by fie... | Implement the Python class `InterfaceConfig` described below.
Class description:
Each field and interrupt in `vhdmmio` can register scalar and vector inputs and outputs, as well as generics. This configuration structure determines how these interfaces are exposed in the entity. By default, the ports are grouped by fie... | d0417925cd72dfb973431d6948e65b662a75c5fa | <|skeleton|>
class InterfaceConfig:
"""Each field and interrupt in `vhdmmio` can register scalar and vector inputs and outputs, as well as generics. This configuration structure determines how these interfaces are exposed in the entity. By default, the ports are grouped by field/interrupt into records while generic... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InterfaceConfig:
"""Each field and interrupt in `vhdmmio` can register scalar and vector inputs and outputs, as well as generics. This configuration structure determines how these interfaces are exposed in the entity. By default, the ports are grouped by field/interrupt into records while generics are flatten... | the_stack_v2_python_sparse | vhdmmio/config/interface.py | abs-tudelft/vhdmmio | train | 5 |
c902d62a7dfe7a5c47f97e34258ecdb136882ce5 | [
"self.curr_round = 0\npayload = {'is_handshake': True}\nlst_replies = self.query_all_parties(payload)\nglobal_counts = self.global_reweighing(lst_replies)\npayload = {'global_weights': True, 'global_counts': global_counts}\nlst_replies = self.query_all_parties(payload)\nwhile not self.reach_termination_criteria(sel... | <|body_start_0|>
self.curr_round = 0
payload = {'is_handshake': True}
lst_replies = self.query_all_parties(payload)
global_counts = self.global_reweighing(lst_replies)
payload = {'global_weights': True, 'global_counts': global_counts}
lst_replies = self.query_all_parties(... | Class for iterative averaging based fusion algorithms. An iterative fusion algorithm here referred to a fusion algorithm that sends out queries at each global round to registered parties for information, and use the collected information from parties to update the global model. The type of queries sent out at each roun... | ReweighFusionHandler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReweighFusionHandler:
"""Class for iterative averaging based fusion algorithms. An iterative fusion algorithm here referred to a fusion algorithm that sends out queries at each global round to registered parties for information, and use the collected information from parties to update the global ... | stack_v2_sparse_classes_36k_train_025055 | 4,100 | permissive | [
{
"docstring": "Starts an iterative global federated learning training process.",
"name": "start_global_training",
"signature": "def start_global_training(self)"
},
{
"docstring": ":param lst_replies: party response with local DP counts for weight calculation :type lst_replies: `dict` :return: g... | 2 | stack_v2_sparse_classes_30k_train_003807 | Implement the Python class `ReweighFusionHandler` described below.
Class description:
Class for iterative averaging based fusion algorithms. An iterative fusion algorithm here referred to a fusion algorithm that sends out queries at each global round to registered parties for information, and use the collected informa... | Implement the Python class `ReweighFusionHandler` described below.
Class description:
Class for iterative averaging based fusion algorithms. An iterative fusion algorithm here referred to a fusion algorithm that sends out queries at each global round to registered parties for information, and use the collected informa... | 64ffa2ee2e906b1bd6b3dd6aabcf6fc3de862608 | <|skeleton|>
class ReweighFusionHandler:
"""Class for iterative averaging based fusion algorithms. An iterative fusion algorithm here referred to a fusion algorithm that sends out queries at each global round to registered parties for information, and use the collected information from parties to update the global ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReweighFusionHandler:
"""Class for iterative averaging based fusion algorithms. An iterative fusion algorithm here referred to a fusion algorithm that sends out queries at each global round to registered parties for information, and use the collected information from parties to update the global model. The ty... | the_stack_v2_python_sparse | debugging-constructs/ibmfl/aggregator/fusion/reweigh_fusion_handler.py | SEED-VT/FedDebug | train | 8 |
1669dd2973df3bb6a46cf842bdfebc0162ecbc12 | [
"self.num_res = len(obj_dict)\nself.obj_dict = obj_dict\nself.res = dict()\ncnt = 0\nfor ip in self.obj_dict:\n obj_elem_dict = self.obj_dict.get(ip)\n drvr_obj = obj_elem_dict.get('drvr_obj')\n self.res[cnt] = {'mgmt_ip': ip, 'quota': drvr_obj.get_max_quota(), 'obj_dict': obj_elem_dict, 'used': 0, 'fw_id_... | <|body_start_0|>
self.num_res = len(obj_dict)
self.obj_dict = obj_dict
self.res = dict()
cnt = 0
for ip in self.obj_dict:
obj_elem_dict = self.obj_dict.get(ip)
drvr_obj = obj_elem_dict.get('drvr_obj')
self.res[cnt] = {'mgmt_ip': ip, 'quota': dr... | Max Sched class. This scheduler will return the first firewall until it reaches its quota. | MaxSched | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaxSched:
"""Max Sched class. This scheduler will return the first firewall until it reaches its quota."""
def __init__(self, obj_dict):
"""Initialization."""
<|body_0|>
def allocate_fw_dev(self, fw_id):
"""Allocate firewall device. Allocate the first Firewall de... | stack_v2_sparse_classes_36k_train_025056 | 9,983 | permissive | [
{
"docstring": "Initialization.",
"name": "__init__",
"signature": "def __init__(self, obj_dict)"
},
{
"docstring": "Allocate firewall device. Allocate the first Firewall device which has resources available.",
"name": "allocate_fw_dev",
"signature": "def allocate_fw_dev(self, fw_id)"
... | 5 | stack_v2_sparse_classes_30k_train_017517 | Implement the Python class `MaxSched` described below.
Class description:
Max Sched class. This scheduler will return the first firewall until it reaches its quota.
Method signatures and docstrings:
- def __init__(self, obj_dict): Initialization.
- def allocate_fw_dev(self, fw_id): Allocate firewall device. Allocate ... | Implement the Python class `MaxSched` described below.
Class description:
Max Sched class. This scheduler will return the first firewall until it reaches its quota.
Method signatures and docstrings:
- def __init__(self, obj_dict): Initialization.
- def allocate_fw_dev(self, fw_id): Allocate firewall device. Allocate ... | 3bd3f90b5ddb4553525abc5f87ba759bf2e7a2e5 | <|skeleton|>
class MaxSched:
"""Max Sched class. This scheduler will return the first firewall until it reaches its quota."""
def __init__(self, obj_dict):
"""Initialization."""
<|body_0|>
def allocate_fw_dev(self, fw_id):
"""Allocate firewall device. Allocate the first Firewall de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MaxSched:
"""Max Sched class. This scheduler will return the first firewall until it reaches its quota."""
def __init__(self, obj_dict):
"""Initialization."""
self.num_res = len(obj_dict)
self.obj_dict = obj_dict
self.res = dict()
cnt = 0
for ip in self.obj... | the_stack_v2_python_sparse | networking_cisco/apps/saf/server/services/firewall/native/drivers/dev_mgr.py | sapcc/networking-cisco | train | 3 |
28ef3042720c18426acc83bc6ccb448982e92ee5 | [
"login_page.LoginPage(self.driver).login()\nsleep(2)\nlogin_page.LoginPage(self.driver)._open(url='/wujiaqu/')\nsleep(2)\npo = tenant_collection_page.TenantCollectionPage(self.driver)\nif po.title() == '收藏':\n po.collection()\nelse:\n po.collection()\n sleep(2)\n po.collection()\nfunction.insert_img(sel... | <|body_start_0|>
login_page.LoginPage(self.driver).login()
sleep(2)
login_page.LoginPage(self.driver)._open(url='/wujiaqu/')
sleep(2)
po = tenant_collection_page.TenantCollectionPage(self.driver)
if po.title() == '收藏':
po.collection()
else:
... | 我的收藏 | TestTenantCollection | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestTenantCollection:
"""我的收藏"""
def test_a_collection(self):
"""五家渠收藏房源"""
<|body_0|>
def test_cancel_collection(self):
"""我的收藏——取消收藏"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
login_page.LoginPage(self.driver).login()
sleep(2)
... | stack_v2_sparse_classes_36k_train_025057 | 1,509 | permissive | [
{
"docstring": "五家渠收藏房源",
"name": "test_a_collection",
"signature": "def test_a_collection(self)"
},
{
"docstring": "我的收藏——取消收藏",
"name": "test_cancel_collection",
"signature": "def test_cancel_collection(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006880 | Implement the Python class `TestTenantCollection` described below.
Class description:
我的收藏
Method signatures and docstrings:
- def test_a_collection(self): 五家渠收藏房源
- def test_cancel_collection(self): 我的收藏——取消收藏 | Implement the Python class `TestTenantCollection` described below.
Class description:
我的收藏
Method signatures and docstrings:
- def test_a_collection(self): 五家渠收藏房源
- def test_cancel_collection(self): 我的收藏——取消收藏
<|skeleton|>
class TestTenantCollection:
"""我的收藏"""
def test_a_collection(self):
"""五家渠收藏... | 192c70c49a8e9e072b9d0d0136f02c653c589410 | <|skeleton|>
class TestTenantCollection:
"""我的收藏"""
def test_a_collection(self):
"""五家渠收藏房源"""
<|body_0|>
def test_cancel_collection(self):
"""我的收藏——取消收藏"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestTenantCollection:
"""我的收藏"""
def test_a_collection(self):
"""五家渠收藏房源"""
login_page.LoginPage(self.driver).login()
sleep(2)
login_page.LoginPage(self.driver)._open(url='/wujiaqu/')
sleep(2)
po = tenant_collection_page.TenantCollectionPage(self.driver)
... | the_stack_v2_python_sparse | mayi/test_case/test_tenant_collection.py | 18701016443/mayi | train | 0 |
a7c7b3de78c499c6bd84626da89b6cf6674bb1ff | [
"size = len(nums)\nl, sum, ans = (0, 0, size + 1)\nfor i in range(0, size):\n sum += nums[i]\n while sum >= s:\n ans = min(ans, i - l + 1)\n sum -= nums[l]\n l += 1\nreturn 0 if ans == size + 1 else ans",
"l, r, sum, ans, size = (0, 0, 0, len(nums) + 1, len(nums))\nwhile l < size and r ... | <|body_start_0|>
size = len(nums)
l, sum, ans = (0, 0, size + 1)
for i in range(0, size):
sum += nums[i]
while sum >= s:
ans = min(ans, i - l + 1)
sum -= nums[l]
l += 1
return 0 if ans == size + 1 else ans
<|end_body... | SolutionMinimumSizeSubarraySum | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SolutionMinimumSizeSubarraySum:
def minSubArrayLen_ON_1(self, s, nums):
""":type s: int :type nums: List[int] :rtype: int"""
<|body_0|>
def minSubArrayLen_ON_2(self, s, nums):
""":type s: int :type nums: List[int] :rtype: int"""
<|body_1|>
def minSubArra... | stack_v2_sparse_classes_36k_train_025058 | 1,796 | no_license | [
{
"docstring": ":type s: int :type nums: List[int] :rtype: int",
"name": "minSubArrayLen_ON_1",
"signature": "def minSubArrayLen_ON_1(self, s, nums)"
},
{
"docstring": ":type s: int :type nums: List[int] :rtype: int",
"name": "minSubArrayLen_ON_2",
"signature": "def minSubArrayLen_ON_2(s... | 3 | stack_v2_sparse_classes_30k_train_008159 | Implement the Python class `SolutionMinimumSizeSubarraySum` described below.
Class description:
Implement the SolutionMinimumSizeSubarraySum class.
Method signatures and docstrings:
- def minSubArrayLen_ON_1(self, s, nums): :type s: int :type nums: List[int] :rtype: int
- def minSubArrayLen_ON_2(self, s, nums): :type... | Implement the Python class `SolutionMinimumSizeSubarraySum` described below.
Class description:
Implement the SolutionMinimumSizeSubarraySum class.
Method signatures and docstrings:
- def minSubArrayLen_ON_1(self, s, nums): :type s: int :type nums: List[int] :rtype: int
- def minSubArrayLen_ON_2(self, s, nums): :type... | a07d8b3cfd5eadb3c3b2f4383cb8ffc32d52f928 | <|skeleton|>
class SolutionMinimumSizeSubarraySum:
def minSubArrayLen_ON_1(self, s, nums):
""":type s: int :type nums: List[int] :rtype: int"""
<|body_0|>
def minSubArrayLen_ON_2(self, s, nums):
""":type s: int :type nums: List[int] :rtype: int"""
<|body_1|>
def minSubArra... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SolutionMinimumSizeSubarraySum:
def minSubArrayLen_ON_1(self, s, nums):
""":type s: int :type nums: List[int] :rtype: int"""
size = len(nums)
l, sum, ans = (0, 0, size + 1)
for i in range(0, size):
sum += nums[i]
while sum >= s:
ans = min... | the_stack_v2_python_sparse | python/MinimumSizeSubarraySum.py | hellocomrade/happycoding | train | 5 | |
ca4ece80a17d014fc4c3c8632f02e5671d27b028 | [
"self.name = 'contextual_model_multi_stimuli'\nself.figures = ['f3a.npz', 'f5.npz']\nself.target_data = 'label_dict'\nself.config = Config()\nself.output_size = [1, 10]\nself.im_size = (10, 51, 51, 75)\nself.repeats = 20\nself.model_input_image_size = [10, 51, 51, 75]\nself.default_loss_function = 'pearson'\nself.s... | <|body_start_0|>
self.name = 'contextual_model_multi_stimuli'
self.figures = ['f3a.npz', 'f5.npz']
self.target_data = 'label_dict'
self.config = Config()
self.output_size = [1, 10]
self.im_size = (10, 51, 51, 75)
self.repeats = 20
self.model_input_image_si... | Tilt-illusion from Contextual modeling paper (fig3a). | data_processing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class data_processing:
"""Tilt-illusion from Contextual modeling paper (fig3a)."""
def __init__(self):
"""Init global variables for contextual circuit bp."""
<|body_0|>
def flatten_and_pad_dict(self, d, flatten=False, constant=0.0):
"""Flatten each dict entry then pad ... | stack_v2_sparse_classes_36k_train_025059 | 4,213 | no_license | [
{
"docstring": "Init global variables for contextual circuit bp.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Flatten each dict entry then pad to the largest sized one.",
"name": "flatten_and_pad_dict",
"signature": "def flatten_and_pad_dict(self, d, flatten... | 3 | null | Implement the Python class `data_processing` described below.
Class description:
Tilt-illusion from Contextual modeling paper (fig3a).
Method signatures and docstrings:
- def __init__(self): Init global variables for contextual circuit bp.
- def flatten_and_pad_dict(self, d, flatten=False, constant=0.0): Flatten each... | Implement the Python class `data_processing` described below.
Class description:
Tilt-illusion from Contextual modeling paper (fig3a).
Method signatures and docstrings:
- def __init__(self): Init global variables for contextual circuit bp.
- def flatten_and_pad_dict(self, d, flatten=False, constant=0.0): Flatten each... | a277bc3146beaa4e3edd2134fc9fb8d3388a6013 | <|skeleton|>
class data_processing:
"""Tilt-illusion from Contextual modeling paper (fig3a)."""
def __init__(self):
"""Init global variables for contextual circuit bp."""
<|body_0|>
def flatten_and_pad_dict(self, d, flatten=False, constant=0.0):
"""Flatten each dict entry then pad ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class data_processing:
"""Tilt-illusion from Contextual modeling paper (fig3a)."""
def __init__(self):
"""Init global variables for contextual circuit bp."""
self.name = 'contextual_model_multi_stimuli'
self.figures = ['f3a.npz', 'f5.npz']
self.target_data = 'label_dict'
... | the_stack_v2_python_sparse | dataset_processing/contextual_model_multi_stimuli.py | dmely/contextual_circuit_bp | train | 0 |
cbb24778f2380ac7ebddb317b14761a8b48f67e1 | [
"super(GSGNN, self).__init__()\nself.n_in = n_in\nself.layers = nn.ModuleList()\nself.layers.append(nn.Linear(n_in, n_h))\nself.layers.append(nn.Dropout(dropout))\nself.layers.append(nn.ReLU())\nfor _ in range(n_layers):\n self.layers.append(nn.Linear(n_h, n_h))\n self.layers.append(nn.Dropout(dropout))\n ... | <|body_start_0|>
super(GSGNN, self).__init__()
self.n_in = n_in
self.layers = nn.ModuleList()
self.layers.append(nn.Linear(n_in, n_h))
self.layers.append(nn.Dropout(dropout))
self.layers.append(nn.ReLU())
for _ in range(n_layers):
self.layers.append(nn... | GSGNN | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GSGNN:
def __init__(self, n_in, n_h=100, n_layers=1, dropout=0.0):
"""FIXME! briefly describe function :param n_in: :param n_h: :param n_layers: :param dropout: :returns: :rtype:"""
<|body_0|>
def forward(self, x):
"""FIXME! briefly describe function :param x: :retur... | stack_v2_sparse_classes_36k_train_025060 | 1,740 | permissive | [
{
"docstring": "FIXME! briefly describe function :param n_in: :param n_h: :param n_layers: :param dropout: :returns: :rtype:",
"name": "__init__",
"signature": "def __init__(self, n_in, n_h=100, n_layers=1, dropout=0.0)"
},
{
"docstring": "FIXME! briefly describe function :param x: :returns: :rt... | 2 | stack_v2_sparse_classes_30k_train_001688 | Implement the Python class `GSGNN` described below.
Class description:
Implement the GSGNN class.
Method signatures and docstrings:
- def __init__(self, n_in, n_h=100, n_layers=1, dropout=0.0): FIXME! briefly describe function :param n_in: :param n_h: :param n_layers: :param dropout: :returns: :rtype:
- def forward(s... | Implement the Python class `GSGNN` described below.
Class description:
Implement the GSGNN class.
Method signatures and docstrings:
- def __init__(self, n_in, n_h=100, n_layers=1, dropout=0.0): FIXME! briefly describe function :param n_in: :param n_h: :param n_layers: :param dropout: :returns: :rtype:
- def forward(s... | fc3fd121fbffb630e3652bacdc74d1b1bddab50e | <|skeleton|>
class GSGNN:
def __init__(self, n_in, n_h=100, n_layers=1, dropout=0.0):
"""FIXME! briefly describe function :param n_in: :param n_h: :param n_layers: :param dropout: :returns: :rtype:"""
<|body_0|>
def forward(self, x):
"""FIXME! briefly describe function :param x: :retur... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GSGNN:
def __init__(self, n_in, n_h=100, n_layers=1, dropout=0.0):
"""FIXME! briefly describe function :param n_in: :param n_h: :param n_layers: :param dropout: :returns: :rtype:"""
super(GSGNN, self).__init__()
self.n_in = n_in
self.layers = nn.ModuleList()
self.layers... | the_stack_v2_python_sparse | src/classicalgsg/nn_models/models.py | kyrarivest/ACRES_REU_Project_2021 | train | 0 | |
544d20a2eaab38d949ad8814f04420c79b50925b | [
"sql = \" select a.id, pbf.full_name as authorize_name, pbf.take_photo, a.mobile_number, a.relation_student from info_authorize a join info_people_basic_facts pbf on a.basic_id = pbf.id join info_student s on a.student_id = s.id join info_guardian g on g.student_id = s.id where g.login_user_id = :login_user_id ... | <|body_start_0|>
sql = " select a.id, pbf.full_name as authorize_name, pbf.take_photo, a.mobile_number, a.relation_student from info_authorize a join info_people_basic_facts pbf on a.basic_id = pbf.id join info_student s on a.student_id = s.id join info_guardian g on g.student_id = s.id where g.login_user_id... | InfoAuthorize | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InfoAuthorize:
def query_auth_info(self, login_user_id):
"""获取授权人列表 :param login_user_id :return:"""
<|body_0|>
def query_sa_auth_tooltips(self, login_user_id, create_dt):
"""获取有无学生变更授权人 :param login_user_id :param create_dt :return:"""
<|body_1|>
def de... | stack_v2_sparse_classes_36k_train_025061 | 3,640 | no_license | [
{
"docstring": "获取授权人列表 :param login_user_id :return:",
"name": "query_auth_info",
"signature": "def query_auth_info(self, login_user_id)"
},
{
"docstring": "获取有无学生变更授权人 :param login_user_id :param create_dt :return:",
"name": "query_sa_auth_tooltips",
"signature": "def query_sa_auth_too... | 4 | stack_v2_sparse_classes_30k_train_008861 | Implement the Python class `InfoAuthorize` described below.
Class description:
Implement the InfoAuthorize class.
Method signatures and docstrings:
- def query_auth_info(self, login_user_id): 获取授权人列表 :param login_user_id :return:
- def query_sa_auth_tooltips(self, login_user_id, create_dt): 获取有无学生变更授权人 :param login_u... | Implement the Python class `InfoAuthorize` described below.
Class description:
Implement the InfoAuthorize class.
Method signatures and docstrings:
- def query_auth_info(self, login_user_id): 获取授权人列表 :param login_user_id :return:
- def query_sa_auth_tooltips(self, login_user_id, create_dt): 获取有无学生变更授权人 :param login_u... | a7cf5a0b6daa372ed860dc43d92c55fcde764eb9 | <|skeleton|>
class InfoAuthorize:
def query_auth_info(self, login_user_id):
"""获取授权人列表 :param login_user_id :return:"""
<|body_0|>
def query_sa_auth_tooltips(self, login_user_id, create_dt):
"""获取有无学生变更授权人 :param login_user_id :param create_dt :return:"""
<|body_1|>
def de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InfoAuthorize:
def query_auth_info(self, login_user_id):
"""获取授权人列表 :param login_user_id :return:"""
sql = " select a.id, pbf.full_name as authorize_name, pbf.take_photo, a.mobile_number, a.relation_student from info_authorize a join info_people_basic_facts pbf on a.basic_id = pbf.id join in... | the_stack_v2_python_sparse | python_project/smart_schoolBus_project/app/schoolbus_situation/models/info_authorize_model.py | malqch/aibus | train | 0 | |
9ad523a355f031dd2ff4c99b8e61cc6a9cdd1ec3 | [
"super(Router, self).__init__()\nself.key_function = key_function\nself.routing_table = routing_table",
"k = self.key_function(msg)\nkey = k[0] if isinstance(k, (tuple, list)) else k\nreturn self.routing_table[key]",
"k = self.key_function(msg)\nif isinstance(k, (tuple, list)):\n key, args, kwargs = {1: tupl... | <|body_start_0|>
super(Router, self).__init__()
self.key_function = key_function
self.routing_table = routing_table
<|end_body_0|>
<|body_start_1|>
k = self.key_function(msg)
key = k[0] if isinstance(k, (tuple, list)) else k
return self.routing_table[key]
<|end_body_1|>
... | Map a message to a handler function, using a **key function** and a **routing table** (dictionary). A *key function* digests a message down to a value. This value is treated as a key to the *routing table* to look up a corresponding handler function. | Router | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Router:
"""Map a message to a handler function, using a **key function** and a **routing table** (dictionary). A *key function* digests a message down to a value. This value is treated as a key to the *routing table* to look up a corresponding handler function."""
def __init__(self, key_func... | stack_v2_sparse_classes_36k_train_025062 | 40,889 | permissive | [
{
"docstring": ":param key_function: A function that takes one argument (the message) and returns one of the following: - a key to the routing table - a 1-tuple (key,) - a 2-tuple (key, (positional, arguments, ...)) - a 3-tuple (key, (positional, arguments, ...), {keyword: arguments, ...}) Extra arguments, if r... | 3 | stack_v2_sparse_classes_30k_train_004357 | Implement the Python class `Router` described below.
Class description:
Map a message to a handler function, using a **key function** and a **routing table** (dictionary). A *key function* digests a message down to a value. This value is treated as a key to the *routing table* to look up a corresponding handler functi... | Implement the Python class `Router` described below.
Class description:
Map a message to a handler function, using a **key function** and a **routing table** (dictionary). A *key function* digests a message down to a value. This value is treated as a key to the *routing table* to look up a corresponding handler functi... | 979ec1c7d50786939eb65ff779e3e03be950d595 | <|skeleton|>
class Router:
"""Map a message to a handler function, using a **key function** and a **routing table** (dictionary). A *key function* digests a message down to a value. This value is treated as a key to the *routing table* to look up a corresponding handler function."""
def __init__(self, key_func... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Router:
"""Map a message to a handler function, using a **key function** and a **routing table** (dictionary). A *key function* digests a message down to a value. This value is treated as a key to the *routing table* to look up a corresponding handler function."""
def __init__(self, key_function, routing... | the_stack_v2_python_sparse | amanobot/helper.py | AmanoTeam/amanobot | train | 25 |
727a4cbb523001d73725c4e26f9462fd039c3b14 | [
"if not route:\n route = '/'\nelse:\n route = route.strip()\nif methods and (not isinstance(methods, (list, tuple, set, frozenset))):\n raise TypeError('methods should be a list')\nself._route = route\nself._methods = methods or ['GET']",
"if not inspect.isroutine(decorated_method):\n raise TypeError(... | <|body_start_0|>
if not route:
route = '/'
else:
route = route.strip()
if methods and (not isinstance(methods, (list, tuple, set, frozenset))):
raise TypeError('methods should be a list')
self._route = route
self._methods = methods or ['GET']
<... | Decorator indicating which route a method handles | Http | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Http:
"""Decorator indicating which route a method handles"""
def __init__(self, route, methods=None):
""":param route: Path handled by the method (beginning with a '/') :param methods: List of HTTP methods allowed (GET, POST, ...)"""
<|body_0|>
def __call__(self, decora... | stack_v2_sparse_classes_36k_train_025063 | 13,381 | permissive | [
{
"docstring": ":param route: Path handled by the method (beginning with a '/') :param methods: List of HTTP methods allowed (GET, POST, ...)",
"name": "__init__",
"signature": "def __init__(self, route, methods=None)"
},
{
"docstring": "Injects the HTTP_ROUTE_ATTRIBUTE to the decorated method t... | 2 | null | Implement the Python class `Http` described below.
Class description:
Decorator indicating which route a method handles
Method signatures and docstrings:
- def __init__(self, route, methods=None): :param route: Path handled by the method (beginning with a '/') :param methods: List of HTTP methods allowed (GET, POST, ... | Implement the Python class `Http` described below.
Class description:
Decorator indicating which route a method handles
Method signatures and docstrings:
- def __init__(self, route, methods=None): :param route: Path handled by the method (beginning with a '/') :param methods: List of HTTP methods allowed (GET, POST, ... | 1d0add361ca219da8fdf72bb9ba8cb0ade01ad2f | <|skeleton|>
class Http:
"""Decorator indicating which route a method handles"""
def __init__(self, route, methods=None):
""":param route: Path handled by the method (beginning with a '/') :param methods: List of HTTP methods allowed (GET, POST, ...)"""
<|body_0|>
def __call__(self, decora... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Http:
"""Decorator indicating which route a method handles"""
def __init__(self, route, methods=None):
""":param route: Path handled by the method (beginning with a '/') :param methods: List of HTTP methods allowed (GET, POST, ...)"""
if not route:
route = '/'
else:
... | the_stack_v2_python_sparse | pelix/http/routing.py | tcalmant/ipopo | train | 67 |
76cb1331f21c9546f2f0f5fa53341440a871eab7 | [
"super().__init__()\nself.gru = rec_gru\nself.node = rec_node\nself.out = rec_output\nself.latent_dim = latent_dim",
"h = torch.zeros(x.shape[0], self.latent_dim * 2).to(x.device)\ntps = torch.cat(((tps[0] - 0.01).unsqueeze(0), tps), 0)\nh_arr, r_fill_mask = (None, None)\nif mask is not None:\n h_arr = torch.z... | <|body_start_0|>
super().__init__()
self.gru = rec_gru
self.node = rec_node
self.out = rec_output
self.latent_dim = latent_dim
<|end_body_0|>
<|body_start_1|>
h = torch.zeros(x.shape[0], self.latent_dim * 2).to(x.device)
tps = torch.cat(((tps[0] - 0.01).unsqueeze... | GRU with hidden dynamics represented by Neural ODE. Implements the GRU-ODE model in: https://arxiv.org/abs/1907.03907. Observations are encoded by a GRU. Between observations, the hidden state is evolved using a Neural ODE. Attributes: gru (nn.Module): GRU unit used to encode input data. node (nn.Module): Neural ODE us... | EncoderGRUODE | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncoderGRUODE:
"""GRU with hidden dynamics represented by Neural ODE. Implements the GRU-ODE model in: https://arxiv.org/abs/1907.03907. Observations are encoded by a GRU. Between observations, the hidden state is evolved using a Neural ODE. Attributes: gru (nn.Module): GRU unit used to encode in... | stack_v2_sparse_classes_36k_train_025064 | 11,020 | no_license | [
{
"docstring": "Initialize GRU-ODE model. This module is intended for use as the encoder of a Latent ODE. Args: latent_dim (int): Dimension of latent state. rec_gru (nn.Module): GRU used to encoder input data. rec_node (nn.Module): NODE used to evolve state between GRU units. rec_output (nn.Module): Final linea... | 4 | stack_v2_sparse_classes_30k_train_004854 | Implement the Python class `EncoderGRUODE` described below.
Class description:
GRU with hidden dynamics represented by Neural ODE. Implements the GRU-ODE model in: https://arxiv.org/abs/1907.03907. Observations are encoded by a GRU. Between observations, the hidden state is evolved using a Neural ODE. Attributes: gru ... | Implement the Python class `EncoderGRUODE` described below.
Class description:
GRU with hidden dynamics represented by Neural ODE. Implements the GRU-ODE model in: https://arxiv.org/abs/1907.03907. Observations are encoded by a GRU. Between observations, the hidden state is evolved using a Neural ODE. Attributes: gru ... | 3a84121a33e7fa7a97f81a33c09a2369e613b700 | <|skeleton|>
class EncoderGRUODE:
"""GRU with hidden dynamics represented by Neural ODE. Implements the GRU-ODE model in: https://arxiv.org/abs/1907.03907. Observations are encoded by a GRU. Between observations, the hidden state is evolved using a Neural ODE. Attributes: gru (nn.Module): GRU unit used to encode in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncoderGRUODE:
"""GRU with hidden dynamics represented by Neural ODE. Implements the GRU-ODE model in: https://arxiv.org/abs/1907.03907. Observations are encoded by a GRU. Between observations, the hidden state is evolved using a Neural ODE. Attributes: gru (nn.Module): GRU unit used to encode input data. nod... | the_stack_v2_python_sparse | latode_model.py | IanShi1996/LatentSegmentedODE | train | 2 |
02c5d18dede9dd26448811ea2bffc91ad73f2965 | [
"methods = self.mapped('force_tax_rounding_method')\nif len(methods) > 1:\n raise ValidationError(_('> 1 force rounding method!'))\nif len(methods) == 1:\n self = self.with_context(force_tax_rounding_method=methods[0])\nreturn super(SaleOrder, self)._amount_all(field_name, arg)",
"val = 0.0\nline_obj = self... | <|body_start_0|>
methods = self.mapped('force_tax_rounding_method')
if len(methods) > 1:
raise ValidationError(_('> 1 force rounding method!'))
if len(methods) == 1:
self = self.with_context(force_tax_rounding_method=methods[0])
return super(SaleOrder, self)._amou... | SaleOrder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SaleOrder:
def _amount_all(self, field_name, arg):
"""Pass context when force_tax_rounding_method is set"""
<|body_0|>
def _amount_line_tax(self, cr, uid, line, context=None):
"""Overwrite, to add context to compute_all()"""
<|body_1|>
<|end_skeleton|>
<|bo... | stack_v2_sparse_classes_36k_train_025065 | 1,437 | no_license | [
{
"docstring": "Pass context when force_tax_rounding_method is set",
"name": "_amount_all",
"signature": "def _amount_all(self, field_name, arg)"
},
{
"docstring": "Overwrite, to add context to compute_all()",
"name": "_amount_line_tax",
"signature": "def _amount_line_tax(self, cr, uid, ... | 2 | stack_v2_sparse_classes_30k_train_020958 | Implement the Python class `SaleOrder` described below.
Class description:
Implement the SaleOrder class.
Method signatures and docstrings:
- def _amount_all(self, field_name, arg): Pass context when force_tax_rounding_method is set
- def _amount_line_tax(self, cr, uid, line, context=None): Overwrite, to add context ... | Implement the Python class `SaleOrder` described below.
Class description:
Implement the SaleOrder class.
Method signatures and docstrings:
- def _amount_all(self, field_name, arg): Pass context when force_tax_rounding_method is set
- def _amount_line_tax(self, cr, uid, line, context=None): Overwrite, to add context ... | e8c21082c187f4639373b29a7a0905d069d770f2 | <|skeleton|>
class SaleOrder:
def _amount_all(self, field_name, arg):
"""Pass context when force_tax_rounding_method is set"""
<|body_0|>
def _amount_line_tax(self, cr, uid, line, context=None):
"""Overwrite, to add context to compute_all()"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SaleOrder:
def _amount_all(self, field_name, arg):
"""Pass context when force_tax_rounding_method is set"""
methods = self.mapped('force_tax_rounding_method')
if len(methods) > 1:
raise ValidationError(_('> 1 force rounding method!'))
if len(methods) == 1:
... | the_stack_v2_python_sparse | account_tax_force_rounding_method/models/sale.py | pabi2/pb2_addons | train | 6 | |
ed32bf2653aa8715fd18dcee636f620cc5f917b4 | [
"QtGui.QWidget.__init__(self, parent)\nself.rowCount = 1\nself.colCount = 1\ngridLayout = QtGui.QGridLayout(self)\ngridLayout.setSpacing(0)\nself.setLayout(gridLayout)\nlabel = QVirtualCellLabel('')\nself.layout().addWidget(label, 0, 0, 1, 1, QtCore.Qt.AlignCenter)\nself.cells = [[label]]\nself.numCell = 1",
"whi... | <|body_start_0|>
QtGui.QWidget.__init__(self, parent)
self.rowCount = 1
self.colCount = 1
gridLayout = QtGui.QGridLayout(self)
gridLayout.setSpacing(0)
self.setLayout(gridLayout)
label = QVirtualCellLabel('')
self.layout().addWidget(label, 0, 0, 1, 1, QtCo... | QVirtualCellConfiguration is a widget provide a virtual layout of the spreadsheet cell. Given a number of cells want to layout, it will let users interactively select where to put a cell in a table layout to construct a virtual cell out of that. | QVirtualCellConfiguration | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QVirtualCellConfiguration:
"""QVirtualCellConfiguration is a widget provide a virtual layout of the spreadsheet cell. Given a number of cells want to layout, it will let users interactively select where to put a cell in a table layout to construct a virtual cell out of that."""
def __init__(... | stack_v2_sparse_classes_36k_train_025066 | 25,543 | permissive | [
{
"docstring": "QVirtualCellConfiguration(parent: QWidget) -> QVirtualCellConfiguration Initialize the widget",
"name": "__init__",
"signature": "def __init__(self, parent=None)"
},
{
"docstring": "clear() -> None Remove and delete all widgets in self.gridLayout",
"name": "clear",
"signa... | 6 | null | Implement the Python class `QVirtualCellConfiguration` described below.
Class description:
QVirtualCellConfiguration is a widget provide a virtual layout of the spreadsheet cell. Given a number of cells want to layout, it will let users interactively select where to put a cell in a table layout to construct a virtual ... | Implement the Python class `QVirtualCellConfiguration` described below.
Class description:
QVirtualCellConfiguration is a widget provide a virtual layout of the spreadsheet cell. Given a number of cells want to layout, it will let users interactively select where to put a cell in a table layout to construct a virtual ... | 23ef56ec24b85c82416e1437a08381635328abe5 | <|skeleton|>
class QVirtualCellConfiguration:
"""QVirtualCellConfiguration is a widget provide a virtual layout of the spreadsheet cell. Given a number of cells want to layout, it will let users interactively select where to put a cell in a table layout to construct a virtual cell out of that."""
def __init__(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QVirtualCellConfiguration:
"""QVirtualCellConfiguration is a widget provide a virtual layout of the spreadsheet cell. Given a number of cells want to layout, it will let users interactively select where to put a cell in a table layout to construct a virtual cell out of that."""
def __init__(self, parent=... | the_stack_v2_python_sparse | vistrails_current/vistrails/gui/paramexplore/virtual_cell.py | lumig242/VisTrailsRecommendation | train | 3 |
581b4545b1ecc60b756adea3486c6b03c95fb0d3 | [
"super().__init__()\nself.gats = nn.ModuleList([GATConv(in_dim, out_dim, num_heads, dropout, dropout, activation=F.elu) for _ in range(num_metapaths)])\nself.semantic_attention = SemanticAttention(in_dim=num_heads * out_dim)",
"zp = [gat(g, h).flatten(start_dim=1) for gat, g in zip(self.gats, gs)]\nzp = torch.sta... | <|body_start_0|>
super().__init__()
self.gats = nn.ModuleList([GATConv(in_dim, out_dim, num_heads, dropout, dropout, activation=F.elu) for _ in range(num_metapaths)])
self.semantic_attention = SemanticAttention(in_dim=num_heads * out_dim)
<|end_body_0|>
<|body_start_1|>
zp = [gat(g, h).... | HANLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HANLayer:
def __init__(self, num_metapaths, in_dim, out_dim, num_heads, dropout):
"""HAN层 :param num_metapaths: int 元路径个数 :param in_dim: int 输入特征维数 :param out_dim: int 输出特征维数 :param num_heads: int 注意力头数K :param dropout: float Dropout概率"""
<|body_0|>
def forward(self, gs, h):... | stack_v2_sparse_classes_36k_train_025067 | 3,582 | no_license | [
{
"docstring": "HAN层 :param num_metapaths: int 元路径个数 :param in_dim: int 输入特征维数 :param out_dim: int 输出特征维数 :param num_heads: int 注意力头数K :param dropout: float Dropout概率",
"name": "__init__",
"signature": "def __init__(self, num_metapaths, in_dim, out_dim, num_heads, dropout)"
},
{
"docstring": ":p... | 2 | null | Implement the Python class `HANLayer` described below.
Class description:
Implement the HANLayer class.
Method signatures and docstrings:
- def __init__(self, num_metapaths, in_dim, out_dim, num_heads, dropout): HAN层 :param num_metapaths: int 元路径个数 :param in_dim: int 输入特征维数 :param out_dim: int 输出特征维数 :param num_heads... | Implement the Python class `HANLayer` described below.
Class description:
Implement the HANLayer class.
Method signatures and docstrings:
- def __init__(self, num_metapaths, in_dim, out_dim, num_heads, dropout): HAN层 :param num_metapaths: int 元路径个数 :param in_dim: int 输入特征维数 :param out_dim: int 输出特征维数 :param num_heads... | b40071dc9f9fb20f081f4ed4944a7b65de919c18 | <|skeleton|>
class HANLayer:
def __init__(self, num_metapaths, in_dim, out_dim, num_heads, dropout):
"""HAN层 :param num_metapaths: int 元路径个数 :param in_dim: int 输入特征维数 :param out_dim: int 输出特征维数 :param num_heads: int 注意力头数K :param dropout: float Dropout概率"""
<|body_0|>
def forward(self, gs, h):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HANLayer:
def __init__(self, num_metapaths, in_dim, out_dim, num_heads, dropout):
"""HAN层 :param num_metapaths: int 元路径个数 :param in_dim: int 输入特征维数 :param out_dim: int 输出特征维数 :param num_heads: int 注意力头数K :param dropout: float Dropout概率"""
super().__init__()
self.gats = nn.ModuleList([G... | the_stack_v2_python_sparse | gnn/han/model.py | deepdumbo/pytorch-tutorial-1 | train | 0 | |
6b73ac4b3bc78b60632af28d95923be1a41b7ded | [
"classstudy_obj = models.ClassStudyRecord.objects.all()\nprint(classstudy_obj)\nreturn render(request, 'jilu/classstudyrecord.html', {'classstudy_obj': classstudy_obj})",
"action = request.POST.get('action')\nselected_id = request.POST.get('selected_id')\nif hasattr(self, action):\n print(1)\n getattr(self,... | <|body_start_0|>
classstudy_obj = models.ClassStudyRecord.objects.all()
print(classstudy_obj)
return render(request, 'jilu/classstudyrecord.html', {'classstudy_obj': classstudy_obj})
<|end_body_0|>
<|body_start_1|>
action = request.POST.get('action')
selected_id = request.POST.g... | ClassStudyRecord | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassStudyRecord:
def get(self, request):
"""获取班级课程记录表的信息并将其呈现在网页上 :param request: :return:"""
<|body_0|>
def post(self, request):
"""从网页上获取用户进行的操作action :param request: :return:"""
<|body_1|>
def batch_delete(self, selected_id):
"""用户在班级课程页面上进行的... | stack_v2_sparse_classes_36k_train_025068 | 19,697 | no_license | [
{
"docstring": "获取班级课程记录表的信息并将其呈现在网页上 :param request: :return:",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "从网页上获取用户进行的操作action :param request: :return:",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "用户在班级课程页面上进行的操作的后台执行流程... | 3 | stack_v2_sparse_classes_30k_train_007805 | Implement the Python class `ClassStudyRecord` described below.
Class description:
Implement the ClassStudyRecord class.
Method signatures and docstrings:
- def get(self, request): 获取班级课程记录表的信息并将其呈现在网页上 :param request: :return:
- def post(self, request): 从网页上获取用户进行的操作action :param request: :return:
- def batch_delete(... | Implement the Python class `ClassStudyRecord` described below.
Class description:
Implement the ClassStudyRecord class.
Method signatures and docstrings:
- def get(self, request): 获取班级课程记录表的信息并将其呈现在网页上 :param request: :return:
- def post(self, request): 从网页上获取用户进行的操作action :param request: :return:
- def batch_delete(... | 8751baf67a744867a93ac02dc5ef96b70431e35c | <|skeleton|>
class ClassStudyRecord:
def get(self, request):
"""获取班级课程记录表的信息并将其呈现在网页上 :param request: :return:"""
<|body_0|>
def post(self, request):
"""从网页上获取用户进行的操作action :param request: :return:"""
<|body_1|>
def batch_delete(self, selected_id):
"""用户在班级课程页面上进行的... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassStudyRecord:
def get(self, request):
"""获取班级课程记录表的信息并将其呈现在网页上 :param request: :return:"""
classstudy_obj = models.ClassStudyRecord.objects.all()
print(classstudy_obj)
return render(request, 'jilu/classstudyrecord.html', {'classstudy_obj': classstudy_obj})
def post(sel... | the_stack_v2_python_sparse | crmtest/app01/views.py | jingdaonb/personal | train | 0 | |
7eefa4659b288fb2898642600fc25b5a02727a94 | [
"super(CachingDescriptorSystem, self).__init__(self.load_item, resources_fs, error_tracker, render_template)\nself.modulestore = modulestore\nself.module_data = module_data\nself.default_class = default_class\nself.course_id = None\nself.cached_metadata = cached_metadata",
"location = Location(location)\njson_dat... | <|body_start_0|>
super(CachingDescriptorSystem, self).__init__(self.load_item, resources_fs, error_tracker, render_template)
self.modulestore = modulestore
self.module_data = module_data
self.default_class = default_class
self.course_id = None
self.cached_metadata = cache... | A system that has a cache of module json that it will use to load modules from, with a backup of calling to the underlying modulestore for more data TODO (cdodge) when the 'split module store' work has been completed we can remove all references to metadata_inheritance_tree | CachingDescriptorSystem | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CachingDescriptorSystem:
"""A system that has a cache of module json that it will use to load modules from, with a backup of calling to the underlying modulestore for more data TODO (cdodge) when the 'split module store' work has been completed we can remove all references to metadata_inheritance... | stack_v2_sparse_classes_36k_train_025069 | 32,025 | no_license | [
{
"docstring": "modulestore: the module store that can be used to retrieve additional modules module_data: a dict mapping Location -> json that was cached from the underlying modulestore default_class: The default_class to use when loading an XModuleDescriptor from the module_data resources_fs: a filesystem, as... | 2 | null | Implement the Python class `CachingDescriptorSystem` described below.
Class description:
A system that has a cache of module json that it will use to load modules from, with a backup of calling to the underlying modulestore for more data TODO (cdodge) when the 'split module store' work has been completed we can remove... | Implement the Python class `CachingDescriptorSystem` described below.
Class description:
A system that has a cache of module json that it will use to load modules from, with a backup of calling to the underlying modulestore for more data TODO (cdodge) when the 'split module store' work has been completed we can remove... | 5fa3a818c3d41bd9c3eb25122e1d376c8910269c | <|skeleton|>
class CachingDescriptorSystem:
"""A system that has a cache of module json that it will use to load modules from, with a backup of calling to the underlying modulestore for more data TODO (cdodge) when the 'split module store' work has been completed we can remove all references to metadata_inheritance... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CachingDescriptorSystem:
"""A system that has a cache of module json that it will use to load modules from, with a backup of calling to the underlying modulestore for more data TODO (cdodge) when the 'split module store' work has been completed we can remove all references to metadata_inheritance_tree"""
... | the_stack_v2_python_sparse | ExtractFeatures/Data/pratik98/mongo.py | vivekaxl/LexisNexis | train | 9 |
976a57487acd724d1d05916f81826fe8d3f65d92 | [
"if map_kwargs is None:\n map_kwargs = {}\nsuper(MappedDataset, self).__init__(*args, **kwargs)\nself.map_function = map_function\nself.map_kwargs = map_kwargs\nself.transpose = transpose\nself.force2d = force2d",
"if self.transpose or self.force2d:\n array_buf = numpy.zeros(shape=self.shape, dtype=self.dty... | <|body_start_0|>
if map_kwargs is None:
map_kwargs = {}
super(MappedDataset, self).__init__(*args, **kwargs)
self.map_function = map_function
self.map_kwargs = map_kwargs
self.transpose = transpose
self.force2d = force2d
<|end_body_0|>
<|body_start_1|>
... | h5py.Dataset that applies a function to the results of __getitem__ before returning the data. Intended to dynamically generate certain datasets that are simple derivatives of others. | MappedDataset | [
"BSD-3-Clause-LBNL",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MappedDataset:
"""h5py.Dataset that applies a function to the results of __getitem__ before returning the data. Intended to dynamically generate certain datasets that are simple derivatives of others."""
def __init__(self, map_function=None, map_kwargs=None, transpose=False, force2d=False, *... | stack_v2_sparse_classes_36k_train_025070 | 10,579 | permissive | [
{
"docstring": "Configure a MappedDatset Attach a map function to a h5py.Dataset (or derivative) and store the arguments to be fed into that map function whenever this object gets sliced. Args: map_function (function): function to be called on the value returned when parent class is sliced map_kwargs (dict): kw... | 2 | stack_v2_sparse_classes_30k_train_001494 | Implement the Python class `MappedDataset` described below.
Class description:
h5py.Dataset that applies a function to the results of __getitem__ before returning the data. Intended to dynamically generate certain datasets that are simple derivatives of others.
Method signatures and docstrings:
- def __init__(self, m... | Implement the Python class `MappedDataset` described below.
Class description:
h5py.Dataset that applies a function to the results of __getitem__ before returning the data. Intended to dynamically generate certain datasets that are simple derivatives of others.
Method signatures and docstrings:
- def __init__(self, m... | 9e2f2f08742281c4550bf03d70fc96d8f02ea92b | <|skeleton|>
class MappedDataset:
"""h5py.Dataset that applies a function to the results of __getitem__ before returning the data. Intended to dynamically generate certain datasets that are simple derivatives of others."""
def __init__(self, map_function=None, map_kwargs=None, transpose=False, force2d=False, *... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MappedDataset:
"""h5py.Dataset that applies a function to the results of __getitem__ before returning the data. Intended to dynamically generate certain datasets that are simple derivatives of others."""
def __init__(self, map_function=None, map_kwargs=None, transpose=False, force2d=False, *args, **kwarg... | the_stack_v2_python_sparse | tokio/connectors/_hdf5.py | NERSC/pytokio | train | 25 |
a79f2b320a2c60c1959ab1d201676f48dcaa96a8 | [
"input_shape = (64, 64, 3)\nlatent_size = 5\ninputs = Input(latent_size)\ngenerator = WGenerator(image_shape=input_shape)(inputs)\ngenerator = Model(inputs=inputs, outputs=generator)\nprior = tf.random.normal([1, latent_size])\npred = generator.predict(prior)\nassert np.all(pred <= 1)\nassert np.all(0 <= pred)",
... | <|body_start_0|>
input_shape = (64, 64, 3)
latent_size = 5
inputs = Input(latent_size)
generator = WGenerator(image_shape=input_shape)(inputs)
generator = Model(inputs=inputs, outputs=generator)
prior = tf.random.normal([1, latent_size])
pred = generator.predict(p... | TestWGAN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestWGAN:
def test_generator_output(self):
"""Generator should yield image with range [0, 1]"""
<|body_0|>
def test_discriminator_weight_updates(self):
"""Discriminator weights should be restrcited into constraint range"""
<|body_1|>
<|end_skeleton|>
<|body... | stack_v2_sparse_classes_36k_train_025071 | 3,384 | no_license | [
{
"docstring": "Generator should yield image with range [0, 1]",
"name": "test_generator_output",
"signature": "def test_generator_output(self)"
},
{
"docstring": "Discriminator weights should be restrcited into constraint range",
"name": "test_discriminator_weight_updates",
"signature":... | 2 | stack_v2_sparse_classes_30k_train_007337 | Implement the Python class `TestWGAN` described below.
Class description:
Implement the TestWGAN class.
Method signatures and docstrings:
- def test_generator_output(self): Generator should yield image with range [0, 1]
- def test_discriminator_weight_updates(self): Discriminator weights should be restrcited into con... | Implement the Python class `TestWGAN` described below.
Class description:
Implement the TestWGAN class.
Method signatures and docstrings:
- def test_generator_output(self): Generator should yield image with range [0, 1]
- def test_discriminator_weight_updates(self): Discriminator weights should be restrcited into con... | 5da5317cedd380c244f20a96213e883d6ef29de2 | <|skeleton|>
class TestWGAN:
def test_generator_output(self):
"""Generator should yield image with range [0, 1]"""
<|body_0|>
def test_discriminator_weight_updates(self):
"""Discriminator weights should be restrcited into constraint range"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestWGAN:
def test_generator_output(self):
"""Generator should yield image with range [0, 1]"""
input_shape = (64, 64, 3)
latent_size = 5
inputs = Input(latent_size)
generator = WGenerator(image_shape=input_shape)(inputs)
generator = Model(inputs=inputs, outputs... | the_stack_v2_python_sparse | Models/tf/Keras/_unittests/test_WGAN.py | MingRuey/mlbox | train | 2 | |
8830b92cf1f4d2413a1d2bd00a326583606ab299 | [
"super().__init__(**kwargs)\nself.duration = self.template_args['duration']['default'] if not (isinstance(duration, int) and self.template_args['duration']['min'] > 0) else duration\nself.schema = 'https' if self.secure else 'http'\nself.headers = {'User-Agent': self.app_id, 'Content-Type': 'application/json'}\nsel... | <|body_start_0|>
super().__init__(**kwargs)
self.duration = self.template_args['duration']['default'] if not (isinstance(duration, int) and self.template_args['duration']['min'] > 0) else duration
self.schema = 'https' if self.secure else 'http'
self.headers = {'User-Agent': self.app_id,... | A wrapper for XBMC/KODI Notifications | NotifyXBMC | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NotifyXBMC:
"""A wrapper for XBMC/KODI Notifications"""
def __init__(self, include_image=True, duration=None, **kwargs):
"""Initialize XBMC/KODI Object"""
<|body_0|>
def _payload_60(self, title, body, notify_type, **kwargs):
"""Builds payload for KODI API v6.0 Re... | stack_v2_sparse_classes_36k_train_025072 | 12,301 | permissive | [
{
"docstring": "Initialize XBMC/KODI Object",
"name": "__init__",
"signature": "def __init__(self, include_image=True, duration=None, **kwargs)"
},
{
"docstring": "Builds payload for KODI API v6.0 Returns (headers, payload)",
"name": "_payload_60",
"signature": "def _payload_60(self, tit... | 6 | stack_v2_sparse_classes_30k_train_005606 | Implement the Python class `NotifyXBMC` described below.
Class description:
A wrapper for XBMC/KODI Notifications
Method signatures and docstrings:
- def __init__(self, include_image=True, duration=None, **kwargs): Initialize XBMC/KODI Object
- def _payload_60(self, title, body, notify_type, **kwargs): Builds payload... | Implement the Python class `NotifyXBMC` described below.
Class description:
A wrapper for XBMC/KODI Notifications
Method signatures and docstrings:
- def __init__(self, include_image=True, duration=None, **kwargs): Initialize XBMC/KODI Object
- def _payload_60(self, title, body, notify_type, **kwargs): Builds payload... | be3baed7e3d33bae973f1714df4ebbf65aa33f85 | <|skeleton|>
class NotifyXBMC:
"""A wrapper for XBMC/KODI Notifications"""
def __init__(self, include_image=True, duration=None, **kwargs):
"""Initialize XBMC/KODI Object"""
<|body_0|>
def _payload_60(self, title, body, notify_type, **kwargs):
"""Builds payload for KODI API v6.0 Re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NotifyXBMC:
"""A wrapper for XBMC/KODI Notifications"""
def __init__(self, include_image=True, duration=None, **kwargs):
"""Initialize XBMC/KODI Object"""
super().__init__(**kwargs)
self.duration = self.template_args['duration']['default'] if not (isinstance(duration, int) and sel... | the_stack_v2_python_sparse | apprise/plugins/NotifyXBMC.py | caronc/apprise | train | 8,426 |
ad00c214a13b2dd22b621a5aef6234637fc6ceaa | [
"self.alphabet_size = alphabet_size\nself.ngram = ngram\nself.dictionary = {}",
"if len(password) < self.ngram:\n return\nfor letter in password:\n if letter in ['\\t']:\n continue\n if letter in self.dictionary:\n self.dictionary[letter] += 1\n else:\n self.dictionary[letter] = 1... | <|body_start_0|>
self.alphabet_size = alphabet_size
self.ngram = ngram
self.dictionary = {}
<|end_body_0|>
<|body_start_1|>
if len(password) < self.ngram:
return
for letter in password:
if letter in ['\t']:
continue
if letter i... | Class that generates and alphabet of the most common letters seen Making this a class so I can re-use trainer_file_io to read the passwords one at a time, and pass them into this | AlphabetGenerator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlphabetGenerator:
"""Class that generates and alphabet of the most common letters seen Making this a class so I can re-use trainer_file_io to read the passwords one at a time, and pass them into this"""
def __init__(self, alphabet_size, ngram):
"""Initialize the alphabet generator V... | stack_v2_sparse_classes_36k_train_025073 | 2,752 | no_license | [
{
"docstring": "Initialize the alphabet generator Values: alphabet_size: The number of characters to save to the alphabet ngram: The ngram count for this grammar Used to identify the minimum size of passwords to train on",
"name": "__init__",
"signature": "def __init__(self, alphabet_size, ngram)"
},
... | 3 | stack_v2_sparse_classes_30k_train_003902 | Implement the Python class `AlphabetGenerator` described below.
Class description:
Class that generates and alphabet of the most common letters seen Making this a class so I can re-use trainer_file_io to read the passwords one at a time, and pass them into this
Method signatures and docstrings:
- def __init__(self, a... | Implement the Python class `AlphabetGenerator` described below.
Class description:
Class that generates and alphabet of the most common letters seen Making this a class so I can re-use trainer_file_io to read the passwords one at a time, and pass them into this
Method signatures and docstrings:
- def __init__(self, a... | 6fed1047838091edec7ce96679c3d0887073ed3b | <|skeleton|>
class AlphabetGenerator:
"""Class that generates and alphabet of the most common letters seen Making this a class so I can re-use trainer_file_io to read the passwords one at a time, and pass them into this"""
def __init__(self, alphabet_size, ngram):
"""Initialize the alphabet generator V... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AlphabetGenerator:
"""Class that generates and alphabet of the most common letters seen Making this a class so I can re-use trainer_file_io to read the passwords one at a time, and pass them into this"""
def __init__(self, alphabet_size, ngram):
"""Initialize the alphabet generator Values: alphab... | the_stack_v2_python_sparse | lib_trainer/omen/alphabet_generator.py | lakiw/pcfg_cracker | train | 286 |
1dde1989edbc3ec619c4edf24ea611f8632a3b63 | [
"super().__init__()\nself.encoder = Encoder(N, dm, h, hidden, input_vocab, max_seq_input, drop_rate)\nself.decoder = Decoder(N, dm, h, hidden, target_vocab, max_seq_input, drop_rate)\nself.linear = tf.keras.layers.Dense(units=target_vocab)",
"out1, _ = self.mha(x, x, x, mask)\nout1 = self.dropout1(out1, training=... | <|body_start_0|>
super().__init__()
self.encoder = Encoder(N, dm, h, hidden, input_vocab, max_seq_input, drop_rate)
self.decoder = Decoder(N, dm, h, hidden, target_vocab, max_seq_input, drop_rate)
self.linear = tf.keras.layers.Dense(units=target_vocab)
<|end_body_0|>
<|body_start_1|>
... | DecoderBlock class | Transformer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Transformer:
"""DecoderBlock class"""
def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, drop_rate=0.1):
"""Initializer. Args: dm: (int) the dimensionality of the model. h: (int) the number of heads. hidden: (int) the number of hidden units... | stack_v2_sparse_classes_36k_train_025074 | 1,800 | no_license | [
{
"docstring": "Initializer. Args: dm: (int) the dimensionality of the model. h: (int) the number of heads. hidden: (int) the number of hidden units in the fully connected layer. drop_rate: (float) the dropout rate.",
"name": "__init__",
"signature": "def __init__(self, N, dm, h, hidden, input_vocab, ta... | 2 | stack_v2_sparse_classes_30k_train_018092 | Implement the Python class `Transformer` described below.
Class description:
DecoderBlock class
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, drop_rate=0.1): Initializer. Args: dm: (int) the dimensionality of the model. h: (int) the ... | Implement the Python class `Transformer` described below.
Class description:
DecoderBlock class
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, drop_rate=0.1): Initializer. Args: dm: (int) the dimensionality of the model. h: (int) the ... | 75274394adb52d740f6cd4000cc00bbde44b9b72 | <|skeleton|>
class Transformer:
"""DecoderBlock class"""
def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, drop_rate=0.1):
"""Initializer. Args: dm: (int) the dimensionality of the model. h: (int) the number of heads. hidden: (int) the number of hidden units... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Transformer:
"""DecoderBlock class"""
def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, drop_rate=0.1):
"""Initializer. Args: dm: (int) the dimensionality of the model. h: (int) the number of heads. hidden: (int) the number of hidden units in the fully... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/11-transformer.py | jdarangop/holbertonschool-machine_learning | train | 2 |
87489201fd16eb33154ec958b656c835c9093e2e | [
"from sage.schemes.product_projective.space import is_ProductProjectiveSpaces\nif is_ProductProjectiveSpaces(self.codomain()):\n raise TypeError('this point must be a point on a subscheme of a product of projective spaces')\nreturn self.codomain().intersection_multiplicity(X, self)",
"from sage.schemes.product... | <|body_start_0|>
from sage.schemes.product_projective.space import is_ProductProjectiveSpaces
if is_ProductProjectiveSpaces(self.codomain()):
raise TypeError('this point must be a point on a subscheme of a product of projective spaces')
return self.codomain().intersection_multiplicit... | ProductProjectiveSpaces_point_field | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProductProjectiveSpaces_point_field:
def intersection_multiplicity(self, X):
"""Return the intersection multiplicity of the codomain of this point and subscheme ``X`` at this point. This uses the subscheme implementation of intersection_multiplicity. This point must be a point on a subsc... | stack_v2_sparse_classes_36k_train_025075 | 19,274 | no_license | [
{
"docstring": "Return the intersection multiplicity of the codomain of this point and subscheme ``X`` at this point. This uses the subscheme implementation of intersection_multiplicity. This point must be a point on a subscheme of a product of projective spaces. INPUT: - ``X`` -- a subscheme in the same ambien... | 2 | stack_v2_sparse_classes_30k_train_008798 | Implement the Python class `ProductProjectiveSpaces_point_field` described below.
Class description:
Implement the ProductProjectiveSpaces_point_field class.
Method signatures and docstrings:
- def intersection_multiplicity(self, X): Return the intersection multiplicity of the codomain of this point and subscheme ``X... | Implement the Python class `ProductProjectiveSpaces_point_field` described below.
Class description:
Implement the ProductProjectiveSpaces_point_field class.
Method signatures and docstrings:
- def intersection_multiplicity(self, X): Return the intersection multiplicity of the codomain of this point and subscheme ``X... | 0d9eacbf74e2acffefde93e39f8bcbec745cdaba | <|skeleton|>
class ProductProjectiveSpaces_point_field:
def intersection_multiplicity(self, X):
"""Return the intersection multiplicity of the codomain of this point and subscheme ``X`` at this point. This uses the subscheme implementation of intersection_multiplicity. This point must be a point on a subsc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProductProjectiveSpaces_point_field:
def intersection_multiplicity(self, X):
"""Return the intersection multiplicity of the codomain of this point and subscheme ``X`` at this point. This uses the subscheme implementation of intersection_multiplicity. This point must be a point on a subscheme of a prod... | the_stack_v2_python_sparse | sage/src/sage/schemes/product_projective/point.py | bopopescu/geosci | train | 0 | |
96724c43d250e93473a694c17134e1c7a1248d04 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn MicrosoftAuthenticatorAuthenticationMethodConfiguration()",
"from .authentication_method_configuration import AuthenticationMethodConfiguration\nfrom .microsoft_authenticator_authentication_method_target import MicrosoftAuthenticatorAu... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return MicrosoftAuthenticatorAuthenticationMethodConfiguration()
<|end_body_0|>
<|body_start_1|>
from .authentication_method_configuration import AuthenticationMethodConfiguration
from .microso... | MicrosoftAuthenticatorAuthenticationMethodConfiguration | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MicrosoftAuthenticatorAuthenticationMethodConfiguration:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> MicrosoftAuthenticatorAuthenticationMethodConfiguration:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node:... | stack_v2_sparse_classes_36k_train_025076 | 4,083 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: MicrosoftAuthenticatorAuthenticationMethodConfiguration",
"name": "create_from_discriminator_value",
"signat... | 3 | stack_v2_sparse_classes_30k_test_000314 | Implement the Python class `MicrosoftAuthenticatorAuthenticationMethodConfiguration` described below.
Class description:
Implement the MicrosoftAuthenticatorAuthenticationMethodConfiguration class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Microso... | Implement the Python class `MicrosoftAuthenticatorAuthenticationMethodConfiguration` described below.
Class description:
Implement the MicrosoftAuthenticatorAuthenticationMethodConfiguration class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Microso... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class MicrosoftAuthenticatorAuthenticationMethodConfiguration:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> MicrosoftAuthenticatorAuthenticationMethodConfiguration:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MicrosoftAuthenticatorAuthenticationMethodConfiguration:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> MicrosoftAuthenticatorAuthenticationMethodConfiguration:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse nod... | the_stack_v2_python_sparse | msgraph/generated/models/microsoft_authenticator_authentication_method_configuration.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
790e1bc996d03b7d6f4aeee8cb1f4bb932117324 | [
"self.amount_total = 0.0\nfor data in self.commission_line:\n self.amount_total += data.amount",
"account_jrnl_obj = self.env['account.journal'].search([('type', '=', 'purchase')], limit=1)\nfor data in self:\n inv_line_values = {'name': 'Commission For ' + data.number or '', 'analytic_account_id': data.ten... | <|body_start_0|>
self.amount_total = 0.0
for data in self.commission_line:
self.amount_total += data.amount
<|end_body_0|>
<|body_start_1|>
account_jrnl_obj = self.env['account.journal'].search([('type', '=', 'purchase')], limit=1)
for data in self:
inv_line_valu... | CommissionInvoice | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommissionInvoice:
def _amount_all(self):
"""Compute the total amounts of the SO."""
<|body_0|>
def create_invoice(self):
"""This method is used to create supplier invoice. ------------------------------------------------------------ @param self: The object pointer""... | stack_v2_sparse_classes_36k_train_025077 | 11,342 | no_license | [
{
"docstring": "Compute the total amounts of the SO.",
"name": "_amount_all",
"signature": "def _amount_all(self)"
},
{
"docstring": "This method is used to create supplier invoice. ------------------------------------------------------------ @param self: The object pointer",
"name": "create... | 5 | stack_v2_sparse_classes_30k_train_009711 | Implement the Python class `CommissionInvoice` described below.
Class description:
Implement the CommissionInvoice class.
Method signatures and docstrings:
- def _amount_all(self): Compute the total amounts of the SO.
- def create_invoice(self): This method is used to create supplier invoice. ------------------------... | Implement the Python class `CommissionInvoice` described below.
Class description:
Implement the CommissionInvoice class.
Method signatures and docstrings:
- def _amount_all(self): Compute the total amounts of the SO.
- def create_invoice(self): This method is used to create supplier invoice. ------------------------... | 163136f382faa8607db8fb6cda42a5ba07c4076b | <|skeleton|>
class CommissionInvoice:
def _amount_all(self):
"""Compute the total amounts of the SO."""
<|body_0|>
def create_invoice(self):
"""This method is used to create supplier invoice. ------------------------------------------------------------ @param self: The object pointer""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommissionInvoice:
def _amount_all(self):
"""Compute the total amounts of the SO."""
self.amount_total = 0.0
for data in self.commission_line:
self.amount_total += data.amount
def create_invoice(self):
"""This method is used to create supplier invoice. --------... | the_stack_v2_python_sparse | property_commission_ee/models/property_commission.py | maarejsys/Roya | train | 0 | |
41d0a4eeef47b8a2478b32980f723c1eab1456b0 | [
"request_dict = rest_utils.get_json_and_verify_params({'tenant_name': {'type': unicode}, 'group_name': {'type': unicode}, 'role': {'type': unicode}})\nrest_utils.validate_inputs(request_dict)\nrole_name = request_dict.get('role')\nif role_name:\n rest_utils.verify_role(role_name)\nelse:\n role_name = constant... | <|body_start_0|>
request_dict = rest_utils.get_json_and_verify_params({'tenant_name': {'type': unicode}, 'group_name': {'type': unicode}, 'role': {'type': unicode}})
rest_utils.validate_inputs(request_dict)
role_name = request_dict.get('role')
if role_name:
rest_utils.verify_... | TenantGroups | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TenantGroups:
def put(self, multi_tenancy):
"""Add a group to a tenant"""
<|body_0|>
def patch(self, multi_tenancy):
"""Update role in group tenant association."""
<|body_1|>
def delete(self, multi_tenancy):
"""Remove a group from a tenant"""
... | stack_v2_sparse_classes_36k_train_025078 | 9,658 | permissive | [
{
"docstring": "Add a group to a tenant",
"name": "put",
"signature": "def put(self, multi_tenancy)"
},
{
"docstring": "Update role in group tenant association.",
"name": "patch",
"signature": "def patch(self, multi_tenancy)"
},
{
"docstring": "Remove a group from a tenant",
... | 3 | stack_v2_sparse_classes_30k_train_007219 | Implement the Python class `TenantGroups` described below.
Class description:
Implement the TenantGroups class.
Method signatures and docstrings:
- def put(self, multi_tenancy): Add a group to a tenant
- def patch(self, multi_tenancy): Update role in group tenant association.
- def delete(self, multi_tenancy): Remove... | Implement the Python class `TenantGroups` described below.
Class description:
Implement the TenantGroups class.
Method signatures and docstrings:
- def put(self, multi_tenancy): Add a group to a tenant
- def patch(self, multi_tenancy): Update role in group tenant association.
- def delete(self, multi_tenancy): Remove... | 760affb83facbe154c35c6ce20acb9432daa8bbd | <|skeleton|>
class TenantGroups:
def put(self, multi_tenancy):
"""Add a group to a tenant"""
<|body_0|>
def patch(self, multi_tenancy):
"""Update role in group tenant association."""
<|body_1|>
def delete(self, multi_tenancy):
"""Remove a group from a tenant"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TenantGroups:
def put(self, multi_tenancy):
"""Add a group to a tenant"""
request_dict = rest_utils.get_json_and_verify_params({'tenant_name': {'type': unicode}, 'group_name': {'type': unicode}, 'role': {'type': unicode}})
rest_utils.validate_inputs(request_dict)
role_name = re... | the_stack_v2_python_sparse | rest-service/manager_rest/rest/resources_v3/tenants.py | Metaswitch/cloudify-manager | train | 0 | |
8cd6b630fe3755d48fbd8218cc395b12b8528bff | [
"super().__init__()\nargs = parse_args()\nif args.model_name == 'faster_rcnn':\n self.model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True).eval()\nelif args.model_name == 'yolov3':\n yolov3_url = 'https://github.com/ultralytics/yolov3/releases/download/v9.6.0/yolov3.pt'\n if not os.path.exists... | <|body_start_0|>
super().__init__()
args = parse_args()
if args.model_name == 'faster_rcnn':
self.model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True).eval()
elif args.model_name == 'yolov3':
yolov3_url = 'https://github.com/ultralytics/yolov3/release... | python inference model | Predictor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Predictor:
"""python inference model"""
def __init__(self):
"""model name"""
<|body_0|>
def forward(self, x):
"""model forward inference Args: x: input Returns: y_pred: output"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super().__init__()
... | stack_v2_sparse_classes_36k_train_025079 | 6,047 | no_license | [
{
"docstring": "model name",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "model forward inference Args: x: input Returns: y_pred: output",
"name": "forward",
"signature": "def forward(self, x)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009942 | Implement the Python class `Predictor` described below.
Class description:
python inference model
Method signatures and docstrings:
- def __init__(self): model name
- def forward(self, x): model forward inference Args: x: input Returns: y_pred: output | Implement the Python class `Predictor` described below.
Class description:
python inference model
Method signatures and docstrings:
- def __init__(self): model name
- def forward(self, x): model forward inference Args: x: input Returns: y_pred: output
<|skeleton|>
class Predictor:
"""python inference model"""
... | bd3790ce72a2a26611b5eda3901651b5a809348f | <|skeleton|>
class Predictor:
"""python inference model"""
def __init__(self):
"""model name"""
<|body_0|>
def forward(self, x):
"""model forward inference Args: x: input Returns: y_pred: output"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Predictor:
"""python inference model"""
def __init__(self):
"""model name"""
super().__init__()
args = parse_args()
if args.model_name == 'faster_rcnn':
self.model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True).eval()
elif args.model_name =... | the_stack_v2_python_sparse | inference/benchmark/python/torch/detection_benchmark.py | PaddlePaddle/PaddleTest | train | 42 |
4e3e9e8730cfd84c5ebcbb0e960f2edca827b38f | [
"super(STSeqCls, self).__init__()\nself.enc = StarTransEnc(embed=embed, hidden_size=hidden_size, num_layers=num_layers, num_head=num_head, head_dim=head_dim, max_len=max_len, emb_dropout=emb_dropout, dropout=dropout)\nself.cls = _Cls(hidden_size, num_cls, cls_hidden_size, dropout=dropout)",
"mask = seq_len_to_mas... | <|body_start_0|>
super(STSeqCls, self).__init__()
self.enc = StarTransEnc(embed=embed, hidden_size=hidden_size, num_layers=num_layers, num_head=num_head, head_dim=head_dim, max_len=max_len, emb_dropout=emb_dropout, dropout=dropout)
self.cls = _Cls(hidden_size, num_cls, cls_hidden_size, dropout=d... | 用于分类任务的Star-Transformer | STSeqCls | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class STSeqCls:
"""用于分类任务的Star-Transformer"""
def __init__(self, embed, num_cls, hidden_size=300, num_layers=4, num_head=8, head_dim=32, max_len=512, cls_hidden_size=600, emb_dropout=0.1, dropout=0.1):
""":param embed: 单词词典, 可以是 tuple, 包括(num_embedings, embedding_dim), 即 embedding的大小和每个词的维... | stack_v2_sparse_classes_36k_train_025080 | 11,601 | permissive | [
{
"docstring": ":param embed: 单词词典, 可以是 tuple, 包括(num_embedings, embedding_dim), 即 embedding的大小和每个词的维度. 也可以传入 nn.Embedding 对象, 此时就以传入的对象作为embedding :param num_cls: 输出类别个数 :param hidden_size: 模型中特征维度. Default: 300 :param num_layers: 模型层数. Default: 4 :param num_head: 模型中multi-head的head个数. Default: 8 :param head_d... | 3 | stack_v2_sparse_classes_30k_train_008481 | Implement the Python class `STSeqCls` described below.
Class description:
用于分类任务的Star-Transformer
Method signatures and docstrings:
- def __init__(self, embed, num_cls, hidden_size=300, num_layers=4, num_head=8, head_dim=32, max_len=512, cls_hidden_size=600, emb_dropout=0.1, dropout=0.1): :param embed: 单词词典, 可以是 tupl... | Implement the Python class `STSeqCls` described below.
Class description:
用于分类任务的Star-Transformer
Method signatures and docstrings:
- def __init__(self, embed, num_cls, hidden_size=300, num_layers=4, num_head=8, head_dim=32, max_len=512, cls_hidden_size=600, emb_dropout=0.1, dropout=0.1): :param embed: 单词词典, 可以是 tupl... | dffc7a06cdbff2671a3ca73d2398159d91a4a7db | <|skeleton|>
class STSeqCls:
"""用于分类任务的Star-Transformer"""
def __init__(self, embed, num_cls, hidden_size=300, num_layers=4, num_head=8, head_dim=32, max_len=512, cls_hidden_size=600, emb_dropout=0.1, dropout=0.1):
""":param embed: 单词词典, 可以是 tuple, 包括(num_embedings, embedding_dim), 即 embedding的大小和每个词的维... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class STSeqCls:
"""用于分类任务的Star-Transformer"""
def __init__(self, embed, num_cls, hidden_size=300, num_layers=4, num_head=8, head_dim=32, max_len=512, cls_hidden_size=600, emb_dropout=0.1, dropout=0.1):
""":param embed: 单词词典, 可以是 tuple, 包括(num_embedings, embedding_dim), 即 embedding的大小和每个词的维度. 也可以传入 nn.E... | the_stack_v2_python_sparse | phenobert/utils/fastNLP/models/star_transformer.py | TianlabTech/PhenoBERT | train | 2 |
feae9baf17c6df0ae31df2687f79190cb2d9a611 | [
"self._logger = logger\nif logger == None:\n self._logger = Logger('OneWireTemp', Logger.INFO)\nself._logger.debug('Initialize OneWireTemp')\nself._test = False",
"device_folder = glob.glob(base_dir + '28*')[x]\ndevice_file = device_folder + '/w1_slave'\nself._logger.debug('{} {}, {} {}, {} {}'.format('In read... | <|body_start_0|>
self._logger = logger
if logger == None:
self._logger = Logger('OneWireTemp', Logger.INFO)
self._logger.debug('Initialize OneWireTemp')
self._test = False
<|end_body_0|>
<|body_start_1|>
device_folder = glob.glob(base_dir + '28*')[x]
device_f... | OneWireTemp | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OneWireTemp:
def __init__(self, logger=None):
"""Create sensor object Args: None Returns: None Raises: None"""
<|body_0|>
def read_temp_raw(self, x):
"""Read sensor buffer Args: x: number of the sensor Returns: lines: lines read Raises: None"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_025081 | 2,998 | no_license | [
{
"docstring": "Create sensor object Args: None Returns: None Raises: None",
"name": "__init__",
"signature": "def __init__(self, logger=None)"
},
{
"docstring": "Read sensor buffer Args: x: number of the sensor Returns: lines: lines read Raises: None",
"name": "read_temp_raw",
"signatur... | 3 | stack_v2_sparse_classes_30k_train_017205 | Implement the Python class `OneWireTemp` described below.
Class description:
Implement the OneWireTemp class.
Method signatures and docstrings:
- def __init__(self, logger=None): Create sensor object Args: None Returns: None Raises: None
- def read_temp_raw(self, x): Read sensor buffer Args: x: number of the sensor R... | Implement the Python class `OneWireTemp` described below.
Class description:
Implement the OneWireTemp class.
Method signatures and docstrings:
- def __init__(self, logger=None): Create sensor object Args: None Returns: None Raises: None
- def read_temp_raw(self, x): Read sensor buffer Args: x: number of the sensor R... | f93a15eaa81914f2041185dc3fbcc67036bd9d2a | <|skeleton|>
class OneWireTemp:
def __init__(self, logger=None):
"""Create sensor object Args: None Returns: None Raises: None"""
<|body_0|>
def read_temp_raw(self, x):
"""Read sensor buffer Args: x: number of the sensor Returns: lines: lines read Raises: None"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OneWireTemp:
def __init__(self, logger=None):
"""Create sensor object Args: None Returns: None Raises: None"""
self._logger = logger
if logger == None:
self._logger = Logger('OneWireTemp', Logger.INFO)
self._logger.debug('Initialize OneWireTemp')
self._test ... | the_stack_v2_python_sparse | MVP/python/OneWireTemp.py | webbhm/NerdFarm | train | 1 | |
eae7869fdc8163dce9c31051a4e39b7f98cc0330 | [
"self.main_window = QtGui.QWidget()\nself.gui = Gui()\nself.gui.setupUi(self.main_window)\nself.gui.drawing_widget.mousePressEvent = self.mouse_press\nself.gui.drawing_widget.paintEvent = self.paint_event\nself.ttt = TicTacToeModel()",
"w = self.gui.drawing_widget.width() // 3\nh = self.gui.drawing_widget.height(... | <|body_start_0|>
self.main_window = QtGui.QWidget()
self.gui = Gui()
self.gui.setupUi(self.main_window)
self.gui.drawing_widget.mousePressEvent = self.mouse_press
self.gui.drawing_widget.paintEvent = self.paint_event
self.ttt = TicTacToeModel()
<|end_body_0|>
<|body_star... | Application class to create and control the gui. This version implements the Tic Tac Toe game using a drawing widget. | App | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class App:
"""Application class to create and control the gui. This version implements the Tic Tac Toe game using a drawing widget."""
def __init__(self):
"""Initialize the gui."""
<|body_0|>
def mouse_press(self, event):
"""Called automatically whenever the drawing wi... | stack_v2_sparse_classes_36k_train_025082 | 4,727 | no_license | [
{
"docstring": "Initialize the gui.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Called automatically whenever the drawing widget is clicked. :param PyQt.QtGui.QMouseEvent event: The event object from PyQt. :return: None",
"name": "mouse_press",
"signature":... | 3 | stack_v2_sparse_classes_30k_test_000717 | Implement the Python class `App` described below.
Class description:
Application class to create and control the gui. This version implements the Tic Tac Toe game using a drawing widget.
Method signatures and docstrings:
- def __init__(self): Initialize the gui.
- def mouse_press(self, event): Called automatically wh... | Implement the Python class `App` described below.
Class description:
Application class to create and control the gui. This version implements the Tic Tac Toe game using a drawing widget.
Method signatures and docstrings:
- def __init__(self): Initialize the gui.
- def mouse_press(self, event): Called automatically wh... | 0e3470085083012f893adb22aa46d46039016965 | <|skeleton|>
class App:
"""Application class to create and control the gui. This version implements the Tic Tac Toe game using a drawing widget."""
def __init__(self):
"""Initialize the gui."""
<|body_0|>
def mouse_press(self, event):
"""Called automatically whenever the drawing wi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class App:
"""Application class to create and control the gui. This version implements the Tic Tac Toe game using a drawing widget."""
def __init__(self):
"""Initialize the gui."""
self.main_window = QtGui.QWidget()
self.gui = Gui()
self.gui.setupUi(self.main_window)
sel... | the_stack_v2_python_sparse | CS_210 (Introduction to Programming)/TicTacToe/DrawApp.py | JacobOrner/USAFA | train | 0 |
e7f218f9bd56620e872389aa1adccae21993341a | [
"res = []\nif not root:\n return res\nqueue = [root, None]\nlevel = []\nwhile queue:\n node = queue.pop(0)\n if not node:\n if not level:\n break\n res.append(level.copy())\n level.clear()\n queue.append(None)\n continue\n level.append(node.val)\n if node... | <|body_start_0|>
res = []
if not root:
return res
queue = [root, None]
level = []
while queue:
node = queue.pop(0)
if not node:
if not level:
break
res.append(level.copy())
lev... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def printFromTopToBottom(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_0|>
def printFromTopToBottom_2(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res... | stack_v2_sparse_classes_36k_train_025083 | 2,922 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: List[List[int]]",
"name": "printFromTopToBottom",
"signature": "def printFromTopToBottom(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: List[List[int]]",
"name": "printFromTopToBottom_2",
"signature": "def printFromTopToBottom_2(... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def printFromTopToBottom(self, root): :type root: TreeNode :rtype: List[List[int]]
- def printFromTopToBottom_2(self, root): :type root: TreeNode :rtype: List[List[int]] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def printFromTopToBottom(self, root): :type root: TreeNode :rtype: List[List[int]]
- def printFromTopToBottom_2(self, root): :type root: TreeNode :rtype: List[List[int]]
<|skele... | 967b0fbb40ae491b552bc3365a481e66324cb6f2 | <|skeleton|>
class Solution:
def printFromTopToBottom(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_0|>
def printFromTopToBottom_2(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def printFromTopToBottom(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
res = []
if not root:
return res
queue = [root, None]
level = []
while queue:
node = queue.pop(0)
if not node:
... | the_stack_v2_python_sparse | jianzhi_offer/26_分行从上到下打印二叉树.py | ryanatgz/data_structure_and_algorithm | train | 0 | |
907c79640dd9504afd7acba644f7f111948317d8 | [
"data = DataObject()\ndata.add_value_string('ps_mode', ps_mode)\ndata.add_value_string('user_registry', user_registry)\ndata.add_value_string('admin_cert_lifetime', admin_cert_lifetime)\ndata.add_value_string('ssl_compliance', ssl_compliance)\ndata.add_value_string('admin_pwd', admin_password)\ndata.add_value_strin... | <|body_start_0|>
data = DataObject()
data.add_value_string('ps_mode', ps_mode)
data.add_value_string('user_registry', user_registry)
data.add_value_string('admin_cert_lifetime', admin_cert_lifetime)
data.add_value_string('ssl_compliance', ssl_compliance)
data.add_value_st... | RuntimeComponent10000 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RuntimeComponent10000:
def configure(self, ps_mode=None, user_registry=None, admin_password=None, ldap_password=None, admin_cert_lifetime=None, ssl_compliance=None, ldap_host=None, ldap_port=None, isam_domain=None, ldap_dn=None, ldap_suffix=None, ldap_ssl_db=None, ldap_ssl_label=None, isam_host=... | stack_v2_sparse_classes_36k_train_025084 | 19,491 | permissive | [
{
"docstring": "Configure the reverse proxy runtime component, including the policy server and user registry. Args: ps_mode (:obj:`str`): The mode for the policy server. Valid values are local and remote. user_registry (:obj:`str`): The type of user registry to be configured against. Valid values are local, lda... | 2 | null | Implement the Python class `RuntimeComponent10000` described below.
Class description:
Implement the RuntimeComponent10000 class.
Method signatures and docstrings:
- def configure(self, ps_mode=None, user_registry=None, admin_password=None, ldap_password=None, admin_cert_lifetime=None, ssl_compliance=None, ldap_host=... | Implement the Python class `RuntimeComponent10000` described below.
Class description:
Implement the RuntimeComponent10000 class.
Method signatures and docstrings:
- def configure(self, ps_mode=None, user_registry=None, admin_password=None, ldap_password=None, admin_cert_lifetime=None, ssl_compliance=None, ldap_host=... | 168fe90d73e783fdc901c8da9c8a84644e9f8846 | <|skeleton|>
class RuntimeComponent10000:
def configure(self, ps_mode=None, user_registry=None, admin_password=None, ldap_password=None, admin_cert_lifetime=None, ssl_compliance=None, ldap_host=None, ldap_port=None, isam_domain=None, ldap_dn=None, ldap_suffix=None, ldap_ssl_db=None, ldap_ssl_label=None, isam_host=... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RuntimeComponent10000:
def configure(self, ps_mode=None, user_registry=None, admin_password=None, ldap_password=None, admin_cert_lifetime=None, ssl_compliance=None, ldap_host=None, ldap_port=None, isam_domain=None, ldap_dn=None, ldap_suffix=None, ldap_ssl_db=None, ldap_ssl_label=None, isam_host=None, isam_por... | the_stack_v2_python_sparse | pyisva/core/web/runtimecomponent.py | lachlan-ibm/pyisva | train | 0 | |
d92774fea778843de6d1e1f3a2c76b4a48942e3f | [
"cnt = 0\nfor i in range(len(nums)):\n cur_sum = 0\n for j in range(i, -1, -1):\n cur_sum += nums[j]\n if cur_sum == k:\n cnt = cnt + 1\nreturn cnt",
"cnt = 0\nsum_dict = {0: 1}\ncur_sum = 0\nfor i in range(len(nums)):\n cur_sum += nums[i]\n if sum_dict.get(cur_sum - k):\n ... | <|body_start_0|>
cnt = 0
for i in range(len(nums)):
cur_sum = 0
for j in range(i, -1, -1):
cur_sum += nums[j]
if cur_sum == k:
cnt = cnt + 1
return cnt
<|end_body_0|>
<|body_start_1|>
cnt = 0
sum_dict = ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def subarraySum(self, nums: List[int], k: int) -> int:
"""note：暴力求解 :param nums: :param k: :return:"""
<|body_0|>
def subarraySum2(self, nums: List[int], k: int) -> int:
"""note：sum([j,...,i]=k, pre[i]=sum([0,...,i]) , pre[i]-pre[j-1]=k :param nums: :param ... | stack_v2_sparse_classes_36k_train_025085 | 1,305 | no_license | [
{
"docstring": "note：暴力求解 :param nums: :param k: :return:",
"name": "subarraySum",
"signature": "def subarraySum(self, nums: List[int], k: int) -> int"
},
{
"docstring": "note：sum([j,...,i]=k, pre[i]=sum([0,...,i]) , pre[i]-pre[j-1]=k :param nums: :param k: :return:",
"name": "subarraySum2",... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def subarraySum(self, nums: List[int], k: int) -> int: note：暴力求解 :param nums: :param k: :return:
- def subarraySum2(self, nums: List[int], k: int) -> int: note：sum([j,...,i]=k, p... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def subarraySum(self, nums: List[int], k: int) -> int: note：暴力求解 :param nums: :param k: :return:
- def subarraySum2(self, nums: List[int], k: int) -> int: note：sum([j,...,i]=k, p... | f7421522c437c952698736dbac8fb7ac6c0b8b88 | <|skeleton|>
class Solution:
def subarraySum(self, nums: List[int], k: int) -> int:
"""note：暴力求解 :param nums: :param k: :return:"""
<|body_0|>
def subarraySum2(self, nums: List[int], k: int) -> int:
"""note：sum([j,...,i]=k, pre[i]=sum([0,...,i]) , pre[i]-pre[j-1]=k :param nums: :param ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def subarraySum(self, nums: List[int], k: int) -> int:
"""note：暴力求解 :param nums: :param k: :return:"""
cnt = 0
for i in range(len(nums)):
cur_sum = 0
for j in range(i, -1, -1):
cur_sum += nums[j]
if cur_sum == k:
... | the_stack_v2_python_sparse | leetcode/daily_question/20200515_subarray_sum.py | whitepaper2/data_beauty | train | 0 | |
227cd1b352128c1d5120dc5da1d73ce8e9cdd9c0 | [
"hash_set = set(words)\nfor word in words:\n for i in range(1, len(word)):\n if word[i:] in hash_set:\n hash_set.remove(word[i:])\nans = 0\nfor word in hash_set:\n ans += len(word) + 1\nreturn ans",
"words = sorted((w[::-1] for w in words)) + ['']\nans = 0\nfor i in range(len(words) - 1):\... | <|body_start_0|>
hash_set = set(words)
for word in words:
for i in range(1, len(word)):
if word[i:] in hash_set:
hash_set.remove(word[i:])
ans = 0
for word in hash_set:
ans += len(word) + 1
return ans
<|end_body_0|>
<|b... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minimumLengthEncoding_MK1(self, words: List[str]) -> int:
"""暴力哈希"""
<|body_0|>
def minimumLengthEncoding_MK2(self, words: List[str]) -> int:
"""反转排序"""
<|body_1|>
def minimumLengthEncoding_MK3(self, words: List[str]) -> int:
"""Tri... | stack_v2_sparse_classes_36k_train_025086 | 1,219 | no_license | [
{
"docstring": "暴力哈希",
"name": "minimumLengthEncoding_MK1",
"signature": "def minimumLengthEncoding_MK1(self, words: List[str]) -> int"
},
{
"docstring": "反转排序",
"name": "minimumLengthEncoding_MK2",
"signature": "def minimumLengthEncoding_MK2(self, words: List[str]) -> int"
},
{
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumLengthEncoding_MK1(self, words: List[str]) -> int: 暴力哈希
- def minimumLengthEncoding_MK2(self, words: List[str]) -> int: 反转排序
- def minimumLengthEncoding_MK3(self, word... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumLengthEncoding_MK1(self, words: List[str]) -> int: 暴力哈希
- def minimumLengthEncoding_MK2(self, words: List[str]) -> int: 反转排序
- def minimumLengthEncoding_MK3(self, word... | d7ba416d22becfa8f2a2ae4eee04c86617cd9332 | <|skeleton|>
class Solution:
def minimumLengthEncoding_MK1(self, words: List[str]) -> int:
"""暴力哈希"""
<|body_0|>
def minimumLengthEncoding_MK2(self, words: List[str]) -> int:
"""反转排序"""
<|body_1|>
def minimumLengthEncoding_MK3(self, words: List[str]) -> int:
"""Tri... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minimumLengthEncoding_MK1(self, words: List[str]) -> int:
"""暴力哈希"""
hash_set = set(words)
for word in words:
for i in range(1, len(word)):
if word[i:] in hash_set:
hash_set.remove(word[i:])
ans = 0
for word ... | the_stack_v2_python_sparse | 0820. Short Encoding of Words/Solution.py | faterazer/LeetCode | train | 4 | |
4f418842a5376a1bd7a9c0d3fbbb0887f9e8cdbf | [
"def removeInvalidRight(s, brackets='()'):\n count = 0\n ans = []\n for c in s:\n if c == brackets[0]:\n count += 1\n elif c == brackets[1]:\n count -= 1\n if count < 0:\n count += 1\n continue\n else:\n ans.append(c)\n r... | <|body_start_0|>
def removeInvalidRight(s, brackets='()'):
count = 0
ans = []
for c in s:
if c == brackets[0]:
count += 1
elif c == brackets[1]:
count -= 1
if count < 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minRemoveToMakeValid(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def minRemoveToMakeValid2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def removeInvalidRight(s, brackets='()'):
... | stack_v2_sparse_classes_36k_train_025087 | 2,824 | no_license | [
{
"docstring": ":type s: str :rtype: str",
"name": "minRemoveToMakeValid",
"signature": "def minRemoveToMakeValid(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name": "minRemoveToMakeValid2",
"signature": "def minRemoveToMakeValid2(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minRemoveToMakeValid(self, s): :type s: str :rtype: str
- def minRemoveToMakeValid2(self, s): :type s: str :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minRemoveToMakeValid(self, s): :type s: str :rtype: str
- def minRemoveToMakeValid2(self, s): :type s: str :rtype: str
<|skeleton|>
class Solution:
def minRemoveToMakeV... | d953abe2c9680f636563e76287d2f907e90ced63 | <|skeleton|>
class Solution:
def minRemoveToMakeValid(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def minRemoveToMakeValid2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minRemoveToMakeValid(self, s):
""":type s: str :rtype: str"""
def removeInvalidRight(s, brackets='()'):
count = 0
ans = []
for c in s:
if c == brackets[0]:
count += 1
elif c == brackets[1]:
... | the_stack_v2_python_sparse | python_leetcode_2020/Python_Leetcode_2020/1249_min_remove_to_make_valid_parentheses.py | xiangcao/Leetcode | train | 0 | |
8dadf69a07909e886bb340691cf6cbd9a8e4868e | [
"for dtype in (tf.int32, tf.int64, tf.float32, tf.float64):\n identity_permutation = tf.range(10, dtype=dtype)\n random_shuffle_seed_1 = tff_rnd.stateless_random_shuffle(identity_permutation, seed=tf.constant((1, 42), tf.int64))\n random_shuffle_seed_2 = tff_rnd.stateless_random_shuffle(identity_permutatio... | <|body_start_0|>
for dtype in (tf.int32, tf.int64, tf.float32, tf.float64):
identity_permutation = tf.range(10, dtype=dtype)
random_shuffle_seed_1 = tff_rnd.stateless_random_shuffle(identity_permutation, seed=tf.constant((1, 42), tf.int64))
random_shuffle_seed_2 = tff_rnd.sta... | StatelessRandomOpsTest | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StatelessRandomOpsTest:
def testOutputIsPermutation(self):
"""Checks that stateless_random_shuffle outputs a permutation."""
<|body_0|>
def testOutputIsStateless(self):
"""Checks that stateless_random_shuffle is stateless."""
<|body_1|>
def testOutputIsI... | stack_v2_sparse_classes_36k_train_025088 | 6,357 | permissive | [
{
"docstring": "Checks that stateless_random_shuffle outputs a permutation.",
"name": "testOutputIsPermutation",
"signature": "def testOutputIsPermutation(self)"
},
{
"docstring": "Checks that stateless_random_shuffle is stateless.",
"name": "testOutputIsStateless",
"signature": "def tes... | 5 | null | Implement the Python class `StatelessRandomOpsTest` described below.
Class description:
Implement the StatelessRandomOpsTest class.
Method signatures and docstrings:
- def testOutputIsPermutation(self): Checks that stateless_random_shuffle outputs a permutation.
- def testOutputIsStateless(self): Checks that stateles... | Implement the Python class `StatelessRandomOpsTest` described below.
Class description:
Implement the StatelessRandomOpsTest class.
Method signatures and docstrings:
- def testOutputIsPermutation(self): Checks that stateless_random_shuffle outputs a permutation.
- def testOutputIsStateless(self): Checks that stateles... | 0d3a2193c0f2d320b65e602cf01d7a617da484df | <|skeleton|>
class StatelessRandomOpsTest:
def testOutputIsPermutation(self):
"""Checks that stateless_random_shuffle outputs a permutation."""
<|body_0|>
def testOutputIsStateless(self):
"""Checks that stateless_random_shuffle is stateless."""
<|body_1|>
def testOutputIsI... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StatelessRandomOpsTest:
def testOutputIsPermutation(self):
"""Checks that stateless_random_shuffle outputs a permutation."""
for dtype in (tf.int32, tf.int64, tf.float32, tf.float64):
identity_permutation = tf.range(10, dtype=dtype)
random_shuffle_seed_1 = tff_rnd.state... | the_stack_v2_python_sparse | tf_quant_finance/math/random_ops/stateless_test.py | google/tf-quant-finance | train | 4,165 | |
0bda9f6572a872c51895eb44ebea3c386176da9a | [
"self._pt_1 = pt_3d_1\nself._pt_2 = pt_3d_2\nself._pt_3 = pt_3d_3",
"def versor3d(pt_1, pt_2):\n \"\"\"\n\n :param pt_1:\n :param pt_2:\n :return:\n \"\"\"\n return Segment(pt_1, pt_2).vector().versor_full()\n\ndef is_pt_in_fascio(pt_1, pt_2, pt_3):\n \"\"\"\n\... | <|body_start_0|>
self._pt_1 = pt_3d_1
self._pt_2 = pt_3d_2
self._pt_3 = pt_3d_3
<|end_body_0|>
<|body_start_1|>
def versor3d(pt_1, pt_2):
"""
:param pt_1:
:param pt_2:
:return:
"""
r... | CartesianTriangle | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CartesianTriangle:
def __init__(self, pt_3d_1, pt_3d_2, pt_3d_3):
""":param pt_3d_1: :param pt_3d_2: :param pt_3d_3:"""
<|body_0|>
def is_pt_within(self, pt_3d):
""":param pt_3d: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self._pt_1 = ... | stack_v2_sparse_classes_36k_train_025089 | 18,232 | no_license | [
{
"docstring": ":param pt_3d_1: :param pt_3d_2: :param pt_3d_3:",
"name": "__init__",
"signature": "def __init__(self, pt_3d_1, pt_3d_2, pt_3d_3)"
},
{
"docstring": ":param pt_3d: :return:",
"name": "is_pt_within",
"signature": "def is_pt_within(self, pt_3d)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019402 | Implement the Python class `CartesianTriangle` described below.
Class description:
Implement the CartesianTriangle class.
Method signatures and docstrings:
- def __init__(self, pt_3d_1, pt_3d_2, pt_3d_3): :param pt_3d_1: :param pt_3d_2: :param pt_3d_3:
- def is_pt_within(self, pt_3d): :param pt_3d: :return: | Implement the Python class `CartesianTriangle` described below.
Class description:
Implement the CartesianTriangle class.
Method signatures and docstrings:
- def __init__(self, pt_3d_1, pt_3d_2, pt_3d_3): :param pt_3d_1: :param pt_3d_2: :param pt_3d_3:
- def is_pt_within(self, pt_3d): :param pt_3d: :return:
<|skelet... | b07ab23400b4ff4151555c2e81392a7adf99fc33 | <|skeleton|>
class CartesianTriangle:
def __init__(self, pt_3d_1, pt_3d_2, pt_3d_3):
""":param pt_3d_1: :param pt_3d_2: :param pt_3d_3:"""
<|body_0|>
def is_pt_within(self, pt_3d):
""":param pt_3d: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CartesianTriangle:
def __init__(self, pt_3d_1, pt_3d_2, pt_3d_3):
""":param pt_3d_1: :param pt_3d_2: :param pt_3d_3:"""
self._pt_1 = pt_3d_1
self._pt_2 = pt_3d_2
self._pt_3 = pt_3d_3
def is_pt_within(self, pt_3d):
""":param pt_3d: :return:"""
def versor3d(p... | the_stack_v2_python_sparse | pygsf/spatial/vectorial/meshes.py | mauroalberti/qgSurf | train | 5 | |
9ee58d9c3d533edbfef1cc1c36903fb204a22742 | [
"self.id = id\nself.date = APIHelper.RFC3339DateTime(date) if date else None\nself.product_id = product_id\nself.description = description\nself.count = count\nself.customer_number = customer_number\nself.external_reference = external_reference\nself.department_id = department_id\nself.additional_properties = addit... | <|body_start_0|>
self.id = id
self.date = APIHelper.RFC3339DateTime(date) if date else None
self.product_id = product_id
self.description = description
self.count = count
self.customer_number = customer_number
self.external_reference = external_reference
s... | Implementation of the 'Transaction' model. TODO: type model description here. Attributes: id (string): Transaction ID date (datetime): The date for the transaction product_id (string): Product ID (SIGN, IDENTIFICATION etc) description (string): Transaction description count (int): Number of transactions for the selecte... | Transaction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Transaction:
"""Implementation of the 'Transaction' model. TODO: type model description here. Attributes: id (string): Transaction ID date (datetime): The date for the transaction product_id (string): Product ID (SIGN, IDENTIFICATION etc) description (string): Transaction description count (int):... | stack_v2_sparse_classes_36k_train_025090 | 3,800 | permissive | [
{
"docstring": "Constructor for the Transaction class",
"name": "__init__",
"signature": "def __init__(self, id=None, date=None, product_id=None, description=None, count=None, customer_number=None, external_reference=None, department_id=None, additional_properties={})"
},
{
"docstring": "Creates... | 2 | stack_v2_sparse_classes_30k_train_007137 | Implement the Python class `Transaction` described below.
Class description:
Implementation of the 'Transaction' model. TODO: type model description here. Attributes: id (string): Transaction ID date (datetime): The date for the transaction product_id (string): Product ID (SIGN, IDENTIFICATION etc) description (string... | Implement the Python class `Transaction` described below.
Class description:
Implementation of the 'Transaction' model. TODO: type model description here. Attributes: id (string): Transaction ID date (datetime): The date for the transaction product_id (string): Product ID (SIGN, IDENTIFICATION etc) description (string... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class Transaction:
"""Implementation of the 'Transaction' model. TODO: type model description here. Attributes: id (string): Transaction ID date (datetime): The date for the transaction product_id (string): Product ID (SIGN, IDENTIFICATION etc) description (string): Transaction description count (int):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Transaction:
"""Implementation of the 'Transaction' model. TODO: type model description here. Attributes: id (string): Transaction ID date (datetime): The date for the transaction product_id (string): Product ID (SIGN, IDENTIFICATION etc) description (string): Transaction description count (int): Number of tr... | the_stack_v2_python_sparse | idfy_rest_client/models/transaction.py | dealflowteam/Idfy | train | 0 |
da25fbbe28fe1df1fc1952bb4bbd3d1ff74cb13b | [
"assert len(resultNodes) > 1\nqueryAttribute = queryNode.toDict()[attribute]\ntotalValue = relevanceScores[queryAttribute, resultNodes[0].toDict()[attribute]]\nfor i in xrange(1, len(resultNodes)):\n totalValue += relevanceScores[queryAttribute, resultNodes[i].toDict()[attribute]] / math.log(i + 1, 2)\nreturn to... | <|body_start_0|>
assert len(resultNodes) > 1
queryAttribute = queryNode.toDict()[attribute]
totalValue = relevanceScores[queryAttribute, resultNodes[0].toDict()[attribute]]
for i in xrange(1, len(resultNodes)):
totalValue += relevanceScores[queryAttribute, resultNodes[i].toDi... | Implements discounted cumulative gain, given some query (or queries) and relevance labels from 0 to 3 (least to most relevant). | CumulativeGainMeasures | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CumulativeGainMeasures:
"""Implements discounted cumulative gain, given some query (or queries) and relevance labels from 0 to 3 (least to most relevant)."""
def discountedCumulativeGain(queryNode, resultNodes, relevanceScores, attribute='name'):
"""Computes the discounted cumulative... | stack_v2_sparse_classes_36k_train_025091 | 1,351 | no_license | [
{
"docstring": "Computes the discounted cumulative gain of some query",
"name": "discountedCumulativeGain",
"signature": "def discountedCumulativeGain(queryNode, resultNodes, relevanceScores, attribute='name')"
},
{
"docstring": "Calculate",
"name": "normalizedDiscountedCumulativeGain",
... | 2 | null | Implement the Python class `CumulativeGainMeasures` described below.
Class description:
Implements discounted cumulative gain, given some query (or queries) and relevance labels from 0 to 3 (least to most relevant).
Method signatures and docstrings:
- def discountedCumulativeGain(queryNode, resultNodes, relevanceScor... | Implement the Python class `CumulativeGainMeasures` described below.
Class description:
Implements discounted cumulative gain, given some query (or queries) and relevance labels from 0 to 3 (least to most relevant).
Method signatures and docstrings:
- def discountedCumulativeGain(queryNode, resultNodes, relevanceScor... | 253906f9a2fe4fb4d3451ebd1d3b51de51e0d239 | <|skeleton|>
class CumulativeGainMeasures:
"""Implements discounted cumulative gain, given some query (or queries) and relevance labels from 0 to 3 (least to most relevant)."""
def discountedCumulativeGain(queryNode, resultNodes, relevanceScores, attribute='name'):
"""Computes the discounted cumulative... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CumulativeGainMeasures:
"""Implements discounted cumulative gain, given some query (or queries) and relevance labels from 0 to 3 (least to most relevant)."""
def discountedCumulativeGain(queryNode, resultNodes, relevanceScores, attribute='name'):
"""Computes the discounted cumulative gain of some... | the_stack_v2_python_sparse | src/experiment/measure/CumulativeGainMeasures.py | wfnuser/RicherPathSIM | train | 0 |
231b9c86fcad9a224466518886a8356e98813ff4 | [
"for arg in self.non_number_values:\n self.assertRaises(TypeError, prev1.primes_list, arg)\nfor arg, val in self.primes_values:\n self.assertEqual(prev1.primes_list(arg), val)",
"for arg in self.non_number_values:\n self.assertRaises(TypeError, prev1.primes_fun, arg)\nfor arg, val in self.primes_values:\... | <|body_start_0|>
for arg in self.non_number_values:
self.assertRaises(TypeError, prev1.primes_list, arg)
for arg, val in self.primes_values:
self.assertEqual(prev1.primes_list(arg), val)
<|end_body_0|>
<|body_start_1|>
for arg in self.non_number_values:
self.... | TestPrimes | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPrimes:
def test_primes_list(self):
"""Testing primes_list function"""
<|body_0|>
def test_primes_fun(self):
"""Testing primes_fun function"""
<|body_1|>
def test_primes_map(self):
"""Testing primes_map function"""
<|body_2|>
def... | stack_v2_sparse_classes_36k_train_025092 | 8,451 | no_license | [
{
"docstring": "Testing primes_list function",
"name": "test_primes_list",
"signature": "def test_primes_list(self)"
},
{
"docstring": "Testing primes_fun function",
"name": "test_primes_fun",
"signature": "def test_primes_fun(self)"
},
{
"docstring": "Testing primes_map function... | 4 | stack_v2_sparse_classes_30k_train_005242 | Implement the Python class `TestPrimes` described below.
Class description:
Implement the TestPrimes class.
Method signatures and docstrings:
- def test_primes_list(self): Testing primes_list function
- def test_primes_fun(self): Testing primes_fun function
- def test_primes_map(self): Testing primes_map function
- d... | Implement the Python class `TestPrimes` described below.
Class description:
Implement the TestPrimes class.
Method signatures and docstrings:
- def test_primes_list(self): Testing primes_list function
- def test_primes_fun(self): Testing primes_fun function
- def test_primes_map(self): Testing primes_map function
- d... | 0cd4bbe3feb63b248d643303433f9fb2fc2def79 | <|skeleton|>
class TestPrimes:
def test_primes_list(self):
"""Testing primes_list function"""
<|body_0|>
def test_primes_fun(self):
"""Testing primes_fun function"""
<|body_1|>
def test_primes_map(self):
"""Testing primes_map function"""
<|body_2|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestPrimes:
def test_primes_list(self):
"""Testing primes_list function"""
for arg in self.non_number_values:
self.assertRaises(TypeError, prev1.primes_list, arg)
for arg, val in self.primes_values:
self.assertEqual(prev1.primes_list(arg), val)
def test_pri... | the_stack_v2_python_sparse | Python - advanced course/Solutions/9/9.1.py | maxymilianz/CS-at-University-of-Wroclaw | train | 0 | |
ec06aacb55ae8a6fb88b3528b33fffcc2bdfc405 | [
"queue = [root]\nret = []\nwhile queue:\n node = queue[0]\n queue = queue[1:]\n if not node:\n ret.append('null')\n continue\n ret.append(str(node.val))\n queue.append(node.left)\n queue.append(node.right)\nreturn ','.join(ret)",
"if not data or data == 'null':\n return None\nno... | <|body_start_0|>
queue = [root]
ret = []
while queue:
node = queue[0]
queue = queue[1:]
if not node:
ret.append('null')
continue
ret.append(str(node.val))
queue.append(node.left)
queue.append(... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_025093 | 1,649 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 9687f8e743a8b6396fff192f22b5256d1025f86b | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
queue = [root]
ret = []
while queue:
node = queue[0]
queue = queue[1:]
if not node:
ret.append('null')
... | the_stack_v2_python_sparse | 2022/practice/tree/xu-lie-hua-er-cha-shu-lcof.py | buhuipao/LeetCode | train | 5 | |
6bfd1fb98173ed4620edfc9ff4f1c0c88a59c8e1 | [
"if basic_approximations is None:\n basic_approximations = generate_basic_approximations(basis_gates=['h', 't', 'tdg'], depth=10)\nself.basic_approximations = self.load_basic_approximations(basic_approximations)",
"if isinstance(data, list):\n return data\nif isinstance(data, str):\n data = np.load(data,... | <|body_start_0|>
if basic_approximations is None:
basic_approximations = generate_basic_approximations(basis_gates=['h', 't', 'tdg'], depth=10)
self.basic_approximations = self.load_basic_approximations(basic_approximations)
<|end_body_0|>
<|body_start_1|>
if isinstance(data, list):... | The Solovay Kitaev discrete decomposition algorithm. This class is called recursively by the transpiler pass, which is why it is separeted. See :class:`qiskit.transpiler.passes.SolovayKitaev` for more information. | SolovayKitaevDecomposition | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SolovayKitaevDecomposition:
"""The Solovay Kitaev discrete decomposition algorithm. This class is called recursively by the transpiler pass, which is why it is separeted. See :class:`qiskit.transpiler.passes.SolovayKitaev` for more information."""
def __init__(self, basic_approximations: str... | stack_v2_sparse_classes_36k_train_025094 | 8,166 | permissive | [
{
"docstring": "Args: basic_approximations: A specification of the basic SU(2) approximations in terms of discrete gates. At each iteration this algorithm, the remaining error is approximated with the closest sequence of gates in this set. If a ``str``, this specifies a ``.npy`` filename from which to load the ... | 5 | stack_v2_sparse_classes_30k_train_006031 | Implement the Python class `SolovayKitaevDecomposition` described below.
Class description:
The Solovay Kitaev discrete decomposition algorithm. This class is called recursively by the transpiler pass, which is why it is separeted. See :class:`qiskit.transpiler.passes.SolovayKitaev` for more information.
Method signa... | Implement the Python class `SolovayKitaevDecomposition` described below.
Class description:
The Solovay Kitaev discrete decomposition algorithm. This class is called recursively by the transpiler pass, which is why it is separeted. See :class:`qiskit.transpiler.passes.SolovayKitaev` for more information.
Method signa... | 0b51250e219ca303654fc28a318c21366584ccd3 | <|skeleton|>
class SolovayKitaevDecomposition:
"""The Solovay Kitaev discrete decomposition algorithm. This class is called recursively by the transpiler pass, which is why it is separeted. See :class:`qiskit.transpiler.passes.SolovayKitaev` for more information."""
def __init__(self, basic_approximations: str... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SolovayKitaevDecomposition:
"""The Solovay Kitaev discrete decomposition algorithm. This class is called recursively by the transpiler pass, which is why it is separeted. See :class:`qiskit.transpiler.passes.SolovayKitaev` for more information."""
def __init__(self, basic_approximations: str | dict[str, ... | the_stack_v2_python_sparse | qiskit/synthesis/discrete_basis/solovay_kitaev.py | 1ucian0/qiskit-terra | train | 6 |
84560eaf255e62046345ffe183427c23a1f61a47 | [
"Parametre.__init__(self, 'liste', 'list')\nself.tronquer = True\nself.aide_courte = 'affiche les chambres libres'\nself.aide_longue = \"Cette commande permet de lister les chambres libres d'une auberge ainsi que leur prix au jour. Vous devez vous trouver auprès d'un aubergiste pour cela. Les chambres affichées son... | <|body_start_0|>
Parametre.__init__(self, 'liste', 'list')
self.tronquer = True
self.aide_courte = 'affiche les chambres libres'
self.aide_longue = "Cette commande permet de lister les chambres libres d'une auberge ainsi que leur prix au jour. Vous devez vous trouver auprès d'un aubergis... | Commande 'louer liste' | PrmListe | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrmListe:
"""Commande 'louer liste'"""
def __init__(self):
"""Constructeur du paramètre."""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Méthode d'interprétation de commande"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
Parame... | stack_v2_sparse_classes_36k_train_025095 | 4,343 | permissive | [
{
"docstring": "Constructeur du paramètre.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Méthode d'interprétation de commande",
"name": "interpreter",
"signature": "def interpreter(self, personnage, dic_masques)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006780 | Implement the Python class `PrmListe` described below.
Class description:
Commande 'louer liste'
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre.
- def interpreter(self, personnage, dic_masques): Méthode d'interprétation de commande | Implement the Python class `PrmListe` described below.
Class description:
Commande 'louer liste'
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre.
- def interpreter(self, personnage, dic_masques): Méthode d'interprétation de commande
<|skeleton|>
class PrmListe:
"""Commande 'loue... | 7e93bff08cdf891352efba587e89c40f3b4a2301 | <|skeleton|>
class PrmListe:
"""Commande 'louer liste'"""
def __init__(self):
"""Constructeur du paramètre."""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Méthode d'interprétation de commande"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PrmListe:
"""Commande 'louer liste'"""
def __init__(self):
"""Constructeur du paramètre."""
Parametre.__init__(self, 'liste', 'list')
self.tronquer = True
self.aide_courte = 'affiche les chambres libres'
self.aide_longue = "Cette commande permet de lister les chamb... | the_stack_v2_python_sparse | src/secondaires/auberge/commandes/louer/liste.py | vincent-lg/tsunami | train | 5 |
8f7b0b4aa4c06e4252afcf563cf9fdd7cfa09f76 | [
"self.head = head\nn = 0\nhead = self.head\nwhile head:\n head = head.next\n n += 1\nself.length = n",
"index = random.randint(0, self.length - 1)\nhead = self.head\nfor i in range(index):\n head = head.next\nreturn head.val"
] | <|body_start_0|>
self.head = head
n = 0
head = self.head
while head:
head = head.next
n += 1
self.length = n
<|end_body_0|>
<|body_start_1|>
index = random.randint(0, self.length - 1)
head = self.head
for i in range(index):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, head: ListNode):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node."""
<|body_0|>
def getRandom(self) -> int:
"""Returns a random node's value."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_025096 | 1,645 | no_license | [
{
"docstring": "@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node.",
"name": "__init__",
"signature": "def __init__(self, head: ListNode)"
},
{
"docstring": "Returns a random node's value.",
"name": "getRandom",
"signatu... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, head: ListNode): @param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node.
- def getRandom(self) -... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, head: ListNode): @param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node.
- def getRandom(self) -... | 5be09b4a804cb600e61e24617b9b2a1cc78fab3f | <|skeleton|>
class Solution:
def __init__(self, head: ListNode):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node."""
<|body_0|>
def getRandom(self) -> int:
"""Returns a random node's value."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, head: ListNode):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node."""
self.head = head
n = 0
head = self.head
while head:
head = head.next
n += 1... | the_stack_v2_python_sparse | 382.py | aiifabbf/leetcode-memo | train | 10 | |
050355171af2ff18d5c1f598ae5913cb1222b63a | [
"self.count = 0\nself.alist = []\nself.wlist = []",
"ndata = len(data)\nif self.count == 0:\n self.nchannels = ndata\n self.count += 1\n self.alist.append(data)\n self.wlist.append(wt)\nelif self.count > 0 and ndata != self.nchannels:\n print('accum:load - WARNING - number of points in spectrum doe... | <|body_start_0|>
self.count = 0
self.alist = []
self.wlist = []
<|end_body_0|>
<|body_start_1|>
ndata = len(data)
if self.count == 0:
self.nchannels = ndata
self.count += 1
self.alist.append(data)
self.wlist.append(wt)
elif... | Class to manage spectral averaging. All spectra to be averaged must be aligned in channel/index space. Weighted averages are possible using weight provided when spectrum is added to the stack. | Accum | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Accum:
"""Class to manage spectral averaging. All spectra to be averaged must be aligned in channel/index space. Weighted averages are possible using weight provided when spectrum is added to the stack."""
def __init__(self):
"""Constructor for Accum class. Args: none Returns: none""... | stack_v2_sparse_classes_36k_train_025097 | 37,646 | no_license | [
{
"docstring": "Constructor for Accum class. Args: none Returns: none",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Loads a set of data to be averaged. Args: data (array): data array wt (float): weighting of data (default is 1) Returns: none",
"name": "load",
... | 3 | stack_v2_sparse_classes_30k_train_019897 | Implement the Python class `Accum` described below.
Class description:
Class to manage spectral averaging. All spectra to be averaged must be aligned in channel/index space. Weighted averages are possible using weight provided when spectrum is added to the stack.
Method signatures and docstrings:
- def __init__(self)... | Implement the Python class `Accum` described below.
Class description:
Class to manage spectral averaging. All spectra to be averaged must be aligned in channel/index space. Weighted averages are possible using weight provided when spectrum is added to the stack.
Method signatures and docstrings:
- def __init__(self)... | 4064f6ca5d2807fbb99626838493d0f91cbd8748 | <|skeleton|>
class Accum:
"""Class to manage spectral averaging. All spectra to be averaged must be aligned in channel/index space. Weighted averages are possible using weight provided when spectrum is added to the stack."""
def __init__(self):
"""Constructor for Accum class. Args: none Returns: none""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Accum:
"""Class to manage spectral averaging. All spectra to be averaged must be aligned in channel/index space. Weighted averages are possible using weight provided when spectrum is added to the stack."""
def __init__(self):
"""Constructor for Accum class. Args: none Returns: none"""
sel... | the_stack_v2_python_sparse | lmtslr/reduction/line_reduction.py | myunm82/SpectralLineReduction | train | 0 |
3db7feb1de79faab52114f4bd7c60039622595ab | [
"cpu_state_proto = cpu_state_pb2.ArmV7mCpuState()\ncpu_state_info = exception_analyzer.CortexMExceptionAnalyzer(cpu_state_proto)\nself.assertFalse(cpu_state_info.is_fault_active())",
"cpu_state_proto = cpu_state_pb2.ArmV7mCpuState()\ncpu_state_proto.cfsr = cortex_m_constants.PW_CORTEX_M_CFSR_STKOF_MASK | cortex_m... | <|body_start_0|>
cpu_state_proto = cpu_state_pb2.ArmV7mCpuState()
cpu_state_info = exception_analyzer.CortexMExceptionAnalyzer(cpu_state_proto)
self.assertFalse(cpu_state_info.is_fault_active())
<|end_body_0|>
<|body_start_1|>
cpu_state_proto = cpu_state_pb2.ArmV7mCpuState()
cpu... | Test basic fault analysis functions. | BasicFaultTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicFaultTest:
"""Test basic fault analysis functions."""
def test_empty_state(self):
"""Ensure an empty CPU state proto doesn't indicate an active fault."""
<|body_0|>
def test_cfsr_fault(self):
"""Ensure a fault is active if CFSR bits are set."""
<|bod... | stack_v2_sparse_classes_36k_train_025098 | 9,689 | permissive | [
{
"docstring": "Ensure an empty CPU state proto doesn't indicate an active fault.",
"name": "test_empty_state",
"signature": "def test_empty_state(self)"
},
{
"docstring": "Ensure a fault is active if CFSR bits are set.",
"name": "test_cfsr_fault",
"signature": "def test_cfsr_fault(self)... | 4 | stack_v2_sparse_classes_30k_train_013135 | Implement the Python class `BasicFaultTest` described below.
Class description:
Test basic fault analysis functions.
Method signatures and docstrings:
- def test_empty_state(self): Ensure an empty CPU state proto doesn't indicate an active fault.
- def test_cfsr_fault(self): Ensure a fault is active if CFSR bits are ... | Implement the Python class `BasicFaultTest` described below.
Class description:
Test basic fault analysis functions.
Method signatures and docstrings:
- def test_empty_state(self): Ensure an empty CPU state proto doesn't indicate an active fault.
- def test_cfsr_fault(self): Ensure a fault is active if CFSR bits are ... | 7f3590b58e8398aad68c1e59702c459d2f8ca38e | <|skeleton|>
class BasicFaultTest:
"""Test basic fault analysis functions."""
def test_empty_state(self):
"""Ensure an empty CPU state proto doesn't indicate an active fault."""
<|body_0|>
def test_cfsr_fault(self):
"""Ensure a fault is active if CFSR bits are set."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BasicFaultTest:
"""Test basic fault analysis functions."""
def test_empty_state(self):
"""Ensure an empty CPU state proto doesn't indicate an active fault."""
cpu_state_proto = cpu_state_pb2.ArmV7mCpuState()
cpu_state_info = exception_analyzer.CortexMExceptionAnalyzer(cpu_state_pr... | the_stack_v2_python_sparse | pw_cpu_exception_cortex_m/py/exception_analyzer_test.py | waelbarakat/pigweed | train | 0 |
00eb25f819bddd566efd738bd13406b57ac48100 | [
"self.canvas = canvas\nself.fig = canvas.fig\nself.tp = None\nself.hover_connection = None\nself.annot = None",
"self.fig.clear()\nself.fig.ax = self.fig.add_subplot(1, 1, 1)\nself.fig.subplots_adjust(left=0.1, bottom=0.15, right=0.98, top=0.98, wspace=0.1, hspace=0)\ntemp, serial_time = meas.compute_time_series(... | <|body_start_0|>
self.canvas = canvas
self.fig = canvas.fig
self.tp = None
self.hover_connection = None
self.annot = None
<|end_body_0|>
<|body_start_1|>
self.fig.clear()
self.fig.ax = self.fig.add_subplot(1, 1, 1)
self.fig.subplots_adjust(left=0.1, botto... | Class to generate a time series graph of the ADCP temperature. Attributes ---------- canvas: MplCanvas Object of MplCanvas a FigureCanvas tp: list Reference to time series plot hover_connection: bool Switch to allow user to use the data cursor annot: Annotation Annotation object for data cursor | TemperatureTS | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain",
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TemperatureTS:
"""Class to generate a time series graph of the ADCP temperature. Attributes ---------- canvas: MplCanvas Object of MplCanvas a FigureCanvas tp: list Reference to time series plot hover_connection: bool Switch to allow user to use the data cursor annot: Annotation Annotation object... | stack_v2_sparse_classes_36k_train_025099 | 6,879 | permissive | [
{
"docstring": "Initialize object using the specified canvas. Parameters ---------- canvas: MplCanvas Object of MplCanvas",
"name": "__init__",
"signature": "def __init__(self, canvas)"
},
{
"docstring": "Creates the figure on the canvas. Parameters ---------- meas: Measurement Object of class M... | 5 | null | Implement the Python class `TemperatureTS` described below.
Class description:
Class to generate a time series graph of the ADCP temperature. Attributes ---------- canvas: MplCanvas Object of MplCanvas a FigureCanvas tp: list Reference to time series plot hover_connection: bool Switch to allow user to use the data cur... | Implement the Python class `TemperatureTS` described below.
Class description:
Class to generate a time series graph of the ADCP temperature. Attributes ---------- canvas: MplCanvas Object of MplCanvas a FigureCanvas tp: list Reference to time series plot hover_connection: bool Switch to allow user to use the data cur... | d82e18bcd8183c16ed2a9f57639933fac133624b | <|skeleton|>
class TemperatureTS:
"""Class to generate a time series graph of the ADCP temperature. Attributes ---------- canvas: MplCanvas Object of MplCanvas a FigureCanvas tp: list Reference to time series plot hover_connection: bool Switch to allow user to use the data cursor annot: Annotation Annotation object... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TemperatureTS:
"""Class to generate a time series graph of the ADCP temperature. Attributes ---------- canvas: MplCanvas Object of MplCanvas a FigureCanvas tp: list Reference to time series plot hover_connection: bool Switch to allow user to use the data cursor annot: Annotation Annotation object for data cur... | the_stack_v2_python_sparse | UI/TemperatureTS.py | ricorx7/QRevPy | train | 0 |
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