blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 467 8.64k | id stringlengths 40 40 | length_bytes int64 515 49.7k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 160 3.93k | prompted_full_text stringlengths 681 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.09k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 331 8.3k | source stringclasses 1
value | source_path stringlengths 5 177 | source_repo stringlengths 6 88 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e8917b7bcabb16ec9c35d4f475362ded7101711d | [
"results = []\nnums.sort()\ni = 0\nwhile i < len(nums):\n j = i + 1\n k = len(nums) - 1\n while j < k:\n v = nums[i] + nums[j] + nums[k] - target\n if v == 0:\n results.append([nums[i], nums[j], nums[k]])\n while j < len(nums) - 2 and nums[j] == nums[j + 1]:\n ... | <|body_start_0|>
results = []
nums.sort()
i = 0
while i < len(nums):
j = i + 1
k = len(nums) - 1
while j < k:
v = nums[i] + nums[j] + nums[k] - target
if v == 0:
results.append([nums[i], nums[j], nums... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def threeSum(self, nums, target):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_0|>
def fourSum(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_005300 | 1,456 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[List[int]]",
"name": "threeSum",
"signature": "def threeSum(self, nums, target)"
},
{
"docstring": ":type nums: List[int] :type target: int :rtype: List[List[int]]",
"name": "fourSum",
"signature": "def fourSum(self, nums, target)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003665 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSum(self, nums, target): :type nums: List[int] :rtype: List[List[int]]
- def fourSum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[List[int]] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSum(self, nums, target): :type nums: List[int] :rtype: List[List[int]]
- def fourSum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[List[int]]... | d8ed762d1005975f0de4f07760c9671195621c88 | <|skeleton|>
class Solution:
def threeSum(self, nums, target):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_0|>
def fourSum(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def threeSum(self, nums, target):
""":type nums: List[int] :rtype: List[List[int]]"""
results = []
nums.sort()
i = 0
while i < len(nums):
j = i + 1
k = len(nums) - 1
while j < k:
v = nums[i] + nums[j] + nums[... | the_stack_v2_python_sparse | 4sum/solution.py | uxlsl/leetcode_practice | train | 0 | |
d0c44c99119ac2e02260ff2f0b0d23a3c6d45be4 | [
"super().__init__()\nself.weights = torch.nn.Parameter(torch.zeros(feature_number, feature_number))\ntorch.nn.init.xavier_uniform_(self.weights)",
"attention = torch.nn.functional.normalize(self.weights, dim=-1)\nleft_representations = torch.nn.functional.normalize(left_representations, dim=-1)\nright_representat... | <|body_start_0|>
super().__init__()
self.weights = torch.nn.Parameter(torch.zeros(feature_number, feature_number))
torch.nn.init.xavier_uniform_(self.weights)
<|end_body_0|>
<|body_start_1|>
attention = torch.nn.functional.normalize(self.weights, dim=-1)
left_representations = t... | Attention layer. | EmbeddingLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmbeddingLayer:
"""Attention layer."""
def __init__(self, feature_number: int):
"""Initialize the relational embedding layer. :param feature_number: Number of features."""
<|body_0|>
def forward(self, left_representations: torch.FloatTensor, right_representations: torch.... | stack_v2_sparse_classes_10k_train_005301 | 25,672 | no_license | [
{
"docstring": "Initialize the relational embedding layer. :param feature_number: Number of features.",
"name": "__init__",
"signature": "def __init__(self, feature_number: int)"
},
{
"docstring": "Make a forward pass with the drug representations. :param left_representations: Left side drug rep... | 2 | stack_v2_sparse_classes_30k_train_003706 | Implement the Python class `EmbeddingLayer` described below.
Class description:
Attention layer.
Method signatures and docstrings:
- def __init__(self, feature_number: int): Initialize the relational embedding layer. :param feature_number: Number of features.
- def forward(self, left_representations: torch.FloatTenso... | Implement the Python class `EmbeddingLayer` described below.
Class description:
Attention layer.
Method signatures and docstrings:
- def __init__(self, feature_number: int): Initialize the relational embedding layer. :param feature_number: Number of features.
- def forward(self, left_representations: torch.FloatTenso... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class EmbeddingLayer:
"""Attention layer."""
def __init__(self, feature_number: int):
"""Initialize the relational embedding layer. :param feature_number: Number of features."""
<|body_0|>
def forward(self, left_representations: torch.FloatTensor, right_representations: torch.... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EmbeddingLayer:
"""Attention layer."""
def __init__(self, feature_number: int):
"""Initialize the relational embedding layer. :param feature_number: Number of features."""
super().__init__()
self.weights = torch.nn.Parameter(torch.zeros(feature_number, feature_number))
tor... | the_stack_v2_python_sparse | generated/test_AstraZeneca_chemicalx.py | jansel/pytorch-jit-paritybench | train | 35 |
337dc67ed4620a488f66b001d77dd9753dde6486 | [
"try:\n activity = request.json\n (services.log_service().upsert_activity(activity), 201)\nexcept Exception as e:\n nsp.abort(500, 'An internal error has occurred: {}'.format(e))",
"try:\n activity = request.json\n (services.log_service().upsert_activity(activity), 204)\nexcept Exception as e:\n ... | <|body_start_0|>
try:
activity = request.json
(services.log_service().upsert_activity(activity), 201)
except Exception as e:
nsp.abort(500, 'An internal error has occurred: {}'.format(e))
<|end_body_0|>
<|body_start_1|>
try:
activity = request.jso... | Activity | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Activity:
def post(self):
"""Insert a new activity log"""
<|body_0|>
def put(self):
"""Update an activity object by it's id."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
activity = request.json
(services.log_service()... | stack_v2_sparse_classes_10k_train_005302 | 4,427 | no_license | [
{
"docstring": "Insert a new activity log",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Update an activity object by it's id.",
"name": "put",
"signature": "def put(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002949 | Implement the Python class `Activity` described below.
Class description:
Implement the Activity class.
Method signatures and docstrings:
- def post(self): Insert a new activity log
- def put(self): Update an activity object by it's id. | Implement the Python class `Activity` described below.
Class description:
Implement the Activity class.
Method signatures and docstrings:
- def post(self): Insert a new activity log
- def put(self): Update an activity object by it's id.
<|skeleton|>
class Activity:
def post(self):
"""Insert a new activi... | df826cf7098aee59e0a1ced6f465c2e8bb3df9a5 | <|skeleton|>
class Activity:
def post(self):
"""Insert a new activity log"""
<|body_0|>
def put(self):
"""Update an activity object by it's id."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Activity:
def post(self):
"""Insert a new activity log"""
try:
activity = request.json
(services.log_service().upsert_activity(activity), 201)
except Exception as e:
nsp.abort(500, 'An internal error has occurred: {}'.format(e))
def put(self):
... | the_stack_v2_python_sparse | patient_portal/patient_portal/api/logs.py | bkh148/patient-cloud | train | 0 | |
e2f8e186dd4e84c355fece22dfbccc7e0d81834a | [
"self.is_categorical = is_categorical\nself.is_binary = len(unique_values) == 2\nself.unique_values = unique_values\nif not is_categorical and (not self.is_binary):\n self.unique_values = self.__get_stdev_band(unique_values)",
"mean = stats.mean(unique_values)\nstdev = stats.stdev(unique_values)\nreturn [mean ... | <|body_start_0|>
self.is_categorical = is_categorical
self.is_binary = len(unique_values) == 2
self.unique_values = unique_values
if not is_categorical and (not self.is_binary):
self.unique_values = self.__get_stdev_band(unique_values)
<|end_body_0|>
<|body_start_1|>
... | Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
def __init__(self, unique_values, is_categorical):
"""Constructor of an Encoder using one-hot-encoding"""
<|body_0|>
def __get_stdev_band(self, unique_values):
"""Get the lower bound and upper bound for the standard devaitation band for continuous value."""
... | stack_v2_sparse_classes_10k_train_005303 | 1,683 | no_license | [
{
"docstring": "Constructor of an Encoder using one-hot-encoding",
"name": "__init__",
"signature": "def __init__(self, unique_values, is_categorical)"
},
{
"docstring": "Get the lower bound and upper bound for the standard devaitation band for continuous value.",
"name": "__get_stdev_band",... | 3 | stack_v2_sparse_classes_30k_train_002081 | Implement the Python class `Encoder` described below.
Class description:
Implement the Encoder class.
Method signatures and docstrings:
- def __init__(self, unique_values, is_categorical): Constructor of an Encoder using one-hot-encoding
- def __get_stdev_band(self, unique_values): Get the lower bound and upper bound... | Implement the Python class `Encoder` described below.
Class description:
Implement the Encoder class.
Method signatures and docstrings:
- def __init__(self, unique_values, is_categorical): Constructor of an Encoder using one-hot-encoding
- def __get_stdev_band(self, unique_values): Get the lower bound and upper bound... | 9ae339f81fc7134ba9058fe975dec9ac7e3aaba4 | <|skeleton|>
class Encoder:
def __init__(self, unique_values, is_categorical):
"""Constructor of an Encoder using one-hot-encoding"""
<|body_0|>
def __get_stdev_band(self, unique_values):
"""Get the lower bound and upper bound for the standard devaitation band for continuous value."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Encoder:
def __init__(self, unique_values, is_categorical):
"""Constructor of an Encoder using one-hot-encoding"""
self.is_categorical = is_categorical
self.is_binary = len(unique_values) == 2
self.unique_values = unique_values
if not is_categorical and (not self.is_bin... | the_stack_v2_python_sparse | Project5/encoding.py | vincy0320/School_Intro_to_ML | train | 0 | |
ab52207902ac62cb372c9c550e8a29caa4abae0c | [
"independent_pc = param_domain.ParamChange('a', 'Copier', {'value': 'firstValue', 'parse_with_jinja': False})\ndependent_pc = param_domain.ParamChange('b', 'Copier', {'value': '{{a}}', 'parse_with_jinja': True})\nexp_param_specs = {'a': param_domain.ParamSpec('UnicodeString'), 'b': param_domain.ParamSpec('UnicodeSt... | <|body_start_0|>
independent_pc = param_domain.ParamChange('a', 'Copier', {'value': 'firstValue', 'parse_with_jinja': False})
dependent_pc = param_domain.ParamChange('b', 'Copier', {'value': '{{a}}', 'parse_with_jinja': True})
exp_param_specs = {'a': param_domain.ParamSpec('UnicodeString'), 'b':... | Test methods relating to exploration parameters. | ExplorationParametersUnitTests | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExplorationParametersUnitTests:
"""Test methods relating to exploration parameters."""
def test_get_init_params(self):
"""Test the get_init_params() method."""
<|body_0|>
def test_update_learner_params(self):
"""Test the update_learner_params() method."""
... | stack_v2_sparse_classes_10k_train_005304 | 15,833 | permissive | [
{
"docstring": "Test the get_init_params() method.",
"name": "test_get_init_params",
"signature": "def test_get_init_params(self)"
},
{
"docstring": "Test the update_learner_params() method.",
"name": "test_update_learner_params",
"signature": "def test_update_learner_params(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004507 | Implement the Python class `ExplorationParametersUnitTests` described below.
Class description:
Test methods relating to exploration parameters.
Method signatures and docstrings:
- def test_get_init_params(self): Test the get_init_params() method.
- def test_update_learner_params(self): Test the update_learner_params... | Implement the Python class `ExplorationParametersUnitTests` described below.
Class description:
Test methods relating to exploration parameters.
Method signatures and docstrings:
- def test_get_init_params(self): Test the get_init_params() method.
- def test_update_learner_params(self): Test the update_learner_params... | 50994926e9e4fab925a0cf1f366cad3de2ed4d7b | <|skeleton|>
class ExplorationParametersUnitTests:
"""Test methods relating to exploration parameters."""
def test_get_init_params(self):
"""Test the get_init_params() method."""
<|body_0|>
def test_update_learner_params(self):
"""Test the update_learner_params() method."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ExplorationParametersUnitTests:
"""Test methods relating to exploration parameters."""
def test_get_init_params(self):
"""Test the get_init_params() method."""
independent_pc = param_domain.ParamChange('a', 'Copier', {'value': 'firstValue', 'parse_with_jinja': False})
dependent_pc... | the_stack_v2_python_sparse | core/controllers/reader_test.py | CMDann/oppia | train | 2 |
0dbb563dc5e7ac920c597c3a48abb2fc84c0c2bf | [
"self.assertEqual(D20Coin.pp(1), 1000)\nself.assertEqual(D20Coin.gp(1), 100)\nself.assertEqual(D20Coin.sp(1), 10)\nself.assertEqual(D20Coin.cp(1), 1)",
"treasure = D20Coin(pp=1, gp=2, sp=3, cp=4)\nself.assertEqual(treasure.value, 1234)\nself.assertEqual(treasure.sale_value, 1234)\nself.assertEqual(treasure.name, ... | <|body_start_0|>
self.assertEqual(D20Coin.pp(1), 1000)
self.assertEqual(D20Coin.gp(1), 100)
self.assertEqual(D20Coin.sp(1), 10)
self.assertEqual(D20Coin.cp(1), 1)
<|end_body_0|>
<|body_start_1|>
treasure = D20Coin(pp=1, gp=2, sp=3, cp=4)
self.assertEqual(treasure.value, ... | A test suite for the D20Coin class | TestD20Coin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestD20Coin:
"""A test suite for the D20Coin class"""
def test_convert_coin_types(self):
"""Try the four coin type conversions"""
<|body_0|>
def test_coin_treasure(self):
"""Create a coin-only treasure object"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_10k_train_005305 | 3,068 | permissive | [
{
"docstring": "Try the four coin type conversions",
"name": "test_convert_coin_types",
"signature": "def test_convert_coin_types(self)"
},
{
"docstring": "Create a coin-only treasure object",
"name": "test_coin_treasure",
"signature": "def test_coin_treasure(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006099 | Implement the Python class `TestD20Coin` described below.
Class description:
A test suite for the D20Coin class
Method signatures and docstrings:
- def test_convert_coin_types(self): Try the four coin type conversions
- def test_coin_treasure(self): Create a coin-only treasure object | Implement the Python class `TestD20Coin` described below.
Class description:
A test suite for the D20Coin class
Method signatures and docstrings:
- def test_convert_coin_types(self): Try the four coin type conversions
- def test_coin_treasure(self): Create a coin-only treasure object
<|skeleton|>
class TestD20Coin:
... | 75504d2443cdc80db120c5dcdc14db379d15396e | <|skeleton|>
class TestD20Coin:
"""A test suite for the D20Coin class"""
def test_convert_coin_types(self):
"""Try the four coin type conversions"""
<|body_0|>
def test_coin_treasure(self):
"""Create a coin-only treasure object"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestD20Coin:
"""A test suite for the D20Coin class"""
def test_convert_coin_types(self):
"""Try the four coin type conversions"""
self.assertEqual(D20Coin.pp(1), 1000)
self.assertEqual(D20Coin.gp(1), 100)
self.assertEqual(D20Coin.sp(1), 10)
self.assertEqual(D20Coin... | the_stack_v2_python_sparse | games/d20/pathfinder/test_pathfindertreasure.py | ajs/tools | train | 5 |
18a986925e2e8bf1b4f45ec1f4cdc3312485112d | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | The greeting service definition. | GreeterServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GreeterServicer:
"""The greeting service definition."""
def SayHello(self, request, context):
"""Sends a greeting"""
<|body_0|>
def SayHelloAgain(self, request, context):
"""Sends another greeting"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_005306 | 2,740 | permissive | [
{
"docstring": "Sends a greeting",
"name": "SayHello",
"signature": "def SayHello(self, request, context)"
},
{
"docstring": "Sends another greeting",
"name": "SayHelloAgain",
"signature": "def SayHelloAgain(self, request, context)"
}
] | 2 | null | Implement the Python class `GreeterServicer` described below.
Class description:
The greeting service definition.
Method signatures and docstrings:
- def SayHello(self, request, context): Sends a greeting
- def SayHelloAgain(self, request, context): Sends another greeting | Implement the Python class `GreeterServicer` described below.
Class description:
The greeting service definition.
Method signatures and docstrings:
- def SayHello(self, request, context): Sends a greeting
- def SayHelloAgain(self, request, context): Sends another greeting
<|skeleton|>
class GreeterServicer:
"""T... | 44e819e713c3885e38c99c16dc73b7d7478acfe8 | <|skeleton|>
class GreeterServicer:
"""The greeting service definition."""
def SayHello(self, request, context):
"""Sends a greeting"""
<|body_0|>
def SayHelloAgain(self, request, context):
"""Sends another greeting"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GreeterServicer:
"""The greeting service definition."""
def SayHello(self, request, context):
"""Sends a greeting"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def... | the_stack_v2_python_sparse | endpoints/getting-started-grpc/helloworld_pb2_grpc.py | GoogleCloudPlatform/python-docs-samples | train | 7,035 |
51090fa6641f4d07d527782c325f98819873d476 | [
"for t in self.rotationTests:\n point = Geometry.rotate(t[0][0], t[0][1], t[0][2], t[0][3], t[0][4])\n self.assertEqual(point, t[1])",
"for t in self.translationTests:\n point = Geometry.translate(t[0][0], t[0][1], t[0][2], t[0][3])\n self.assertEqual(point, t[1])",
"for t in self.scalingTests:\n ... | <|body_start_0|>
for t in self.rotationTests:
point = Geometry.rotate(t[0][0], t[0][1], t[0][2], t[0][3], t[0][4])
self.assertEqual(point, t[1])
<|end_body_0|>
<|body_start_1|>
for t in self.translationTests:
point = Geometry.translate(t[0][0], t[0][1], t[0][2], t[0]... | testGeometry | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class testGeometry:
def testRotation(self):
"""testing rotation primitive"""
<|body_0|>
def testTranslation(self):
"""testing translation primitive"""
<|body_1|>
def testScaling(self):
"""testing scaling primitive"""
<|body_2|>
<|end_skeleton|... | stack_v2_sparse_classes_10k_train_005307 | 27,361 | no_license | [
{
"docstring": "testing rotation primitive",
"name": "testRotation",
"signature": "def testRotation(self)"
},
{
"docstring": "testing translation primitive",
"name": "testTranslation",
"signature": "def testTranslation(self)"
},
{
"docstring": "testing scaling primitive",
"na... | 3 | null | Implement the Python class `testGeometry` described below.
Class description:
Implement the testGeometry class.
Method signatures and docstrings:
- def testRotation(self): testing rotation primitive
- def testTranslation(self): testing translation primitive
- def testScaling(self): testing scaling primitive | Implement the Python class `testGeometry` described below.
Class description:
Implement the testGeometry class.
Method signatures and docstrings:
- def testRotation(self): testing rotation primitive
- def testTranslation(self): testing translation primitive
- def testScaling(self): testing scaling primitive
<|skelet... | d900f58f0ddc1891831b298d9b37fbe98193719d | <|skeleton|>
class testGeometry:
def testRotation(self):
"""testing rotation primitive"""
<|body_0|>
def testTranslation(self):
"""testing translation primitive"""
<|body_1|>
def testScaling(self):
"""testing scaling primitive"""
<|body_2|>
<|end_skeleton|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class testGeometry:
def testRotation(self):
"""testing rotation primitive"""
for t in self.rotationTests:
point = Geometry.rotate(t[0][0], t[0][1], t[0][2], t[0][3], t[0][4])
self.assertEqual(point, t[1])
def testTranslation(self):
"""testing translation primitiv... | the_stack_v2_python_sparse | Assignment4/atom3/Kernel/GraphicEditor/testGraphics.py | pombreda/comp304 | train | 1 | |
e6f517acbb6bd64f7c13cbc13ba6b6e320dc3174 | [
"startTime = datetime.datetime.now()\nopener = urllib.request.build_opener()\nopener.addheaders = [('User-agent', 'Mozilla/5.0')]\nurllib.request.install_opener(opener)\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('arshadr_rcallah_shaikh1', 'arshadr_rcallah_shaikh1')\nprint('Connected'... | <|body_start_0|>
startTime = datetime.datetime.now()
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
urllib.request.install_opener(opener)
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('arshadr_r... | income_data | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class income_data:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything h... | stack_v2_sparse_classes_10k_train_005308 | 4,837 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | null | Implement the Python class `income_data` described below.
Class description:
Implement the income_data class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTi... | Implement the Python class `income_data` described below.
Class description:
Implement the income_data class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTi... | 90284cf3debbac36eead07b8d2339cdd191b86cf | <|skeleton|>
class income_data:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything h... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class income_data:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
urllib.request.instal... | the_stack_v2_python_sparse | arshadr_rcallah_shaikh1/income_data.py | maximega/course-2019-spr-proj | train | 2 | |
d48eaf3f2ca2f639f53e1be750b170bc47851085 | [
"try:\n email = username\n user = self.user_class.objects.get(contact__email_addresses__email_address__iexact=email, contact__email_addresses__is_primary=True)\n if user.is_active and (user.check_password(password) or no_pass):\n return user\n return None\nexcept self.user_class.DoesNotExist:\n ... | <|body_start_0|>
try:
email = username
user = self.user_class.objects.get(contact__email_addresses__email_address__iexact=email, contact__email_addresses__is_primary=True)
if user.is_active and (user.check_password(password) or no_pass):
return user
... | Authenticate a CustomUser with e-mail address instead of username. | EmailAuthenticationBackend | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmailAuthenticationBackend:
"""Authenticate a CustomUser with e-mail address instead of username."""
def authenticate(self, username=None, password=None, no_pass=False):
"""Check if the user is properly authenticated, either logging in by e-mail address and password or programmatical... | stack_v2_sparse_classes_10k_train_005309 | 1,808 | no_license | [
{
"docstring": "Check if the user is properly authenticated, either logging in by e-mail address and password or programmatically logged in (e.g. upon activation of account) using no_pass=True.",
"name": "authenticate",
"signature": "def authenticate(self, username=None, password=None, no_pass=False)"
... | 3 | stack_v2_sparse_classes_30k_train_001971 | Implement the Python class `EmailAuthenticationBackend` described below.
Class description:
Authenticate a CustomUser with e-mail address instead of username.
Method signatures and docstrings:
- def authenticate(self, username=None, password=None, no_pass=False): Check if the user is properly authenticated, either lo... | Implement the Python class `EmailAuthenticationBackend` described below.
Class description:
Authenticate a CustomUser with e-mail address instead of username.
Method signatures and docstrings:
- def authenticate(self, username=None, password=None, no_pass=False): Check if the user is properly authenticated, either lo... | 0a284e2aae3ca08955215418a76bb70ad9af1f81 | <|skeleton|>
class EmailAuthenticationBackend:
"""Authenticate a CustomUser with e-mail address instead of username."""
def authenticate(self, username=None, password=None, no_pass=False):
"""Check if the user is properly authenticated, either logging in by e-mail address and password or programmatical... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EmailAuthenticationBackend:
"""Authenticate a CustomUser with e-mail address instead of username."""
def authenticate(self, username=None, password=None, no_pass=False):
"""Check if the user is properly authenticated, either logging in by e-mail address and password or programmatically logged in ... | the_stack_v2_python_sparse | lily/users/auth_backends.py | rmoorman/hellolily | train | 0 |
0c1b699d18933ca76dcc0358803abd2a50a8c082 | [
"mix = mix.to(self.device)\nmix_w = self.modules.encoder(mix)\nest_mask = self.modules.masknet(mix_w)\nmix_w = torch.stack([mix_w] * self.hparams.num_spks)\nsep_h = mix_w * est_mask\nest_source = torch.cat([self.modules.decoder(sep_h[i]).unsqueeze(-1) for i in range(self.hparams.num_spks)], dim=-1)\nT_origin = mix.... | <|body_start_0|>
mix = mix.to(self.device)
mix_w = self.modules.encoder(mix)
est_mask = self.modules.masknet(mix_w)
mix_w = torch.stack([mix_w] * self.hparams.num_spks)
sep_h = mix_w * est_mask
est_source = torch.cat([self.modules.decoder(sep_h[i]).unsqueeze(-1) for i in ... | A "ready-to-use" speech separation model. Uses Sepformer architecture. Example ------- >>> tmpdir = getfixture("tmpdir") >>> model = SepformerSeparation.from_hparams( ... source="speechbrain/sepformer-wsj02mix", ... savedir=tmpdir) >>> mix = torch.randn(1, 400) >>> est_sources = model.separate_batch(mix) >>> print(est_... | SepformerSeparation | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference",
"GPL-1.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SepformerSeparation:
"""A "ready-to-use" speech separation model. Uses Sepformer architecture. Example ------- >>> tmpdir = getfixture("tmpdir") >>> model = SepformerSeparation.from_hparams( ... source="speechbrain/sepformer-wsj02mix", ... savedir=tmpdir) >>> mix = torch.randn(1, 400) >>> est_sou... | stack_v2_sparse_classes_10k_train_005310 | 35,100 | permissive | [
{
"docstring": "Run source separation on batch of audio. Arguments --------- mix : torch.tensor The mixture of sources. Returns ------- tensor Separated sources",
"name": "separate_batch",
"signature": "def separate_batch(self, mix)"
},
{
"docstring": "Separate sources from file. Arguments -----... | 2 | null | Implement the Python class `SepformerSeparation` described below.
Class description:
A "ready-to-use" speech separation model. Uses Sepformer architecture. Example ------- >>> tmpdir = getfixture("tmpdir") >>> model = SepformerSeparation.from_hparams( ... source="speechbrain/sepformer-wsj02mix", ... savedir=tmpdir) >>... | Implement the Python class `SepformerSeparation` described below.
Class description:
A "ready-to-use" speech separation model. Uses Sepformer architecture. Example ------- >>> tmpdir = getfixture("tmpdir") >>> model = SepformerSeparation.from_hparams( ... source="speechbrain/sepformer-wsj02mix", ... savedir=tmpdir) >>... | 92acc188d3a0f634de58463b6676e70df83ef808 | <|skeleton|>
class SepformerSeparation:
"""A "ready-to-use" speech separation model. Uses Sepformer architecture. Example ------- >>> tmpdir = getfixture("tmpdir") >>> model = SepformerSeparation.from_hparams( ... source="speechbrain/sepformer-wsj02mix", ... savedir=tmpdir) >>> mix = torch.randn(1, 400) >>> est_sou... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SepformerSeparation:
"""A "ready-to-use" speech separation model. Uses Sepformer architecture. Example ------- >>> tmpdir = getfixture("tmpdir") >>> model = SepformerSeparation.from_hparams( ... source="speechbrain/sepformer-wsj02mix", ... savedir=tmpdir) >>> mix = torch.randn(1, 400) >>> est_sources = model.... | the_stack_v2_python_sparse | ACL_PyTorch/contrib/audio/tdnn/interfaces.py | Ascend/ModelZoo-PyTorch | train | 23 |
ce3749ee1424c6adce799fe1e348471b79ae42af | [
"super(BowElmoEmbedder, self).__init__()\nself.emb_dim = emb_dim\nself.dropout_value = dropout_value\nself.layer_aggregation_type = layer_aggregation\nself.allowed_layer_aggregation_types = ['sum', 'average', 'last', 'first']\nself.cuda_device_id = cuda_device_id\nself.device = torch.device('cpu') if cuda_device_id... | <|body_start_0|>
super(BowElmoEmbedder, self).__init__()
self.emb_dim = emb_dim
self.dropout_value = dropout_value
self.layer_aggregation_type = layer_aggregation
self.allowed_layer_aggregation_types = ['sum', 'average', 'last', 'first']
self.cuda_device_id = cuda_device_... | BowElmoEmbedder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BowElmoEmbedder:
def __init__(self, emb_dim: int=1024, dropout_value: float=0.0, layer_aggregation: str='sum', cuda_device_id: int=-1):
"""Bag of words Elmo Embedder which aggregates elmo embedding for every token Parameters ---------- emb_dim : int Embedding dimension dropout_value : fl... | stack_v2_sparse_classes_10k_train_005311 | 4,125 | permissive | [
{
"docstring": "Bag of words Elmo Embedder which aggregates elmo embedding for every token Parameters ---------- emb_dim : int Embedding dimension dropout_value : float Any input dropout to be applied to the embeddings layer_aggregation : str You can chose one of ``[sum, average, last, first]`` which decides ho... | 2 | stack_v2_sparse_classes_30k_train_002649 | Implement the Python class `BowElmoEmbedder` described below.
Class description:
Implement the BowElmoEmbedder class.
Method signatures and docstrings:
- def __init__(self, emb_dim: int=1024, dropout_value: float=0.0, layer_aggregation: str='sum', cuda_device_id: int=-1): Bag of words Elmo Embedder which aggregates e... | Implement the Python class `BowElmoEmbedder` described below.
Class description:
Implement the BowElmoEmbedder class.
Method signatures and docstrings:
- def __init__(self, emb_dim: int=1024, dropout_value: float=0.0, layer_aggregation: str='sum', cuda_device_id: int=-1): Bag of words Elmo Embedder which aggregates e... | cb4c1413ddc3c749835e1cb80db31c0060e7a1eb | <|skeleton|>
class BowElmoEmbedder:
def __init__(self, emb_dim: int=1024, dropout_value: float=0.0, layer_aggregation: str='sum', cuda_device_id: int=-1):
"""Bag of words Elmo Embedder which aggregates elmo embedding for every token Parameters ---------- emb_dim : int Embedding dimension dropout_value : fl... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BowElmoEmbedder:
def __init__(self, emb_dim: int=1024, dropout_value: float=0.0, layer_aggregation: str='sum', cuda_device_id: int=-1):
"""Bag of words Elmo Embedder which aggregates elmo embedding for every token Parameters ---------- emb_dim : int Embedding dimension dropout_value : float Any input ... | the_stack_v2_python_sparse | sciwing/modules/embedders/bow_elmo_embedder.py | yaxche-io/sciwing | train | 0 | |
97ba2c8dbb90199871ebead20570ddb79ccca4d5 | [
"args = movies_parser.parse_args()\npage = args['page']\nper_page = args['per_page']\nsort_by = args['sort_by']\nsort_order = args['order']\nstart = per_page * (page - 1)\nstop = start + per_page\ndescending = sort_order == 'desc'\nkwargs = {'start': start, 'stop': stop, 'list_id': list_id, 'order_by': sort_by, 'de... | <|body_start_0|>
args = movies_parser.parse_args()
page = args['page']
per_page = args['per_page']
sort_by = args['sort_by']
sort_order = args['order']
start = per_page * (page - 1)
stop = start + per_page
descending = sort_order == 'desc'
kwargs =... | MovieListMoviesAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovieListMoviesAPI:
def get(self, list_id, session=None):
"""Get movies by list ID"""
<|body_0|>
def post(self, list_id, session=None):
"""Add movies to list by ID"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
args = movies_parser.parse_args()
... | stack_v2_sparse_classes_10k_train_005312 | 12,846 | permissive | [
{
"docstring": "Get movies by list ID",
"name": "get",
"signature": "def get(self, list_id, session=None)"
},
{
"docstring": "Add movies to list by ID",
"name": "post",
"signature": "def post(self, list_id, session=None)"
}
] | 2 | null | Implement the Python class `MovieListMoviesAPI` described below.
Class description:
Implement the MovieListMoviesAPI class.
Method signatures and docstrings:
- def get(self, list_id, session=None): Get movies by list ID
- def post(self, list_id, session=None): Add movies to list by ID | Implement the Python class `MovieListMoviesAPI` described below.
Class description:
Implement the MovieListMoviesAPI class.
Method signatures and docstrings:
- def get(self, list_id, session=None): Get movies by list ID
- def post(self, list_id, session=None): Add movies to list by ID
<|skeleton|>
class MovieListMov... | ea95ff60041beaea9aacbc2d93549e3a6b981dc5 | <|skeleton|>
class MovieListMoviesAPI:
def get(self, list_id, session=None):
"""Get movies by list ID"""
<|body_0|>
def post(self, list_id, session=None):
"""Add movies to list by ID"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MovieListMoviesAPI:
def get(self, list_id, session=None):
"""Get movies by list ID"""
args = movies_parser.parse_args()
page = args['page']
per_page = args['per_page']
sort_by = args['sort_by']
sort_order = args['order']
start = per_page * (page - 1)
... | the_stack_v2_python_sparse | flexget/components/managed_lists/lists/movie_list/api.py | BrutuZ/Flexget | train | 1 | |
608eda567b1c98079ef6384daee47e21bb89f84b | [
"writer = KvDbWriter(KvDbClient(**config))\nfor configured_stream in configured_catalog.streams:\n if configured_stream.destination_sync_mode == DestinationSyncMode.overwrite:\n writer.delete_stream_entries(configured_stream.stream.name)\nfor message in input_messages:\n if message.type == Type.STATE:\... | <|body_start_0|>
writer = KvDbWriter(KvDbClient(**config))
for configured_stream in configured_catalog.streams:
if configured_stream.destination_sync_mode == DestinationSyncMode.overwrite:
writer.delete_stream_entries(configured_stream.stream.name)
for message in inpu... | DestinationKvdb | [
"MIT",
"Apache-2.0",
"BSD-3-Clause",
"Elastic-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DestinationKvdb:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method return... | stack_v2_sparse_classes_10k_train_005313 | 3,439 | permissive | [
{
"docstring": "Reads the input stream of messages, config, and catalog to write data to the destination. This method returns an iterable (typically a generator of AirbyteMessages via yield) containing state messages received in the input message stream. Outputting a state message means that every AirbyteRecord... | 2 | stack_v2_sparse_classes_30k_train_006855 | Implement the Python class `DestinationKvdb` described below.
Class description:
Implement the DestinationKvdb class.
Method signatures and docstrings:
- def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]: Read... | Implement the Python class `DestinationKvdb` described below.
Class description:
Implement the DestinationKvdb class.
Method signatures and docstrings:
- def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]: Read... | 8d5f9a2d49ab8f9e85ccf058cb02c2fda287afc6 | <|skeleton|>
class DestinationKvdb:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method return... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DestinationKvdb:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method returns an iterable ... | the_stack_v2_python_sparse | dts/airbyte/airbyte-integrations/connectors/destination-kvdb/destination_kvdb/destination.py | alldatacenter/alldata | train | 774 | |
af50065004b0f65f1417d3839533b261a29f9b2a | [
"self.queue = []\nself.front = None\nself.rare = None\nself.max_size = 5",
"if self.rare == self.max_size - 1:\n print('Overflow')\nelse:\n self.queue.append(item)\n if self.front == None:\n self.front = 0\n self.rare = 0\n else:\n self.rare += 1",
"if self.front == None:\n p... | <|body_start_0|>
self.queue = []
self.front = None
self.rare = None
self.max_size = 5
<|end_body_0|>
<|body_start_1|>
if self.rare == self.max_size - 1:
print('Overflow')
else:
self.queue.append(item)
if self.front == None:
... | This class contains functions for queue data structure implementation. Enqueue: To add at rare position. Dequeue: To remove at front position. | Queue | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Queue:
"""This class contains functions for queue data structure implementation. Enqueue: To add at rare position. Dequeue: To remove at front position."""
def __init__(self):
"""Constructor function. Argument: self -- represents the object of the class."""
<|body_0|>
de... | stack_v2_sparse_classes_10k_train_005314 | 2,051 | no_license | [
{
"docstring": "Constructor function. Argument: self -- represents the object of the class.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "This function will add the item to the queue. Argument: self -- represents the object of the class. item -- integer value.",
... | 4 | stack_v2_sparse_classes_30k_train_006935 | Implement the Python class `Queue` described below.
Class description:
This class contains functions for queue data structure implementation. Enqueue: To add at rare position. Dequeue: To remove at front position.
Method signatures and docstrings:
- def __init__(self): Constructor function. Argument: self -- represen... | Implement the Python class `Queue` described below.
Class description:
This class contains functions for queue data structure implementation. Enqueue: To add at rare position. Dequeue: To remove at front position.
Method signatures and docstrings:
- def __init__(self): Constructor function. Argument: self -- represen... | 6870426104aef417086788221dad29e887ddfe3f | <|skeleton|>
class Queue:
"""This class contains functions for queue data structure implementation. Enqueue: To add at rare position. Dequeue: To remove at front position."""
def __init__(self):
"""Constructor function. Argument: self -- represents the object of the class."""
<|body_0|>
de... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Queue:
"""This class contains functions for queue data structure implementation. Enqueue: To add at rare position. Dequeue: To remove at front position."""
def __init__(self):
"""Constructor function. Argument: self -- represents the object of the class."""
self.queue = []
self.fr... | the_stack_v2_python_sparse | Data Structure/03. Queue/01. Queue Implementation/py_code.py | Slothfulwave612/Coding-Problems | train | 5 |
8cc0dc9633f9b9cee52b2c36367c06a63ea75666 | [
"self.terms = terms\n\ndef form():\n res = 0\n for x in terms:\n res += x.base ** x.power\n return res\nself.form = form",
"if isinstance(target, Formula) == False:\n raise ValueError('Require Formula instance!')\n\ndef form():\n res = 0\n for t in target.terms:\n for x in self.ter... | <|body_start_0|>
self.terms = terms
def form():
res = 0
for x in terms:
res += x.base ** x.power
return res
self.form = form
<|end_body_0|>
<|body_start_1|>
if isinstance(target, Formula) == False:
raise ValueError('Requir... | Formula class. Have base number and multiplier. | Formula | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Formula:
"""Formula class. Have base number and multiplier."""
def __init__(self, terms):
"""Recieved Term object list."""
<|body_0|>
def __mul__(self, target):
"""Multiply out."""
<|body_1|>
def calc(self):
"""Retaining formula caluculate.""... | stack_v2_sparse_classes_10k_train_005315 | 7,281 | no_license | [
{
"docstring": "Recieved Term object list.",
"name": "__init__",
"signature": "def __init__(self, terms)"
},
{
"docstring": "Multiply out.",
"name": "__mul__",
"signature": "def __mul__(self, target)"
},
{
"docstring": "Retaining formula caluculate.",
"name": "calc",
"sig... | 3 | stack_v2_sparse_classes_30k_train_003045 | Implement the Python class `Formula` described below.
Class description:
Formula class. Have base number and multiplier.
Method signatures and docstrings:
- def __init__(self, terms): Recieved Term object list.
- def __mul__(self, target): Multiply out.
- def calc(self): Retaining formula caluculate. | Implement the Python class `Formula` described below.
Class description:
Formula class. Have base number and multiplier.
Method signatures and docstrings:
- def __init__(self, terms): Recieved Term object list.
- def __mul__(self, target): Multiply out.
- def calc(self): Retaining formula caluculate.
<|skeleton|>
cl... | 0c4f79ce5c370027b76ec9a336b392ee61b12a7a | <|skeleton|>
class Formula:
"""Formula class. Have base number and multiplier."""
def __init__(self, terms):
"""Recieved Term object list."""
<|body_0|>
def __mul__(self, target):
"""Multiply out."""
<|body_1|>
def calc(self):
"""Retaining formula caluculate.""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Formula:
"""Formula class. Have base number and multiplier."""
def __init__(self, terms):
"""Recieved Term object list."""
self.terms = terms
def form():
res = 0
for x in terms:
res += x.base ** x.power
return res
self.f... | the_stack_v2_python_sparse | pheasant/numtheory.py | moguonyanko/pheasant | train | 0 |
a94619b76fd9dd0dc0e3afa02ececd22d1290059 | [
"if data is None:\n raise ValidationError('No data was provided')\nreturn Performance(**data)",
"if data['start_datetime'].date() > data['end_datetime'].date():\n raise ValidationError('Start date must be before end date.')\nelif data['start_datetime'].date() == data['end_datetime'].date() and data['start_d... | <|body_start_0|>
if data is None:
raise ValidationError('No data was provided')
return Performance(**data)
<|end_body_0|>
<|body_start_1|>
if data['start_datetime'].date() > data['end_datetime'].date():
raise ValidationError('Start date must be before end date.')
... | Class to serialize and deserialize Performance objects. | PerformanceSchema | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PerformanceSchema:
"""Class to serialize and deserialize Performance objects."""
def make_object(self, data, **kwargs):
"""Return a performance object from the validated data."""
<|body_0|>
def validate_datetimes(self, data, **kwargs):
"""Raise a ValidationError ... | stack_v2_sparse_classes_10k_train_005316 | 2,121 | no_license | [
{
"docstring": "Return a performance object from the validated data.",
"name": "make_object",
"signature": "def make_object(self, data, **kwargs)"
},
{
"docstring": "Raise a ValidationError if the start_datetime is after the end_datetime.",
"name": "validate_datetimes",
"signature": "def... | 2 | stack_v2_sparse_classes_30k_train_002659 | Implement the Python class `PerformanceSchema` described below.
Class description:
Class to serialize and deserialize Performance objects.
Method signatures and docstrings:
- def make_object(self, data, **kwargs): Return a performance object from the validated data.
- def validate_datetimes(self, data, **kwargs): Rai... | Implement the Python class `PerformanceSchema` described below.
Class description:
Class to serialize and deserialize Performance objects.
Method signatures and docstrings:
- def make_object(self, data, **kwargs): Return a performance object from the validated data.
- def validate_datetimes(self, data, **kwargs): Rai... | d5ae552d383f5f971e29a38055c518fc68172f32 | <|skeleton|>
class PerformanceSchema:
"""Class to serialize and deserialize Performance objects."""
def make_object(self, data, **kwargs):
"""Return a performance object from the validated data."""
<|body_0|>
def validate_datetimes(self, data, **kwargs):
"""Raise a ValidationError ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PerformanceSchema:
"""Class to serialize and deserialize Performance objects."""
def make_object(self, data, **kwargs):
"""Return a performance object from the validated data."""
if data is None:
raise ValidationError('No data was provided')
return Performance(**data)
... | the_stack_v2_python_sparse | server/app/api/schemas/performance.py | EricMontague/MailChimp-Newsletter-Project | train | 0 |
12bfaad3d93abc8989bf3065f638200430de6f5c | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\ntry:\n mapping_value = parse_node.get_child_node('@odata.type').get_str_value()\nexcept AttributeError:\n mapping_value = None\nif mapping_value and mapping_value.casefold() == '#microsoft.graph.chatMessageHostedContent'.casefold():\n ... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
try:
mapping_value = parse_node.get_child_node('@odata.type').get_str_value()
except AttributeError:
mapping_value = None
if mapping_value and mapping_value.casefold() ==... | TeamworkHostedContent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TeamworkHostedContent:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TeamworkHostedContent:
"""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 th... | stack_v2_sparse_classes_10k_train_005317 | 2,909 | 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: TeamworkHostedContent",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminat... | 3 | null | Implement the Python class `TeamworkHostedContent` described below.
Class description:
Implement the TeamworkHostedContent class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TeamworkHostedContent: Creates a new instance of the appropriate class base... | Implement the Python class `TeamworkHostedContent` described below.
Class description:
Implement the TeamworkHostedContent class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TeamworkHostedContent: Creates a new instance of the appropriate class base... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class TeamworkHostedContent:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TeamworkHostedContent:
"""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 th... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TeamworkHostedContent:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TeamworkHostedContent:
"""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 Retur... | the_stack_v2_python_sparse | msgraph/generated/models/teamwork_hosted_content.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
28658cda49c965f5add0ab92473812c45c28d8e7 | [
"self.label = label\nself.paths = paths\nif self.paths is None:\n self.paths = {}\nself.reset()",
"str_ = \"Vertex '{label}' - visited: {visited}, parent: {parent}, cost: {cost}\\n\"\nstr_ += ' paths: {paths}'\nreturn str_.format(**self.__dict__)",
"self.visited = False\nself.parent = None\nself.cost = No... | <|body_start_0|>
self.label = label
self.paths = paths
if self.paths is None:
self.paths = {}
self.reset()
<|end_body_0|>
<|body_start_1|>
str_ = "Vertex '{label}' - visited: {visited}, parent: {parent}, cost: {cost}\n"
str_ += ' paths: {paths}'
re... | A graph vertex with a set of paths to other vertices. A vertex is characterized by a label (or name) and a dictionary of paths from the vertex to other vertices. For application of Dijkstra's algorithm for calculating the shortest path from any vertex to all other vertices, each vertex also has the 'visited', 'parent' ... | Vertex | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Vertex:
"""A graph vertex with a set of paths to other vertices. A vertex is characterized by a label (or name) and a dictionary of paths from the vertex to other vertices. For application of Dijkstra's algorithm for calculating the shortest path from any vertex to all other vertices, each vertex... | stack_v2_sparse_classes_10k_train_005318 | 15,275 | no_license | [
{
"docstring": "Create a new vertex.",
"name": "__init__",
"signature": "def __init__(self, label, paths=None)"
},
{
"docstring": "Format the vertex as a string.",
"name": "__str__",
"signature": "def __str__(self)"
},
{
"docstring": "Reset the vertex to its default values. This ... | 3 | stack_v2_sparse_classes_30k_train_002581 | Implement the Python class `Vertex` described below.
Class description:
A graph vertex with a set of paths to other vertices. A vertex is characterized by a label (or name) and a dictionary of paths from the vertex to other vertices. For application of Dijkstra's algorithm for calculating the shortest path from any ve... | Implement the Python class `Vertex` described below.
Class description:
A graph vertex with a set of paths to other vertices. A vertex is characterized by a label (or name) and a dictionary of paths from the vertex to other vertices. For application of Dijkstra's algorithm for calculating the shortest path from any ve... | c80ea145c758f3b392f956e4311f11cfc099a149 | <|skeleton|>
class Vertex:
"""A graph vertex with a set of paths to other vertices. A vertex is characterized by a label (or name) and a dictionary of paths from the vertex to other vertices. For application of Dijkstra's algorithm for calculating the shortest path from any vertex to all other vertices, each vertex... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Vertex:
"""A graph vertex with a set of paths to other vertices. A vertex is characterized by a label (or name) and a dictionary of paths from the vertex to other vertices. For application of Dijkstra's algorithm for calculating the shortest path from any vertex to all other vertices, each vertex also has the... | the_stack_v2_python_sparse | dailyprogrammer/challenges/038e.py | UltimateTimmeh/r-daily-programmer | train | 0 |
f9505cb6e584b53da247837e9d22c998696971b5 | [
"if tree:\n print(tree.get_root_val())\n Orders.preorder(tree.get_left_child())\n Orders.preorder(tree.get_right_child())",
"if tree != None:\n Orders.inorder(tree.get_left_child())\n print(tree.get_root_val())\n Orders.inorder(tree.get_right_child())",
"if tree != None:\n Orders.postorder(... | <|body_start_0|>
if tree:
print(tree.get_root_val())
Orders.preorder(tree.get_left_child())
Orders.preorder(tree.get_right_child())
<|end_body_0|>
<|body_start_1|>
if tree != None:
Orders.inorder(tree.get_left_child())
print(tree.get_root_val(... | Стат методы для обхода дерева | Orders | [
"WTFPL"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Orders:
"""Стат методы для обхода дерева"""
def preorder(tree):
"""Прямой обход дерева"""
<|body_0|>
def inorder(tree):
"""Симметричный обход дерева"""
<|body_1|>
def postorder(tree):
"""Обратный обход"""
<|body_2|>
<|end_skeleton|>
... | stack_v2_sparse_classes_10k_train_005319 | 2,441 | permissive | [
{
"docstring": "Прямой обход дерева",
"name": "preorder",
"signature": "def preorder(tree)"
},
{
"docstring": "Симметричный обход дерева",
"name": "inorder",
"signature": "def inorder(tree)"
},
{
"docstring": "Обратный обход",
"name": "postorder",
"signature": "def postor... | 3 | stack_v2_sparse_classes_30k_train_002886 | Implement the Python class `Orders` described below.
Class description:
Стат методы для обхода дерева
Method signatures and docstrings:
- def preorder(tree): Прямой обход дерева
- def inorder(tree): Симметричный обход дерева
- def postorder(tree): Обратный обход | Implement the Python class `Orders` described below.
Class description:
Стат методы для обхода дерева
Method signatures and docstrings:
- def preorder(tree): Прямой обход дерева
- def inorder(tree): Симметричный обход дерева
- def postorder(tree): Обратный обход
<|skeleton|>
class Orders:
"""Стат методы для обхо... | 9575c43fa01c261ea1ed573df9b5686b5a6f4211 | <|skeleton|>
class Orders:
"""Стат методы для обхода дерева"""
def preorder(tree):
"""Прямой обход дерева"""
<|body_0|>
def inorder(tree):
"""Симметричный обход дерева"""
<|body_1|>
def postorder(tree):
"""Обратный обход"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Orders:
"""Стат методы для обхода дерева"""
def preorder(tree):
"""Прямой обход дерева"""
if tree:
print(tree.get_root_val())
Orders.preorder(tree.get_left_child())
Orders.preorder(tree.get_right_child())
def inorder(tree):
"""Симметричный ... | the_stack_v2_python_sparse | Course_I/Алгоритмы Python/Part2/семинары/pract6/task3/task.py | GeorgiyDemo/FA | train | 46 |
f23e2f45bb2cad751652bd5bc4fcc3f1d08e49a7 | [
"if user_id is None:\n return None\nSessionId = super().create_session(user_id)\nif SessionId is None:\n return None\nusInstance = UserSession()\nusInstance.user_id = user_id\nusInstance.session_id = SessionId\nusInstance.save()\nreturn SessionId",
"UserSession.load_from_file()\nobj = UserSession.search({'s... | <|body_start_0|>
if user_id is None:
return None
SessionId = super().create_session(user_id)
if SessionId is None:
return None
usInstance = UserSession()
usInstance.user_id = user_id
usInstance.session_id = SessionId
usInstance.save()
... | [summary] Args: SessionExpAuth ([type]): [description] | SessionDBAuth | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SessionDBAuth:
"""[summary] Args: SessionExpAuth ([type]): [description]"""
def create_session(self, user_id=None):
"""[summary] Args: user_id ([type], optional): [description]. Defaults to None."""
<|body_0|>
def user_id_for_session_id(self, session_id=None):
""... | stack_v2_sparse_classes_10k_train_005320 | 2,155 | no_license | [
{
"docstring": "[summary] Args: user_id ([type], optional): [description]. Defaults to None.",
"name": "create_session",
"signature": "def create_session(self, user_id=None)"
},
{
"docstring": "[Request database and return user_id based on the session_id] Args: session_id ([type], optional): [de... | 3 | stack_v2_sparse_classes_30k_train_006694 | Implement the Python class `SessionDBAuth` described below.
Class description:
[summary] Args: SessionExpAuth ([type]): [description]
Method signatures and docstrings:
- def create_session(self, user_id=None): [summary] Args: user_id ([type], optional): [description]. Defaults to None.
- def user_id_for_session_id(se... | Implement the Python class `SessionDBAuth` described below.
Class description:
[summary] Args: SessionExpAuth ([type]): [description]
Method signatures and docstrings:
- def create_session(self, user_id=None): [summary] Args: user_id ([type], optional): [description]. Defaults to None.
- def user_id_for_session_id(se... | 94cae2ce3aa4cd72fc5907bd0148694054a9e60f | <|skeleton|>
class SessionDBAuth:
"""[summary] Args: SessionExpAuth ([type]): [description]"""
def create_session(self, user_id=None):
"""[summary] Args: user_id ([type], optional): [description]. Defaults to None."""
<|body_0|>
def user_id_for_session_id(self, session_id=None):
""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SessionDBAuth:
"""[summary] Args: SessionExpAuth ([type]): [description]"""
def create_session(self, user_id=None):
"""[summary] Args: user_id ([type], optional): [description]. Defaults to None."""
if user_id is None:
return None
SessionId = super().create_session(use... | the_stack_v2_python_sparse | 0x07-Session_authentication/api/v1/auth/session_db_auth.py | nakadorx/holbertonschool-web_back_end | train | 0 |
6e496121999f5a37a17d3e184866cc98b9a7d96e | [
"if not matrix:\n return\nn = matrix.__len__()\nrotate = [[0 for _ in range(n)] for _ in range(n)]\nfor i in range(n):\n for j in range(n):\n rotate[j][n - 1 - i] = matrix[i][j]\nmatrix[:] = rotate[:]",
"if not matrix:\n return\nn = matrix.__len__()\nmatrix.reverse()\nfor i in range(n):\n for j... | <|body_start_0|>
if not matrix:
return
n = matrix.__len__()
rotate = [[0 for _ in range(n)] for _ in range(n)]
for i in range(n):
for j in range(n):
rotate[j][n - 1 - i] = matrix[i][j]
matrix[:] = rotate[:]
<|end_body_0|>
<|body_start_1|>
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rotate(self, matrix):
""":type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead."""
<|body_0|>
def rotate1(self, matrix):
""":type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-p... | stack_v2_sparse_classes_10k_train_005321 | 1,454 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead.",
"name": "rotate",
"signature": "def rotate(self, matrix)"
},
{
"docstring": ":type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead.",
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotate(self, matrix): :type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead.
- def rotate1(self, matrix): :type matrix: List[List[... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotate(self, matrix): :type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead.
- def rotate1(self, matrix): :type matrix: List[List[... | 472f780c3214aab5c713612812d834ccbe589434 | <|skeleton|>
class Solution:
def rotate(self, matrix):
""":type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead."""
<|body_0|>
def rotate1(self, matrix):
""":type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-p... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def rotate(self, matrix):
""":type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead."""
if not matrix:
return
n = matrix.__len__()
rotate = [[0 for _ in range(n)] for _ in range(n)]
for i in range(n):
... | the_stack_v2_python_sparse | 2/48-Rotate_Image.py | ChangXiaodong/Leetcode-solutions | train | 4 | |
221af954ec827e037fdab8c1c32d0d14bcb7daeb | [
"if accepting_variables is None and rejecting_variables is None and (not no_variables):\n raise ValueError('Cannot create a symbolic subring since nothing is specified.')\nif accepting_variables is not None and rejecting_variables is not None or (rejecting_variables is not None and no_variables) or (no_variables... | <|body_start_0|>
if accepting_variables is None and rejecting_variables is None and (not no_variables):
raise ValueError('Cannot create a symbolic subring since nothing is specified.')
if accepting_variables is not None and rejecting_variables is not None or (rejecting_variables is not None ... | A factory creating a symbolic subring. INPUT: Specify one of the following keywords to create a subring. - ``accepting_variables`` (default: ``None``) -- a tuple or other iterable of variables. If specified, then a symbolic subring of expressions in only these variables is created. - ``rejecting_variables`` (default: `... | SymbolicSubringFactory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SymbolicSubringFactory:
"""A factory creating a symbolic subring. INPUT: Specify one of the following keywords to create a subring. - ``accepting_variables`` (default: ``None``) -- a tuple or other iterable of variables. If specified, then a symbolic subring of expressions in only these variables... | stack_v2_sparse_classes_10k_train_005322 | 31,870 | no_license | [
{
"docstring": "Given the arguments and keyword, create a key that uniquely determines this object. See :class:`SymbolicSubringFactory` for details. TESTS:: sage: from sage.symbolic.subring import SymbolicSubring sage: SymbolicSubring.create_key_and_extra_args() Traceback (most recent call last): ... ValueError... | 2 | null | Implement the Python class `SymbolicSubringFactory` described below.
Class description:
A factory creating a symbolic subring. INPUT: Specify one of the following keywords to create a subring. - ``accepting_variables`` (default: ``None``) -- a tuple or other iterable of variables. If specified, then a symbolic subring... | Implement the Python class `SymbolicSubringFactory` described below.
Class description:
A factory creating a symbolic subring. INPUT: Specify one of the following keywords to create a subring. - ``accepting_variables`` (default: ``None``) -- a tuple or other iterable of variables. If specified, then a symbolic subring... | 0d9eacbf74e2acffefde93e39f8bcbec745cdaba | <|skeleton|>
class SymbolicSubringFactory:
"""A factory creating a symbolic subring. INPUT: Specify one of the following keywords to create a subring. - ``accepting_variables`` (default: ``None``) -- a tuple or other iterable of variables. If specified, then a symbolic subring of expressions in only these variables... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SymbolicSubringFactory:
"""A factory creating a symbolic subring. INPUT: Specify one of the following keywords to create a subring. - ``accepting_variables`` (default: ``None``) -- a tuple or other iterable of variables. If specified, then a symbolic subring of expressions in only these variables is created. ... | the_stack_v2_python_sparse | sage/src/sage/symbolic/subring.py | bopopescu/geosci | train | 0 |
0e5a14d238bc7cc34fd3aad87fc634e42a176871 | [
"super(WordDatatype_iter_with_caching, self).__init__(parent, iter, length)\nself._data, self._gen = itertools.tee(self._data)\nself._list = []\nself._last_index = -1",
"for a in self._list:\n yield a\nfor a in self._gen:\n self._list.append(a)\n self._last_index += 1\n yield a\nif self._len is None:\... | <|body_start_0|>
super(WordDatatype_iter_with_caching, self).__init__(parent, iter, length)
self._data, self._gen = itertools.tee(self._data)
self._list = []
self._last_index = -1
<|end_body_0|>
<|body_start_1|>
for a in self._list:
yield a
for a in self._gen... | WordDatatype_iter_with_caching | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordDatatype_iter_with_caching:
def __init__(self, parent, iter, length=None):
"""INPUT: - ``parent`` - a parent - ``iter`` - an iterator - ``length`` - (default: ``None``) the length of the word EXAMPLES:: sage: import itertools sage: Word(itertools.cycle("abbabaab")) word: abbabaababba... | stack_v2_sparse_classes_10k_train_005323 | 39,600 | no_license | [
{
"docstring": "INPUT: - ``parent`` - a parent - ``iter`` - an iterator - ``length`` - (default: ``None``) the length of the word EXAMPLES:: sage: import itertools sage: Word(itertools.cycle(\"abbabaab\")) word: abbabaababbabaababbabaababbabaababbabaab... sage: w = Word(iter(\"abbabaab\"), length=\"finite\"); w... | 5 | null | Implement the Python class `WordDatatype_iter_with_caching` described below.
Class description:
Implement the WordDatatype_iter_with_caching class.
Method signatures and docstrings:
- def __init__(self, parent, iter, length=None): INPUT: - ``parent`` - a parent - ``iter`` - an iterator - ``length`` - (default: ``None... | Implement the Python class `WordDatatype_iter_with_caching` described below.
Class description:
Implement the WordDatatype_iter_with_caching class.
Method signatures and docstrings:
- def __init__(self, parent, iter, length=None): INPUT: - ``parent`` - a parent - ``iter`` - an iterator - ``length`` - (default: ``None... | 0d9eacbf74e2acffefde93e39f8bcbec745cdaba | <|skeleton|>
class WordDatatype_iter_with_caching:
def __init__(self, parent, iter, length=None):
"""INPUT: - ``parent`` - a parent - ``iter`` - an iterator - ``length`` - (default: ``None``) the length of the word EXAMPLES:: sage: import itertools sage: Word(itertools.cycle("abbabaab")) word: abbabaababba... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WordDatatype_iter_with_caching:
def __init__(self, parent, iter, length=None):
"""INPUT: - ``parent`` - a parent - ``iter`` - an iterator - ``length`` - (default: ``None``) the length of the word EXAMPLES:: sage: import itertools sage: Word(itertools.cycle("abbabaab")) word: abbabaababbabaababbabaabab... | the_stack_v2_python_sparse | sage/src/sage/combinat/words/word_infinite_datatypes.py | bopopescu/geosci | train | 0 | |
a47911233f844849c984eb6c3d5768e95fc92b51 | [
"self.out_filters = out_filters\nself.strides = strides\nself.in_filters = None\nsuper(Upscore, self).__init__(name)",
"if self.in_filters is None:\n self.in_filters = x.get_shape().as_list()[-1]\nassert self.in_filters == x.get_shape().as_list()[-1], 'Module was initialised for a different input shape'\nif se... | <|body_start_0|>
self.out_filters = out_filters
self.strides = strides
self.in_filters = None
super(Upscore, self).__init__(name)
<|end_body_0|>
<|body_start_1|>
if self.in_filters is None:
self.in_filters = x.get_shape().as_list()[-1]
assert self.in_filters ... | Upscore module according to J. Long. | Upscore | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Upscore:
"""Upscore module according to J. Long."""
def __init__(self, out_filters, strides, name='upscore'):
"""Constructs an Upscore module Parameters ---------- out_filters : int number of output filters strides : list or tuple strides to use for upsampling name : string name of t... | stack_v2_sparse_classes_10k_train_005324 | 11,734 | permissive | [
{
"docstring": "Constructs an Upscore module Parameters ---------- out_filters : int number of output filters strides : list or tuple strides to use for upsampling name : string name of the module",
"name": "__init__",
"signature": "def __init__(self, out_filters, strides, name='upscore')"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_002764 | Implement the Python class `Upscore` described below.
Class description:
Upscore module according to J. Long.
Method signatures and docstrings:
- def __init__(self, out_filters, strides, name='upscore'): Constructs an Upscore module Parameters ---------- out_filters : int number of output filters strides : list or tu... | Implement the Python class `Upscore` described below.
Class description:
Upscore module according to J. Long.
Method signatures and docstrings:
- def __init__(self, out_filters, strides, name='upscore'): Constructs an Upscore module Parameters ---------- out_filters : int number of output filters strides : list or tu... | 80d1a04dc163590aa44b62688b06aece78fb7fd6 | <|skeleton|>
class Upscore:
"""Upscore module according to J. Long."""
def __init__(self, out_filters, strides, name='upscore'):
"""Constructs an Upscore module Parameters ---------- out_filters : int number of output filters strides : list or tuple strides to use for upsampling name : string name of t... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Upscore:
"""Upscore module according to J. Long."""
def __init__(self, out_filters, strides, name='upscore'):
"""Constructs an Upscore module Parameters ---------- out_filters : int number of output filters strides : list or tuple strides to use for upsampling name : string name of the module"""
... | the_stack_v2_python_sparse | dltk/models/segmentation/deepmedic.py | pawni/DLTK-1 | train | 1 |
5969474fa3c92f5089d35dbfabadb8e6b0364fb8 | [
"kth = None\ncnt = 0\n\ndef find_kth_smallest(node):\n if not node:\n return False\n if find_kth_smallest(node.left):\n return True\n nonlocal cnt, kth\n cnt += 1\n if cnt == k:\n kth = node.val\n return True\n return find_kth_smallest(node.right)\nfind_kth_smallest(roo... | <|body_start_0|>
kth = None
cnt = 0
def find_kth_smallest(node):
if not node:
return False
if find_kth_smallest(node.left):
return True
nonlocal cnt, kth
cnt += 1
if cnt == k:
kth = node.... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def kthSmallest(self, root: TreeNode, k: int) -> int:
"""08/25/2019 16:16"""
<|body_0|>
def kthSmallest(self, root: Optional[TreeNode], k: int) -> int:
"""05/01/2022 19:49"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
kth = None
... | stack_v2_sparse_classes_10k_train_005325 | 2,893 | no_license | [
{
"docstring": "08/25/2019 16:16",
"name": "kthSmallest",
"signature": "def kthSmallest(self, root: TreeNode, k: int) -> int"
},
{
"docstring": "05/01/2022 19:49",
"name": "kthSmallest",
"signature": "def kthSmallest(self, root: Optional[TreeNode], k: int) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_004134 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kthSmallest(self, root: TreeNode, k: int) -> int: 08/25/2019 16:16
- def kthSmallest(self, root: Optional[TreeNode], k: int) -> int: 05/01/2022 19:49 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kthSmallest(self, root: TreeNode, k: int) -> int: 08/25/2019 16:16
- def kthSmallest(self, root: Optional[TreeNode], k: int) -> int: 05/01/2022 19:49
<|skeleton|>
class Solu... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def kthSmallest(self, root: TreeNode, k: int) -> int:
"""08/25/2019 16:16"""
<|body_0|>
def kthSmallest(self, root: Optional[TreeNode], k: int) -> int:
"""05/01/2022 19:49"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def kthSmallest(self, root: TreeNode, k: int) -> int:
"""08/25/2019 16:16"""
kth = None
cnt = 0
def find_kth_smallest(node):
if not node:
return False
if find_kth_smallest(node.left):
return True
non... | the_stack_v2_python_sparse | leetcode/solved/230_Kth_Smallest_Element_in_a_BST/solution.py | sungminoh/algorithms | train | 0 | |
52ddaa0a584115434cd4de02da4cc3118a7a2b63 | [
"try:\n res = requests.get(url, params=params, **kwargs)\nexcept Exception:\n logging.info('访问get请求不成功')\nelse:\n return res",
"try:\n res = requests.post(url, data=data, json=json, **kwargs)\nexcept Exception:\n logging.info(url, data, json, **kwargs)\n logging.info('访问post请求不成功')\nelse:\n r... | <|body_start_0|>
try:
res = requests.get(url, params=params, **kwargs)
except Exception:
logging.info('访问get请求不成功')
else:
return res
<|end_body_0|>
<|body_start_1|>
try:
res = requests.post(url, data=data, json=json, **kwargs)
exce... | 不需要记住cookie信息的请求类 | RequestsHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RequestsHandler:
"""不需要记住cookie信息的请求类"""
def get(self, url, params=None, **kwargs):
"""发送get请求 params 传递参数就是放到URL里面传递 data 在form表单中传递参数"""
<|body_0|>
def post(self, url, data=None, json=None, **kwargs):
"""发送post请求"""
<|body_1|>
def visit(self, metho... | stack_v2_sparse_classes_10k_train_005326 | 4,741 | no_license | [
{
"docstring": "发送get请求 params 传递参数就是放到URL里面传递 data 在form表单中传递参数",
"name": "get",
"signature": "def get(self, url, params=None, **kwargs)"
},
{
"docstring": "发送post请求",
"name": "post",
"signature": "def post(self, url, data=None, json=None, **kwargs)"
},
{
"docstring": "访问 get 和 ... | 4 | null | Implement the Python class `RequestsHandler` described below.
Class description:
不需要记住cookie信息的请求类
Method signatures and docstrings:
- def get(self, url, params=None, **kwargs): 发送get请求 params 传递参数就是放到URL里面传递 data 在form表单中传递参数
- def post(self, url, data=None, json=None, **kwargs): 发送post请求
- def visit(self, method, u... | Implement the Python class `RequestsHandler` described below.
Class description:
不需要记住cookie信息的请求类
Method signatures and docstrings:
- def get(self, url, params=None, **kwargs): 发送get请求 params 传递参数就是放到URL里面传递 data 在form表单中传递参数
- def post(self, url, data=None, json=None, **kwargs): 发送post请求
- def visit(self, method, u... | cfadd3132c2c7c518c784589e0dab6510a662a6c | <|skeleton|>
class RequestsHandler:
"""不需要记住cookie信息的请求类"""
def get(self, url, params=None, **kwargs):
"""发送get请求 params 传递参数就是放到URL里面传递 data 在form表单中传递参数"""
<|body_0|>
def post(self, url, data=None, json=None, **kwargs):
"""发送post请求"""
<|body_1|>
def visit(self, metho... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RequestsHandler:
"""不需要记住cookie信息的请求类"""
def get(self, url, params=None, **kwargs):
"""发送get请求 params 传递参数就是放到URL里面传递 data 在form表单中传递参数"""
try:
res = requests.get(url, params=params, **kwargs)
except Exception:
logging.info('访问get请求不成功')
else:
... | the_stack_v2_python_sparse | yiqihai/tebiemiao/Interface/common/requests_handler.py | songyongzhuang/PythonCode_office | train | 0 |
a01f40def4606dbbb33e812126fdf5e3713e20a7 | [
"n = len(nums)\ndp = [0] * (n + 2)\nfor i in reversed(range(n)):\n dp[i] = max(nums[i] + dp[i + 2], dp[i + 1])\nreturn dp[0]",
"last, now = (0, 0)\nfor x in nums:\n last, now = (now, max(last + x, now))\nreturn now"
] | <|body_start_0|>
n = len(nums)
dp = [0] * (n + 2)
for i in reversed(range(n)):
dp[i] = max(nums[i] + dp[i + 2], dp[i + 1])
return dp[0]
<|end_body_0|>
<|body_start_1|>
last, now = (0, 0)
for x in nums:
last, now = (now, max(last + x, now))
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
n = len(nums)
dp = [0] * (n + 2)
for i in re... | stack_v2_sparse_classes_10k_train_005327 | 1,446 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob",
"signature": "def rob(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob2",
"signature": "def rob2(self, nums)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob2(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 rob(self, nums): :type nums: List[int] :rtype: int
- def rob2(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def rob(self, nums):
"... | fa6504cb5145d10952f4615478fa745f4b35ba13 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
n = len(nums)
dp = [0] * (n + 2)
for i in reversed(range(n)):
dp[i] = max(nums[i] + dp[i + 2], dp[i + 1])
return dp[0]
def rob2(self, nums):
""":type nums: List[int] :rtype: ... | the_stack_v2_python_sparse | Algorithms/challenges/lc198_house_robber.py | snowdj/cs_course | train | 0 | |
e32dd38bec112369a53caf2b775e3f6a0665dee2 | [
"if not nums:\n return 0\nactive = []\nfor num in nums:\n if not active:\n active.append(num)\n continue\n if num <= active[0]:\n active[0] = num\n elif num > active[-1]:\n active.append(num)\n else:\n i = 0\n while i < len(active) and num > active[i]:\n ... | <|body_start_0|>
if not nums:
return 0
active = []
for num in nums:
if not active:
active.append(num)
continue
if num <= active[0]:
active[0] = num
elif num > active[-1]:
active.append... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def lengthOfLIS_DP(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not nums:
return 0
... | stack_v2_sparse_classes_10k_train_005328 | 3,192 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "lengthOfLIS",
"signature": "def lengthOfLIS(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "lengthOfLIS_DP",
"signature": "def lengthOfLIS_DP(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007299 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
- def lengthOfLIS_DP(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 lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
- def lengthOfLIS_DP(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def lengthOf... | d308e0e41c288f23a846b8505e572943d30b1392 | <|skeleton|>
class Solution:
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def lengthOfLIS_DP(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
if not nums:
return 0
active = []
for num in nums:
if not active:
active.append(num)
continue
if num <= active[0]:
... | the_stack_v2_python_sparse | python/300_Longest_Increasing_Subsequence.py | HankerZheng/LeetCode-Problems | train | 2 | |
91b6f562d8058a2b569f1b7b7736b02e24d6348d | [
"supercategorys = []\ncategories_id = {}\nfor item in categories:\n supercategory = item['supercategory']\n name = item['name']\n id = item['id']\n categories_id[name] = id\nreturn categories_id",
"annotations_id = []\nfor item in annotations:\n id = item['id']\n annotations_id.append(id)\nretur... | <|body_start_0|>
supercategorys = []
categories_id = {}
for item in categories:
supercategory = item['supercategory']
name = item['name']
id = item['id']
categories_id[name] = id
return categories_id
<|end_body_0|>
<|body_start_1|>
... | COCO Tools | COCOTools | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class COCOTools:
"""COCO Tools"""
def get_categories_id(categories):
"""get categories id dict :param categories: :return: dict:{name:id}"""
<|body_0|>
def get_annotations_id(annotations):
"""get annotations id list :param annotations: :return: annotations id list"""
... | stack_v2_sparse_classes_10k_train_005329 | 11,741 | no_license | [
{
"docstring": "get categories id dict :param categories: :return: dict:{name:id}",
"name": "get_categories_id",
"signature": "def get_categories_id(categories)"
},
{
"docstring": "get annotations id list :param annotations: :return: annotations id list",
"name": "get_annotations_id",
"s... | 5 | stack_v2_sparse_classes_30k_train_007047 | Implement the Python class `COCOTools` described below.
Class description:
COCO Tools
Method signatures and docstrings:
- def get_categories_id(categories): get categories id dict :param categories: :return: dict:{name:id}
- def get_annotations_id(annotations): get annotations id list :param annotations: :return: ann... | Implement the Python class `COCOTools` described below.
Class description:
COCO Tools
Method signatures and docstrings:
- def get_categories_id(categories): get categories id dict :param categories: :return: dict:{name:id}
- def get_annotations_id(annotations): get annotations id list :param annotations: :return: ann... | f45f0879cc70eb59de67a270a6ec8dbb2cf8e742 | <|skeleton|>
class COCOTools:
"""COCO Tools"""
def get_categories_id(categories):
"""get categories id dict :param categories: :return: dict:{name:id}"""
<|body_0|>
def get_annotations_id(annotations):
"""get annotations id list :param annotations: :return: annotations id list"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class COCOTools:
"""COCO Tools"""
def get_categories_id(categories):
"""get categories id dict :param categories: :return: dict:{name:id}"""
supercategorys = []
categories_id = {}
for item in categories:
supercategory = item['supercategory']
name = item['... | the_stack_v2_python_sparse | modules/dataset_tool/coco_tools/convert_voc2coco.py | zuiyueyin/python-learning-notes | train | 0 |
6b45c00a9bc4dbb74d987b80149d4e7f1fa85b78 | [
"self.tailed_file = tailed_file\nself.check_file_validity()\nself.tailed_file = tailed_file",
"with open(self.tailed_file, 'r') as file_:\n file_.seek(0, 2)\n fsize = file_.tell()\n file_.seek(max(fsize - 10000, 0), 0)\n lines = file_.readlines()\nlines = lines[-max_lines:]\nfor line in lines:\n pr... | <|body_start_0|>
self.tailed_file = tailed_file
self.check_file_validity()
self.tailed_file = tailed_file
<|end_body_0|>
<|body_start_1|>
with open(self.tailed_file, 'r') as file_:
file_.seek(0, 2)
fsize = file_.tell()
file_.seek(max(fsize - 10000, 0)... | Represent a tail command. | File | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class File:
"""Represent a tail command."""
def __init__(self, tailed_file):
"""Method that instantiates the class. Check for file validity, assigns callback function to standard out. Args: tailed_file - File to be followed."""
<|body_0|>
def tail(self, seconds=1, max_lines=50... | stack_v2_sparse_classes_10k_train_005330 | 3,083 | permissive | [
{
"docstring": "Method that instantiates the class. Check for file validity, assigns callback function to standard out. Args: tailed_file - File to be followed.",
"name": "__init__",
"signature": "def __init__(self, tailed_file)"
},
{
"docstring": "Do a tail follow. If a callback function is reg... | 3 | stack_v2_sparse_classes_30k_test_000147 | Implement the Python class `File` described below.
Class description:
Represent a tail command.
Method signatures and docstrings:
- def __init__(self, tailed_file): Method that instantiates the class. Check for file validity, assigns callback function to standard out. Args: tailed_file - File to be followed.
- def ta... | Implement the Python class `File` described below.
Class description:
Represent a tail command.
Method signatures and docstrings:
- def __init__(self, tailed_file): Method that instantiates the class. Check for file validity, assigns callback function to standard out. Args: tailed_file - File to be followed.
- def ta... | ae82589fbbab77fef6d6be09c1fcca5846f595a8 | <|skeleton|>
class File:
"""Represent a tail command."""
def __init__(self, tailed_file):
"""Method that instantiates the class. Check for file validity, assigns callback function to standard out. Args: tailed_file - File to be followed."""
<|body_0|>
def tail(self, seconds=1, max_lines=50... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class File:
"""Represent a tail command."""
def __init__(self, tailed_file):
"""Method that instantiates the class. Check for file validity, assigns callback function to standard out. Args: tailed_file - File to be followed."""
self.tailed_file = tailed_file
self.check_file_validity()
... | the_stack_v2_python_sparse | switchmap/utils/input_output.py | PalisadoesFoundation/switchmap-ng | train | 8 |
3dbf0ac49449127672d21b514c773321ffae9278 | [
"self.host = host\nself.port = port\nself.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nself.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\nself.sock.bind((self.host, self.port))\nself.messagesList = []\nself.connected = []",
"self.sock.listen(5)\nwhile True:\n client, address = self.s... | <|body_start_0|>
self.host = host
self.port = port
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
self.sock.bind((self.host, self.port))
self.messagesList = []
self.connected = []
<|end... | ChatServer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChatServer:
def __init__(self, host, port):
"""Initialization for the server"""
<|body_0|>
def listen(self):
"""Loop for accepting clients"""
<|body_1|>
def listenToClient(self, client, address):
"""Client thread method"""
<|body_2|>
... | stack_v2_sparse_classes_10k_train_005331 | 2,925 | no_license | [
{
"docstring": "Initialization for the server",
"name": "__init__",
"signature": "def __init__(self, host, port)"
},
{
"docstring": "Loop for accepting clients",
"name": "listen",
"signature": "def listen(self)"
},
{
"docstring": "Client thread method",
"name": "listenToClien... | 4 | stack_v2_sparse_classes_30k_train_003498 | Implement the Python class `ChatServer` described below.
Class description:
Implement the ChatServer class.
Method signatures and docstrings:
- def __init__(self, host, port): Initialization for the server
- def listen(self): Loop for accepting clients
- def listenToClient(self, client, address): Client thread method... | Implement the Python class `ChatServer` described below.
Class description:
Implement the ChatServer class.
Method signatures and docstrings:
- def __init__(self, host, port): Initialization for the server
- def listen(self): Loop for accepting clients
- def listenToClient(self, client, address): Client thread method... | 0249a73062f6bef9e40d0ab792f9cf30eaa363ed | <|skeleton|>
class ChatServer:
def __init__(self, host, port):
"""Initialization for the server"""
<|body_0|>
def listen(self):
"""Loop for accepting clients"""
<|body_1|>
def listenToClient(self, client, address):
"""Client thread method"""
<|body_2|>
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ChatServer:
def __init__(self, host, port):
"""Initialization for the server"""
self.host = host
self.port = port
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
self.sock.bind((self.h... | the_stack_v2_python_sparse | Bimester2/Class10/ChatServer.py | aloysiogl/CES-22_solutions | train | 0 | |
bdd16bb6870ea5a9ded39bef95448c15cdc1223b | [
"super(GraphVisualizerPointDraw, self).__init__()\nself.setMinimumSize(QSize(13, 13))\nself.setMaximumSize(QSize(13, 13))",
"painter = QPainter(self)\npainter.drawEllipse(self.rect().center(), 6, 6)\npainter.setBrush(Qt.black)\npainter.drawEllipse(self.rect().center(), 2, 2)"
] | <|body_start_0|>
super(GraphVisualizerPointDraw, self).__init__()
self.setMinimumSize(QSize(13, 13))
self.setMaximumSize(QSize(13, 13))
<|end_body_0|>
<|body_start_1|>
painter = QPainter(self)
painter.drawEllipse(self.rect().center(), 6, 6)
painter.setBrush(Qt.black)
... | Define an empty widget with a point drew. | GraphVisualizerPointDraw | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraphVisualizerPointDraw:
"""Define an empty widget with a point drew."""
def __init__(self):
"""Initialize a GraphVisualizerPointDraw instance."""
<|body_0|>
def paintEvent(self, event):
"""Paint an event."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_10k_train_005332 | 24,840 | permissive | [
{
"docstring": "Initialize a GraphVisualizerPointDraw instance.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Paint an event.",
"name": "paintEvent",
"signature": "def paintEvent(self, event)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003757 | Implement the Python class `GraphVisualizerPointDraw` described below.
Class description:
Define an empty widget with a point drew.
Method signatures and docstrings:
- def __init__(self): Initialize a GraphVisualizerPointDraw instance.
- def paintEvent(self, event): Paint an event. | Implement the Python class `GraphVisualizerPointDraw` described below.
Class description:
Define an empty widget with a point drew.
Method signatures and docstrings:
- def __init__(self): Initialize a GraphVisualizerPointDraw instance.
- def paintEvent(self, event): Paint an event.
<|skeleton|>
class GraphVisualizer... | bbcf475a4b4e85836123452053bbbf34cc44063a | <|skeleton|>
class GraphVisualizerPointDraw:
"""Define an empty widget with a point drew."""
def __init__(self):
"""Initialize a GraphVisualizerPointDraw instance."""
<|body_0|>
def paintEvent(self, event):
"""Paint an event."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GraphVisualizerPointDraw:
"""Define an empty widget with a point drew."""
def __init__(self):
"""Initialize a GraphVisualizerPointDraw instance."""
super(GraphVisualizerPointDraw, self).__init__()
self.setMinimumSize(QSize(13, 13))
self.setMaximumSize(QSize(13, 13))
d... | the_stack_v2_python_sparse | posydon/visualization/VH_diagram/GraphVisualizer.py | POSYDON-code/POSYDON | train | 11 |
f5583083a3c3cec400b7c5689b140822ca889d5f | [
"result = []\nfor model in cls.model_list:\n result += list(model.objects.filter(*args, **kwargs))\nreturn result",
"try:\n ls = cls.filter(*args, **kwargs)\n if len(ls) > 1:\n raise MultipleObjectsReturned()\n return cls.filter(*args, **kwargs)[0]\nexcept IndexError:\n raise ObjectDoesNotEx... | <|body_start_0|>
result = []
for model in cls.model_list:
result += list(model.objects.filter(*args, **kwargs))
return result
<|end_body_0|>
<|body_start_1|>
try:
ls = cls.filter(*args, **kwargs)
if len(ls) > 1:
raise MultipleObjectsRe... | This is a class made for querying abstract models. This class is itself abstract. create subclasses to query your own abstract models. | AbstractModelQuery | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AbstractModelQuery:
"""This is a class made for querying abstract models. This class is itself abstract. create subclasses to query your own abstract models."""
def filter(cls, *args, **kwargs):
"""Query all concrete model classes. Iterates over the model list and returns a list of a... | stack_v2_sparse_classes_10k_train_005333 | 1,753 | permissive | [
{
"docstring": "Query all concrete model classes. Iterates over the model list and returns a list of all matching models from the classes given. Filter queries are given here as normal and are passed into the Django ORM for each concrete model",
"name": "filter",
"signature": "def filter(cls, *args, **k... | 2 | stack_v2_sparse_classes_30k_train_007312 | Implement the Python class `AbstractModelQuery` described below.
Class description:
This is a class made for querying abstract models. This class is itself abstract. create subclasses to query your own abstract models.
Method signatures and docstrings:
- def filter(cls, *args, **kwargs): Query all concrete model clas... | Implement the Python class `AbstractModelQuery` described below.
Class description:
This is a class made for querying abstract models. This class is itself abstract. create subclasses to query your own abstract models.
Method signatures and docstrings:
- def filter(cls, *args, **kwargs): Query all concrete model clas... | 886a644432ff53f97babccbae4eee555338caec1 | <|skeleton|>
class AbstractModelQuery:
"""This is a class made for querying abstract models. This class is itself abstract. create subclasses to query your own abstract models."""
def filter(cls, *args, **kwargs):
"""Query all concrete model classes. Iterates over the model list and returns a list of a... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AbstractModelQuery:
"""This is a class made for querying abstract models. This class is itself abstract. create subclasses to query your own abstract models."""
def filter(cls, *args, **kwargs):
"""Query all concrete model classes. Iterates over the model list and returns a list of all matching m... | the_stack_v2_python_sparse | src/dashboard/utils.py | opnfv/laas | train | 3 |
3ae99b698b17e25fe5fc4832727a51c6df6142f7 | [
"super(Model, self).__init__()\nself.model1 = MlpNet(layer_sizes1, input_size1).double()\nself.model2 = MlpNet(layer_sizes2, input_size2).double()\nself.loss = cca_loss(outdim_size, use_all_singular_values, device).loss",
"output1 = self.model1(x1)\noutput2 = self.model2(x2)\nreturn (output1, output2)"
] | <|body_start_0|>
super(Model, self).__init__()
self.model1 = MlpNet(layer_sizes1, input_size1).double()
self.model2 = MlpNet(layer_sizes2, input_size2).double()
self.loss = cca_loss(outdim_size, use_all_singular_values, device).loss
<|end_body_0|>
<|body_start_1|>
output1 = self... | Model | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Model:
def __init__(self, layer_sizes1: list, layer_sizes2: list, input_size1: int, input_size2: int, outdim_size: int, use_all_singular_values: bool=False, device=torch.device('cpu')):
"""model initialization Parameters ---------- layer_sizes1 (list): list of layer shape of view 1 layer... | stack_v2_sparse_classes_10k_train_005334 | 2,797 | permissive | [
{
"docstring": "model initialization Parameters ---------- layer_sizes1 (list): list of layer shape of view 1 layer_sizes2 (list): list of layer shape of view 1 input_size1 (int): input dimension of view 1 input_size2 (int): input dimension of view 2 outdim_size (int): output dimension of data use_all_singular_... | 2 | stack_v2_sparse_classes_30k_train_007004 | Implement the Python class `Model` described below.
Class description:
Implement the Model class.
Method signatures and docstrings:
- def __init__(self, layer_sizes1: list, layer_sizes2: list, input_size1: int, input_size2: int, outdim_size: int, use_all_singular_values: bool=False, device=torch.device('cpu')): model... | Implement the Python class `Model` described below.
Class description:
Implement the Model class.
Method signatures and docstrings:
- def __init__(self, layer_sizes1: list, layer_sizes2: list, input_size1: int, input_size2: int, outdim_size: int, use_all_singular_values: bool=False, device=torch.device('cpu')): model... | 89ba01c18d3ed36942ffdf3e1f3c68fd08b05324 | <|skeleton|>
class Model:
def __init__(self, layer_sizes1: list, layer_sizes2: list, input_size1: int, input_size2: int, outdim_size: int, use_all_singular_values: bool=False, device=torch.device('cpu')):
"""model initialization Parameters ---------- layer_sizes1 (list): list of layer shape of view 1 layer... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Model:
def __init__(self, layer_sizes1: list, layer_sizes2: list, input_size1: int, input_size2: int, outdim_size: int, use_all_singular_values: bool=False, device=torch.device('cpu')):
"""model initialization Parameters ---------- layer_sizes1 (list): list of layer shape of view 1 layer_sizes2 (list)... | the_stack_v2_python_sparse | Groups/Group_ID_7/DeepCCA/DeepCCAModels.py | aryapushpa/DataScience | train | 0 | |
a5428b1a799611dac61e81dbc7ee2f8a16a80f57 | [
"if str2bool(value):\n return queryset.filter(status__in=BuildStatusGroups.ACTIVE_CODES)\nelse:\n return queryset.exclude(status__in=BuildStatusGroups.ACTIVE_CODES)",
"if str2bool(value):\n return queryset.filter(Build.OVERDUE_FILTER)\nelse:\n return queryset.exclude(Build.OVERDUE_FILTER)",
"value =... | <|body_start_0|>
if str2bool(value):
return queryset.filter(status__in=BuildStatusGroups.ACTIVE_CODES)
else:
return queryset.exclude(status__in=BuildStatusGroups.ACTIVE_CODES)
<|end_body_0|>
<|body_start_1|>
if str2bool(value):
return queryset.filter(Build.OV... | Custom filterset for BuildList API endpoint. | BuildFilter | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BuildFilter:
"""Custom filterset for BuildList API endpoint."""
def filter_active(self, queryset, name, value):
"""Filter the queryset to either include or exclude orders which are active."""
<|body_0|>
def filter_overdue(self, queryset, name, value):
"""Filter t... | stack_v2_sparse_classes_10k_train_005335 | 20,912 | permissive | [
{
"docstring": "Filter the queryset to either include or exclude orders which are active.",
"name": "filter_active",
"signature": "def filter_active(self, queryset, name, value)"
},
{
"docstring": "Filter the queryset to either include or exclude orders which are overdue.",
"name": "filter_o... | 5 | null | Implement the Python class `BuildFilter` described below.
Class description:
Custom filterset for BuildList API endpoint.
Method signatures and docstrings:
- def filter_active(self, queryset, name, value): Filter the queryset to either include or exclude orders which are active.
- def filter_overdue(self, queryset, n... | Implement the Python class `BuildFilter` described below.
Class description:
Custom filterset for BuildList API endpoint.
Method signatures and docstrings:
- def filter_active(self, queryset, name, value): Filter the queryset to either include or exclude orders which are active.
- def filter_overdue(self, queryset, n... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class BuildFilter:
"""Custom filterset for BuildList API endpoint."""
def filter_active(self, queryset, name, value):
"""Filter the queryset to either include or exclude orders which are active."""
<|body_0|>
def filter_overdue(self, queryset, name, value):
"""Filter t... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BuildFilter:
"""Custom filterset for BuildList API endpoint."""
def filter_active(self, queryset, name, value):
"""Filter the queryset to either include or exclude orders which are active."""
if str2bool(value):
return queryset.filter(status__in=BuildStatusGroups.ACTIVE_CODES)... | the_stack_v2_python_sparse | InvenTree/build/api.py | inventree/InvenTree | train | 3,077 |
1f149501ee1f991a2fe0e31947b627d399d8a74a | [
"self.distance_x = distance_x\nself.distance_y = distance_y\nself.rho = rho\nself.eps = eps\nself.auditor_nsteps = auditor_nsteps\nself.auditor_lr = auditor_lr\nsuper().__init__(module=module, criterion=criterion, regression=regression, **kwargs)",
"self.initialize_criterion()\nkwargs = self.get_params_for('modul... | <|body_start_0|>
self.distance_x = distance_x
self.distance_y = distance_y
self.rho = rho
self.eps = eps
self.auditor_nsteps = auditor_nsteps
self.auditor_lr = auditor_lr
super().__init__(module=module, criterion=criterion, regression=regression, **kwargs)
<|end_b... | Sensitive Set Invariance (SenSeI). SenSeI is an in-processing method for individual fairness [#yurochkin20]_. In this method, individual fairness is formulated as invariance on certain sensitive sets. SenSeI minimizes a transport-based regularizer that enforces this version of individual fairness. References: .. [#yuro... | SenSeI | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SenSeI:
"""Sensitive Set Invariance (SenSeI). SenSeI is an in-processing method for individual fairness [#yurochkin20]_. In this method, individual fairness is formulated as invariance on certain sensitive sets. SenSeI minimizes a transport-based regularizer that enforces this version of individu... | stack_v2_sparse_classes_10k_train_005336 | 15,710 | permissive | [
{
"docstring": "Args: module (torch.nn.Module): Network architecture. criterion (torch.nn.Module): Loss function. distance_x (inFairness.distances.Distance): Distance metric in the input space. distance_y (inFairness.distances.Distance): Distance metric in the output space. rho (float): :math:`\\\\rho` paramete... | 2 | stack_v2_sparse_classes_30k_train_000864 | Implement the Python class `SenSeI` described below.
Class description:
Sensitive Set Invariance (SenSeI). SenSeI is an in-processing method for individual fairness [#yurochkin20]_. In this method, individual fairness is formulated as invariance on certain sensitive sets. SenSeI minimizes a transport-based regularizer... | Implement the Python class `SenSeI` described below.
Class description:
Sensitive Set Invariance (SenSeI). SenSeI is an in-processing method for individual fairness [#yurochkin20]_. In this method, individual fairness is formulated as invariance on certain sensitive sets. SenSeI minimizes a transport-based regularizer... | 6f9972e4a7dbca2402f29b86ea67889143dbeb3e | <|skeleton|>
class SenSeI:
"""Sensitive Set Invariance (SenSeI). SenSeI is an in-processing method for individual fairness [#yurochkin20]_. In this method, individual fairness is formulated as invariance on certain sensitive sets. SenSeI minimizes a transport-based regularizer that enforces this version of individu... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SenSeI:
"""Sensitive Set Invariance (SenSeI). SenSeI is an in-processing method for individual fairness [#yurochkin20]_. In this method, individual fairness is formulated as invariance on certain sensitive sets. SenSeI minimizes a transport-based regularizer that enforces this version of individual fairness. ... | the_stack_v2_python_sparse | aif360/sklearn/inprocessing/infairness.py | Trusted-AI/AIF360 | train | 1,157 |
5614132ffaceb5ea3e84b0434d7dc4e71450f20b | [
"super().__init__(adguard, entry)\nself.entity_description = description\nself._attr_unique_id = '_'.join([DOMAIN, adguard.host, str(adguard.port), 'sensor', description.key])",
"value = await self.entity_description.value_fn(self.adguard)\nself._attr_native_value = value\nif isinstance(value, float):\n self._... | <|body_start_0|>
super().__init__(adguard, entry)
self.entity_description = description
self._attr_unique_id = '_'.join([DOMAIN, adguard.host, str(adguard.port), 'sensor', description.key])
<|end_body_0|>
<|body_start_1|>
value = await self.entity_description.value_fn(self.adguard)
... | Defines a AdGuard Home sensor. | AdGuardHomeSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdGuardHomeSensor:
"""Defines a AdGuard Home sensor."""
def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None:
"""Initialize AdGuard Home sensor."""
<|body_0|>
async def _adguard_update(self) -> None:
"""U... | stack_v2_sparse_classes_10k_train_005337 | 4,982 | permissive | [
{
"docstring": "Initialize AdGuard Home sensor.",
"name": "__init__",
"signature": "def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None"
},
{
"docstring": "Update AdGuard Home entity.",
"name": "_adguard_update",
"signature": "a... | 2 | null | Implement the Python class `AdGuardHomeSensor` described below.
Class description:
Defines a AdGuard Home sensor.
Method signatures and docstrings:
- def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None: Initialize AdGuard Home sensor.
- async def _adguard_up... | Implement the Python class `AdGuardHomeSensor` described below.
Class description:
Defines a AdGuard Home sensor.
Method signatures and docstrings:
- def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None: Initialize AdGuard Home sensor.
- async def _adguard_up... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class AdGuardHomeSensor:
"""Defines a AdGuard Home sensor."""
def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None:
"""Initialize AdGuard Home sensor."""
<|body_0|>
async def _adguard_update(self) -> None:
"""U... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AdGuardHomeSensor:
"""Defines a AdGuard Home sensor."""
def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None:
"""Initialize AdGuard Home sensor."""
super().__init__(adguard, entry)
self.entity_description = description
... | the_stack_v2_python_sparse | homeassistant/components/adguard/sensor.py | home-assistant/core | train | 35,501 |
4f5627fc3183b6714c6c39d26d80be832e9f5f16 | [
"self.fileHandle = fileHandle\nself.dagPath = dagPath\nself.fFluid = OpenMayaFX.MFnFluid(dagPath)",
"xPtr = OpenMaya.MScriptUtil().asDoublePtr()\nyPtr = OpenMaya.MScriptUtil().asDoublePtr()\nzPtr = OpenMaya.MScriptUtil().asDoublePtr()\nself.fFluid.getDimensions(xPtr, yPtr, zPtr)\ndimX = OpenMaya.MScriptUtil(xPtr)... | <|body_start_0|>
self.fileHandle = fileHandle
self.dagPath = dagPath
self.fFluid = OpenMayaFX.MFnFluid(dagPath)
<|end_body_0|>
<|body_start_1|>
xPtr = OpenMaya.MScriptUtil().asDoublePtr()
yPtr = OpenMaya.MScriptUtil().asDoublePtr()
zPtr = OpenMaya.MScriptUtil().asDoubleP... | Fluid volume export module | Volume | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Volume:
"""Fluid volume export module"""
def __init__(self, fileHandle, dagPath):
"""Set up the objects we're dealing with"""
<|body_0|>
def getOutput(self):
"""Read Fluid data and export as volumegrid"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_005338 | 2,972 | no_license | [
{
"docstring": "Set up the objects we're dealing with",
"name": "__init__",
"signature": "def __init__(self, fileHandle, dagPath)"
},
{
"docstring": "Read Fluid data and export as volumegrid",
"name": "getOutput",
"signature": "def getOutput(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004572 | Implement the Python class `Volume` described below.
Class description:
Fluid volume export module
Method signatures and docstrings:
- def __init__(self, fileHandle, dagPath): Set up the objects we're dealing with
- def getOutput(self): Read Fluid data and export as volumegrid | Implement the Python class `Volume` described below.
Class description:
Fluid volume export module
Method signatures and docstrings:
- def __init__(self, fileHandle, dagPath): Set up the objects we're dealing with
- def getOutput(self): Read Fluid data and export as volumegrid
<|skeleton|>
class Volume:
"""Fluid... | 3891e40c3c4c3a054e5ff1ff16d051d4e690cc4a | <|skeleton|>
class Volume:
"""Fluid volume export module"""
def __init__(self, fileHandle, dagPath):
"""Set up the objects we're dealing with"""
<|body_0|>
def getOutput(self):
"""Read Fluid data and export as volumegrid"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Volume:
"""Fluid volume export module"""
def __init__(self, fileHandle, dagPath):
"""Set up the objects we're dealing with"""
self.fileHandle = fileHandle
self.dagPath = dagPath
self.fFluid = OpenMayaFX.MFnFluid(dagPath)
def getOutput(self):
"""Read Fluid data... | the_stack_v2_python_sparse | luxPlugin/Lux/LuxExportModules/Volume.py | LuxRender/LuxMaya | train | 0 |
08d63d9a573f23c0ae83b2507add617971dbbd47 | [
"Model.__init__(self, data, verbose=verbose)\nself.α = α\nif G is None:\n self.G = stats.norm(loc=0, scale=10000)\nelse:\n self.G = G\nself.col_lookups = [{unique_val: count for unique_val, count in zip(*np.unique(self.X.data[:, d][~self.X.mask[:, d]], return_counts=True))} for d in range(self.D)]\nself._calc... | <|body_start_0|>
Model.__init__(self, data, verbose=verbose)
self.α = α
if G is None:
self.G = stats.norm(loc=0, scale=10000)
else:
self.G = G
self.col_lookups = [{unique_val: count for unique_val, count in zip(*np.unique(self.X.data[:, d][~self.X.mask[:, ... | DP | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DP:
def __init__(self, data, verbose=None, α=1, G=None):
"""Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not information should be written to std_err. α: floating point concentration parameter. G: prior ... | stack_v2_sparse_classes_10k_train_005339 | 7,946 | permissive | [
{
"docstring": "Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not information should be written to std_err. α: floating point concentration parameter. G: prior distribution: scipy.stats objects or other objects with similar inte... | 6 | stack_v2_sparse_classes_30k_train_005463 | Implement the Python class `DP` described below.
Class description:
Implement the DP class.
Method signatures and docstrings:
- def __init__(self, data, verbose=None, α=1, G=None): Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not inf... | Implement the Python class `DP` described below.
Class description:
Implement the DP class.
Method signatures and docstrings:
- def __init__(self, data, verbose=None, α=1, G=None): Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not inf... | 99aaa6364898e5e67a9fc7e21d8c5dc0052d9edc | <|skeleton|>
class DP:
def __init__(self, data, verbose=None, α=1, G=None):
"""Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not information should be written to std_err. α: floating point concentration parameter. G: prior ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DP:
def __init__(self, data, verbose=None, α=1, G=None):
"""Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not information should be written to std_err. α: floating point concentration parameter. G: prior distribution: ... | the_stack_v2_python_sparse | auto_impute/dp.py | JamesAllingham/AutoImpute | train | 1 | |
f81f8f325de4d2b29662b70777cf97b8fc5957d1 | [
"Parametre.__init__(self, 'vitesse', 'speed')\nself.schema = '<vitesse_rames>'\nself.aide_courte = 'change la vitesse des rames'\nself.aide_longue = \"Cette commande permet de modifier la vitesse des rames que vous tenez en main. Les vitesses disponibles sont |cmd|arrière|ff| (pour aller en marche arrière), |cmd|im... | <|body_start_0|>
Parametre.__init__(self, 'vitesse', 'speed')
self.schema = '<vitesse_rames>'
self.aide_courte = 'change la vitesse des rames'
self.aide_longue = "Cette commande permet de modifier la vitesse des rames que vous tenez en main. Les vitesses disponibles sont |cmd|arrière|ff|... | Commande 'rames vitesse'. | PrmVitesse | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrmVitesse:
"""Commande 'rames vitesse'."""
def __init__(self):
"""Constructeur du paramètre"""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Interprétation du paramètre"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
Parametre._... | stack_v2_sparse_classes_10k_train_005340 | 3,270 | permissive | [
{
"docstring": "Constructeur du paramètre",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Interprétation du paramètre",
"name": "interpreter",
"signature": "def interpreter(self, personnage, dic_masques)"
}
] | 2 | null | Implement the Python class `PrmVitesse` described below.
Class description:
Commande 'rames vitesse'.
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre | Implement the Python class `PrmVitesse` described below.
Class description:
Commande 'rames vitesse'.
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre
<|skeleton|>
class PrmVitesse:
"""Commande 'rames v... | 7e93bff08cdf891352efba587e89c40f3b4a2301 | <|skeleton|>
class PrmVitesse:
"""Commande 'rames vitesse'."""
def __init__(self):
"""Constructeur du paramètre"""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Interprétation du paramètre"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PrmVitesse:
"""Commande 'rames vitesse'."""
def __init__(self):
"""Constructeur du paramètre"""
Parametre.__init__(self, 'vitesse', 'speed')
self.schema = '<vitesse_rames>'
self.aide_courte = 'change la vitesse des rames'
self.aide_longue = "Cette commande permet d... | the_stack_v2_python_sparse | src/secondaires/navigation/commandes/rames/vitesse.py | vincent-lg/tsunami | train | 5 |
6f5f9de9c3ff9f3ff37e76660fd3d93a701d479f | [
"self.mb_dir_path.mkdir(parents=True, exist_ok=True)\nfor i in range(self.n_boxes):\n mb_path = self.path_to_mailbox(i)\n with mb_path.open('w') as fh:\n fh.write(header)",
"if index_name is None:\n start = '\\t'\nelse:\n start = f'{index_name}\\t'\ncolstring = '\\t'.join(columns)\nself.mb_dir_... | <|body_start_0|>
self.mb_dir_path.mkdir(parents=True, exist_ok=True)
for i in range(self.n_boxes):
mb_path = self.path_to_mailbox(i)
with mb_path.open('w') as fh:
fh.write(header)
<|end_body_0|>
<|body_start_1|>
if index_name is None:
start = ... | Pass data to and from on-disk FIFOs. | DataMailboxes | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataMailboxes:
"""Pass data to and from on-disk FIFOs."""
def write_headers(self, header):
"""Initialize the mailboxes, writing a free-form header."""
<|body_0|>
def write_tsv_headers(self, columns, index_name=None):
"""Initialize the mailboxes, writing a tab-sep... | stack_v2_sparse_classes_10k_train_005341 | 7,728 | permissive | [
{
"docstring": "Initialize the mailboxes, writing a free-form header.",
"name": "write_headers",
"signature": "def write_headers(self, header)"
},
{
"docstring": "Initialize the mailboxes, writing a tab-separated header.",
"name": "write_tsv_headers",
"signature": "def write_tsv_headers(... | 6 | stack_v2_sparse_classes_30k_train_005899 | Implement the Python class `DataMailboxes` described below.
Class description:
Pass data to and from on-disk FIFOs.
Method signatures and docstrings:
- def write_headers(self, header): Initialize the mailboxes, writing a free-form header.
- def write_tsv_headers(self, columns, index_name=None): Initialize the mailbox... | Implement the Python class `DataMailboxes` described below.
Class description:
Pass data to and from on-disk FIFOs.
Method signatures and docstrings:
- def write_headers(self, header): Initialize the mailboxes, writing a free-form header.
- def write_tsv_headers(self, columns, index_name=None): Initialize the mailbox... | 90b6f52d9208458053001e49a9537cd9870c5f15 | <|skeleton|>
class DataMailboxes:
"""Pass data to and from on-disk FIFOs."""
def write_headers(self, header):
"""Initialize the mailboxes, writing a free-form header."""
<|body_0|>
def write_tsv_headers(self, columns, index_name=None):
"""Initialize the mailboxes, writing a tab-sep... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DataMailboxes:
"""Pass data to and from on-disk FIFOs."""
def write_headers(self, header):
"""Initialize the mailboxes, writing a free-form header."""
self.mb_dir_path.mkdir(parents=True, exist_ok=True)
for i in range(self.n_boxes):
mb_path = self.path_to_mailbox(i)
... | the_stack_v2_python_sparse | azulejo/mailboxes.py | legumeinfo/azulejo | train | 0 |
0e5e7d507e358cab8c33d98a1957c9316c750f38 | [
"merge_df = graph.merge_by_year(1807)\nself.assertEqual(['Country', 'Region', 'Income'], merge_df.columns.values.tolist())\nself.assertEqual(merge_df.ix[0, 'Country'], 'Algeria')\nself.assertEqual(merge_df.ix[0, 'Region'], 'AFRICA')\nself.assertEqual(merge_df.ix[0, 'Income'], 766.121479698518)",
"self.assertEqual... | <|body_start_0|>
merge_df = graph.merge_by_year(1807)
self.assertEqual(['Country', 'Region', 'Income'], merge_df.columns.values.tolist())
self.assertEqual(merge_df.ix[0, 'Country'], 'Algeria')
self.assertEqual(merge_df.ix[0, 'Region'], 'AFRICA')
self.assertEqual(merge_df.ix[0, 'I... | Test | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test:
def test_merge_by_year(self):
"""Unit test for the merge_by_year function."""
<|body_0|>
def test_year_string_to_int(self):
"""Unit test for the year_string_to_int function."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
merge_df = graph.merg... | stack_v2_sparse_classes_10k_train_005342 | 1,235 | no_license | [
{
"docstring": "Unit test for the merge_by_year function.",
"name": "test_merge_by_year",
"signature": "def test_merge_by_year(self)"
},
{
"docstring": "Unit test for the year_string_to_int function.",
"name": "test_year_string_to_int",
"signature": "def test_year_string_to_int(self)"
... | 2 | null | Implement the Python class `Test` described below.
Class description:
Implement the Test class.
Method signatures and docstrings:
- def test_merge_by_year(self): Unit test for the merge_by_year function.
- def test_year_string_to_int(self): Unit test for the year_string_to_int function. | Implement the Python class `Test` described below.
Class description:
Implement the Test class.
Method signatures and docstrings:
- def test_merge_by_year(self): Unit test for the merge_by_year function.
- def test_year_string_to_int(self): Unit test for the year_string_to_int function.
<|skeleton|>
class Test:
... | f5bb1e51de4f84ab3dd62d3073aee4f56534afa1 | <|skeleton|>
class Test:
def test_merge_by_year(self):
"""Unit test for the merge_by_year function."""
<|body_0|>
def test_year_string_to_int(self):
"""Unit test for the year_string_to_int function."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Test:
def test_merge_by_year(self):
"""Unit test for the merge_by_year function."""
merge_df = graph.merge_by_year(1807)
self.assertEqual(['Country', 'Region', 'Income'], merge_df.columns.values.tolist())
self.assertEqual(merge_df.ix[0, 'Country'], 'Algeria')
self.asser... | the_stack_v2_python_sparse | lj1035/package/test.py | ds-ga-1007/assignment9 | train | 2 | |
79f1f9403e408b557a41330ebb7d2d08d8b3f800 | [
"try:\n self.assertEqual(add(17, 23), 40)\nexcept Exception as error:\n print(error)",
"try:\n self.assertEqual(add(-7, -11), -18)\nexcept Exception as error:\n print(error)",
"try:\n self.assertEqual(add(0, 15), 15)\nexcept Exception as error:\n print(error)"
] | <|body_start_0|>
try:
self.assertEqual(add(17, 23), 40)
except Exception as error:
print(error)
<|end_body_0|>
<|body_start_1|>
try:
self.assertEqual(add(-7, -11), -18)
except Exception as error:
print(error)
<|end_body_1|>
<|body_start_2... | Test add function from calculation.py module. | TestAddFunction | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestAddFunction:
"""Test add function from calculation.py module."""
def test_add_all_args_greater_zero(self):
"""Test add function if all arguments are greater than zero."""
<|body_0|>
def test_add_all_args_less_zero(self):
"""Test add function if all arguments ... | stack_v2_sparse_classes_10k_train_005343 | 1,838 | no_license | [
{
"docstring": "Test add function if all arguments are greater than zero.",
"name": "test_add_all_args_greater_zero",
"signature": "def test_add_all_args_greater_zero(self)"
},
{
"docstring": "Test add function if all arguments are less than zero.",
"name": "test_add_all_args_less_zero",
... | 3 | stack_v2_sparse_classes_30k_train_005481 | Implement the Python class `TestAddFunction` described below.
Class description:
Test add function from calculation.py module.
Method signatures and docstrings:
- def test_add_all_args_greater_zero(self): Test add function if all arguments are greater than zero.
- def test_add_all_args_less_zero(self): Test add funct... | Implement the Python class `TestAddFunction` described below.
Class description:
Test add function from calculation.py module.
Method signatures and docstrings:
- def test_add_all_args_greater_zero(self): Test add function if all arguments are greater than zero.
- def test_add_all_args_less_zero(self): Test add funct... | 3a500c9d55fecf4032b5faf59a1cbecf64592e9a | <|skeleton|>
class TestAddFunction:
"""Test add function from calculation.py module."""
def test_add_all_args_greater_zero(self):
"""Test add function if all arguments are greater than zero."""
<|body_0|>
def test_add_all_args_less_zero(self):
"""Test add function if all arguments ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestAddFunction:
"""Test add function from calculation.py module."""
def test_add_all_args_greater_zero(self):
"""Test add function if all arguments are greater than zero."""
try:
self.assertEqual(add(17, 23), 40)
except Exception as error:
print(error)
... | the_stack_v2_python_sparse | python10/test_calculation.py | maksimok93/Dp-189 | train | 0 |
34eb3eabde4d8665502d141338d7b82776449095 | [
"if len(s) == 0:\n return ''\nvowelsTable = ['a', 'A', 'i', 'I', 'e', 'E', 'o', 'O', 'u', 'U']\ntemp = []\nans = []\nvowels = []\nfor i in xrange(len(s)):\n if s[i] in vowelsTable:\n x = '~'\n vowels.append(s[i])\n else:\n x = s[i]\n temp.append(x)\nfor i in temp:\n if i == '~':\... | <|body_start_0|>
if len(s) == 0:
return ''
vowelsTable = ['a', 'A', 'i', 'I', 'e', 'E', 'o', 'O', 'u', 'U']
temp = []
ans = []
vowels = []
for i in xrange(len(s)):
if s[i] in vowelsTable:
x = '~'
vowels.append(s[i])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseVowels(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def reverseVowels2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
def reverseVowels_2_ptr(self, s):
""":type s: str :rtype: str"""
<|body_2|>
<|end_skel... | stack_v2_sparse_classes_10k_train_005344 | 2,402 | no_license | [
{
"docstring": ":type s: str :rtype: str",
"name": "reverseVowels",
"signature": "def reverseVowels(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name": "reverseVowels2",
"signature": "def reverseVowels2(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name... | 3 | stack_v2_sparse_classes_30k_train_004310 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseVowels(self, s): :type s: str :rtype: str
- def reverseVowels2(self, s): :type s: str :rtype: str
- def reverseVowels_2_ptr(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 reverseVowels(self, s): :type s: str :rtype: str
- def reverseVowels2(self, s): :type s: str :rtype: str
- def reverseVowels_2_ptr(self, s): :type s: str :rtype: str
<|skele... | 2d5fa4cd696d5035ea8859befeadc5cc436959c9 | <|skeleton|>
class Solution:
def reverseVowels(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def reverseVowels2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
def reverseVowels_2_ptr(self, s):
""":type s: str :rtype: str"""
<|body_2|>
<|end_skel... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def reverseVowels(self, s):
""":type s: str :rtype: str"""
if len(s) == 0:
return ''
vowelsTable = ['a', 'A', 'i', 'I', 'e', 'E', 'o', 'O', 'u', 'U']
temp = []
ans = []
vowels = []
for i in xrange(len(s)):
if s[i] in vow... | the_stack_v2_python_sparse | SourceCode/Python/Problem/00345.Reverse_Vowels_of_a_String.py | roger6blog/LeetCode | train | 0 | |
7800f605df3594d34514037b2dc687591114871b | [
"def traverse(arr, start, path, seen):\n if len(path) >= 2 and path not in self.res and (path == sorted(path)):\n self.res.append(path[:])\n for i in range(start, len(arr)):\n if i not in seen:\n path.append(arr[i])\n seen.add(i)\n traverse(arr, i + 1, path, seen... | <|body_start_0|>
def traverse(arr, start, path, seen):
if len(path) >= 2 and path not in self.res and (path == sorted(path)):
self.res.append(path[:])
for i in range(start, len(arr)):
if i not in seen:
path.append(arr[i])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findSubsequences(self, nums: List[int]) -> List[List[int]]:
"""Purpose: Returns all different possible increasing subsequences of a given array with at least two elements. Note: Answer can be returned in any order."""
<|body_0|>
def findSubsequences1(self, nums... | stack_v2_sparse_classes_10k_train_005345 | 1,854 | no_license | [
{
"docstring": "Purpose: Returns all different possible increasing subsequences of a given array with at least two elements. Note: Answer can be returned in any order.",
"name": "findSubsequences",
"signature": "def findSubsequences(self, nums: List[int]) -> List[List[int]]"
},
{
"docstring": "I... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findSubsequences(self, nums: List[int]) -> List[List[int]]: Purpose: Returns all different possible increasing subsequences of a given array with at least two elements. Note:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findSubsequences(self, nums: List[int]) -> List[List[int]]: Purpose: Returns all different possible increasing subsequences of a given array with at least two elements. Note:... | 95a86cbbca28d0c0f6d72d28a2f1cb5a86327934 | <|skeleton|>
class Solution:
def findSubsequences(self, nums: List[int]) -> List[List[int]]:
"""Purpose: Returns all different possible increasing subsequences of a given array with at least two elements. Note: Answer can be returned in any order."""
<|body_0|>
def findSubsequences1(self, nums... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def findSubsequences(self, nums: List[int]) -> List[List[int]]:
"""Purpose: Returns all different possible increasing subsequences of a given array with at least two elements. Note: Answer can be returned in any order."""
def traverse(arr, start, path, seen):
if len(path)... | the_stack_v2_python_sparse | backtrackIncSubseq.py | tashakim/puzzles_python | train | 8 | |
444c933da2aa8a9ba07727fc24653096bed66861 | [
"self.device = device\nself.conn_cmd = conn_cmd\nself.device.conn_cmd = conn_cmd",
"bft_pexpect_helper.spawn.__init__(self.device, command='/bin/bash', args=['-c', self.conn_cmd])\ntry:\n result = self.device.expect(['assword:', 'ser2net.*\\r\\n', 'OpenGear Serial Server', 'to access the port escape menu'])\ne... | <|body_start_0|>
self.device = device
self.conn_cmd = conn_cmd
self.device.conn_cmd = conn_cmd
<|end_body_0|>
<|body_start_1|>
bft_pexpect_helper.spawn.__init__(self.device, command='/bin/bash', args=['-c', self.conn_cmd])
try:
result = self.device.expect(['assword:'... | The ser2net daemon allows telnet and tcp sessions to be established with a unit's serial ports. If a board is connected serially to a server running ser2net daemon, this class can be used to connect to the board. | Ser2NetConnection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Ser2NetConnection:
"""The ser2net daemon allows telnet and tcp sessions to be established with a unit's serial ports. If a board is connected serially to a server running ser2net daemon, this class can be used to connect to the board."""
def __init__(self, device=None, conn_cmd=None, **kwarg... | stack_v2_sparse_classes_10k_train_005346 | 2,190 | permissive | [
{
"docstring": "This method initializes the class instance to open a pexpect session. :param device: device to connect, defaults to None :type device: object :param conn_cmd: conn_cmd to connect to device, defaults to None :type conn_cmd: string :param **kwargs: args to be used :type **kwargs: dict",
"name"... | 3 | stack_v2_sparse_classes_30k_train_001552 | Implement the Python class `Ser2NetConnection` described below.
Class description:
The ser2net daemon allows telnet and tcp sessions to be established with a unit's serial ports. If a board is connected serially to a server running ser2net daemon, this class can be used to connect to the board.
Method signatures and ... | Implement the Python class `Ser2NetConnection` described below.
Class description:
The ser2net daemon allows telnet and tcp sessions to be established with a unit's serial ports. If a board is connected serially to a server running ser2net daemon, this class can be used to connect to the board.
Method signatures and ... | 100521fde1fb67536682cafecc2f91a6e2e8a6f8 | <|skeleton|>
class Ser2NetConnection:
"""The ser2net daemon allows telnet and tcp sessions to be established with a unit's serial ports. If a board is connected serially to a server running ser2net daemon, this class can be used to connect to the board."""
def __init__(self, device=None, conn_cmd=None, **kwarg... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Ser2NetConnection:
"""The ser2net daemon allows telnet and tcp sessions to be established with a unit's serial ports. If a board is connected serially to a server running ser2net daemon, this class can be used to connect to the board."""
def __init__(self, device=None, conn_cmd=None, **kwargs):
"... | the_stack_v2_python_sparse | boardfarm/devices/ser2net_connection.py | mattsm/boardfarm | train | 45 |
9d4e352c6c1384e32d4fbdc8faa94da3bb24bb8c | [
"self.server_host = server_host\nself.password = password\nself.from_user = from_user\nself.to_user_list = to_user_list\nself.server = self._init_server()",
"server = smtplib.SMTP(self.server_host, 25)\nserver.login(self.from_user, self.password)\nreturn server",
"from_user_format = Header(str(self.from_user) +... | <|body_start_0|>
self.server_host = server_host
self.password = password
self.from_user = from_user
self.to_user_list = to_user_list
self.server = self._init_server()
<|end_body_0|>
<|body_start_1|>
server = smtplib.SMTP(self.server_host, 25)
server.login(self.fr... | EmailUtil | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmailUtil:
def __init__(self, server_host, password, from_user, to_user_list):
"""初始化发送邮件设置 :param server_host: 发送邮件的邮件服务器 :param password: 发送用户的密码 :param from_user: 发送邮件的用户邮箱 :param to_user_list: 接收邮件的用户邮箱列表"""
<|body_0|>
def _init_server(self):
"""邮件服务器初始化 :return:... | stack_v2_sparse_classes_10k_train_005347 | 1,955 | permissive | [
{
"docstring": "初始化发送邮件设置 :param server_host: 发送邮件的邮件服务器 :param password: 发送用户的密码 :param from_user: 发送邮件的用户邮箱 :param to_user_list: 接收邮件的用户邮箱列表",
"name": "__init__",
"signature": "def __init__(self, server_host, password, from_user, to_user_list)"
},
{
"docstring": "邮件服务器初始化 :return: 邮件服务器对象",
... | 3 | stack_v2_sparse_classes_30k_train_003156 | Implement the Python class `EmailUtil` described below.
Class description:
Implement the EmailUtil class.
Method signatures and docstrings:
- def __init__(self, server_host, password, from_user, to_user_list): 初始化发送邮件设置 :param server_host: 发送邮件的邮件服务器 :param password: 发送用户的密码 :param from_user: 发送邮件的用户邮箱 :param to_user... | Implement the Python class `EmailUtil` described below.
Class description:
Implement the EmailUtil class.
Method signatures and docstrings:
- def __init__(self, server_host, password, from_user, to_user_list): 初始化发送邮件设置 :param server_host: 发送邮件的邮件服务器 :param password: 发送用户的密码 :param from_user: 发送邮件的用户邮箱 :param to_user... | ed360d5aa7f733991fbbc8ab5af96e739c9e1142 | <|skeleton|>
class EmailUtil:
def __init__(self, server_host, password, from_user, to_user_list):
"""初始化发送邮件设置 :param server_host: 发送邮件的邮件服务器 :param password: 发送用户的密码 :param from_user: 发送邮件的用户邮箱 :param to_user_list: 接收邮件的用户邮箱列表"""
<|body_0|>
def _init_server(self):
"""邮件服务器初始化 :return:... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EmailUtil:
def __init__(self, server_host, password, from_user, to_user_list):
"""初始化发送邮件设置 :param server_host: 发送邮件的邮件服务器 :param password: 发送用户的密码 :param from_user: 发送邮件的用户邮箱 :param to_user_list: 接收邮件的用户邮箱列表"""
self.server_host = server_host
self.password = password
self.from_... | the_stack_v2_python_sparse | automated_testing/requests_unittest_test/app/utils/EmailUtil.py | tzytammy/requests_unittest | train | 0 | |
15738df061286b930ffd74a960dab282029e65a7 | [
"self = object.__new__(cls)\nself.name = cls.DEFAULT_NAME\nself.value = value\nself.max_per_guild = 1\nself.metadata_type = AutoModerationRuleTriggerMetadataBase\nreturn self",
"self.value = value\nself.name = name\nself.max_per_guild = max_per_guild\nself.metadata_type = metadata_type\nself.INSTANCES[value] = se... | <|body_start_0|>
self = object.__new__(cls)
self.name = cls.DEFAULT_NAME
self.value = value
self.max_per_guild = 1
self.metadata_type = AutoModerationRuleTriggerMetadataBase
return self
<|end_body_0|>
<|body_start_1|>
self.value = value
self.name = name
... | Represents an auto moderation rule's trigger type. Attributes ---------- value : `int` The Discord side identifier value of the auto moderation trigger type. name : `str` The default name of the auto moderation trigger type. max_per_guild : `int` The maximal amount of rules of this type per guild. metadata_type : `Auto... | AutoModerationRuleTriggerType | [
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AutoModerationRuleTriggerType:
"""Represents an auto moderation rule's trigger type. Attributes ---------- value : `int` The Discord side identifier value of the auto moderation trigger type. name : `str` The default name of the auto moderation trigger type. max_per_guild : `int` The maximal amou... | stack_v2_sparse_classes_10k_train_005348 | 7,201 | permissive | [
{
"docstring": "Creates a new auto moderation trigger type with the given value. Parameters ---------- value : `int` The auto moderation trigger type's identifier value. Returns ------- self : ``AutoModerationRuleTriggerType`` The created instance.",
"name": "_from_value",
"signature": "def _from_value(... | 2 | stack_v2_sparse_classes_30k_train_004355 | Implement the Python class `AutoModerationRuleTriggerType` described below.
Class description:
Represents an auto moderation rule's trigger type. Attributes ---------- value : `int` The Discord side identifier value of the auto moderation trigger type. name : `str` The default name of the auto moderation trigger type.... | Implement the Python class `AutoModerationRuleTriggerType` described below.
Class description:
Represents an auto moderation rule's trigger type. Attributes ---------- value : `int` The Discord side identifier value of the auto moderation trigger type. name : `str` The default name of the auto moderation trigger type.... | 53f24fdb38459dc5a4fd04f11bdbfee8295b76a4 | <|skeleton|>
class AutoModerationRuleTriggerType:
"""Represents an auto moderation rule's trigger type. Attributes ---------- value : `int` The Discord side identifier value of the auto moderation trigger type. name : `str` The default name of the auto moderation trigger type. max_per_guild : `int` The maximal amou... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AutoModerationRuleTriggerType:
"""Represents an auto moderation rule's trigger type. Attributes ---------- value : `int` The Discord side identifier value of the auto moderation trigger type. name : `str` The default name of the auto moderation trigger type. max_per_guild : `int` The maximal amount of rules o... | the_stack_v2_python_sparse | hata/discord/auto_moderation/rule/preinstanced.py | HuyaneMatsu/hata | train | 3 |
d5b4d11dedf2138bffd1c08e7f3cb0ff528c2f91 | [
"self.p0 = p0\nself.p1 = p1\nself.p2 = p2\nself.p3 = p3",
"\"\"\"\n Caso en que la coordenada \"y\" es igual a cero. \n \"\"\"\nif self.y == 0:\n '\\n Caso en que la coordenada \"x\" es mayor que cero. \\n '\n if checkSign(self.x) == 2:\n return 0\n '\\n ... | <|body_start_0|>
self.p0 = p0
self.p1 = p1
self.p2 = p2
self.p3 = p3
<|end_body_0|>
<|body_start_1|>
"""
Caso en que la coordenada "y" es igual a cero.
"""
if self.y == 0:
'\n Caso en que la coordenada "x" es mayor ... | Face3d | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Face3d:
def __init__(self, p0, p1, p2, p3):
"""@param p0: instancia de Point3d @param p1: instancia de Point3d @param p2: instancia de Point3d @param p3: instancia de Point3d"""
<|body_0|>
def Triangulate(self):
"""Metodo que se encarga de triangular una cara en el e... | stack_v2_sparse_classes_10k_train_005349 | 3,592 | no_license | [
{
"docstring": "@param p0: instancia de Point3d @param p1: instancia de Point3d @param p2: instancia de Point3d @param p3: instancia de Point3d",
"name": "__init__",
"signature": "def __init__(self, p0, p1, p2, p3)"
},
{
"docstring": "Metodo que se encarga de triangular una cara en el espacio 3D... | 3 | stack_v2_sparse_classes_30k_train_001892 | Implement the Python class `Face3d` described below.
Class description:
Implement the Face3d class.
Method signatures and docstrings:
- def __init__(self, p0, p1, p2, p3): @param p0: instancia de Point3d @param p1: instancia de Point3d @param p2: instancia de Point3d @param p3: instancia de Point3d
- def Triangulate(... | Implement the Python class `Face3d` described below.
Class description:
Implement the Face3d class.
Method signatures and docstrings:
- def __init__(self, p0, p1, p2, p3): @param p0: instancia de Point3d @param p1: instancia de Point3d @param p2: instancia de Point3d @param p3: instancia de Point3d
- def Triangulate(... | a93de278ea92ad8d57d66fcb76744d394400bd11 | <|skeleton|>
class Face3d:
def __init__(self, p0, p1, p2, p3):
"""@param p0: instancia de Point3d @param p1: instancia de Point3d @param p2: instancia de Point3d @param p3: instancia de Point3d"""
<|body_0|>
def Triangulate(self):
"""Metodo que se encarga de triangular una cara en el e... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Face3d:
def __init__(self, p0, p1, p2, p3):
"""@param p0: instancia de Point3d @param p1: instancia de Point3d @param p2: instancia de Point3d @param p3: instancia de Point3d"""
self.p0 = p0
self.p1 = p1
self.p2 = p2
self.p3 = p3
def Triangulate(self):
"""M... | the_stack_v2_python_sparse | geometry/controller/geometry_3d/face_3d.py | nvergarayi/Cubicador | train | 0 | |
5629ad020469bb4f0749842a5e0a615cc8c15d4c | [
"Frame.__init__(self, master)\nself.pack()\nself.createAlbumWidgets()",
"top_frame = Frame(self)\nself.labelInput = Label(top_frame, text='Album Name')\nself.text_in = Entry(top_frame)\nself.labelResult = Label(top_frame, text='Result')\nself.labelInput.pack()\nself.text_in.pack()\nself.labelResult.pack()\ntop_fr... | <|body_start_0|>
Frame.__init__(self, master)
self.pack()
self.createAlbumWidgets()
<|end_body_0|>
<|body_start_1|>
top_frame = Frame(self)
self.labelInput = Label(top_frame, text='Album Name')
self.text_in = Entry(top_frame)
self.labelResult = Label(top_frame, t... | Application main window class. | getAlbum_UI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class getAlbum_UI:
"""Application main window class."""
def __init__(self, master=None):
"""Main frame initialization (mostly delegated)"""
<|body_0|>
def createAlbumWidgets(self):
"""Add all the widgets to the main frame."""
<|body_1|>
def handle(self):
... | stack_v2_sparse_classes_10k_train_005350 | 10,077 | no_license | [
{
"docstring": "Main frame initialization (mostly delegated)",
"name": "__init__",
"signature": "def __init__(self, master=None)"
},
{
"docstring": "Add all the widgets to the main frame.",
"name": "createAlbumWidgets",
"signature": "def createAlbumWidgets(self)"
},
{
"docstring"... | 3 | stack_v2_sparse_classes_30k_val_000194 | Implement the Python class `getAlbum_UI` described below.
Class description:
Application main window class.
Method signatures and docstrings:
- def __init__(self, master=None): Main frame initialization (mostly delegated)
- def createAlbumWidgets(self): Add all the widgets to the main frame.
- def handle(self): Handl... | Implement the Python class `getAlbum_UI` described below.
Class description:
Application main window class.
Method signatures and docstrings:
- def __init__(self, master=None): Main frame initialization (mostly delegated)
- def createAlbumWidgets(self): Add all the widgets to the main frame.
- def handle(self): Handl... | 2dba11861f91e4bdc1ef28279132a6d8dd4ccf54 | <|skeleton|>
class getAlbum_UI:
"""Application main window class."""
def __init__(self, master=None):
"""Main frame initialization (mostly delegated)"""
<|body_0|>
def createAlbumWidgets(self):
"""Add all the widgets to the main frame."""
<|body_1|>
def handle(self):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class getAlbum_UI:
"""Application main window class."""
def __init__(self, master=None):
"""Main frame initialization (mostly delegated)"""
Frame.__init__(self, master)
self.pack()
self.createAlbumWidgets()
def createAlbumWidgets(self):
"""Add all the widgets to the... | the_stack_v2_python_sparse | Mux_src/Fix_All_Music_Guis.py | rduvalwa5/Mux | train | 0 |
ce98243afa4fc7e5ce7810748beff8c2a791c298 | [
"if surface is not None:\n self.t = surface.index\n assert np.all(self.t == profile.index.unique())\n self.surface = surface\nelse:\n self.t = profile.index.unique()\n self.surface = pd.DataFrame(index=self.t)\nself.z = profile['z'].unique()\nself.info = info\nself.N = len(self.z)\nself.info['levels'... | <|body_start_0|>
if surface is not None:
self.t = surface.index
assert np.all(self.t == profile.index.unique())
self.surface = surface
else:
self.t = profile.index.unique()
self.surface = pd.DataFrame(index=self.t)
self.z = profile['z']... | MMCdata | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MMCdata:
def __init__(self, profile, surface, info, na_values=-999.0):
"""Create object for standardized MMC data output profile: pandas.DataFrame Dataframe with datetime index and columns corresponding to the data arrays described above; a 'z' column describes the height AGL. surface: p... | stack_v2_sparse_classes_10k_train_005351 | 6,740 | no_license | [
{
"docstring": "Create object for standardized MMC data output profile: pandas.DataFrame Dataframe with datetime index and columns corresponding to the data arrays described above; a 'z' column describes the height AGL. surface: pandas.DataFrame, optional Dataframe with datetime index and columns corresponding ... | 3 | stack_v2_sparse_classes_30k_train_000795 | Implement the Python class `MMCdata` described below.
Class description:
Implement the MMCdata class.
Method signatures and docstrings:
- def __init__(self, profile, surface, info, na_values=-999.0): Create object for standardized MMC data output profile: pandas.DataFrame Dataframe with datetime index and columns cor... | Implement the Python class `MMCdata` described below.
Class description:
Implement the MMCdata class.
Method signatures and docstrings:
- def __init__(self, profile, surface, info, na_values=-999.0): Create object for standardized MMC data output profile: pandas.DataFrame Dataframe with datetime index and columns cor... | c34afb2a13fc0075f95a43bac99219b25b3984a2 | <|skeleton|>
class MMCdata:
def __init__(self, profile, surface, info, na_values=-999.0):
"""Create object for standardized MMC data output profile: pandas.DataFrame Dataframe with datetime index and columns corresponding to the data arrays described above; a 'z' column describes the height AGL. surface: p... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MMCdata:
def __init__(self, profile, surface, info, na_values=-999.0):
"""Create object for standardized MMC data output profile: pandas.DataFrame Dataframe with datetime index and columns corresponding to the data arrays described above; a 'z' column describes the height AGL. surface: pandas.DataFram... | the_stack_v2_python_sparse | MMC/output_profile.py | ewquon/pylib | train | 2 | |
e84cbe88cb65b7ae6709666f376c9453d867f726 | [
"super().__init__(reduction=reduction, name='fenchel_young')\nself._batched = batched\nself._maximize = maximize\nself.func = func\nself.perturbed = perturbations.perturbed(func=func, num_samples=num_samples, sigma=sigma, noise=noise, batched=batched)",
"@tf.custom_gradient\ndef forward(theta):\n diff = self.p... | <|body_start_0|>
super().__init__(reduction=reduction, name='fenchel_young')
self._batched = batched
self._maximize = maximize
self.func = func
self.perturbed = perturbations.perturbed(func=func, num_samples=num_samples, sigma=sigma, noise=noise, batched=batched)
<|end_body_0|>
... | Implementation of a Fenchel Young loss. | FenchelYoungLoss | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FenchelYoungLoss:
"""Implementation of a Fenchel Young loss."""
def __init__(self, func=None, num_samples=1000, sigma=0.01, noise=perturbations._NORMAL, batched=True, maximize=True, reduction=tf.keras.losses.Reduction.SUM):
"""Initializes the Fenchel-Young loss. Args: func: the funct... | stack_v2_sparse_classes_10k_train_005352 | 3,623 | permissive | [
{
"docstring": "Initializes the Fenchel-Young loss. Args: func: the function whose argmax is to be differentiated by perturbation. num_samples: (int) the number of perturbed inputs. sigma: (float) the amount of noise to be considered noise: (str) the noise distribution to be used to sample perturbations. batche... | 2 | stack_v2_sparse_classes_30k_train_004255 | Implement the Python class `FenchelYoungLoss` described below.
Class description:
Implementation of a Fenchel Young loss.
Method signatures and docstrings:
- def __init__(self, func=None, num_samples=1000, sigma=0.01, noise=perturbations._NORMAL, batched=True, maximize=True, reduction=tf.keras.losses.Reduction.SUM): ... | Implement the Python class `FenchelYoungLoss` described below.
Class description:
Implementation of a Fenchel Young loss.
Method signatures and docstrings:
- def __init__(self, func=None, num_samples=1000, sigma=0.01, noise=perturbations._NORMAL, batched=True, maximize=True, reduction=tf.keras.losses.Reduction.SUM): ... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class FenchelYoungLoss:
"""Implementation of a Fenchel Young loss."""
def __init__(self, func=None, num_samples=1000, sigma=0.01, noise=perturbations._NORMAL, batched=True, maximize=True, reduction=tf.keras.losses.Reduction.SUM):
"""Initializes the Fenchel-Young loss. Args: func: the funct... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FenchelYoungLoss:
"""Implementation of a Fenchel Young loss."""
def __init__(self, func=None, num_samples=1000, sigma=0.01, noise=perturbations._NORMAL, batched=True, maximize=True, reduction=tf.keras.losses.Reduction.SUM):
"""Initializes the Fenchel-Young loss. Args: func: the function whose arg... | the_stack_v2_python_sparse | perturbations/fenchel_young.py | Jimmy-INL/google-research | train | 1 |
9d9a710003f6c1d85d64fb7af652826307c50ef9 | [
"if isinstance(value, collections.abc.Iterable):\n return numpy.ones_like(value, dtype=numpy.bool_)\nelse:\n return True",
"data = None if data is None else numpy.array(data, copy=False)\nif data is None or data.size == 0:\n return self.DEFAULT_RANGE\nif mode == 'minmax':\n vmin, vmax = self.autoscale... | <|body_start_0|>
if isinstance(value, collections.abc.Iterable):
return numpy.ones_like(value, dtype=numpy.bool_)
else:
return True
<|end_body_0|>
<|body_start_1|>
data = None if data is None else numpy.array(data, copy=False)
if data is None or data.size == 0:
... | Colormap normalization mix-in class | _NormalizationMixIn | [
"MIT",
"LicenseRef-scancode-public-domain-disclaimer",
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _NormalizationMixIn:
"""Colormap normalization mix-in class"""
def is_valid(self, value):
"""Check if a value is in the valid range for this normalization. Override in subclass. :param Union[float,numpy.ndarray] value: :rtype: Union[bool,numpy.ndarray]"""
<|body_0|>
def ... | stack_v2_sparse_classes_10k_train_005353 | 16,802 | permissive | [
{
"docstring": "Check if a value is in the valid range for this normalization. Override in subclass. :param Union[float,numpy.ndarray] value: :rtype: Union[bool,numpy.ndarray]",
"name": "is_valid",
"signature": "def is_valid(self, value)"
},
{
"docstring": "Returns range for given data and autos... | 4 | null | Implement the Python class `_NormalizationMixIn` described below.
Class description:
Colormap normalization mix-in class
Method signatures and docstrings:
- def is_valid(self, value): Check if a value is in the valid range for this normalization. Override in subclass. :param Union[float,numpy.ndarray] value: :rtype: ... | Implement the Python class `_NormalizationMixIn` described below.
Class description:
Colormap normalization mix-in class
Method signatures and docstrings:
- def is_valid(self, value): Check if a value is in the valid range for this normalization. Override in subclass. :param Union[float,numpy.ndarray] value: :rtype: ... | 5e33cb69afd2a8b1cfe3183282acdd8b34c1a74f | <|skeleton|>
class _NormalizationMixIn:
"""Colormap normalization mix-in class"""
def is_valid(self, value):
"""Check if a value is in the valid range for this normalization. Override in subclass. :param Union[float,numpy.ndarray] value: :rtype: Union[bool,numpy.ndarray]"""
<|body_0|>
def ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class _NormalizationMixIn:
"""Colormap normalization mix-in class"""
def is_valid(self, value):
"""Check if a value is in the valid range for this normalization. Override in subclass. :param Union[float,numpy.ndarray] value: :rtype: Union[bool,numpy.ndarray]"""
if isinstance(value, collections.... | the_stack_v2_python_sparse | src/silx/math/colormap.py | silx-kit/silx | train | 120 |
0ebc8acedc20dab13f0f55b1fb2a72636170567f | [
"self.session = session\nsuper().__init__(cmdlist)\nself.sighandle = self.session.sighandle\nfor cmd in cmdlist:\n cmdname = cmd.__name__.lower()\n cmdinst = getattr(self, cmdname)\n cmdinst.sighandle = self.sighandle\nself.session_lock_string = ''",
"self.mem_handle = self.session.mem_handle\nself.cmdlo... | <|body_start_0|>
self.session = session
super().__init__(cmdlist)
self.sighandle = self.session.sighandle
for cmd in cmdlist:
cmdname = cmd.__name__.lower()
cmdinst = getattr(self, cmdname)
cmdinst.sighandle = self.sighandle
self.session_lock_s... | @brief Overloading the logging functions and user input methods to allow for interactions through the web GUI | GUIcontrolterm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GUIcontrolterm:
"""@brief Overloading the logging functions and user input methods to allow for interactions through the web GUI"""
def __init__(self, cmdlist, session, **kwargs):
"""User action prompts requires a reference to the socketio interface"""
<|body_0|>
def ini... | stack_v2_sparse_classes_10k_train_005354 | 16,012 | no_license | [
{
"docstring": "User action prompts requires a reference to the socketio interface",
"name": "__init__",
"signature": "def __init__(self, cmdlist, session, **kwargs)"
},
{
"docstring": "@brief Overloading the settings for the logger instances. @details Additional settings for the memory settings... | 3 | stack_v2_sparse_classes_30k_train_000638 | Implement the Python class `GUIcontrolterm` described below.
Class description:
@brief Overloading the logging functions and user input methods to allow for interactions through the web GUI
Method signatures and docstrings:
- def __init__(self, cmdlist, session, **kwargs): User action prompts requires a reference to ... | Implement the Python class `GUIcontrolterm` described below.
Class description:
@brief Overloading the logging functions and user input methods to allow for interactions through the web GUI
Method signatures and docstrings:
- def __init__(self, cmdlist, session, **kwargs): User action prompts requires a reference to ... | 592a21b2361969faab075f31cc70e1fc05af7fd1 | <|skeleton|>
class GUIcontrolterm:
"""@brief Overloading the logging functions and user input methods to allow for interactions through the web GUI"""
def __init__(self, cmdlist, session, **kwargs):
"""User action prompts requires a reference to the socketio interface"""
<|body_0|>
def ini... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GUIcontrolterm:
"""@brief Overloading the logging functions and user input methods to allow for interactions through the web GUI"""
def __init__(self, cmdlist, session, **kwargs):
"""User action prompts requires a reference to the socketio interface"""
self.session = session
super... | the_stack_v2_python_sparse | server/session.py | yimuchen/SiPMCalibControl | train | 0 |
8c9f11bf8c5da7f13b577e1eb7ace6f51bf87516 | [
"inv.Inventory.__init__(self, item_code, description, market_price, rental_price)\nself.material = material\nself.size = size",
"output_dict = {}\noutput_dict['item_code'] = self.item_code\noutput_dict['description'] = self.description\noutput_dict['market_price'] = self.market_price\noutput_dict['rental_price'] ... | <|body_start_0|>
inv.Inventory.__init__(self, item_code, description, market_price, rental_price)
self.material = material
self.size = size
<|end_body_0|>
<|body_start_1|>
output_dict = {}
output_dict['item_code'] = self.item_code
output_dict['description'] = self.descri... | Contains class methods and attributes for furniture items. | Furniture | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Furniture:
"""Contains class methods and attributes for furniture items."""
def __init__(self, item_code, description, market_price, rental_price, material, size):
"""Creates furniture item."""
<|body_0|>
def return_as_dictionary(self):
"""Return furniture item i... | stack_v2_sparse_classes_10k_train_005355 | 1,070 | no_license | [
{
"docstring": "Creates furniture item.",
"name": "__init__",
"signature": "def __init__(self, item_code, description, market_price, rental_price, material, size)"
},
{
"docstring": "Return furniture item information as a dictionary.",
"name": "return_as_dictionary",
"signature": "def re... | 2 | stack_v2_sparse_classes_30k_train_002489 | Implement the Python class `Furniture` described below.
Class description:
Contains class methods and attributes for furniture items.
Method signatures and docstrings:
- def __init__(self, item_code, description, market_price, rental_price, material, size): Creates furniture item.
- def return_as_dictionary(self): Re... | Implement the Python class `Furniture` described below.
Class description:
Contains class methods and attributes for furniture items.
Method signatures and docstrings:
- def __init__(self, item_code, description, market_price, rental_price, material, size): Creates furniture item.
- def return_as_dictionary(self): Re... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class Furniture:
"""Contains class methods and attributes for furniture items."""
def __init__(self, item_code, description, market_price, rental_price, material, size):
"""Creates furniture item."""
<|body_0|>
def return_as_dictionary(self):
"""Return furniture item i... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Furniture:
"""Contains class methods and attributes for furniture items."""
def __init__(self, item_code, description, market_price, rental_price, material, size):
"""Creates furniture item."""
inv.Inventory.__init__(self, item_code, description, market_price, rental_price)
self.m... | the_stack_v2_python_sparse | students/alexander_boone/lesson01/assignment/inventory_management/furniture_class.py | JavaRod/SP_Python220B_2019 | train | 1 |
9b47e728a7d3d53fe94ca8d616f41a45c4d59fd9 | [
"super(AutomaticWeightedLoss, self).__init__()\nself.mode = mode\nif self.mode not in ['cls', 'reg']:\n raise ValueError('mode argument must be cls or reg.')\nparams = torch.ones(num, requires_grad=True)\nself.params = torch.nn.Parameter(params)",
"loss_sum = 0\nloss_num = len(x)\nfor i, loss in enumerate(x):\... | <|body_start_0|>
super(AutomaticWeightedLoss, self).__init__()
self.mode = mode
if self.mode not in ['cls', 'reg']:
raise ValueError('mode argument must be cls or reg.')
params = torch.ones(num, requires_grad=True)
self.params = torch.nn.Parameter(params)
<|end_body_0... | automatically weighted multi-task(classification task) loss Examples： loss1=1 loss2=2 awl = AutomaticWeightedLoss(2) loss_sum = awl(loss1, loss2) | AutomaticWeightedLoss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AutomaticWeightedLoss:
"""automatically weighted multi-task(classification task) loss Examples： loss1=1 loss2=2 awl = AutomaticWeightedLoss(2) loss_sum = awl(loss1, loss2)"""
def __init__(self, num=2, mode='cls'):
"""Args: num (int, optional): the number of loss. Defaults to 2. mode ... | stack_v2_sparse_classes_10k_train_005356 | 1,891 | permissive | [
{
"docstring": "Args: num (int, optional): the number of loss. Defaults to 2. mode (str, optional): 'cls' for classification multi-task, 'reg' for regression multi-task. Defaults to 'cls'.",
"name": "__init__",
"signature": "def __init__(self, num=2, mode='cls')"
},
{
"docstring": "[summary] Arg... | 2 | stack_v2_sparse_classes_30k_train_000576 | Implement the Python class `AutomaticWeightedLoss` described below.
Class description:
automatically weighted multi-task(classification task) loss Examples： loss1=1 loss2=2 awl = AutomaticWeightedLoss(2) loss_sum = awl(loss1, loss2)
Method signatures and docstrings:
- def __init__(self, num=2, mode='cls'): Args: num ... | Implement the Python class `AutomaticWeightedLoss` described below.
Class description:
automatically weighted multi-task(classification task) loss Examples： loss1=1 loss2=2 awl = AutomaticWeightedLoss(2) loss_sum = awl(loss1, loss2)
Method signatures and docstrings:
- def __init__(self, num=2, mode='cls'): Args: num ... | b4c049fd30db39b67984edfadc49b4354d52be83 | <|skeleton|>
class AutomaticWeightedLoss:
"""automatically weighted multi-task(classification task) loss Examples： loss1=1 loss2=2 awl = AutomaticWeightedLoss(2) loss_sum = awl(loss1, loss2)"""
def __init__(self, num=2, mode='cls'):
"""Args: num (int, optional): the number of loss. Defaults to 2. mode ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AutomaticWeightedLoss:
"""automatically weighted multi-task(classification task) loss Examples： loss1=1 loss2=2 awl = AutomaticWeightedLoss(2) loss_sum = awl(loss1, loss2)"""
def __init__(self, num=2, mode='cls'):
"""Args: num (int, optional): the number of loss. Defaults to 2. mode (str, optiona... | the_stack_v2_python_sparse | pasaie/losses/autoweighted_loss.py | tracy-talent/AIPolicy | train | 0 |
a7b71c941db485bfa8b423f413a785dfb4fd4da2 | [
"versions = []\nfor key, data in VERSIONS.items():\n v = BaseVersion(data['id'], data['status'], request.application_url, data['updated'])\n versions.append(v)\nreturn wsgi.Result(VersionsDataView(versions))",
"data = VERSIONS[request.url_version]\nv = Version(data['id'], data['status'], request.application... | <|body_start_0|>
versions = []
for key, data in VERSIONS.items():
v = BaseVersion(data['id'], data['status'], request.application_url, data['updated'])
versions.append(v)
return wsgi.Result(VersionsDataView(versions))
<|end_body_0|>
<|body_start_1|>
data = VERSIO... | VersionsController | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VersionsController:
def index(self, request):
"""Respond to a request for API versions."""
<|body_0|>
def show(self, request):
"""Respond to a request for a specific API version."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
versions = []
... | stack_v2_sparse_classes_10k_train_005357 | 3,164 | permissive | [
{
"docstring": "Respond to a request for API versions.",
"name": "index",
"signature": "def index(self, request)"
},
{
"docstring": "Respond to a request for a specific API version.",
"name": "show",
"signature": "def show(self, request)"
}
] | 2 | null | Implement the Python class `VersionsController` described below.
Class description:
Implement the VersionsController class.
Method signatures and docstrings:
- def index(self, request): Respond to a request for API versions.
- def show(self, request): Respond to a request for a specific API version. | Implement the Python class `VersionsController` described below.
Class description:
Implement the VersionsController class.
Method signatures and docstrings:
- def index(self, request): Respond to a request for API versions.
- def show(self, request): Respond to a request for a specific API version.
<|skeleton|>
cla... | 4288b8f78250cc3a1c93b019e2c3b4bf78df177c | <|skeleton|>
class VersionsController:
def index(self, request):
"""Respond to a request for API versions."""
<|body_0|>
def show(self, request):
"""Respond to a request for a specific API version."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class VersionsController:
def index(self, request):
"""Respond to a request for API versions."""
versions = []
for key, data in VERSIONS.items():
v = BaseVersion(data['id'], data['status'], request.application_url, data['updated'])
versions.append(v)
return ws... | the_stack_v2_python_sparse | trove/versions.py | openstack/trove | train | 258 | |
26f1b91faa9f85f22214419e9e526798dee252e7 | [
"cache = {}\n\ndef dfs(n, rpl):\n if n in cache:\n return cache[n]\n if n == 1:\n return rpl\n if n & 1:\n temp = 1 + min(dfs(n + 1, rpl), dfs(n - 1, rpl))\n else:\n temp = 1 + dfs(n // 2, rpl)\n cache[n] = temp\n return temp\nreturn dfs(n, 0)",
"res = 0\nwhile n > 1:... | <|body_start_0|>
cache = {}
def dfs(n, rpl):
if n in cache:
return cache[n]
if n == 1:
return rpl
if n & 1:
temp = 1 + min(dfs(n + 1, rpl), dfs(n - 1, rpl))
else:
temp = 1 + dfs(n // 2, rpl)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def integerReplacement(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def integerReplacement2(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
cache = {}
def dfs(n, rpl):
if n ... | stack_v2_sparse_classes_10k_train_005358 | 899 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "integerReplacement",
"signature": "def integerReplacement(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "integerReplacement2",
"signature": "def integerReplacement2(self, n)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def integerReplacement(self, n): :type n: int :rtype: int
- def integerReplacement2(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def integerReplacement(self, n): :type n: int :rtype: int
- def integerReplacement2(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def integerReplacement(s... | dbdb227e12f329e4ca064b338f1fbdca42f3a848 | <|skeleton|>
class Solution:
def integerReplacement(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def integerReplacement2(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def integerReplacement(self, n):
""":type n: int :rtype: int"""
cache = {}
def dfs(n, rpl):
if n in cache:
return cache[n]
if n == 1:
return rpl
if n & 1:
temp = 1 + min(dfs(n + 1, rpl), dfs(... | the_stack_v2_python_sparse | LC397.py | Qiao-Liang/LeetCode | train | 0 | |
8f4a9be4611d012c6f8a05fe6183fda335c322e5 | [
"self.nums = nums\nself.dicts = {}\n\ndef dfs(start, end):\n if start > end:\n return 0\n if (start, end) in self.dicts.keys():\n return self.dicts[start, end]\n if start == end:\n self.dicts[start, end] = self.nums[start]\n return self.nums[start]\n else:\n a = dfs(st... | <|body_start_0|>
self.nums = nums
self.dicts = {}
def dfs(start, end):
if start > end:
return 0
if (start, end) in self.dicts.keys():
return self.dicts[start, end]
if start == end:
self.dicts[start, end] = self.... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def PredictTheWinner(self, nums):
""":type nums: List[int] :rtype: bool 62ms"""
<|body_0|>
def PredictTheWinner_1(self, nums):
""":type nums: List[int] :rtype: bool 32ms"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.nums = nums
... | stack_v2_sparse_classes_10k_train_005359 | 2,954 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: bool 62ms",
"name": "PredictTheWinner",
"signature": "def PredictTheWinner(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: bool 32ms",
"name": "PredictTheWinner_1",
"signature": "def PredictTheWinner_1(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002177 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def PredictTheWinner(self, nums): :type nums: List[int] :rtype: bool 62ms
- def PredictTheWinner_1(self, nums): :type nums: List[int] :rtype: bool 32ms | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def PredictTheWinner(self, nums): :type nums: List[int] :rtype: bool 62ms
- def PredictTheWinner_1(self, nums): :type nums: List[int] :rtype: bool 32ms
<|skeleton|>
class Soluti... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def PredictTheWinner(self, nums):
""":type nums: List[int] :rtype: bool 62ms"""
<|body_0|>
def PredictTheWinner_1(self, nums):
""":type nums: List[int] :rtype: bool 32ms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def PredictTheWinner(self, nums):
""":type nums: List[int] :rtype: bool 62ms"""
self.nums = nums
self.dicts = {}
def dfs(start, end):
if start > end:
return 0
if (start, end) in self.dicts.keys():
return self.di... | the_stack_v2_python_sparse | PredictTheWinner_MID_486.py | 953250587/leetcode-python | train | 2 | |
d1996d89dfe707a7f259692889eb85139eaa28d0 | [
"ans = []\nfrom collections import deque\nqueue = deque()\nfor i in range(len(nums)):\n while queue and queue[0] < i - k + 1:\n queue.popleft()\n while queue and nums[i] > nums[queue[-1]]:\n queue.pop()\n queue.append(i)\n if i >= k - 1:\n ans.append(nums[queue[0]])\nreturn ans",
... | <|body_start_0|>
ans = []
from collections import deque
queue = deque()
for i in range(len(nums)):
while queue and queue[0] < i - k + 1:
queue.popleft()
while queue and nums[i] > nums[queue[-1]]:
queue.pop()
queue.append... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxSlidingWindow(self, nums, k):
"""Disscussion Method 算法：单调双向队列 思路： 和单调栈类似，单调栈是栈内元素保持某种单调性，单调双向队列是队列内保持某种单调性，然后由于本题的背景， 所以使用双向队列 首先要明确➡️单调队列保持队列内有序 用一个单调队列来记录遍历过的值的下标 比如1，3，2，0遍历到3的时候，由于3进来后，前面比3小的元素一定不可能是目标的元素，所以将其弹出，而遍历 到2的时候，2不大于3，并且不知道2后面的元素会不会比2更小，也就是2可能是一个候选的最大值，所以入队... | stack_v2_sparse_classes_10k_train_005360 | 3,046 | no_license | [
{
"docstring": "Disscussion Method 算法：单调双向队列 思路： 和单调栈类似，单调栈是栈内元素保持某种单调性，单调双向队列是队列内保持某种单调性，然后由于本题的背景， 所以使用双向队列 首先要明确➡️单调队列保持队列内有序 用一个单调队列来记录遍历过的值的下标 比如1，3，2，0遍历到3的时候，由于3进来后，前面比3小的元素一定不可能是目标的元素，所以将其弹出，而遍历 到2的时候，2不大于3，并且不知道2后面的元素会不会比2更小，也就是2可能是一个候选的最大值，所以入队列。如此 一来便可以在遍历到第i个元素时确认这个元素的留存状态，即它大于队列尾元素，队列尾弹出，然后第i元素入队列。... | 2 | stack_v2_sparse_classes_30k_val_000292 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow(self, nums, k): Disscussion Method 算法：单调双向队列 思路： 和单调栈类似，单调栈是栈内元素保持某种单调性，单调双向队列是队列内保持某种单调性，然后由于本题的背景， 所以使用双向队列 首先要明确➡️单调队列保持队列内有序 用一个单调队列来记录遍历过的值的下标 比如1，3，2，0... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow(self, nums, k): Disscussion Method 算法：单调双向队列 思路： 和单调栈类似，单调栈是栈内元素保持某种单调性，单调双向队列是队列内保持某种单调性，然后由于本题的背景， 所以使用双向队列 首先要明确➡️单调队列保持队列内有序 用一个单调队列来记录遍历过的值的下标 比如1，3，2，0... | a1c074ff0d542f7ef0e5e01e280b16e52fa7a33d | <|skeleton|>
class Solution:
def maxSlidingWindow(self, nums, k):
"""Disscussion Method 算法：单调双向队列 思路： 和单调栈类似，单调栈是栈内元素保持某种单调性，单调双向队列是队列内保持某种单调性，然后由于本题的背景， 所以使用双向队列 首先要明确➡️单调队列保持队列内有序 用一个单调队列来记录遍历过的值的下标 比如1，3，2，0遍历到3的时候，由于3进来后，前面比3小的元素一定不可能是目标的元素，所以将其弹出，而遍历 到2的时候，2不大于3，并且不知道2后面的元素会不会比2更小，也就是2可能是一个候选的最大值，所以入队... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def maxSlidingWindow(self, nums, k):
"""Disscussion Method 算法：单调双向队列 思路： 和单调栈类似，单调栈是栈内元素保持某种单调性，单调双向队列是队列内保持某种单调性，然后由于本题的背景， 所以使用双向队列 首先要明确➡️单调队列保持队列内有序 用一个单调队列来记录遍历过的值的下标 比如1，3，2，0遍历到3的时候，由于3进来后，前面比3小的元素一定不可能是目标的元素，所以将其弹出，而遍历 到2的时候，2不大于3，并且不知道2后面的元素会不会比2更小，也就是2可能是一个候选的最大值，所以入队列。如此 一来便可以在遍历到... | the_stack_v2_python_sparse | 239_Sliding Window Maximum.py | xhwupup/xhw_project | train | 0 | |
c41d30eab0f767478cf32aa263c7a3335ca48226 | [
"tax_amount = 0\nself.tax_amount = tax_amount\nself.amount_with_tax = self.amount_without_tax + tax_amount",
"res = super(sale_anticipated_invoice, self).default_get(fields_list=fields_list)\nsale_id = self.env.context.get('active_id')\nif sale_id and self.env.context.get('active_model') == 'sale.order':\n res... | <|body_start_0|>
tax_amount = 0
self.tax_amount = tax_amount
self.amount_with_tax = self.amount_without_tax + tax_amount
<|end_body_0|>
<|body_start_1|>
res = super(sale_anticipated_invoice, self).default_get(fields_list=fields_list)
sale_id = self.env.context.get('active_id')
... | Wizard to create an anticipated invoice from the sale | sale_anticipated_invoice | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class sale_anticipated_invoice:
"""Wizard to create an anticipated invoice from the sale"""
def _compute_amount_with_tax(self):
"""Calcul du montant total avec les taxes"""
<|body_0|>
def default_get(self, fields_list):
"""Surcharge afin de récupérer la vente pour laqu... | stack_v2_sparse_classes_10k_train_005361 | 7,286 | no_license | [
{
"docstring": "Calcul du montant total avec les taxes",
"name": "_compute_amount_with_tax",
"signature": "def _compute_amount_with_tax(self)"
},
{
"docstring": "Surcharge afin de récupérer la vente pour laquelle on effectue la facture anticipée",
"name": "default_get",
"signature": "def... | 5 | stack_v2_sparse_classes_30k_train_000477 | Implement the Python class `sale_anticipated_invoice` described below.
Class description:
Wizard to create an anticipated invoice from the sale
Method signatures and docstrings:
- def _compute_amount_with_tax(self): Calcul du montant total avec les taxes
- def default_get(self, fields_list): Surcharge afin de récupér... | Implement the Python class `sale_anticipated_invoice` described below.
Class description:
Wizard to create an anticipated invoice from the sale
Method signatures and docstrings:
- def _compute_amount_with_tax(self): Calcul du montant total avec les taxes
- def default_get(self, fields_list): Surcharge afin de récupér... | eb394e1f79ba1995da2dcd81adfdd511c22caff9 | <|skeleton|>
class sale_anticipated_invoice:
"""Wizard to create an anticipated invoice from the sale"""
def _compute_amount_with_tax(self):
"""Calcul du montant total avec les taxes"""
<|body_0|>
def default_get(self, fields_list):
"""Surcharge afin de récupérer la vente pour laqu... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class sale_anticipated_invoice:
"""Wizard to create an anticipated invoice from the sale"""
def _compute_amount_with_tax(self):
"""Calcul du montant total avec les taxes"""
tax_amount = 0
self.tax_amount = tax_amount
self.amount_with_tax = self.amount_without_tax + tax_amount
... | the_stack_v2_python_sparse | OpenPROD/openprod-addons/sale/wizard/anticipated_invoice.py | kazacube-mziouadi/ceci | train | 0 |
463ef791225c225cde13a3c88c80e1896fce1606 | [
"data = {'id': str(user_id), 'first_name': '', 'last_name': '', 'fullname': '', 'email': '', 'internal': False}\ntry:\n _ = UUID(user_id)\nexcept (ValueError, AttributeError):\n logger.error(f'Actor id is not a valid UUID: {user_id}')\nelse:\n if user_id == SystemUser.id:\n raw_data = SystemUser\n ... | <|body_start_0|>
data = {'id': str(user_id), 'first_name': '', 'last_name': '', 'fullname': '', 'email': '', 'internal': False}
try:
_ = UUID(user_id)
except (ValueError, AttributeError):
logger.error(f'Actor id is not a valid UUID: {user_id}')
else:
i... | Stub implementation of the IUserProfileQuery interface. | BaseUserProfileQuery | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseUserProfileQuery:
"""Stub implementation of the IUserProfileQuery interface."""
def get_data(self, user_id: str) -> dict:
"""Get a map with user data."""
<|body_0|>
def update_wf_history(self, state_history: list) -> list:
"""Update workflow history with user... | stack_v2_sparse_classes_10k_train_005362 | 1,795 | no_license | [
{
"docstring": "Get a map with user data.",
"name": "get_data",
"signature": "def get_data(self, user_id: str) -> dict"
},
{
"docstring": "Update workflow history with user data.",
"name": "update_wf_history",
"signature": "def update_wf_history(self, state_history: list) -> list"
}
] | 2 | stack_v2_sparse_classes_30k_train_003023 | Implement the Python class `BaseUserProfileQuery` described below.
Class description:
Stub implementation of the IUserProfileQuery interface.
Method signatures and docstrings:
- def get_data(self, user_id: str) -> dict: Get a map with user data.
- def update_wf_history(self, state_history: list) -> list: Update workf... | Implement the Python class `BaseUserProfileQuery` described below.
Class description:
Stub implementation of the IUserProfileQuery interface.
Method signatures and docstrings:
- def get_data(self, user_id: str) -> dict: Get a map with user data.
- def update_wf_history(self, state_history: list) -> list: Update workf... | 0f27d5de4b04fe1d0ce2c2c9ccd3f2893b833128 | <|skeleton|>
class BaseUserProfileQuery:
"""Stub implementation of the IUserProfileQuery interface."""
def get_data(self, user_id: str) -> dict:
"""Get a map with user data."""
<|body_0|>
def update_wf_history(self, state_history: list) -> list:
"""Update workflow history with user... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BaseUserProfileQuery:
"""Stub implementation of the IUserProfileQuery interface."""
def get_data(self, user_id: str) -> dict:
"""Get a map with user data."""
data = {'id': str(user_id), 'first_name': '', 'last_name': '', 'fullname': '', 'email': '', 'internal': False}
try:
... | the_stack_v2_python_sparse | src/briefy/common/utilities/userprofile.py | BriefyHQ/briefy.common | train | 0 |
d899a9406e323751205c9405d8c20b1af8791ea9 | [
"self.email_addresses = email_addresses\nself.email_delivery_targets = email_delivery_targets\nself.raise_object_level_failure_alert = raise_object_level_failure_alert",
"if dictionary is None:\n return None\nemail_addresses = dictionary.get('emailAddresses')\nemail_delivery_targets = None\nif dictionary.get('... | <|body_start_0|>
self.email_addresses = email_addresses
self.email_delivery_targets = email_delivery_targets
self.raise_object_level_failure_alert = raise_object_level_failure_alert
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
email_addresses = ... | Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of EmailDeliveryTarget): Specifies additional email addresses where alert notificati... | AlertingConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlertingConfig:
"""Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of EmailDeliveryTarget): Specifies additio... | stack_v2_sparse_classes_10k_train_005363 | 2,719 | permissive | [
{
"docstring": "Constructor for the AlertingConfig class",
"name": "__init__",
"signature": "def __init__(self, email_addresses=None, email_delivery_targets=None, raise_object_level_failure_alert=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dict... | 2 | stack_v2_sparse_classes_30k_test_000394 | Implement the Python class `AlertingConfig` described below.
Class description:
Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of ... | Implement the Python class `AlertingConfig` described below.
Class description:
Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class AlertingConfig:
"""Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of EmailDeliveryTarget): Specifies additio... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AlertingConfig:
"""Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of EmailDeliveryTarget): Specifies additional email add... | the_stack_v2_python_sparse | cohesity_management_sdk/models/alerting_config.py | cohesity/management-sdk-python | train | 24 |
f8a8c0ae49a906382844e3bf70f9c24985b78624 | [
"ts = TopologicalSorter()\nfor cur, pre in prerequisites:\n ts.add(cur, pre)\ntry:\n ts.prepare()\n return True\nexcept CycleError:\n return False",
"adjList = [[] for _ in range(numCourses)]\ndeg = [0] * numCourses\nfor cur, pre in prerequisites:\n adjList[pre].append(cur)\n deg[cur] += 1\nretu... | <|body_start_0|>
ts = TopologicalSorter()
for cur, pre in prerequisites:
ts.add(cur, pre)
try:
ts.prepare()
return True
except CycleError:
return False
<|end_body_0|>
<|body_start_1|>
adjList = [[] for _ in range(numCourses)]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canFinish(self, numCourses: int, prerequisites: list[list[int]]) -> bool:
"""有向图是否无环"""
<|body_0|>
def canFinish2(self, numCourses: int, prerequisites: list[list[int]]) -> bool:
"""有向图是否无环"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_005364 | 1,875 | no_license | [
{
"docstring": "有向图是否无环",
"name": "canFinish",
"signature": "def canFinish(self, numCourses: int, prerequisites: list[list[int]]) -> bool"
},
{
"docstring": "有向图是否无环",
"name": "canFinish2",
"signature": "def canFinish2(self, numCourses: int, prerequisites: list[list[int]]) -> bool"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canFinish(self, numCourses: int, prerequisites: list[list[int]]) -> bool: 有向图是否无环
- def canFinish2(self, numCourses: int, prerequisites: list[list[int]]) -> bool: 有向图是否无环 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canFinish(self, numCourses: int, prerequisites: list[list[int]]) -> bool: 有向图是否无环
- def canFinish2(self, numCourses: int, prerequisites: list[list[int]]) -> bool: 有向图是否无环
<|... | 7e79e26bb8f641868561b186e34c1127ed63c9e0 | <|skeleton|>
class Solution:
def canFinish(self, numCourses: int, prerequisites: list[list[int]]) -> bool:
"""有向图是否无环"""
<|body_0|>
def canFinish2(self, numCourses: int, prerequisites: list[list[int]]) -> bool:
"""有向图是否无环"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def canFinish(self, numCourses: int, prerequisites: list[list[int]]) -> bool:
"""有向图是否无环"""
ts = TopologicalSorter()
for cur, pre in prerequisites:
ts.add(cur, pre)
try:
ts.prepare()
return True
except CycleError:
... | the_stack_v2_python_sparse | 7_graph/拓扑排序/课程表/207. 课程表拓扑排序调库.py | 981377660LMT/algorithm-study | train | 225 | |
fdf52c53e8c01d13ae12d342deb658977903b435 | [
"self.host = host\nself.port = port\nself.sock = None\nself._sensor = sensor\nself.stopped = threading.Event()",
"_LOGGER.debug('Setting up socket...')\nself.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nself.sock.settimeout(10)\nself.sock.setsockopt(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1)\ntry:\n ... | <|body_start_0|>
self.host = host
self.port = port
self.sock = None
self._sensor = sensor
self.stopped = threading.Event()
<|end_body_0|>
<|body_start_1|>
_LOGGER.debug('Setting up socket...')
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
... | Event listener to monitor calls on the Fritz!Box. | FritzBoxCallMonitor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FritzBoxCallMonitor:
"""Event listener to monitor calls on the Fritz!Box."""
def __init__(self, host, port, sensor):
"""Initialize Fritz!Box monitor instance."""
<|body_0|>
def connect(self):
"""Connect to the Fritz!Box."""
<|body_1|>
def _listen(sel... | stack_v2_sparse_classes_10k_train_005365 | 9,842 | permissive | [
{
"docstring": "Initialize Fritz!Box monitor instance.",
"name": "__init__",
"signature": "def __init__(self, host, port, sensor)"
},
{
"docstring": "Connect to the Fritz!Box.",
"name": "connect",
"signature": "def connect(self)"
},
{
"docstring": "Listen to incoming or outgoing ... | 4 | null | Implement the Python class `FritzBoxCallMonitor` described below.
Class description:
Event listener to monitor calls on the Fritz!Box.
Method signatures and docstrings:
- def __init__(self, host, port, sensor): Initialize Fritz!Box monitor instance.
- def connect(self): Connect to the Fritz!Box.
- def _listen(self): ... | Implement the Python class `FritzBoxCallMonitor` described below.
Class description:
Event listener to monitor calls on the Fritz!Box.
Method signatures and docstrings:
- def __init__(self, host, port, sensor): Initialize Fritz!Box monitor instance.
- def connect(self): Connect to the Fritz!Box.
- def _listen(self): ... | ed4ab403deaed9e8c95e0db728477fcb012bf4fa | <|skeleton|>
class FritzBoxCallMonitor:
"""Event listener to monitor calls on the Fritz!Box."""
def __init__(self, host, port, sensor):
"""Initialize Fritz!Box monitor instance."""
<|body_0|>
def connect(self):
"""Connect to the Fritz!Box."""
<|body_1|>
def _listen(sel... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FritzBoxCallMonitor:
"""Event listener to monitor calls on the Fritz!Box."""
def __init__(self, host, port, sensor):
"""Initialize Fritz!Box monitor instance."""
self.host = host
self.port = port
self.sock = None
self._sensor = sensor
self.stopped = threadi... | the_stack_v2_python_sparse | homeassistant/components/fritzbox_callmonitor/sensor.py | tchellomello/home-assistant | train | 8 |
2f9b042a47b9ab8e5f5919eac4c752cf41f5ba49 | [
"if not root:\n return []\nret = deque()\nstk = [root]\nvisited = set()\nwhile stk:\n cur = stk.pop()\n ret.appendleft(cur.val)\n for c in cur.children:\n stk.append(c)\nreturn list(ret)",
"ret = []\nif not root:\n return ret\nstk = [root]\nvisited = set()\nwhile stk:\n cur = stk[-1]\n ... | <|body_start_0|>
if not root:
return []
ret = deque()
stk = [root]
visited = set()
while stk:
cur = stk.pop()
ret.appendleft(cur.val)
for c in cur.children:
stk.append(c)
return list(ret)
<|end_body_0|>
<|bo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def postorder(self, root: 'Node') -> List[int]:
"""maintain a stack, pop and reverse"""
<|body_0|>
def postorder_visited(self, root: 'Node') -> List[int]:
"""maintain a stack, if visited before, then pop"""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_10k_train_005366 | 1,410 | no_license | [
{
"docstring": "maintain a stack, pop and reverse",
"name": "postorder",
"signature": "def postorder(self, root: 'Node') -> List[int]"
},
{
"docstring": "maintain a stack, if visited before, then pop",
"name": "postorder_visited",
"signature": "def postorder_visited(self, root: 'Node') -... | 2 | stack_v2_sparse_classes_30k_train_000497 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def postorder(self, root: 'Node') -> List[int]: maintain a stack, pop and reverse
- def postorder_visited(self, root: 'Node') -> List[int]: maintain a stack, if visited before, t... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def postorder(self, root: 'Node') -> List[int]: maintain a stack, pop and reverse
- def postorder_visited(self, root: 'Node') -> List[int]: maintain a stack, if visited before, t... | 929dde1723fb2f54870c8a9badc80fc23e8400d3 | <|skeleton|>
class Solution:
def postorder(self, root: 'Node') -> List[int]:
"""maintain a stack, pop and reverse"""
<|body_0|>
def postorder_visited(self, root: 'Node') -> List[int]:
"""maintain a stack, if visited before, then pop"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def postorder(self, root: 'Node') -> List[int]:
"""maintain a stack, pop and reverse"""
if not root:
return []
ret = deque()
stk = [root]
visited = set()
while stk:
cur = stk.pop()
ret.appendleft(cur.val)
... | the_stack_v2_python_sparse | _algorithms_challenges/leetcode/LeetCode/590 N-ary Tree Postorder Traversal.py | syurskyi/Algorithms_and_Data_Structure | train | 4 | |
ad29b1837ce39a4be0200f090d59eb124731e5f3 | [
"self.number = number\nself.current_color = '000000'\nself.log = logging.getLogger('RASPLed')\nself.strip = strip",
"new_color = '{0}{1}{2}'.format(hex(int(color[0]))[2:].zfill(2), hex(int(color[1]))[2:].zfill(2), hex(int(color[2]))[2:].zfill(2))\ntry:\n self.current_color = new_color\n self.strip.setPixelC... | <|body_start_0|>
self.number = number
self.current_color = '000000'
self.log = logging.getLogger('RASPLed')
self.strip = strip
<|end_body_0|>
<|body_start_1|>
new_color = '{0}{1}{2}'.format(hex(int(color[0]))[2:].zfill(2), hex(int(color[1]))[2:].zfill(2), hex(int(color[2]))[2:].... | RASPLed | [
"MIT",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RASPLed:
def __init__(self, config, number, strip):
"""Initialise led."""
<|body_0|>
def color(self, color):
"""Set the LED to the specified color. Args: color: a list of int colors. one for each channel. Returns: None"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_005367 | 20,244 | permissive | [
{
"docstring": "Initialise led.",
"name": "__init__",
"signature": "def __init__(self, config, number, strip)"
},
{
"docstring": "Set the LED to the specified color. Args: color: a list of int colors. one for each channel. Returns: None",
"name": "color",
"signature": "def color(self, co... | 2 | stack_v2_sparse_classes_30k_train_005234 | Implement the Python class `RASPLed` described below.
Class description:
Implement the RASPLed class.
Method signatures and docstrings:
- def __init__(self, config, number, strip): Initialise led.
- def color(self, color): Set the LED to the specified color. Args: color: a list of int colors. one for each channel. Re... | Implement the Python class `RASPLed` described below.
Class description:
Implement the RASPLed class.
Method signatures and docstrings:
- def __init__(self, config, number, strip): Initialise led.
- def color(self, color): Set the LED to the specified color. Args: color: a list of int colors. one for each channel. Re... | 00937ab2ff51b1dc668bf465282ffa8ff1eebbd8 | <|skeleton|>
class RASPLed:
def __init__(self, config, number, strip):
"""Initialise led."""
<|body_0|>
def color(self, color):
"""Set the LED to the specified color. Args: color: a list of int colors. one for each channel. Returns: None"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RASPLed:
def __init__(self, config, number, strip):
"""Initialise led."""
self.number = number
self.current_color = '000000'
self.log = logging.getLogger('RASPLed')
self.strip = strip
def color(self, color):
"""Set the LED to the specified color. Args: colo... | the_stack_v2_python_sparse | mpf/platforms/rasppinball/rasppinball.py | vgrillot/mpf | train | 0 | |
7d38576cba07c7d04df5b702cea8c998a3d6cfd5 | [
"super(Conv2dSubsampling, self).__init__()\nself.conv = torch.nn.Sequential(torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU(), torch.nn.Conv2d(odim, odim, 3, 2), torch.nn.ReLU())\nself.out = torch.nn.Sequential(torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim), pos_enc)",
"x = x.unsqueeze(1)\nx = self.conv... | <|body_start_0|>
super(Conv2dSubsampling, self).__init__()
self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU(), torch.nn.Conv2d(odim, odim, 3, 2), torch.nn.ReLU())
self.out = torch.nn.Sequential(torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim), pos_enc)
<|en... | Convolutional 2D subsampling (to 1/4 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer. | Conv2dSubsampling | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Conv2dSubsampling:
"""Convolutional 2D subsampling (to 1/4 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer."""
def __init__(self, idim, odim, dropout_rate, pos_enc):
... | stack_v2_sparse_classes_10k_train_005368 | 2,435 | permissive | [
{
"docstring": "Construct an Conv2dSubsampling object.",
"name": "__init__",
"signature": "def __init__(self, idim, odim, dropout_rate, pos_enc)"
},
{
"docstring": "Subsample x. Args: x (torch.Tensor): Input tensor (#batch, time, idim). x_mask (torch.Tensor): Input mask (#batch, 1, time). Return... | 2 | stack_v2_sparse_classes_30k_train_003834 | Implement the Python class `Conv2dSubsampling` described below.
Class description:
Convolutional 2D subsampling (to 1/4 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer.
Method signatures and docstri... | Implement the Python class `Conv2dSubsampling` described below.
Class description:
Convolutional 2D subsampling (to 1/4 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer.
Method signatures and docstri... | e2f834dd60e7939672c1795b4ac62e89ad0bca49 | <|skeleton|>
class Conv2dSubsampling:
"""Convolutional 2D subsampling (to 1/4 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer."""
def __init__(self, idim, odim, dropout_rate, pos_enc):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Conv2dSubsampling:
"""Convolutional 2D subsampling (to 1/4 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer."""
def __init__(self, idim, odim, dropout_rate, pos_enc):
"""Construc... | the_stack_v2_python_sparse | speech/conformer/pytorch/src/layers/subsampling.py | graphcore/examples | train | 311 |
f168839eeabc277e5920dd9e2f0e72d6306efd6c | [
"logger.debug('Start validator_email.')\nemail_from_database = User.objects.filter(email=email)\nif email_from_database.exists():\n raise ValidationError({'email': [_(constants.EMAIL_EXISTS)]})\nelif email is None:\n raise forms.ValidationError({'email': [_(constants.EMAIL_NONE)]})\nelif len(email) > constant... | <|body_start_0|>
logger.debug('Start validator_email.')
email_from_database = User.objects.filter(email=email)
if email_from_database.exists():
raise ValidationError({'email': [_(constants.EMAIL_EXISTS)]})
elif email is None:
raise forms.ValidationError({'email': ... | Validating user fields. | UserValidator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserValidator:
"""Validating user fields."""
def validator_email(self, email):
"""Validating email."""
<|body_0|>
def validator_email_in_reset_password(self, email):
"""Validating email."""
<|body_1|>
def validator_password(self, password, password_c... | stack_v2_sparse_classes_10k_train_005369 | 4,003 | permissive | [
{
"docstring": "Validating email.",
"name": "validator_email",
"signature": "def validator_email(self, email)"
},
{
"docstring": "Validating email.",
"name": "validator_email_in_reset_password",
"signature": "def validator_email_in_reset_password(self, email)"
},
{
"docstring": "... | 6 | stack_v2_sparse_classes_30k_train_005699 | Implement the Python class `UserValidator` described below.
Class description:
Validating user fields.
Method signatures and docstrings:
- def validator_email(self, email): Validating email.
- def validator_email_in_reset_password(self, email): Validating email.
- def validator_password(self, password, password_confi... | Implement the Python class `UserValidator` described below.
Class description:
Validating user fields.
Method signatures and docstrings:
- def validator_email(self, email): Validating email.
- def validator_email_in_reset_password(self, email): Validating email.
- def validator_password(self, password, password_confi... | 5387eb80dfb354e948abe64f7d8bbe087fc4f136 | <|skeleton|>
class UserValidator:
"""Validating user fields."""
def validator_email(self, email):
"""Validating email."""
<|body_0|>
def validator_email_in_reset_password(self, email):
"""Validating email."""
<|body_1|>
def validator_password(self, password, password_c... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UserValidator:
"""Validating user fields."""
def validator_email(self, email):
"""Validating email."""
logger.debug('Start validator_email.')
email_from_database = User.objects.filter(email=email)
if email_from_database.exists():
raise ValidationError({'email':... | the_stack_v2_python_sparse | medical_prescription/user/validators/uservalidator.py | ristovao/2017.2-Receituario-Medico | train | 0 |
4eca387528e53aa20835173db4bbc588aa27c96d | [
"super().__init__()\nassert d % h == 0, 'd must divide by h'\nself.dk = d // h\nself.h = h\nself.d = d\nself.n1 = nn.Linear(d, d)\nself.n2 = nn.Linear(d, d)\nself.n3 = nn.Linear(d, d)\nself.n4 = nn.Linear(d, d)\nself.dropout = nn.Dropout(drop)",
"N, _, d = Q.size()\nq = self.n1(Q).view(N, -1, self.h, self.dk).tra... | <|body_start_0|>
super().__init__()
assert d % h == 0, 'd must divide by h'
self.dk = d // h
self.h = h
self.d = d
self.n1 = nn.Linear(d, d)
self.n2 = nn.Linear(d, d)
self.n3 = nn.Linear(d, d)
self.n4 = nn.Linear(d, d)
self.dropout = nn.Dro... | MultiAttentionLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiAttentionLayer:
def __init__(self, d, h, drop=0.1):
"""d: hidden size h:split factor"""
<|body_0|>
def forward(self, Q, K, V, mask=None):
"""Q:(N,T1,d) K:(N,T2,d) V:(N,T2,d) mask:(N,T1,T2) return out:(N,T1,d) p_attn:(N,T1,T2)"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_10k_train_005370 | 11,927 | no_license | [
{
"docstring": "d: hidden size h:split factor",
"name": "__init__",
"signature": "def __init__(self, d, h, drop=0.1)"
},
{
"docstring": "Q:(N,T1,d) K:(N,T2,d) V:(N,T2,d) mask:(N,T1,T2) return out:(N,T1,d) p_attn:(N,T1,T2)",
"name": "forward",
"signature": "def forward(self, Q, K, V, mask... | 2 | stack_v2_sparse_classes_30k_train_004025 | Implement the Python class `MultiAttentionLayer` described below.
Class description:
Implement the MultiAttentionLayer class.
Method signatures and docstrings:
- def __init__(self, d, h, drop=0.1): d: hidden size h:split factor
- def forward(self, Q, K, V, mask=None): Q:(N,T1,d) K:(N,T2,d) V:(N,T2,d) mask:(N,T1,T2) r... | Implement the Python class `MultiAttentionLayer` described below.
Class description:
Implement the MultiAttentionLayer class.
Method signatures and docstrings:
- def __init__(self, d, h, drop=0.1): d: hidden size h:split factor
- def forward(self, Q, K, V, mask=None): Q:(N,T1,d) K:(N,T2,d) V:(N,T2,d) mask:(N,T1,T2) r... | 24e60f24b6e442db22507adddd6bf3e2c343c013 | <|skeleton|>
class MultiAttentionLayer:
def __init__(self, d, h, drop=0.1):
"""d: hidden size h:split factor"""
<|body_0|>
def forward(self, Q, K, V, mask=None):
"""Q:(N,T1,d) K:(N,T2,d) V:(N,T2,d) mask:(N,T1,T2) return out:(N,T1,d) p_attn:(N,T1,T2)"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MultiAttentionLayer:
def __init__(self, d, h, drop=0.1):
"""d: hidden size h:split factor"""
super().__init__()
assert d % h == 0, 'd must divide by h'
self.dk = d // h
self.h = h
self.d = d
self.n1 = nn.Linear(d, d)
self.n2 = nn.Linear(d, d)
... | the_stack_v2_python_sparse | daily/8/pytorch_tutoral/nmt/model.py | mckjzhangxk/deepAI | train | 1 | |
7a174be35bef155fa4e649a67ecfae0012fc3153 | [
"l = []\n\ndef preOrder(root):\n if not root:\n l.append('n')\n return\n l.append(str(root.val))\n preOrder(root.left)\n preOrder(root.right)\npreOrder(root)\nreturn ','.join(l)",
"l = list(map(lambda x: int(x) if x != 'n' else None, data.split(',')))\nif not l or l[0] is None:\n retu... | <|body_start_0|>
l = []
def preOrder(root):
if not root:
l.append('n')
return
l.append(str(root.val))
preOrder(root.left)
preOrder(root.right)
preOrder(root)
return ','.join(l)
<|end_body_0|>
<|body_start_1... | Codec | [] | 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|>
l = []
... | stack_v2_sparse_classes_10k_train_005371 | 2,159 | no_license | [
{
"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_001709 | 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... | f6f7b548b29abe53b88a7396296d7edc932450cc | <|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_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
l = []
def preOrder(root):
if not root:
l.append('n')
return
l.append(str(root.val))
preOrder(root.left)
preOrder... | the_stack_v2_python_sparse | leetcode/daily challenges/2020-10/09-serialize-and-deserialize-bst.py | Nayald/algorithm-portfolio | train | 0 | |
5ba893e3eabf8c8b829b30d5dbc442d913aa700d | [
"parser.add_argument('name', help='The name of the peered DNS domain to create.')\nparser.add_argument('--network', metavar='NETWORK', required=True, help='The network in the consumer project peered with the service.')\nparser.add_argument('--service', metavar='SERVICE', default='servicenetworking.googleapis.com', ... | <|body_start_0|>
parser.add_argument('name', help='The name of the peered DNS domain to create.')
parser.add_argument('--network', metavar='NETWORK', required=True, help='The network in the consumer project peered with the service.')
parser.add_argument('--service', metavar='SERVICE', default='s... | Create a peered DNS domain for a private service connection. | Create | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Create:
"""Create a peered DNS domain for a private service connection."""
def Args(parser):
"""Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Position... | stack_v2_sparse_classes_10k_train_005372 | 4,396 | permissive | [
{
"docstring": "Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed.",
"name": "Args",
"signature": "def Args(parser)"
},
{
"docstring"... | 2 | stack_v2_sparse_classes_30k_train_000107 | Implement the Python class `Create` described below.
Class description:
Create a peered DNS domain for a private service connection.
Method signatures and docstrings:
- def Args(parser): Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments... | Implement the Python class `Create` described below.
Class description:
Create a peered DNS domain for a private service connection.
Method signatures and docstrings:
- def Args(parser): Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments... | 392abf004b16203030e6efd2f0af24db7c8d669e | <|skeleton|>
class Create:
"""Create a peered DNS domain for a private service connection."""
def Args(parser):
"""Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Position... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Create:
"""Create a peered DNS domain for a private service connection."""
def Args(parser):
"""Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments ... | the_stack_v2_python_sparse | lib/surface/services/peered_dns_domains/create.py | google-cloud-sdk-unofficial/google-cloud-sdk | train | 9 |
1be80efcea639df77cbd5a9e4bafb825296629bf | [
"assert isinstance(decoding, Decoding), 'Invalid decoding %s' % decoding\nif decoding.doDecode:\n return\nif isinstance(decoding.type, (Iter, Dict)):\n return\nif not decoding.type.isPrimitive:\n return\ndecoding.doDecode = self.createDecode(decoding)",
"assert isinstance(decoding, Decoding), 'Invalid de... | <|body_start_0|>
assert isinstance(decoding, Decoding), 'Invalid decoding %s' % decoding
if decoding.doDecode:
return
if isinstance(decoding.type, (Iter, Dict)):
return
if not decoding.type.isPrimitive:
return
decoding.doDecode = self.createDec... | Implementation for a handler that provides the primitive parameters values decoding. | PrimitiveDecode | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrimitiveDecode:
"""Implementation for a handler that provides the primitive parameters values decoding."""
def process(self, chain, decoding: Decoding, **keyargs):
"""@see: HandlerProcessor.process Create the primitive decode."""
<|body_0|>
def createDecode(self, decodi... | stack_v2_sparse_classes_10k_train_005373 | 3,264 | no_license | [
{
"docstring": "@see: HandlerProcessor.process Create the primitive decode.",
"name": "process",
"signature": "def process(self, chain, decoding: Decoding, **keyargs)"
},
{
"docstring": "Create the primitive do decode.",
"name": "createDecode",
"signature": "def createDecode(self, decodi... | 2 | stack_v2_sparse_classes_30k_train_000580 | Implement the Python class `PrimitiveDecode` described below.
Class description:
Implementation for a handler that provides the primitive parameters values decoding.
Method signatures and docstrings:
- def process(self, chain, decoding: Decoding, **keyargs): @see: HandlerProcessor.process Create the primitive decode.... | Implement the Python class `PrimitiveDecode` described below.
Class description:
Implementation for a handler that provides the primitive parameters values decoding.
Method signatures and docstrings:
- def process(self, chain, decoding: Decoding, **keyargs): @see: HandlerProcessor.process Create the primitive decode.... | e0b3466b34d31548996d57be4a9dac134d904380 | <|skeleton|>
class PrimitiveDecode:
"""Implementation for a handler that provides the primitive parameters values decoding."""
def process(self, chain, decoding: Decoding, **keyargs):
"""@see: HandlerProcessor.process Create the primitive decode."""
<|body_0|>
def createDecode(self, decodi... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PrimitiveDecode:
"""Implementation for a handler that provides the primitive parameters values decoding."""
def process(self, chain, decoding: Decoding, **keyargs):
"""@see: HandlerProcessor.process Create the primitive decode."""
assert isinstance(decoding, Decoding), 'Invalid decoding %... | the_stack_v2_python_sparse | components/ally-core/ally/core/impl/processor/decoder/general/primitive.py | cristidomsa/Ally-Py | train | 0 |
b8996bf48c29938c7c14e599decb94ae6c9945e0 | [
"words_to_counts = {'cat': 1}\nexpected_result = {'cat': 1}\ntweets.common_words(words_to_counts, 1)\nself.assertEqual(words_to_counts, expected_result, 'none removed')",
"dic = {'I': 10, 'you': 5, 'miss': 8, 'here': 6, 'how': 6}\ntweets.common_words(dic, 3)\nexpect_result = {'I': 10, 'miss': 8}\nself.assertEqual... | <|body_start_0|>
words_to_counts = {'cat': 1}
expected_result = {'cat': 1}
tweets.common_words(words_to_counts, 1)
self.assertEqual(words_to_counts, expected_result, 'none removed')
<|end_body_0|>
<|body_start_1|>
dic = {'I': 10, 'you': 5, 'miss': 8, 'here': 6, 'how': 6}
... | TestCommonWords | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestCommonWords:
def test_none_removed(self):
"""Test common_words with N so that no words are removed."""
<|body_0|>
def test_tie_removed(self):
"""Test common_words with N so that tied words are removed."""
<|body_1|>
def test_tie_remained(self):
... | stack_v2_sparse_classes_10k_train_005374 | 1,649 | permissive | [
{
"docstring": "Test common_words with N so that no words are removed.",
"name": "test_none_removed",
"signature": "def test_none_removed(self)"
},
{
"docstring": "Test common_words with N so that tied words are removed.",
"name": "test_tie_removed",
"signature": "def test_tie_removed(se... | 5 | stack_v2_sparse_classes_30k_train_004963 | Implement the Python class `TestCommonWords` described below.
Class description:
Implement the TestCommonWords class.
Method signatures and docstrings:
- def test_none_removed(self): Test common_words with N so that no words are removed.
- def test_tie_removed(self): Test common_words with N so that tied words are re... | Implement the Python class `TestCommonWords` described below.
Class description:
Implement the TestCommonWords class.
Method signatures and docstrings:
- def test_none_removed(self): Test common_words with N so that no words are removed.
- def test_tie_removed(self): Test common_words with N so that tied words are re... | 214525afeeb2da2409f451bf269e792c6940a1ba | <|skeleton|>
class TestCommonWords:
def test_none_removed(self):
"""Test common_words with N so that no words are removed."""
<|body_0|>
def test_tie_removed(self):
"""Test common_words with N so that tied words are removed."""
<|body_1|>
def test_tie_remained(self):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestCommonWords:
def test_none_removed(self):
"""Test common_words with N so that no words are removed."""
words_to_counts = {'cat': 1}
expected_result = {'cat': 1}
tweets.common_words(words_to_counts, 1)
self.assertEqual(words_to_counts, expected_result, 'none removed'... | the_stack_v2_python_sparse | Python/Tweet/test_common_words.py | LilyYC/legendary-train | train | 0 | |
a9cb6a3513a09023b92674b7bae47bd27cbaeac7 | [
"self.gpf_core.float()\nself.likelihood.train()\noptimizer = torch.optim.Adam([{'params': self.gpf_core.parameters()}], lr=0.1)\nmll = gpytorch.mlls.ExactMarginalLogLikelihood(self.likelihood, self.gpf_core)\nfor _ in range(500):\n optimizer.zero_grad()\n output = self.gpf_core(self.tensor_x)\n loss = -mll... | <|body_start_0|>
self.gpf_core.float()
self.likelihood.train()
optimizer = torch.optim.Adam([{'params': self.gpf_core.parameters()}], lr=0.1)
mll = gpytorch.mlls.ExactMarginalLogLikelihood(self.likelihood, self.gpf_core)
for _ in range(500):
optimizer.zero_grad()
... | PytorchGPFitter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PytorchGPFitter:
def fit_gp(self):
"""Fit the GP according to options."""
<|body_0|>
def get_next_gp(self):
"""Get the next GP from previously fitted. Returns: GPWrapper object."""
<|body_1|>
def _init_gpf(self):
"""Initialize the GP fitter."""
... | stack_v2_sparse_classes_10k_train_005375 | 6,453 | permissive | [
{
"docstring": "Fit the GP according to options.",
"name": "fit_gp",
"signature": "def fit_gp(self)"
},
{
"docstring": "Get the next GP from previously fitted. Returns: GPWrapper object.",
"name": "get_next_gp",
"signature": "def get_next_gp(self)"
},
{
"docstring": "Initialize t... | 3 | stack_v2_sparse_classes_30k_train_006444 | Implement the Python class `PytorchGPFitter` described below.
Class description:
Implement the PytorchGPFitter class.
Method signatures and docstrings:
- def fit_gp(self): Fit the GP according to options.
- def get_next_gp(self): Get the next GP from previously fitted. Returns: GPWrapper object.
- def _init_gpf(self)... | Implement the Python class `PytorchGPFitter` described below.
Class description:
Implement the PytorchGPFitter class.
Method signatures and docstrings:
- def fit_gp(self): Fit the GP according to options.
- def get_next_gp(self): Get the next GP from previously fitted. Returns: GPWrapper object.
- def _init_gpf(self)... | fb330ec4ac2ed0f6167eebd849c23fe61692c11c | <|skeleton|>
class PytorchGPFitter:
def fit_gp(self):
"""Fit the GP according to options."""
<|body_0|>
def get_next_gp(self):
"""Get the next GP from previously fitted. Returns: GPWrapper object."""
<|body_1|>
def _init_gpf(self):
"""Initialize the GP fitter."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PytorchGPFitter:
def fit_gp(self):
"""Fit the GP according to options."""
self.gpf_core.float()
self.likelihood.train()
optimizer = torch.optim.Adam([{'params': self.gpf_core.parameters()}], lr=0.1)
mll = gpytorch.mlls.ExactMarginalLogLikelihood(self.likelihood, self.gp... | the_stack_v2_python_sparse | src/gp/gpytorch_interface.py | haowenCS/OCBO_offline | train | 0 | |
e8cdd5a31a81ba6252d02232dcdcd7d0d602c153 | [
"if campo is None:\n return ''\nif campo in request.POST:\n return request.POST[campo].strip().encode('utf8')\nreturn ''",
"if campo is None:\n return ''\nif campo in request.FILES:\n return request.FILES[campo]\nreturn ''"
] | <|body_start_0|>
if campo is None:
return ''
if campo in request.POST:
return request.POST[campo].strip().encode('utf8')
return ''
<|end_body_0|>
<|body_start_1|>
if campo is None:
return ''
if campo in request.FILES:
return reques... | UtilsForAll | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UtilsForAll:
def getfromPost(self, request, campo=None):
"""Given a field return its value if exists. Return an empty string otherwise."""
<|body_0|>
def getfilefromPost(self, request, campo=None):
"""Given a field return its value if exists. Return an empty string o... | stack_v2_sparse_classes_10k_train_005376 | 914 | no_license | [
{
"docstring": "Given a field return its value if exists. Return an empty string otherwise.",
"name": "getfromPost",
"signature": "def getfromPost(self, request, campo=None)"
},
{
"docstring": "Given a field return its value if exists. Return an empty string otherwise.",
"name": "getfilefrom... | 2 | stack_v2_sparse_classes_30k_train_003001 | Implement the Python class `UtilsForAll` described below.
Class description:
Implement the UtilsForAll class.
Method signatures and docstrings:
- def getfromPost(self, request, campo=None): Given a field return its value if exists. Return an empty string otherwise.
- def getfilefromPost(self, request, campo=None): Gi... | Implement the Python class `UtilsForAll` described below.
Class description:
Implement the UtilsForAll class.
Method signatures and docstrings:
- def getfromPost(self, request, campo=None): Given a field return its value if exists. Return an empty string otherwise.
- def getfilefromPost(self, request, campo=None): Gi... | 7a390f98fec62825360c462f65944018ace7c265 | <|skeleton|>
class UtilsForAll:
def getfromPost(self, request, campo=None):
"""Given a field return its value if exists. Return an empty string otherwise."""
<|body_0|>
def getfilefromPost(self, request, campo=None):
"""Given a field return its value if exists. Return an empty string o... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UtilsForAll:
def getfromPost(self, request, campo=None):
"""Given a field return its value if exists. Return an empty string otherwise."""
if campo is None:
return ''
if campo in request.POST:
return request.POST[campo].strip().encode('utf8')
return ''
... | the_stack_v2_python_sparse | Welpe/site_utils.py | itziar/Welpe | train | 1 | |
0c3c1cd131a9b48e3fb2161f5c4ba03364623892 | [
"self.copy_only_backup = copy_only_backup\nself.disable_metadata = disable_metadata\nself.disable_notification = disable_notification\nself.excluded_vss_writers = excluded_vss_writers",
"if dictionary is None:\n return None\ncopy_only_backup = dictionary.get('copyOnlyBackup')\ndisable_metadata = dictionary.get... | <|body_start_0|>
self.copy_only_backup = copy_only_backup
self.disable_metadata = disable_metadata
self.disable_notification = disable_notification
self.excluded_vss_writers = excluded_vss_writers
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
... | Implementation of the 'WindowsHostSnapshotParameters' model. Specifies settings that are meaningful only on Windows hosts. Attributes: copy_only_backup (bool): Specifies whether to backup regardless of the state of each file's backup history. Backup history will not be updated. Refer Microsoft documentation on VSS_BT_C... | WindowsHostSnapshotParameters | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WindowsHostSnapshotParameters:
"""Implementation of the 'WindowsHostSnapshotParameters' model. Specifies settings that are meaningful only on Windows hosts. Attributes: copy_only_backup (bool): Specifies whether to backup regardless of the state of each file's backup history. Backup history will ... | stack_v2_sparse_classes_10k_train_005377 | 2,957 | permissive | [
{
"docstring": "Constructor for the WindowsHostSnapshotParameters class",
"name": "__init__",
"signature": "def __init__(self, copy_only_backup=None, disable_metadata=None, disable_notification=None, excluded_vss_writers=None)"
},
{
"docstring": "Creates an instance of this model from a dictiona... | 2 | null | Implement the Python class `WindowsHostSnapshotParameters` described below.
Class description:
Implementation of the 'WindowsHostSnapshotParameters' model. Specifies settings that are meaningful only on Windows hosts. Attributes: copy_only_backup (bool): Specifies whether to backup regardless of the state of each file... | Implement the Python class `WindowsHostSnapshotParameters` described below.
Class description:
Implementation of the 'WindowsHostSnapshotParameters' model. Specifies settings that are meaningful only on Windows hosts. Attributes: copy_only_backup (bool): Specifies whether to backup regardless of the state of each file... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class WindowsHostSnapshotParameters:
"""Implementation of the 'WindowsHostSnapshotParameters' model. Specifies settings that are meaningful only on Windows hosts. Attributes: copy_only_backup (bool): Specifies whether to backup regardless of the state of each file's backup history. Backup history will ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WindowsHostSnapshotParameters:
"""Implementation of the 'WindowsHostSnapshotParameters' model. Specifies settings that are meaningful only on Windows hosts. Attributes: copy_only_backup (bool): Specifies whether to backup regardless of the state of each file's backup history. Backup history will not be update... | the_stack_v2_python_sparse | cohesity_management_sdk/models/windows_host_snapshot_parameters.py | cohesity/management-sdk-python | train | 24 |
25c6377bf1a7101a2c8440f6ea06152f0e8bb476 | [
"if not root:\n return []\nqueue = [(root, 0)]\nvalues = defaultdict(list)\nwhile queue:\n cur, height = queue.pop(0)\n values[height].append(cur.val)\n if cur.left:\n queue.append((cur.left, height + 1))\n if cur.right:\n queue.append((cur.right, height + 1))\nres = []\niter = True\nfo... | <|body_start_0|>
if not root:
return []
queue = [(root, 0)]
values = defaultdict(list)
while queue:
cur, height = queue.pop(0)
values[height].append(cur.val)
if cur.left:
queue.append((cur.left, height + 1))
if c... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def zigzagLevelOrder(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_0|>
def zigzagLevelOrder2(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:... | stack_v2_sparse_classes_10k_train_005378 | 2,087 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: List[List[int]]",
"name": "zigzagLevelOrder",
"signature": "def zigzagLevelOrder(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: List[List[int]]",
"name": "zigzagLevelOrder2",
"signature": "def zigzagLevelOrder2(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007103 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def zigzagLevelOrder(self, root): :type root: TreeNode :rtype: List[List[int]]
- def zigzagLevelOrder2(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 zigzagLevelOrder(self, root): :type root: TreeNode :rtype: List[List[int]]
- def zigzagLevelOrder2(self, root): :type root: TreeNode :rtype: List[List[int]]
<|skeleton|>
cla... | 0fc4c7af59246e3064db41989a45d9db413a624b | <|skeleton|>
class Solution:
def zigzagLevelOrder(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_0|>
def zigzagLevelOrder2(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def zigzagLevelOrder(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
if not root:
return []
queue = [(root, 0)]
values = defaultdict(list)
while queue:
cur, height = queue.pop(0)
values[height].append(cur.val... | the_stack_v2_python_sparse | 103. Binary Tree Zigzag Level Order Traversal/zigzag.py | Macielyoung/LeetCode | train | 1 | |
6d8eb49159978b4fec61bec4051b7d4e6ed7cb88 | [
"from collections import defaultdict\n\ndef f(path, res):\n word = ''\n i = 0\n while i < max_word and i < len(res):\n word += res[i]\n if word in wordDict:\n path.append(word)\n mem[''.join(path)].append(' '.join(path))\n f(path, res[i + 1:])\n pat... | <|body_start_0|>
from collections import defaultdict
def f(path, res):
word = ''
i = 0
while i < max_word and i < len(res):
word += res[i]
if word in wordDict:
path.append(word)
mem[''.join(path)... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def wordBreak1(self, s, wordDict):
""":type s: str :type wordDict: List[str] :rtype: List[str]"""
<|body_0|>
def wordBreak(self, s, wordDict):
""":type s: str :type wordDict: List[str] :rtype: List[str]"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_10k_train_005379 | 1,443 | no_license | [
{
"docstring": ":type s: str :type wordDict: List[str] :rtype: List[str]",
"name": "wordBreak1",
"signature": "def wordBreak1(self, s, wordDict)"
},
{
"docstring": ":type s: str :type wordDict: List[str] :rtype: List[str]",
"name": "wordBreak",
"signature": "def wordBreak(self, s, wordDi... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wordBreak1(self, s, wordDict): :type s: str :type wordDict: List[str] :rtype: List[str]
- def wordBreak(self, s, wordDict): :type s: str :type wordDict: List[str] :rtype: Lis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wordBreak1(self, s, wordDict): :type s: str :type wordDict: List[str] :rtype: List[str]
- def wordBreak(self, s, wordDict): :type s: str :type wordDict: List[str] :rtype: Lis... | d8ed762d1005975f0de4f07760c9671195621c88 | <|skeleton|>
class Solution:
def wordBreak1(self, s, wordDict):
""":type s: str :type wordDict: List[str] :rtype: List[str]"""
<|body_0|>
def wordBreak(self, s, wordDict):
""":type s: str :type wordDict: List[str] :rtype: List[str]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def wordBreak1(self, s, wordDict):
""":type s: str :type wordDict: List[str] :rtype: List[str]"""
from collections import defaultdict
def f(path, res):
word = ''
i = 0
while i < max_word and i < len(res):
word += res[i]
... | the_stack_v2_python_sparse | word-break-ii/solution.py | uxlsl/leetcode_practice | train | 0 | |
f0c70892e1d24be8bbb1e7328e3ea37404aad208 | [
"dictionary = dict()\nfor i in range(len(nums)):\n if nums[i] in dictionary:\n nums[i].append(i)\n if any(lambda x: x >= abs(i - k) and x <= i + k, nums):\n return True\n else:\n dictionary[nums[i]] = [i]\nreturn False",
"tracker = dict()\nfor i in range(len(nums)):\n if t... | <|body_start_0|>
dictionary = dict()
for i in range(len(nums)):
if nums[i] in dictionary:
nums[i].append(i)
if any(lambda x: x >= abs(i - k) and x <= i + k, nums):
return True
else:
dictionary[nums[i]] = [i]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_0|>
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_10k_train_005380 | 1,257 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :rtype: bool",
"name": "containsNearbyDuplicate",
"signature": "def containsNearbyDuplicate(self, nums, k)"
},
{
"docstring": ":type nums: List[int] :type k: int :rtype: bool",
"name": "containsNearbyDuplicate",
"signature": "def contain... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtyp... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtyp... | d40c24736a6fee43b56aa1c80150c5f14be4ff22 | <|skeleton|>
class Solution:
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_0|>
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
dictionary = dict()
for i in range(len(nums)):
if nums[i] in dictionary:
nums[i].append(i)
if any(lambda x: x >= abs(i - k) and x <= i... | the_stack_v2_python_sparse | LeetCodePractice/219. Contains Duplicate II.py | deepika087/CompetitiveProgramming | train | 10 | |
3e7493bba61b7f6cb13a492764f5d6407b894617 | [
"self.X = X_init\nself.Y = Y_init\nself.sigma_f = sigma_f\nself.l = l\nself.K = self.kernel(self.X, self.X)",
"a = np.sum(X1 ** 2, 1).reshape(-1, 1)\nb = np.sum(X2 ** 2, 1) - 2 * np.dot(X1, X2.T)\nsqdist = a + b\nreturn self.sigma_f ** 2 * np.exp(-0.5 / self.l ** 2 * sqdist)",
"K = self.kernel(self.X, self.X)\n... | <|body_start_0|>
self.X = X_init
self.Y = Y_init
self.sigma_f = sigma_f
self.l = l
self.K = self.kernel(self.X, self.X)
<|end_body_0|>
<|body_start_1|>
a = np.sum(X1 ** 2, 1).reshape(-1, 1)
b = np.sum(X2 ** 2, 1) - 2 * np.dot(X1, X2.T)
sqdist = a + b
... | Gaussian class | GaussianProcess | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianProcess:
"""Gaussian class"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""Create the class GaussianProcess that represents a noiseless 1D Gaussian process: :param X_init: is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box ... | stack_v2_sparse_classes_10k_train_005381 | 2,092 | no_license | [
{
"docstring": "Create the class GaussianProcess that represents a noiseless 1D Gaussian process: :param X_init: is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box function :param Y_init: is a numpy.ndarray of shape (t, 1) representing the outputs of the black-box func... | 3 | stack_v2_sparse_classes_30k_test_000343 | Implement the Python class `GaussianProcess` described below.
Class description:
Gaussian class
Method signatures and docstrings:
- def __init__(self, X_init, Y_init, l=1, sigma_f=1): Create the class GaussianProcess that represents a noiseless 1D Gaussian process: :param X_init: is a numpy.ndarray of shape (t, 1) re... | Implement the Python class `GaussianProcess` described below.
Class description:
Gaussian class
Method signatures and docstrings:
- def __init__(self, X_init, Y_init, l=1, sigma_f=1): Create the class GaussianProcess that represents a noiseless 1D Gaussian process: :param X_init: is a numpy.ndarray of shape (t, 1) re... | f83a60babb1d2a510a4a0e0f58aa3880fd9f93a7 | <|skeleton|>
class GaussianProcess:
"""Gaussian class"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""Create the class GaussianProcess that represents a noiseless 1D Gaussian process: :param X_init: is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GaussianProcess:
"""Gaussian class"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""Create the class GaussianProcess that represents a noiseless 1D Gaussian process: :param X_init: is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box function :par... | the_stack_v2_python_sparse | unsupervised_learning/0x03-hyperparameter_tuning/1-gp.py | jalondono/holbertonschool-machine_learning | train | 2 |
d44f55e449332180c0a5ec051cb0813a453deca6 | [
"batch_size = self.tensors.batch_size\nmask = self._get_mask()\nspatial_temporal_log_loss = self._get_spatial_temporal_loss(n_location_categories, layer_2_output_socres) * mask\ncategorical_log_loss = self._get_categorical_loss(layer_1_output_socres) * mask\nreturn [tf.reduce_sum(categorical_log_loss) / batch_size,... | <|body_start_0|>
batch_size = self.tensors.batch_size
mask = self._get_mask()
spatial_temporal_log_loss = self._get_spatial_temporal_loss(n_location_categories, layer_2_output_socres) * mask
categorical_log_loss = self._get_categorical_loss(layer_1_output_socres) * mask
return [t... | TwoLayerCategoricalLocationLossFunction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TwoLayerCategoricalLocationLossFunction:
def get_loss(self, n_location_categories, layer_1_output_socres, layer_2_output_socres):
"""Get total loss from layer 1 and layer 2 output."""
<|body_0|>
def _get_spatial_temporal_loss(self, n_location_categories, output_socres):
... | stack_v2_sparse_classes_10k_train_005382 | 3,718 | permissive | [
{
"docstring": "Get total loss from layer 1 and layer 2 output.",
"name": "get_loss",
"signature": "def get_loss(self, n_location_categories, layer_1_output_socres, layer_2_output_socres)"
},
{
"docstring": "Compute the spatial-temporal log loss",
"name": "_get_spatial_temporal_loss",
"s... | 2 | stack_v2_sparse_classes_30k_train_004013 | Implement the Python class `TwoLayerCategoricalLocationLossFunction` described below.
Class description:
Implement the TwoLayerCategoricalLocationLossFunction class.
Method signatures and docstrings:
- def get_loss(self, n_location_categories, layer_1_output_socres, layer_2_output_socres): Get total loss from layer 1... | Implement the Python class `TwoLayerCategoricalLocationLossFunction` described below.
Class description:
Implement the TwoLayerCategoricalLocationLossFunction class.
Method signatures and docstrings:
- def get_loss(self, n_location_categories, layer_1_output_socres, layer_2_output_socres): Get total loss from layer 1... | 36f21b46a5c9382f90ece561a3efb1885be3c74f | <|skeleton|>
class TwoLayerCategoricalLocationLossFunction:
def get_loss(self, n_location_categories, layer_1_output_socres, layer_2_output_socres):
"""Get total loss from layer 1 and layer 2 output."""
<|body_0|>
def _get_spatial_temporal_loss(self, n_location_categories, output_socres):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TwoLayerCategoricalLocationLossFunction:
def get_loss(self, n_location_categories, layer_1_output_socres, layer_2_output_socres):
"""Get total loss from layer 1 and layer 2 output."""
batch_size = self.tensors.batch_size
mask = self._get_mask()
spatial_temporal_log_loss = self.... | the_stack_v2_python_sparse | lstm_mobility_model/two_layer_categorical_location/loss_function.py | zihenglin/LSTM-Mobility-Model | train | 20 | |
c5fb65e1f817982556c16501ba58eaad232509c5 | [
"super(GroupL1Norm, self).__init__()\nassert reg_lambda >= 0, 'regularization weight should be 0 or positive'\nassert isinstance(groups, list), 'groups needs to be a list'\nself.reg_lambda = reg_lambda\nself.groups = groups\nself.stabilizing_val = stabilizing_val",
"squared = net.Sqr(param)\nreduced_sum = net.Red... | <|body_start_0|>
super(GroupL1Norm, self).__init__()
assert reg_lambda >= 0, 'regularization weight should be 0 or positive'
assert isinstance(groups, list), 'groups needs to be a list'
self.reg_lambda = reg_lambda
self.groups = groups
self.stabilizing_val = stabilizing_v... | Scardapane, Simone, et al. "Group sparse regularization for deep neural networks." Neurocomputing 241 (2017): 81-89. This regularizer computes l1 norm of a weight matrix based on groups. There are essentially three stages in the computation: 1. Compute the l2 norm on all the members of each group 2. Scale each l2 norm ... | GroupL1Norm | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GroupL1Norm:
"""Scardapane, Simone, et al. "Group sparse regularization for deep neural networks." Neurocomputing 241 (2017): 81-89. This regularizer computes l1 norm of a weight matrix based on groups. There are essentially three stages in the computation: 1. Compute the l2 norm on all the membe... | stack_v2_sparse_classes_10k_train_005383 | 11,669 | permissive | [
{
"docstring": "Args: reg_lambda: The weight of the regularization term. groups: A list of integers describing the size of each group. The length of the list is the number of groups. Optional Args: stabilizing_val: The computation of GroupL1Norm involves the Sqrt operator. When values are small, its gradient ca... | 2 | stack_v2_sparse_classes_30k_train_003183 | Implement the Python class `GroupL1Norm` described below.
Class description:
Scardapane, Simone, et al. "Group sparse regularization for deep neural networks." Neurocomputing 241 (2017): 81-89. This regularizer computes l1 norm of a weight matrix based on groups. There are essentially three stages in the computation: ... | Implement the Python class `GroupL1Norm` described below.
Class description:
Scardapane, Simone, et al. "Group sparse regularization for deep neural networks." Neurocomputing 241 (2017): 81-89. This regularizer computes l1 norm of a weight matrix based on groups. There are essentially three stages in the computation: ... | cabf6e4f1970dc14302f87414f170de19944bac2 | <|skeleton|>
class GroupL1Norm:
"""Scardapane, Simone, et al. "Group sparse regularization for deep neural networks." Neurocomputing 241 (2017): 81-89. This regularizer computes l1 norm of a weight matrix based on groups. There are essentially three stages in the computation: 1. Compute the l2 norm on all the membe... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GroupL1Norm:
"""Scardapane, Simone, et al. "Group sparse regularization for deep neural networks." Neurocomputing 241 (2017): 81-89. This regularizer computes l1 norm of a weight matrix based on groups. There are essentially three stages in the computation: 1. Compute the l2 norm on all the members of each gr... | the_stack_v2_python_sparse | pytorch/source/caffe2/python/regularizer.py | ryfeus/lambda-packs | train | 1,283 |
d3dcd82fe22869609945cb73474af1c159388268 | [
"for pol in self.auth:\n if pol.actor == uid:\n return pol\nreturn None",
"pol = self.get_policy(uid)\nif pol is not None:\n session.delete(pol)",
"del session\npol = self.get_policy(uid)\npol.role = new_role"
] | <|body_start_0|>
for pol in self.auth:
if pol.actor == uid:
return pol
return None
<|end_body_0|>
<|body_start_1|>
pol = self.get_policy(uid)
if pol is not None:
session.delete(pol)
<|end_body_1|>
<|body_start_2|>
del session
pol ... | Base class for models with a list of authorization policies | Authorized | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Authorized:
"""Base class for models with a list of authorization policies"""
def get_policy(self, uid):
"""Retrieve policy associated with a given actor. Args: uid: (str) id of actor Returns: (Policy) or None if no actor in auth list"""
<|body_0|>
def remove_policy(self... | stack_v2_sparse_classes_10k_train_005384 | 3,240 | no_license | [
{
"docstring": "Retrieve policy associated with a given actor. Args: uid: (str) id of actor Returns: (Policy) or None if no actor in auth list",
"name": "get_policy",
"signature": "def get_policy(self, uid)"
},
{
"docstring": "Remove access granted to actor Args: session: (DBSession) uid: (str) ... | 3 | stack_v2_sparse_classes_30k_train_005257 | Implement the Python class `Authorized` described below.
Class description:
Base class for models with a list of authorization policies
Method signatures and docstrings:
- def get_policy(self, uid): Retrieve policy associated with a given actor. Args: uid: (str) id of actor Returns: (Policy) or None if no actor in au... | Implement the Python class `Authorized` described below.
Class description:
Base class for models with a list of authorization policies
Method signatures and docstrings:
- def get_policy(self, uid): Retrieve policy associated with a given actor. Args: uid: (str) id of actor Returns: (Policy) or None if no actor in au... | ff1feea27efa6544c0e443b953951bb50cbdd9bb | <|skeleton|>
class Authorized:
"""Base class for models with a list of authorization policies"""
def get_policy(self, uid):
"""Retrieve policy associated with a given actor. Args: uid: (str) id of actor Returns: (Policy) or None if no actor in auth list"""
<|body_0|>
def remove_policy(self... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Authorized:
"""Base class for models with a list of authorization policies"""
def get_policy(self, uid):
"""Retrieve policy associated with a given actor. Args: uid: (str) id of actor Returns: (Policy) or None if no actor in auth list"""
for pol in self.auth:
if pol.actor == u... | the_stack_v2_python_sparse | seeweb/models/auth.py | pradal/seeweb | train | 0 |
2dfab900e499be7cdca312690e8e338fc11f5091 | [
"for item in os.listdir(source):\n if os.path.isfile(os.path.join(source, item)):\n self.put(os.path.join(source, item), '%s/%s' % (target, item))\n else:\n self.mkdir('%s/%s' % (target, item), ignore_existing=True)\n self.put_dir(os.path.join(source, item), '%s/%s' % (target, item))",
... | <|body_start_0|>
for item in os.listdir(source):
if os.path.isfile(os.path.join(source, item)):
self.put(os.path.join(source, item), '%s/%s' % (target, item))
else:
self.mkdir('%s/%s' % (target, item), ignore_existing=True)
self.put_dir(os.... | RSFTPClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RSFTPClient:
def put_dir(self, source, target):
"""Uploads the contents of the source directory to the target path. The target directory needs to exists. All subdirectories in source are created under target."""
<|body_0|>
def mkdir(self, path, mode=511, ignore_existing=Fals... | stack_v2_sparse_classes_10k_train_005385 | 43,347 | permissive | [
{
"docstring": "Uploads the contents of the source directory to the target path. The target directory needs to exists. All subdirectories in source are created under target.",
"name": "put_dir",
"signature": "def put_dir(self, source, target)"
},
{
"docstring": "Augments mkdir by adding an optio... | 2 | stack_v2_sparse_classes_30k_train_004677 | Implement the Python class `RSFTPClient` described below.
Class description:
Implement the RSFTPClient class.
Method signatures and docstrings:
- def put_dir(self, source, target): Uploads the contents of the source directory to the target path. The target directory needs to exists. All subdirectories in source are c... | Implement the Python class `RSFTPClient` described below.
Class description:
Implement the RSFTPClient class.
Method signatures and docstrings:
- def put_dir(self, source, target): Uploads the contents of the source directory to the target path. The target directory needs to exists. All subdirectories in source are c... | 981afa736547ad08e48a11137788fba5b8980cd9 | <|skeleton|>
class RSFTPClient:
def put_dir(self, source, target):
"""Uploads the contents of the source directory to the target path. The target directory needs to exists. All subdirectories in source are created under target."""
<|body_0|>
def mkdir(self, path, mode=511, ignore_existing=Fals... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RSFTPClient:
def put_dir(self, source, target):
"""Uploads the contents of the source directory to the target path. The target directory needs to exists. All subdirectories in source are created under target."""
for item in os.listdir(source):
if os.path.isfile(os.path.join(source,... | the_stack_v2_python_sparse | src/riaps/ctrl/ctrl.py | RIAPS/riaps-pycom | train | 7 | |
420be07774e250b1b3349a22a54715b27b101592 | [
"gen = ind + FigureControl.minPossibleGenNumber\nfor cplot in gs.cloud_plots:\n fitness = cplot.update_annot(gen)\ntext = '{}'.format(gen)\ngs.fitness_plot.floating_annot.xy = (gen, fitness)\ngs.fitness_plot.floating_annot.set_text(text)",
"for cplot in gs.cloud_plots:\n cplot.annot.set_visible(vis)\ngs.fit... | <|body_start_0|>
gen = ind + FigureControl.minPossibleGenNumber
for cplot in gs.cloud_plots:
fitness = cplot.update_annot(gen)
text = '{}'.format(gen)
gs.fitness_plot.floating_annot.xy = (gen, fitness)
gs.fitness_plot.floating_annot.set_text(text)
<|end_body_0|>
<|bo... | mouse move event on plots | MouseMove | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MouseMove:
"""mouse move event on plots"""
def update_annot(cls, ind):
"""update the parent floating annotations"""
<|body_0|>
def update_plot(cls, vis):
"""update the plots"""
<|body_1|>
def update(cls, event, curve, preferred_idx):
"""updat... | stack_v2_sparse_classes_10k_train_005386 | 4,481 | permissive | [
{
"docstring": "update the parent floating annotations",
"name": "update_annot",
"signature": "def update_annot(cls, ind)"
},
{
"docstring": "update the plots",
"name": "update_plot",
"signature": "def update_plot(cls, vis)"
},
{
"docstring": "update the plots and/or annotations"... | 4 | stack_v2_sparse_classes_30k_train_000260 | Implement the Python class `MouseMove` described below.
Class description:
mouse move event on plots
Method signatures and docstrings:
- def update_annot(cls, ind): update the parent floating annotations
- def update_plot(cls, vis): update the plots
- def update(cls, event, curve, preferred_idx): update the plots and... | Implement the Python class `MouseMove` described below.
Class description:
mouse move event on plots
Method signatures and docstrings:
- def update_annot(cls, ind): update the parent floating annotations
- def update_plot(cls, vis): update the plots
- def update(cls, event, curve, preferred_idx): update the plots and... | d0132c8a64516fbb45eb1e645c6312bbe56a7bc5 | <|skeleton|>
class MouseMove:
"""mouse move event on plots"""
def update_annot(cls, ind):
"""update the parent floating annotations"""
<|body_0|>
def update_plot(cls, vis):
"""update the plots"""
<|body_1|>
def update(cls, event, curve, preferred_idx):
"""updat... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MouseMove:
"""mouse move event on plots"""
def update_annot(cls, ind):
"""update the parent floating annotations"""
gen = ind + FigureControl.minPossibleGenNumber
for cplot in gs.cloud_plots:
fitness = cplot.update_annot(gen)
text = '{}'.format(gen)
gs.... | the_stack_v2_python_sparse | visual_inspector/figure_base/mouse_event.py | justin-nguyen-1996/deep-neuroevolution | train | 1 |
3ae8f4497a46691adefeaf6f67a5cbd1d1491ace | [
"x = input_dict[node.inputs[0]]\nind = input_dict[node.inputs[1]]\nif len(node.inputs) > 2:\n output_shape = input_dict.get(node.inputs[2], None)\nelse:\n output_shape = None\nkernel_shape = node.attrs['kernel_shape']\nspatial_size = len(kernel_shape)\nx_rank = spatial_size + 2\nstorage_format, _ = get_data_f... | <|body_start_0|>
x = input_dict[node.inputs[0]]
ind = input_dict[node.inputs[1]]
if len(node.inputs) > 2:
output_shape = input_dict.get(node.inputs[2], None)
else:
output_shape = None
kernel_shape = node.attrs['kernel_shape']
spatial_size = len(ker... | UnpoolMixin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnpoolMixin:
def max_unpool(cls, node, input_dict):
"""MaxUnpooling operation"""
<|body_0|>
def _get_default_shape(cls, input_shape, kernel_shape, strides):
"""Calculates default shape from kernel_shape and strides Args: input_shape: shape of the input to unpool op k... | stack_v2_sparse_classes_10k_train_005387 | 5,295 | permissive | [
{
"docstring": "MaxUnpooling operation",
"name": "max_unpool",
"signature": "def max_unpool(cls, node, input_dict)"
},
{
"docstring": "Calculates default shape from kernel_shape and strides Args: input_shape: shape of the input to unpool op kernel_shape: the size of the kernel along each axis ou... | 5 | stack_v2_sparse_classes_30k_train_000808 | Implement the Python class `UnpoolMixin` described below.
Class description:
Implement the UnpoolMixin class.
Method signatures and docstrings:
- def max_unpool(cls, node, input_dict): MaxUnpooling operation
- def _get_default_shape(cls, input_shape, kernel_shape, strides): Calculates default shape from kernel_shape ... | Implement the Python class `UnpoolMixin` described below.
Class description:
Implement the UnpoolMixin class.
Method signatures and docstrings:
- def max_unpool(cls, node, input_dict): MaxUnpooling operation
- def _get_default_shape(cls, input_shape, kernel_shape, strides): Calculates default shape from kernel_shape ... | 44c09275a803e04eeeb4e0d24c372adf1f9ff1f5 | <|skeleton|>
class UnpoolMixin:
def max_unpool(cls, node, input_dict):
"""MaxUnpooling operation"""
<|body_0|>
def _get_default_shape(cls, input_shape, kernel_shape, strides):
"""Calculates default shape from kernel_shape and strides Args: input_shape: shape of the input to unpool op k... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UnpoolMixin:
def max_unpool(cls, node, input_dict):
"""MaxUnpooling operation"""
x = input_dict[node.inputs[0]]
ind = input_dict[node.inputs[1]]
if len(node.inputs) > 2:
output_shape = input_dict.get(node.inputs[2], None)
else:
output_shape = Non... | the_stack_v2_python_sparse | onnx_tf/handlers/backend/unpool_mixin.py | sdmonov/onnx-tensorflow | train | 3 | |
f2fc41d312acec66667a9d52162ca4663649520b | [
"def rserialize(root, string):\n if root == None:\n string += '# '\n else:\n string += str(root.val) + ' '\n string = rserialize(root.left, string)\n string = rserialize(root.right, string)\n return string\nstring = rserialize(root, '')\nreturn string",
"def rdeserialize(l):\n... | <|body_start_0|>
def rserialize(root, string):
if root == None:
string += '# '
else:
string += str(root.val) + ' '
string = rserialize(root.left, string)
string = rserialize(root.right, string)
return string
... | Codec | [
"Apache-2.0"
] | 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_10k_train_005388 | 1,755 | permissive | [
{
"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 | stack_v2_sparse_classes_30k_train_001569 | 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:... | fb3fa6df7c77feb2d252feea7f3507569e057c70 | <|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_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def rserialize(root, string):
if root == None:
string += '# '
else:
string += str(root.val) + ' '
string = rserial... | the_stack_v2_python_sparse | 297/serializeanddeserializebinarytree.py | cccccccccccccc/Myleetcode | train | 0 | |
b9b149b2b78d9611ce71b794833735cffeeccbfb | [
"if request.method == 'PUT':\n if 'bio' not in data or 'website' not in data:\n raise ValidationError('Missing one or more fields.')",
"if data is None:\n raise ValidationError('No data was provided')\nreturn Artist(**data)"
] | <|body_start_0|>
if request.method == 'PUT':
if 'bio' not in data or 'website' not in data:
raise ValidationError('Missing one or more fields.')
<|end_body_0|>
<|body_start_1|>
if data is None:
raise ValidationError('No data was provided')
return Artist(*... | Class to serialize and deserialize Artist objects. | ArtistSchema | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ArtistSchema:
"""Class to serialize and deserialize Artist objects."""
def validate_on_put_request(self, data, **kwargs):
"""Raise a ValidationError if certain fields are not sent during a PUT request."""
<|body_0|>
def make_object(self, data, **kwargs):
"""Retur... | stack_v2_sparse_classes_10k_train_005389 | 1,406 | no_license | [
{
"docstring": "Raise a ValidationError if certain fields are not sent during a PUT request.",
"name": "validate_on_put_request",
"signature": "def validate_on_put_request(self, data, **kwargs)"
},
{
"docstring": "Return an artist object from the validated data.",
"name": "make_object",
... | 2 | stack_v2_sparse_classes_30k_train_003976 | Implement the Python class `ArtistSchema` described below.
Class description:
Class to serialize and deserialize Artist objects.
Method signatures and docstrings:
- def validate_on_put_request(self, data, **kwargs): Raise a ValidationError if certain fields are not sent during a PUT request.
- def make_object(self, d... | Implement the Python class `ArtistSchema` described below.
Class description:
Class to serialize and deserialize Artist objects.
Method signatures and docstrings:
- def validate_on_put_request(self, data, **kwargs): Raise a ValidationError if certain fields are not sent during a PUT request.
- def make_object(self, d... | d5ae552d383f5f971e29a38055c518fc68172f32 | <|skeleton|>
class ArtistSchema:
"""Class to serialize and deserialize Artist objects."""
def validate_on_put_request(self, data, **kwargs):
"""Raise a ValidationError if certain fields are not sent during a PUT request."""
<|body_0|>
def make_object(self, data, **kwargs):
"""Retur... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ArtistSchema:
"""Class to serialize and deserialize Artist objects."""
def validate_on_put_request(self, data, **kwargs):
"""Raise a ValidationError if certain fields are not sent during a PUT request."""
if request.method == 'PUT':
if 'bio' not in data or 'website' not in dat... | the_stack_v2_python_sparse | server/app/api/schemas/artist.py | EricMontague/MailChimp-Newsletter-Project | train | 0 |
528c8b5d28b583c4a0e0d04572fdcf3a2e36fd38 | [
"args = {}\nargs.update(cls.args_map_export())\nargs.update({'json_flat': False})\nreturn args",
"super(Json, self).start(**kwargs)\nflat = self.get_arg_value('json_flat')\nself._first_row = True\nself.open_fd()\nbegin = '' if flat else '['\nself._fd.write(begin)",
"super(Json, self).stop(**kwargs)\nflat = self... | <|body_start_0|>
args = {}
args.update(cls.args_map_export())
args.update({'json_flat': False})
return args
<|end_body_0|>
<|body_start_1|>
super(Json, self).start(**kwargs)
flat = self.get_arg_value('json_flat')
self._first_row = True
self.open_fd()
... | Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # or client.users * :meth:`args_map` for callback generic arguments to for... | Json | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Json:
"""Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # or client.users * :meth:`args_map` for c... | stack_v2_sparse_classes_10k_train_005390 | 4,497 | permissive | [
{
"docstring": "Get the custom argument names and their defaults for this callbacks object. Examples: Export the output to STDOUT. If ``export_file`` is not supplied, the default is to print the output to STDOUT. >>> assets = apiobj.get(export=\"json\") Export the output to a file in the default path :attr:`axo... | 6 | stack_v2_sparse_classes_30k_train_007091 | Implement the Python class `Json` described below.
Class description:
Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # o... | Implement the Python class `Json` described below.
Class description:
Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # o... | be49566e590834df1b46494c8588651fa029b8c5 | <|skeleton|>
class Json:
"""Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # or client.users * :meth:`args_map` for c... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Json:
"""Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # or client.users * :meth:`args_map` for callback gener... | the_stack_v2_python_sparse | axonius_api_client/api/asset_callbacks/base_json.py | Axonius/axonius_api_client | train | 17 |
09fe5ee15fa30a41280845e67458dec0e82c22e5 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | AzureSecretServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AzureSecretServiceServicer:
"""Missing associated documentation comment in .proto file."""
def getAzureSecret(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def createAzureSecret(self, request, context):
"""Miss... | stack_v2_sparse_classes_10k_train_005391 | 8,199 | permissive | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "getAzureSecret",
"signature": "def getAzureSecret(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "createAzureSecret",
"signature": "def crea... | 4 | stack_v2_sparse_classes_30k_train_001655 | Implement the Python class `AzureSecretServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def getAzureSecret(self, request, context): Missing associated documentation comment in .proto file.
- def createAzureSecret(self, re... | Implement the Python class `AzureSecretServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def getAzureSecret(self, request, context): Missing associated documentation comment in .proto file.
- def createAzureSecret(self, re... | c69e14b409add099d151434b9add711e41f41b20 | <|skeleton|>
class AzureSecretServiceServicer:
"""Missing associated documentation comment in .proto file."""
def getAzureSecret(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def createAzureSecret(self, request, context):
"""Miss... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AzureSecretServiceServicer:
"""Missing associated documentation comment in .proto file."""
def getAzureSecret(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not imp... | the_stack_v2_python_sparse | python-sdk/src/airavata_mft_sdk/azure/AzureSecretService_pb2_grpc.py | apache/airavata-mft | train | 23 |
2283b88a918d63730ab37f2bfb086374cd614d32 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn ProvisioningErrorInfo()",
"from .provisioning_status_error_category import ProvisioningStatusErrorCategory\nfrom .provisioning_status_error_category import ProvisioningStatusErrorCategory\nfields: Dict[str, Callable[[Any], None]] = {'a... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return ProvisioningErrorInfo()
<|end_body_0|>
<|body_start_1|>
from .provisioning_status_error_category import ProvisioningStatusErrorCategory
from .provisioning_status_error_category import Pr... | ProvisioningErrorInfo | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProvisioningErrorInfo:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProvisioningErrorInfo:
"""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 th... | stack_v2_sparse_classes_10k_train_005392 | 3,926 | 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: ProvisioningErrorInfo",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminat... | 3 | null | Implement the Python class `ProvisioningErrorInfo` described below.
Class description:
Implement the ProvisioningErrorInfo class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProvisioningErrorInfo: Creates a new instance of the appropriate class base... | Implement the Python class `ProvisioningErrorInfo` described below.
Class description:
Implement the ProvisioningErrorInfo class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProvisioningErrorInfo: Creates a new instance of the appropriate class base... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class ProvisioningErrorInfo:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProvisioningErrorInfo:
"""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 th... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ProvisioningErrorInfo:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ProvisioningErrorInfo:
"""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 Retur... | the_stack_v2_python_sparse | msgraph/generated/models/provisioning_error_info.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
4f3901abda7eb63d34bc0f8cb786235a013196b0 | [
"def bits_to_abbr(target, bits):\n abbr = []\n pre = 0\n for i in range(len(target)):\n if bits & 1:\n if i - pre > 0:\n abbr.append(str(i - pre))\n pre = i + 1\n abbr.append(str(target[i]))\n elif i == len(target) - 1:\n abbr.append(... | <|body_start_0|>
def bits_to_abbr(target, bits):
abbr = []
pre = 0
for i in range(len(target)):
if bits & 1:
if i - pre > 0:
abbr.append(str(i - pre))
pre = i + 1
abbr.append(s... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minAbbreviationAC(self, target, dictionary):
""":type target: str :type dictionary: List[str] :rtype: str"""
<|body_0|>
def minAbbreviation(self, target, dictionary):
""":type target: str :type dictionary: List[str] :rtype: str"""
<|body_1|>
<|... | stack_v2_sparse_classes_10k_train_005393 | 3,484 | no_license | [
{
"docstring": ":type target: str :type dictionary: List[str] :rtype: str",
"name": "minAbbreviationAC",
"signature": "def minAbbreviationAC(self, target, dictionary)"
},
{
"docstring": ":type target: str :type dictionary: List[str] :rtype: str",
"name": "minAbbreviation",
"signature": "... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minAbbreviationAC(self, target, dictionary): :type target: str :type dictionary: List[str] :rtype: str
- def minAbbreviation(self, target, dictionary): :type target: str :typ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minAbbreviationAC(self, target, dictionary): :type target: str :type dictionary: List[str] :rtype: str
- def minAbbreviation(self, target, dictionary): :type target: str :typ... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def minAbbreviationAC(self, target, dictionary):
""":type target: str :type dictionary: List[str] :rtype: str"""
<|body_0|>
def minAbbreviation(self, target, dictionary):
""":type target: str :type dictionary: List[str] :rtype: str"""
<|body_1|>
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def minAbbreviationAC(self, target, dictionary):
""":type target: str :type dictionary: List[str] :rtype: str"""
def bits_to_abbr(target, bits):
abbr = []
pre = 0
for i in range(len(target)):
if bits & 1:
if i - ... | the_stack_v2_python_sparse | M/MinimumUniqueWordAbbreviation.py | bssrdf/pyleet | train | 2 | |
60a6bc4edc2623c992baddf2529b7324c1a8bf47 | [
"def dfs(root):\n if not root:\n return\n res.append(str(root.val) + ',')\n dfs(root.left)\n dfs(root.right)\nres = []\ndfs(root)\nreturn ''.join(res)",
"lst = data.split(',')\nlst.pop()\nstack = []\nhead = None\nfor n in lst:\n n = int(n)\n if not head:\n head = TreeNode(n)\n ... | <|body_start_0|>
def dfs(root):
if not root:
return
res.append(str(root.val) + ',')
dfs(root.left)
dfs(root.right)
res = []
dfs(root)
return ''.join(res)
<|end_body_0|>
<|body_start_1|>
lst = data.split(',')
... | Codec2 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec2:
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|>
def dfs(root)... | stack_v2_sparse_classes_10k_train_005394 | 1,478 | no_license | [
{
"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 | null | Implement the Python class `Codec2` described below.
Class description:
Implement the Codec2 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 `Codec2` described below.
Class description:
Implement the Codec2 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 ... | 3f0ffd519404165fd1a735441b212c801fd1ad1e | <|skeleton|>
class Codec2:
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_10k | data/stack_v2_sparse_classes_30k | class Codec2:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
def dfs(root):
if not root:
return
res.append(str(root.val) + ',')
dfs(root.left)
dfs(root.right)
res = []
dfs(root)
... | the_stack_v2_python_sparse | Problems/0400_0499/0449_Serialize_and_Deserialize_BST/Project_Python3/TreeNode/Codec2.py | NobuyukiInoue/LeetCode | train | 0 | |
4986d7562765fae465ddffef852a0071fca82fc4 | [
"conv_dt = naive.astimezone(pytz.timezone(tz)).strftime('%Y-%m-%d %H:%M:%S')\nlogger.logic_log('LOSI13022', tz, naive, conv_dt, request=request)\nreturn conv_dt",
"tz_ex = pytz.timezone(tz)\nnaive = naive.replace('/', '-')\nuser_dt = datetime.datetime.strptime(naive, '%Y-%m-%d %H:%M:%S')\ncou_dt = tz_ex.localize(... | <|body_start_0|>
conv_dt = naive.astimezone(pytz.timezone(tz)).strftime('%Y-%m-%d %H:%M:%S')
logger.logic_log('LOSI13022', tz, naive, conv_dt, request=request)
return conv_dt
<|end_body_0|>
<|body_start_1|>
tz_ex = pytz.timezone(tz)
naive = naive.replace('/', '-')
user_d... | TimeConversion | [
"Apache-2.0",
"BSD-3-Clause",
"LGPL-3.0-only",
"MIT",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TimeConversion:
def get_time_conversion(cls, naive, tz, request=None):
"""[概要] 時刻変換処理を行う [戻り値] 変換した時刻"""
<|body_0|>
def get_time_conversion_utc(cls, naive, tz, request=None):
"""[概要] 時刻変換処理を行う [戻り値] utc_dt : 変換した時刻(UTC)"""
<|body_1|>
<|end_skeleton|>
<|body... | stack_v2_sparse_classes_10k_train_005395 | 9,642 | permissive | [
{
"docstring": "[概要] 時刻変換処理を行う [戻り値] 変換した時刻",
"name": "get_time_conversion",
"signature": "def get_time_conversion(cls, naive, tz, request=None)"
},
{
"docstring": "[概要] 時刻変換処理を行う [戻り値] utc_dt : 変換した時刻(UTC)",
"name": "get_time_conversion_utc",
"signature": "def get_time_conversion_utc(cl... | 2 | stack_v2_sparse_classes_30k_train_000358 | Implement the Python class `TimeConversion` described below.
Class description:
Implement the TimeConversion class.
Method signatures and docstrings:
- def get_time_conversion(cls, naive, tz, request=None): [概要] 時刻変換処理を行う [戻り値] 変換した時刻
- def get_time_conversion_utc(cls, naive, tz, request=None): [概要] 時刻変換処理を行う [戻り値] u... | Implement the Python class `TimeConversion` described below.
Class description:
Implement the TimeConversion class.
Method signatures and docstrings:
- def get_time_conversion(cls, naive, tz, request=None): [概要] 時刻変換処理を行う [戻り値] 変換した時刻
- def get_time_conversion_utc(cls, naive, tz, request=None): [概要] 時刻変換処理を行う [戻り値] u... | c00ea4fe1bf4b4a18d545aabeaaf1d95c7664b94 | <|skeleton|>
class TimeConversion:
def get_time_conversion(cls, naive, tz, request=None):
"""[概要] 時刻変換処理を行う [戻り値] 変換した時刻"""
<|body_0|>
def get_time_conversion_utc(cls, naive, tz, request=None):
"""[概要] 時刻変換処理を行う [戻り値] utc_dt : 変換した時刻(UTC)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TimeConversion:
def get_time_conversion(cls, naive, tz, request=None):
"""[概要] 時刻変換処理を行う [戻り値] 変換した時刻"""
conv_dt = naive.astimezone(pytz.timezone(tz)).strftime('%Y-%m-%d %H:%M:%S')
logger.logic_log('LOSI13022', tz, naive, conv_dt, request=request)
return conv_dt
def get_ti... | the_stack_v2_python_sparse | oase-root/libs/webcommonlibs/common.py | exastro-suite/oase | train | 10 | |
73e2e2d32a970b297706cefd49903747f9c9867d | [
"super().__init__(filterFineData, universeSettings)\nself.NumberOfSymbolsCoarse = 500\nself.NumberOfSymbolsFine = 20\nself.NumberOfSymbolsInPortfolio = 10\nself.lastMonth = -1\nself.dollarVolumeBySymbol = {}",
"month = algorithm.Time.month\nif month == self.lastMonth:\n return Universe.Unchanged\nself.lastMont... | <|body_start_0|>
super().__init__(filterFineData, universeSettings)
self.NumberOfSymbolsCoarse = 500
self.NumberOfSymbolsFine = 20
self.NumberOfSymbolsInPortfolio = 10
self.lastMonth = -1
self.dollarVolumeBySymbol = {}
<|end_body_0|>
<|body_start_1|>
month = algo... | Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm. From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Value to EBITDA (EV/EBITDA) and Return on Assets (ROA). | GreenBlattMagicFormulaUniverseSelectionModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GreenBlattMagicFormulaUniverseSelectionModel:
"""Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm. From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Value to EBITDA (EV/EBITDA) and Return on ... | stack_v2_sparse_classes_10k_train_005396 | 9,868 | permissive | [
{
"docstring": "Initializes a new default instance of the MagicFormulaUniverseSelectionModel",
"name": "__init__",
"signature": "def __init__(self, filterFineData=True, universeSettings=None)"
},
{
"docstring": "Performs coarse selection for constituents. The stocks must have fundamental data",
... | 3 | null | Implement the Python class `GreenBlattMagicFormulaUniverseSelectionModel` described below.
Class description:
Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm. From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Val... | Implement the Python class `GreenBlattMagicFormulaUniverseSelectionModel` described below.
Class description:
Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm. From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Val... | b33dd3bc140e14b883f39ecf848a793cf7292277 | <|skeleton|>
class GreenBlattMagicFormulaUniverseSelectionModel:
"""Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm. From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Value to EBITDA (EV/EBITDA) and Return on ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GreenBlattMagicFormulaUniverseSelectionModel:
"""Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm. From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Value to EBITDA (EV/EBITDA) and Return on Assets (ROA).... | the_stack_v2_python_sparse | Algorithm.Python/Alphas/GreenblattMagicFormulaAlpha.py | Capnode/Algoloop | train | 87 |
5e8b9932734bec2eac26839189e7c997956ec95b | [
"if self.request.version == 'v6':\n return WorkspaceDetailsSerializerV6\nelif self.request.version == 'v7':\n return WorkspaceDetailsSerializerV6",
"if request.version == 'v6':\n return self._get_v6(request, workspace_id)\nelif request.version == 'v7':\n return self._get_v6(request, workspace_id)\nrai... | <|body_start_0|>
if self.request.version == 'v6':
return WorkspaceDetailsSerializerV6
elif self.request.version == 'v7':
return WorkspaceDetailsSerializerV6
<|end_body_0|>
<|body_start_1|>
if request.version == 'v6':
return self._get_v6(request, workspace_id)... | This view is the endpoint for retrieving/updating details of a workspace. | WorkspaceDetailsView | [
"LicenseRef-scancode-free-unknown",
"Apache-2.0",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkspaceDetailsView:
"""This view is the endpoint for retrieving/updating details of a workspace."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
<|body_0|>
def get(self, request, workspace_id):
... | stack_v2_sparse_classes_10k_train_005397 | 19,677 | permissive | [
{
"docstring": "Returns the appropriate serializer based off the requests version of the REST API",
"name": "get_serializer_class",
"signature": "def get_serializer_class(self)"
},
{
"docstring": "Retrieves the details for a workspace and return them in JSON form :param request: the HTTP GET req... | 5 | stack_v2_sparse_classes_30k_train_006896 | Implement the Python class `WorkspaceDetailsView` described below.
Class description:
This view is the endpoint for retrieving/updating details of a workspace.
Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def ge... | Implement the Python class `WorkspaceDetailsView` described below.
Class description:
This view is the endpoint for retrieving/updating details of a workspace.
Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def ge... | 28618aee07ceed9e4a6eb7b8d0e6f05b31d8fd6b | <|skeleton|>
class WorkspaceDetailsView:
"""This view is the endpoint for retrieving/updating details of a workspace."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
<|body_0|>
def get(self, request, workspace_id):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WorkspaceDetailsView:
"""This view is the endpoint for retrieving/updating details of a workspace."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
if self.request.version == 'v6':
return WorkspaceDetail... | the_stack_v2_python_sparse | scale/storage/views.py | kfconsultant/scale | train | 0 |
0e324456ce8625f2fa22a1a85266f646beaf4f7e | [
"def backTrack(n, res, tmp, flag, row):\n if row == n:\n z = []\n for t in tmp:\n z.append(''.join(t))\n res.append(z)\n else:\n for col in range(n):\n if flag[row] and flag[n + col] and flag[2 * n + row + col] and flag[5 * n - 2 + col - row]:\n ... | <|body_start_0|>
def backTrack(n, res, tmp, flag, row):
if row == n:
z = []
for t in tmp:
z.append(''.join(t))
res.append(z)
else:
for col in range(n):
if flag[row] and flag[n + col] a... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def solveNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
<|body_0|>
def solveNQueens0(self, n):
""":type n: int :rtype: List[List[str]]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def backTrack(n, res, tmp, flag, row):
... | stack_v2_sparse_classes_10k_train_005398 | 2,064 | no_license | [
{
"docstring": ":type n: int :rtype: List[List[str]]",
"name": "solveNQueens",
"signature": "def solveNQueens(self, n)"
},
{
"docstring": ":type n: int :rtype: List[List[str]]",
"name": "solveNQueens0",
"signature": "def solveNQueens0(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004286 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def solveNQueens(self, n): :type n: int :rtype: List[List[str]]
- def solveNQueens0(self, n): :type n: int :rtype: List[List[str]] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def solveNQueens(self, n): :type n: int :rtype: List[List[str]]
- def solveNQueens0(self, n): :type n: int :rtype: List[List[str]]
<|skeleton|>
class Solution:
def solveNQu... | 9e49b2c6003b957276737005d4aaac276b44d251 | <|skeleton|>
class Solution:
def solveNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
<|body_0|>
def solveNQueens0(self, n):
""":type n: int :rtype: List[List[str]]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def solveNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
def backTrack(n, res, tmp, flag, row):
if row == n:
z = []
for t in tmp:
z.append(''.join(t))
res.append(z)
else:
... | the_stack_v2_python_sparse | PythonCode/src/0051_N-Queens.py | oneyuan/CodeforFun | train | 0 | |
e0c7cdf2a0e61f20341632eb84be7f031633a20f | [
"extension_path = os.path.join(util.GetUnittestDataDir(), 'foo')\noptions = options_for_unittests.GetCopy()\nself.assertRaises(extension_to_load.ExtensionPathNonExistentException, lambda: extension_to_load.ExtensionToLoad(extension_path, options.browser_type))",
"extension_path = os.path.join(util.GetUnittestData... | <|body_start_0|>
extension_path = os.path.join(util.GetUnittestDataDir(), 'foo')
options = options_for_unittests.GetCopy()
self.assertRaises(extension_to_load.ExtensionPathNonExistentException, lambda: extension_to_load.ExtensionToLoad(extension_path, options.browser_type))
<|end_body_0|>
<|bod... | NonExistentExtensionTest | [
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NonExistentExtensionTest:
def testNonExistentExtensionPath(self):
"""Test that a non-existent extension path will raise an exception."""
<|body_0|>
def testExtensionNotLoaded(self):
"""Querying an extension that was not loaded will return None"""
<|body_1|>
... | stack_v2_sparse_classes_10k_train_005399 | 9,822 | permissive | [
{
"docstring": "Test that a non-existent extension path will raise an exception.",
"name": "testNonExistentExtensionPath",
"signature": "def testNonExistentExtensionPath(self)"
},
{
"docstring": "Querying an extension that was not loaded will return None",
"name": "testExtensionNotLoaded",
... | 2 | null | Implement the Python class `NonExistentExtensionTest` described below.
Class description:
Implement the NonExistentExtensionTest class.
Method signatures and docstrings:
- def testNonExistentExtensionPath(self): Test that a non-existent extension path will raise an exception.
- def testExtensionNotLoaded(self): Query... | Implement the Python class `NonExistentExtensionTest` described below.
Class description:
Implement the NonExistentExtensionTest class.
Method signatures and docstrings:
- def testNonExistentExtensionPath(self): Test that a non-existent extension path will raise an exception.
- def testExtensionNotLoaded(self): Query... | 72a05af97787001756bae2511b7985e61498c965 | <|skeleton|>
class NonExistentExtensionTest:
def testNonExistentExtensionPath(self):
"""Test that a non-existent extension path will raise an exception."""
<|body_0|>
def testExtensionNotLoaded(self):
"""Querying an extension that was not loaded will return None"""
<|body_1|>
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NonExistentExtensionTest:
def testNonExistentExtensionPath(self):
"""Test that a non-existent extension path will raise an exception."""
extension_path = os.path.join(util.GetUnittestDataDir(), 'foo')
options = options_for_unittests.GetCopy()
self.assertRaises(extension_to_load... | the_stack_v2_python_sparse | third_party/catapult/telemetry/telemetry/internal/browser/extension_unittest.py | metux/chromium-suckless | train | 5 |
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