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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
248b4261ea8199e77a5478eafe17e8e2521894c4 | [
"self._modules = args\nself._base_module = kwargs.get('base_module', 'galileo')\nif not self._modules:\n self._modules = [self._base_module]",
"for module in self._modules:\n m = sys.modules[module]\n setattr(m, func.__name__, func)\nreturn func",
"for module in self._modules:\n m = sys.modules[modu... | <|body_start_0|>
self._modules = args
self._base_module = kwargs.get('base_module', 'galileo')
if not self._modules:
self._modules = [self._base_module]
<|end_body_0|>
<|body_start_1|>
for module in self._modules:
m = sys.modules[module]
setattr(m, fu... | \\brief Export galileo APIs | export | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"BSD-2-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class export:
"""\\brief Export galileo APIs"""
def __init__(self, *args, **kwargs):
"""\\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module Default is `galileo`."""
<|body_0|>
def __cal... | stack_v2_sparse_classes_36k_train_016400 | 2,131 | permissive | [
{
"docstring": "\\\\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\\\param **kwargs base_module Default is `galileo`.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "\\\\brief export c... | 4 | stack_v2_sparse_classes_30k_train_015352 | Implement the Python class `export` described below.
Class description:
\\brief Export galileo APIs
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): \\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module D... | Implement the Python class `export` described below.
Class description:
\\brief Export galileo APIs
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): \\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module D... | 48099ec3f0331196c6812208ceb080ba618a588b | <|skeleton|>
class export:
"""\\brief Export galileo APIs"""
def __init__(self, *args, **kwargs):
"""\\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module Default is `galileo`."""
<|body_0|>
def __cal... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class export:
"""\\brief Export galileo APIs"""
def __init__(self, *args, **kwargs):
"""\\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module Default is `galileo`."""
self._modules = args
self._bas... | the_stack_v2_python_sparse | galileo/platform/export.py | 2012fang1/galileo | train | 0 |
df1f7ec18c67941dd46beb4812c532d7686e3bf0 | [
"super(SkyDriveOldLogParser, self).__init__()\nself._last_date_time = None\nself._last_event_data = None\nself.offset = 0",
"time_elements_tuple = self._GetValueFromStructure(structure, 'date_time')\nmonth, day_of_month, year, hours, minutes, seconds, milliseconds = time_elements_tuple\ntime_elements_tuple = (yea... | <|body_start_0|>
super(SkyDriveOldLogParser, self).__init__()
self._last_date_time = None
self._last_event_data = None
self.offset = 0
<|end_body_0|>
<|body_start_1|>
time_elements_tuple = self._GetValueFromStructure(structure, 'date_time')
month, day_of_month, year, hou... | Parse SkyDrive old log files. | SkyDriveOldLogParser | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SkyDriveOldLogParser:
"""Parse SkyDrive old log files."""
def __init__(self):
"""Initializes a parser."""
<|body_0|>
def _ParseLogline(self, parser_mediator, structure):
"""Parse a logline and store appropriate attributes. Args: parser_mediator (ParserMediator): ... | stack_v2_sparse_classes_36k_train_016401 | 16,775 | permissive | [
{
"docstring": "Initializes a parser.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Parse a logline and store appropriate attributes. Args: parser_mediator (ParserMediator): mediates interactions between parsers and other components, such as storage and dfvfs. struct... | 5 | null | Implement the Python class `SkyDriveOldLogParser` described below.
Class description:
Parse SkyDrive old log files.
Method signatures and docstrings:
- def __init__(self): Initializes a parser.
- def _ParseLogline(self, parser_mediator, structure): Parse a logline and store appropriate attributes. Args: parser_mediat... | Implement the Python class `SkyDriveOldLogParser` described below.
Class description:
Parse SkyDrive old log files.
Method signatures and docstrings:
- def __init__(self): Initializes a parser.
- def _ParseLogline(self, parser_mediator, structure): Parse a logline and store appropriate attributes. Args: parser_mediat... | c69b2952b608cfce47ff8fd0d1409d856be35cb1 | <|skeleton|>
class SkyDriveOldLogParser:
"""Parse SkyDrive old log files."""
def __init__(self):
"""Initializes a parser."""
<|body_0|>
def _ParseLogline(self, parser_mediator, structure):
"""Parse a logline and store appropriate attributes. Args: parser_mediator (ParserMediator): ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SkyDriveOldLogParser:
"""Parse SkyDrive old log files."""
def __init__(self):
"""Initializes a parser."""
super(SkyDriveOldLogParser, self).__init__()
self._last_date_time = None
self._last_event_data = None
self.offset = 0
def _ParseLogline(self, parser_media... | the_stack_v2_python_sparse | plaso/parsers/skydrivelog.py | cyb3rfox/plaso | train | 3 |
a9f2ddf7bcf9f7dc674a5f70abfdbd1730a5438f | [
"if not parent:\n raise ValueError('Missing parent value.')\nsuper(VShadowPathSpec, self).__init__(parent=parent, **kwargs)\nself.location = location\nself.store_index = store_index",
"string_parts = []\nif self.location is not None:\n string_parts.append(f'location: {self.location:s}')\nif self.store_index... | <|body_start_0|>
if not parent:
raise ValueError('Missing parent value.')
super(VShadowPathSpec, self).__init__(parent=parent, **kwargs)
self.location = location
self.store_index = store_index
<|end_body_0|>
<|body_start_1|>
string_parts = []
if self.location... | Volume Shadow Snapshots (VSS) path specification. Attributes: location (str): location. store_index (int): store index. | VShadowPathSpec | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VShadowPathSpec:
"""Volume Shadow Snapshots (VSS) path specification. Attributes: location (str): location. store_index (int): store index."""
def __init__(self, location=None, parent=None, store_index=None, **kwargs):
"""Initializes a path specification. Note that the VSS path speci... | stack_v2_sparse_classes_36k_train_016402 | 1,499 | permissive | [
{
"docstring": "Initializes a path specification. Note that the VSS path specification must have a parent. Args: location (Optional[str]): location. parent (Optional[PathSpec]): parent path specification. store_index (Optional[int]): store index. Raises: ValueError: when parent is not set.",
"name": "__init... | 2 | null | Implement the Python class `VShadowPathSpec` described below.
Class description:
Volume Shadow Snapshots (VSS) path specification. Attributes: location (str): location. store_index (int): store index.
Method signatures and docstrings:
- def __init__(self, location=None, parent=None, store_index=None, **kwargs): Initi... | Implement the Python class `VShadowPathSpec` described below.
Class description:
Volume Shadow Snapshots (VSS) path specification. Attributes: location (str): location. store_index (int): store index.
Method signatures and docstrings:
- def __init__(self, location=None, parent=None, store_index=None, **kwargs): Initi... | 28756d910e951a22c5f0b2bcf5184f055a19d544 | <|skeleton|>
class VShadowPathSpec:
"""Volume Shadow Snapshots (VSS) path specification. Attributes: location (str): location. store_index (int): store index."""
def __init__(self, location=None, parent=None, store_index=None, **kwargs):
"""Initializes a path specification. Note that the VSS path speci... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VShadowPathSpec:
"""Volume Shadow Snapshots (VSS) path specification. Attributes: location (str): location. store_index (int): store index."""
def __init__(self, location=None, parent=None, store_index=None, **kwargs):
"""Initializes a path specification. Note that the VSS path specification must... | the_stack_v2_python_sparse | dfvfs/path/vshadow_path_spec.py | log2timeline/dfvfs | train | 197 |
2b5ae912190d192c9800f906ef4ce1e338138b5b | [
"if value is self.field.missing_value:\n return []\nterms = self.widget.updateTerms()\ntry:\n return [terms.getTerm(value).token]\nexcept LookupError:\n return []",
"widget = self.widget\nif not len(value) or value[0] == widget.noValueToken:\n return self.field.missing_value\nwidget.updateTerms()\nret... | <|body_start_0|>
if value is self.field.missing_value:
return []
terms = self.widget.updateTerms()
try:
return [terms.getTerm(value).token]
except LookupError:
return []
<|end_body_0|>
<|body_start_1|>
widget = self.widget
if not len(v... | Basic data converter for ISequenceWidget. | SequenceDataConverter | [
"ZPL-2.1"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SequenceDataConverter:
"""Basic data converter for ISequenceWidget."""
def toWidgetValue(self, value):
"""Convert from Python bool to HTML representation."""
<|body_0|>
def toFieldValue(self, value):
"""See interfaces.IDataConverter"""
<|body_1|>
<|end_s... | stack_v2_sparse_classes_36k_train_016403 | 15,934 | permissive | [
{
"docstring": "Convert from Python bool to HTML representation.",
"name": "toWidgetValue",
"signature": "def toWidgetValue(self, value)"
},
{
"docstring": "See interfaces.IDataConverter",
"name": "toFieldValue",
"signature": "def toFieldValue(self, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001816 | Implement the Python class `SequenceDataConverter` described below.
Class description:
Basic data converter for ISequenceWidget.
Method signatures and docstrings:
- def toWidgetValue(self, value): Convert from Python bool to HTML representation.
- def toFieldValue(self, value): See interfaces.IDataConverter | Implement the Python class `SequenceDataConverter` described below.
Class description:
Basic data converter for ISequenceWidget.
Method signatures and docstrings:
- def toWidgetValue(self, value): Convert from Python bool to HTML representation.
- def toFieldValue(self, value): See interfaces.IDataConverter
<|skelet... | aa47e9b109ad2d7de600fc1d4ea7359d8144f356 | <|skeleton|>
class SequenceDataConverter:
"""Basic data converter for ISequenceWidget."""
def toWidgetValue(self, value):
"""Convert from Python bool to HTML representation."""
<|body_0|>
def toFieldValue(self, value):
"""See interfaces.IDataConverter"""
<|body_1|>
<|end_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SequenceDataConverter:
"""Basic data converter for ISequenceWidget."""
def toWidgetValue(self, value):
"""Convert from Python bool to HTML representation."""
if value is self.field.missing_value:
return []
terms = self.widget.updateTerms()
try:
retu... | the_stack_v2_python_sparse | src/z3c/form/converter.py | zopefoundation/z3c.form | train | 6 |
03d7ef71403e445c77fc720711810bb338f93751 | [
"self.mask = np.copy(mask)\nself.masked_vol = np.copy(vol)\nself.masked_vol[self.mask == 0] = 0",
"min_intensity = np.float64(np.min(self.masked_vol[self.masked_vol != 0]))\nmax_intensity = np.max(self.masked_vol)\nw1 = np.copy(self.masked_vol)\nw1 = (w1 - min_intensity) / (max_intensity - min_intensity)\nw1[w1 <... | <|body_start_0|>
self.mask = np.copy(mask)
self.masked_vol = np.copy(vol)
self.masked_vol[self.mask == 0] = 0
<|end_body_0|>
<|body_start_1|>
min_intensity = np.float64(np.min(self.masked_vol[self.masked_vol != 0]))
max_intensity = np.max(self.masked_vol)
w1 = np.copy(se... | vertices | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class vertices:
def __init__(self, mask, vol):
"""Constructor"""
<|body_0|>
def compute_w1(self):
"""Compute W1 for vertex weight computation"""
<|body_1|>
def compute_w2(self):
"""Compute W2 for vertex weight computation; Distance from boundaries."""
... | stack_v2_sparse_classes_36k_train_016404 | 8,906 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, mask, vol)"
},
{
"docstring": "Compute W1 for vertex weight computation",
"name": "compute_w1",
"signature": "def compute_w1(self)"
},
{
"docstring": "Compute W2 for vertex weight computation; Dist... | 5 | stack_v2_sparse_classes_30k_val_000546 | Implement the Python class `vertices` described below.
Class description:
Implement the vertices class.
Method signatures and docstrings:
- def __init__(self, mask, vol): Constructor
- def compute_w1(self): Compute W1 for vertex weight computation
- def compute_w2(self): Compute W2 for vertex weight computation; Dist... | Implement the Python class `vertices` described below.
Class description:
Implement the vertices class.
Method signatures and docstrings:
- def __init__(self, mask, vol): Constructor
- def compute_w1(self): Compute W1 for vertex weight computation
- def compute_w2(self): Compute W2 for vertex weight computation; Dist... | f966200d09d03a75ff9f56ab5c08b03b7bc3aadb | <|skeleton|>
class vertices:
def __init__(self, mask, vol):
"""Constructor"""
<|body_0|>
def compute_w1(self):
"""Compute W1 for vertex weight computation"""
<|body_1|>
def compute_w2(self):
"""Compute W2 for vertex weight computation; Distance from boundaries."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class vertices:
def __init__(self, mask, vol):
"""Constructor"""
self.mask = np.copy(mask)
self.masked_vol = np.copy(vol)
self.masked_vol[self.mask == 0] = 0
def compute_w1(self):
"""Compute W1 for vertex weight computation"""
min_intensity = np.float64(np.min(se... | the_stack_v2_python_sparse | src/jupyter/kkarbasi/cobalt_tractography/cobalt_tractography/tractography.py | neurodata-cobalt/cobalt | train | 0 | |
d349de07b292bdeab635290cd1dfcd044933b896 | [
"res = 0\nfor i in range(len(nums)):\n res = max(res, nums[i] + self.rob(nums[i + 2:]))\nreturn res",
"if not nums:\n return 0\np_0 = nums[0]\nif len(nums) == 1:\n return p_0\np_1 = max(p_0, nums[1])\nfor i in range(2, len(nums)):\n p_2 = max(p_1, p_0 + nums[i])\n p_0 = p_1\n p_1 = p_2\nreturn p... | <|body_start_0|>
res = 0
for i in range(len(nums)):
res = max(res, nums[i] + self.rob(nums[i + 2:]))
return res
<|end_body_0|>
<|body_start_1|>
if not nums:
return 0
p_0 = nums[0]
if len(nums) == 1:
return p_0
p_1 = max(p_0, nu... | Solution | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rob(self, nums):
"""Brute Force (Time Limit Exceeded)"""
<|body_0|>
def rob2(self, nums):
"""DP"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = 0
for i in range(len(nums)):
res = max(res, nums[i] + self.rob(nu... | stack_v2_sparse_classes_36k_train_016405 | 1,489 | permissive | [
{
"docstring": "Brute Force (Time Limit Exceeded)",
"name": "rob",
"signature": "def rob(self, nums)"
},
{
"docstring": "DP",
"name": "rob2",
"signature": "def rob2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002289 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): Brute Force (Time Limit Exceeded)
- def rob2(self, nums): DP | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): Brute Force (Time Limit Exceeded)
- def rob2(self, nums): DP
<|skeleton|>
class Solution:
def rob(self, nums):
"""Brute Force (Time Limit Excee... | 49a0b03c55d8a702785888d473ef96539265ce9c | <|skeleton|>
class Solution:
def rob(self, nums):
"""Brute Force (Time Limit Exceeded)"""
<|body_0|>
def rob2(self, nums):
"""DP"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rob(self, nums):
"""Brute Force (Time Limit Exceeded)"""
res = 0
for i in range(len(nums)):
res = max(res, nums[i] + self.rob(nums[i + 2:]))
return res
def rob2(self, nums):
"""DP"""
if not nums:
return 0
p_0 = ... | the_stack_v2_python_sparse | leetcode/0198_house_robber.py | chaosWsF/Python-Practice | train | 1 | |
3d3769dfb66f03a6652f6e4821e07e02986e2c9c | [
"active_users_by_email = UserModel._default_manager.filter(**{'%s__iexact' % UserModel.get_email_field_name(): email, 'is_active': True})\nactive_users_by_username = UserModel._default_manager.filter(**{'username__iexact': username, 'is_active': True})\nusers = []\nfor u in active_users_by_email:\n if u.has_usab... | <|body_start_0|>
active_users_by_email = UserModel._default_manager.filter(**{'%s__iexact' % UserModel.get_email_field_name(): email, 'is_active': True})
active_users_by_username = UserModel._default_manager.filter(**{'username__iexact': username, 'is_active': True})
users = []
for u in ... | UsernameAwarePasswordResetForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UsernameAwarePasswordResetForm:
def get_users(self, email=None, username=None):
"""Given an email or username, return matching user(s) who should receive a reset. This allows subclasses to more easily customize the default policies that prevent inactive users and users with unusable pass... | stack_v2_sparse_classes_36k_train_016406 | 11,141 | no_license | [
{
"docstring": "Given an email or username, return matching user(s) who should receive a reset. This allows subclasses to more easily customize the default policies that prevent inactive users and users with unusable passwords from resetting their password.",
"name": "get_users",
"signature": "def get_u... | 2 | stack_v2_sparse_classes_30k_train_014422 | Implement the Python class `UsernameAwarePasswordResetForm` described below.
Class description:
Implement the UsernameAwarePasswordResetForm class.
Method signatures and docstrings:
- def get_users(self, email=None, username=None): Given an email or username, return matching user(s) who should receive a reset. This a... | Implement the Python class `UsernameAwarePasswordResetForm` described below.
Class description:
Implement the UsernameAwarePasswordResetForm class.
Method signatures and docstrings:
- def get_users(self, email=None, username=None): Given an email or username, return matching user(s) who should receive a reset. This a... | 0a90553a04a947175a74dd11fb2eb9dc72385a12 | <|skeleton|>
class UsernameAwarePasswordResetForm:
def get_users(self, email=None, username=None):
"""Given an email or username, return matching user(s) who should receive a reset. This allows subclasses to more easily customize the default policies that prevent inactive users and users with unusable pass... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UsernameAwarePasswordResetForm:
def get_users(self, email=None, username=None):
"""Given an email or username, return matching user(s) who should receive a reset. This allows subclasses to more easily customize the default policies that prevent inactive users and users with unusable passwords from res... | the_stack_v2_python_sparse | fanart/forms.py | bctiemann/tlkfaa | train | 1 | |
c740e18d709baa471bc0f12144c76089953feeac | [
"json = self.json_parse(value)\nif json and json['source'] == self._YOUTUBE_MILESTONE_SOURCE:\n data = transforms.loads(json['data'])\n video_identifier = (data['video_id'], data['instance_id'])\n playhead_position = data['position']\n if playhead_position <= _POS_LIMIT_SECONDS and playhead_position != ... | <|body_start_0|>
json = self.json_parse(value)
if json and json['source'] == self._YOUTUBE_MILESTONE_SOURCE:
data = transforms.loads(json['data'])
video_identifier = (data['video_id'], data['instance_id'])
playhead_position = data['position']
if playhead_p... | Generates time histogram of user video engagement. Input file: EventEntity JSON file. Each event has a 'source' that defines a place in a code where the event was recorded. Each event has a 'user_id' to represent an actor who triggered the event. The event 'data' is a JSON object and its format and content depends on t... | YoutubeHistogramGenerator | [
"Apache-2.0",
"CC-BY-3.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class YoutubeHistogramGenerator:
"""Generates time histogram of user video engagement. Input file: EventEntity JSON file. Each event has a 'source' that defines a place in a code where the event was recorded. Each event has a 'user_id' to represent an actor who triggered the event. The event 'data' is ... | stack_v2_sparse_classes_36k_train_016407 | 20,093 | permissive | [
{
"docstring": "Filters out YouTube video data from EventEntity JSON file. Args: unused_key: int. line number of each EventEntity in file. value: str. instance of EventEntity extracted from file. Yields: A tuple of (video_identifier, time_position) to be passed into reduce function. Video_identifier is a tuple ... | 2 | stack_v2_sparse_classes_30k_train_005712 | Implement the Python class `YoutubeHistogramGenerator` described below.
Class description:
Generates time histogram of user video engagement. Input file: EventEntity JSON file. Each event has a 'source' that defines a place in a code where the event was recorded. Each event has a 'user_id' to represent an actor who tr... | Implement the Python class `YoutubeHistogramGenerator` described below.
Class description:
Generates time histogram of user video engagement. Input file: EventEntity JSON file. Each event has a 'source' that defines a place in a code where the event was recorded. Each event has a 'user_id' to represent an actor who tr... | 64f5ea13a8d85b9ef057dddae888a427b1396df6 | <|skeleton|>
class YoutubeHistogramGenerator:
"""Generates time histogram of user video engagement. Input file: EventEntity JSON file. Each event has a 'source' that defines a place in a code where the event was recorded. Each event has a 'user_id' to represent an actor who triggered the event. The event 'data' is ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class YoutubeHistogramGenerator:
"""Generates time histogram of user video engagement. Input file: EventEntity JSON file. Each event has a 'source' that defines a place in a code where the event was recorded. Each event has a 'user_id' to represent an actor who triggered the event. The event 'data' is a JSON object... | the_stack_v2_python_sparse | coursebuilder/tools/etl/mapreduce_examples.py | ram8647/gcb-clone-v111 | train | 1 |
531c95b2f38de3a513426138e108d8c38222fa57 | [
"sql = '\\n SELECT \\n mobile,\\n `name`,\\n driver_id,\\n city \\n FROM \\n db_cdm.zd_new_driver_message'\nread_data = self.select_all(sql)\nreturn pd.DataFrame(read_data)",
"sql = \"\\n SELECT\\n ucf.driver_id,\\n ubi.`name` driver_na... | <|body_start_0|>
sql = '\n SELECT \n mobile,\n `name`,\n driver_id,\n city \n FROM \n db_cdm.zd_new_driver_message'
read_data = self.select_all(sql)
return pd.DataFrame(read_data)
<|end_body_0|>
<|body_start_1|>
sql = "\n SELECT\n ... | PickLabelDao | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PickLabelDao:
def select_new_driver(self):
"""查询新司机信息 :return:"""
<|body_0|>
def select_user_common_flow(self):
"""查询司机常运流向列表 :return:"""
<|body_1|>
def select_user_common_kind(self, values):
"""查询司机常运品种列表 :return:"""
<|body_2|>
def ... | stack_v2_sparse_classes_36k_train_016408 | 3,525 | no_license | [
{
"docstring": "查询新司机信息 :return:",
"name": "select_new_driver",
"signature": "def select_new_driver(self)"
},
{
"docstring": "查询司机常运流向列表 :return:",
"name": "select_user_common_flow",
"signature": "def select_user_common_flow(self)"
},
{
"docstring": "查询司机常运品种列表 :return:",
"na... | 4 | null | Implement the Python class `PickLabelDao` described below.
Class description:
Implement the PickLabelDao class.
Method signatures and docstrings:
- def select_new_driver(self): 查询新司机信息 :return:
- def select_user_common_flow(self): 查询司机常运流向列表 :return:
- def select_user_common_kind(self, values): 查询司机常运品种列表 :return:
- ... | Implement the Python class `PickLabelDao` described below.
Class description:
Implement the PickLabelDao class.
Method signatures and docstrings:
- def select_new_driver(self): 查询新司机信息 :return:
- def select_user_common_flow(self): 查询司机常运流向列表 :return:
- def select_user_common_kind(self, values): 查询司机常运品种列表 :return:
- ... | 5fb62820fa697ffc45931c4c19a9b0775feb1fc5 | <|skeleton|>
class PickLabelDao:
def select_new_driver(self):
"""查询新司机信息 :return:"""
<|body_0|>
def select_user_common_flow(self):
"""查询司机常运流向列表 :return:"""
<|body_1|>
def select_user_common_kind(self, values):
"""查询司机常运品种列表 :return:"""
<|body_2|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PickLabelDao:
def select_new_driver(self):
"""查询新司机信息 :return:"""
sql = '\n SELECT \n mobile,\n `name`,\n driver_id,\n city \n FROM \n db_cdm.zd_new_driver_message'
read_data = self.select_all(sql)
return pd.DataFrame(read_data)
... | the_stack_v2_python_sparse | app/main/steel_factory/dao/pick_propelling_label_dao.py | 9Echo/gc-goods-allocation | train | 0 | |
afaa7a4364047103fd1bd9444f12f304e1210ccc | [
"super(AttentionalFactorizationMachineModel, self).__init__()\nself.afm = AFMLayer(embed_size, num_fields, attn_size, dropout_p)\nself.bias = nn.Parameter(torch.zeros(1))\nnn.init.uniform_(self.bias.data)",
"linear_out = feat_inputs.sum(dim=1)\nafm_out, _ = self.afm(emb_inputs)\nafm_out = afm_out.sum(dim=2)\noutp... | <|body_start_0|>
super(AttentionalFactorizationMachineModel, self).__init__()
self.afm = AFMLayer(embed_size, num_fields, attn_size, dropout_p)
self.bias = nn.Parameter(torch.zeros(1))
nn.init.uniform_(self.bias.data)
<|end_body_0|>
<|body_start_1|>
linear_out = feat_inputs.sum(... | AttentionalFactorizationMachineModel is a model of attentional factorization machine, which calculate prediction by summing up bias, linear terms and attentional factorization machine values. :Reference: #. `Jun Xiao et al, 2017. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attent... | AttentionalFactorizationMachineModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttentionalFactorizationMachineModel:
"""AttentionalFactorizationMachineModel is a model of attentional factorization machine, which calculate prediction by summing up bias, linear terms and attentional factorization machine values. :Reference: #. `Jun Xiao et al, 2017. Attentional Factorization ... | stack_v2_sparse_classes_36k_train_016409 | 2,613 | permissive | [
{
"docstring": "initialize Attention Factorization Machine Model Args: embed_size (int): embedding size num_fields (int): number of fields in input attn_size (int): attention layer size dropout_p (float, optional): dropout probability after AFM layer. Defaults to 0.0.",
"name": "__init__",
"signature": ... | 2 | stack_v2_sparse_classes_30k_train_000088 | Implement the Python class `AttentionalFactorizationMachineModel` described below.
Class description:
AttentionalFactorizationMachineModel is a model of attentional factorization machine, which calculate prediction by summing up bias, linear terms and attentional factorization machine values. :Reference: #. `Jun Xiao ... | Implement the Python class `AttentionalFactorizationMachineModel` described below.
Class description:
AttentionalFactorizationMachineModel is a model of attentional factorization machine, which calculate prediction by summing up bias, linear terms and attentional factorization machine values. :Reference: #. `Jun Xiao ... | 8b4cdbd5ed126a86da3bd9ef1665a6985dedc07c | <|skeleton|>
class AttentionalFactorizationMachineModel:
"""AttentionalFactorizationMachineModel is a model of attentional factorization machine, which calculate prediction by summing up bias, linear terms and attentional factorization machine values. :Reference: #. `Jun Xiao et al, 2017. Attentional Factorization ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttentionalFactorizationMachineModel:
"""AttentionalFactorizationMachineModel is a model of attentional factorization machine, which calculate prediction by summing up bias, linear terms and attentional factorization machine values. :Reference: #. `Jun Xiao et al, 2017. Attentional Factorization Machines: Lea... | the_stack_v2_python_sparse | torecsys/models/ctr/attentional_factorization_machine.py | codeants2012/torecsys | train | 0 |
cd540ad9e95d9409ff4beb53c26767868f3927d4 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn InternalDomainFederation()",
"from .federated_idp_mfa_behavior import FederatedIdpMfaBehavior\nfrom .prompt_login_behavior import PromptLoginBehavior\nfrom .saml_or_ws_fed_provider import SamlOrWsFedProvider\nfrom .signing_certificate_... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return InternalDomainFederation()
<|end_body_0|>
<|body_start_1|>
from .federated_idp_mfa_behavior import FederatedIdpMfaBehavior
from .prompt_login_behavior import PromptLoginBehavior
... | InternalDomainFederation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InternalDomainFederation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> InternalDomainFederation:
"""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 cre... | stack_v2_sparse_classes_36k_train_016410 | 6,466 | 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: InternalDomainFederation",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrimi... | 3 | stack_v2_sparse_classes_30k_train_014378 | Implement the Python class `InternalDomainFederation` described below.
Class description:
Implement the InternalDomainFederation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> InternalDomainFederation: Creates a new instance of the appropriate c... | Implement the Python class `InternalDomainFederation` described below.
Class description:
Implement the InternalDomainFederation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> InternalDomainFederation: Creates a new instance of the appropriate c... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class InternalDomainFederation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> InternalDomainFederation:
"""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 cre... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InternalDomainFederation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> InternalDomainFederation:
"""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... | the_stack_v2_python_sparse | msgraph/generated/models/internal_domain_federation.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
15c87d4742a17bab9f19927567f9cab6a21d1a45 | [
"header = {'typ': 'JWT', 'alg': 'HS256'}\nheader = json.dumps(header, separators=(',', ':')).encode('utf-8')\nheader = base64.urlsafe_b64encode(header).replace(b'=', b'')\np = json.dumps(data, separators=(',', ':')).encode('utf-8')\np = base64.urlsafe_b64encode(p).replace(b'=', b'')\nsecret_key = properties.get('se... | <|body_start_0|>
header = {'typ': 'JWT', 'alg': 'HS256'}
header = json.dumps(header, separators=(',', ':')).encode('utf-8')
header = base64.urlsafe_b64encode(header).replace(b'=', b'')
p = json.dumps(data, separators=(',', ':')).encode('utf-8')
p = base64.urlsafe_b64encode(p).rep... | JWT | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JWT:
def encode(data):
"""JWT签名 :param data: :return:"""
<|body_0|>
def verify(access_token):
"""JWT验签 :param access_token: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
header = {'typ': 'JWT', 'alg': 'HS256'}
header = json.dumps(... | stack_v2_sparse_classes_36k_train_016411 | 2,016 | no_license | [
{
"docstring": "JWT签名 :param data: :return:",
"name": "encode",
"signature": "def encode(data)"
},
{
"docstring": "JWT验签 :param access_token: :return:",
"name": "verify",
"signature": "def verify(access_token)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020873 | Implement the Python class `JWT` described below.
Class description:
Implement the JWT class.
Method signatures and docstrings:
- def encode(data): JWT签名 :param data: :return:
- def verify(access_token): JWT验签 :param access_token: :return: | Implement the Python class `JWT` described below.
Class description:
Implement the JWT class.
Method signatures and docstrings:
- def encode(data): JWT签名 :param data: :return:
- def verify(access_token): JWT验签 :param access_token: :return:
<|skeleton|>
class JWT:
def encode(data):
"""JWT签名 :param data: ... | 6156ba7e3b87552b80fe20b886fa476d8fc4a277 | <|skeleton|>
class JWT:
def encode(data):
"""JWT签名 :param data: :return:"""
<|body_0|>
def verify(access_token):
"""JWT验签 :param access_token: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JWT:
def encode(data):
"""JWT签名 :param data: :return:"""
header = {'typ': 'JWT', 'alg': 'HS256'}
header = json.dumps(header, separators=(',', ':')).encode('utf-8')
header = base64.urlsafe_b64encode(header).replace(b'=', b'')
p = json.dumps(data, separators=(',', ':')).e... | the_stack_v2_python_sparse | tools/jwt.py | Duo-Shou-Store/DSSP | train | 0 | |
80b5951479a69718770006f817275fc7c576e8f3 | [
"super(LinearRegression, self).setUp()\ndataset = self.get_file('linear_regression_gen.csv')\nschema = [('c1', float), ('c2', float), ('c3', float), ('c4', float), ('label', float)]\nself.frame = self.context.frame.import_csv(dataset, schema=schema)",
"model = self.context.models.regression.linear_regression.trai... | <|body_start_0|>
super(LinearRegression, self).setUp()
dataset = self.get_file('linear_regression_gen.csv')
schema = [('c1', float), ('c2', float), ('c3', float), ('c4', float), ('label', float)]
self.frame = self.context.frame.import_csv(dataset, schema=schema)
<|end_body_0|>
<|body_st... | LinearRegression | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinearRegression:
def setUp(self):
"""Build test frame"""
<|body_0|>
def test_model_scoring(self):
"""Test publishing a linear regression model"""
<|body_1|>
def test_revise_model(self):
"""Tests revise api in scoring engine"""
<|body_2|>... | stack_v2_sparse_classes_36k_train_016412 | 3,490 | permissive | [
{
"docstring": "Build test frame",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test publishing a linear regression model",
"name": "test_model_scoring",
"signature": "def test_model_scoring(self)"
},
{
"docstring": "Tests revise api in scoring engine",
... | 3 | stack_v2_sparse_classes_30k_train_005913 | Implement the Python class `LinearRegression` described below.
Class description:
Implement the LinearRegression class.
Method signatures and docstrings:
- def setUp(self): Build test frame
- def test_model_scoring(self): Test publishing a linear regression model
- def test_revise_model(self): Tests revise api in sco... | Implement the Python class `LinearRegression` described below.
Class description:
Implement the LinearRegression class.
Method signatures and docstrings:
- def setUp(self): Build test frame
- def test_model_scoring(self): Test publishing a linear regression model
- def test_revise_model(self): Tests revise api in sco... | 5548fc925b5c278263cbdebbd9e8c7593320c2f4 | <|skeleton|>
class LinearRegression:
def setUp(self):
"""Build test frame"""
<|body_0|>
def test_model_scoring(self):
"""Test publishing a linear regression model"""
<|body_1|>
def test_revise_model(self):
"""Tests revise api in scoring engine"""
<|body_2|>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinearRegression:
def setUp(self):
"""Build test frame"""
super(LinearRegression, self).setUp()
dataset = self.get_file('linear_regression_gen.csv')
schema = [('c1', float), ('c2', float), ('c3', float), ('c4', float), ('label', float)]
self.frame = self.context.frame.i... | the_stack_v2_python_sparse | regression-tests/sparktkregtests/testcases/scoretests/linear_regression_test.py | trustedanalytics/spark-tk | train | 35 | |
c1aaac53a18741be14da9eb53da728d7fdb56858 | [
"self.n = n = model.n\ncounts = model.counts\nself.probs = probs = dict()\nself.sorted_probs = sorted_probs = dict()\nfor tokens in counts.keys():\n if len(tokens) == n:\n token = tokens[n - 1]\n prev_tokens = tokens[:-1]\n probability = model.cond_prob(token, list(prev_tokens))\n if ... | <|body_start_0|>
self.n = n = model.n
counts = model.counts
self.probs = probs = dict()
self.sorted_probs = sorted_probs = dict()
for tokens in counts.keys():
if len(tokens) == n:
token = tokens[n - 1]
prev_tokens = tokens[:-1]
... | NGramGenerator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NGramGenerator:
def __init__(self, model):
"""model -- n-gram model."""
<|body_0|>
def generate_token(self, prev_tokens=None):
"""Randomly generate a token, given prev_tokens. prev_tokens -- the previous n-1 tokens (optional only if n = 1)."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_016413 | 27,345 | no_license | [
{
"docstring": "model -- n-gram model.",
"name": "__init__",
"signature": "def __init__(self, model)"
},
{
"docstring": "Randomly generate a token, given prev_tokens. prev_tokens -- the previous n-1 tokens (optional only if n = 1).",
"name": "generate_token",
"signature": "def generate_t... | 3 | stack_v2_sparse_classes_30k_val_000891 | Implement the Python class `NGramGenerator` described below.
Class description:
Implement the NGramGenerator class.
Method signatures and docstrings:
- def __init__(self, model): model -- n-gram model.
- def generate_token(self, prev_tokens=None): Randomly generate a token, given prev_tokens. prev_tokens -- the previ... | Implement the Python class `NGramGenerator` described below.
Class description:
Implement the NGramGenerator class.
Method signatures and docstrings:
- def __init__(self, model): model -- n-gram model.
- def generate_token(self, prev_tokens=None): Randomly generate a token, given prev_tokens. prev_tokens -- the previ... | 540cbea09b8e0d609e423c2d8dad192cd3a8e00c | <|skeleton|>
class NGramGenerator:
def __init__(self, model):
"""model -- n-gram model."""
<|body_0|>
def generate_token(self, prev_tokens=None):
"""Randomly generate a token, given prev_tokens. prev_tokens -- the previous n-1 tokens (optional only if n = 1)."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NGramGenerator:
def __init__(self, model):
"""model -- n-gram model."""
self.n = n = model.n
counts = model.counts
self.probs = probs = dict()
self.sorted_probs = sorted_probs = dict()
for tokens in counts.keys():
if len(tokens) == n:
... | the_stack_v2_python_sparse | languagemodeling/ngram.py | famaf/PLN_2017 | train | 0 | |
ecdf7c4683818cf2095a56ccbe027b9dc2c37b9a | [
"super(Discriminator, self).__init__()\nself.first_conv_layer = ConvolutionDown(in_channels=7, out_channels=32, kernel_size=3)\nself.second_conv_layer = ConvolutionDown(in_channels=32, out_channels=64, kernel_size=3)\nself.third_conv_layer = ConvolutionDown(in_channels=64, out_channels=128, kernel_size=3)\nself.fou... | <|body_start_0|>
super(Discriminator, self).__init__()
self.first_conv_layer = ConvolutionDown(in_channels=7, out_channels=32, kernel_size=3)
self.second_conv_layer = ConvolutionDown(in_channels=32, out_channels=64, kernel_size=3)
self.third_conv_layer = ConvolutionDown(in_channels=64, o... | Discriminator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Discriminator:
def __init__(self):
"""In the constructor we instantiate our custom modules and assign them as member variables."""
<|body_0|>
def forward(self, x):
"""In the forward function we accept a Variable of input data and we must return a Variable of output d... | stack_v2_sparse_classes_36k_train_016414 | 2,339 | no_license | [
{
"docstring": "In the constructor we instantiate our custom modules and assign them as member variables.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "In the forward function we accept a Variable of input data and we must return a Variable of output data. We can use... | 2 | null | Implement the Python class `Discriminator` described below.
Class description:
Implement the Discriminator class.
Method signatures and docstrings:
- def __init__(self): In the constructor we instantiate our custom modules and assign them as member variables.
- def forward(self, x): In the forward function we accept ... | Implement the Python class `Discriminator` described below.
Class description:
Implement the Discriminator class.
Method signatures and docstrings:
- def __init__(self): In the constructor we instantiate our custom modules and assign them as member variables.
- def forward(self, x): In the forward function we accept ... | 0adf5a0a5d4c2e4de41faac6fcc75700104c2b53 | <|skeleton|>
class Discriminator:
def __init__(self):
"""In the constructor we instantiate our custom modules and assign them as member variables."""
<|body_0|>
def forward(self, x):
"""In the forward function we accept a Variable of input data and we must return a Variable of output d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Discriminator:
def __init__(self):
"""In the constructor we instantiate our custom modules and assign them as member variables."""
super(Discriminator, self).__init__()
self.first_conv_layer = ConvolutionDown(in_channels=7, out_channels=32, kernel_size=3)
self.second_conv_layer... | the_stack_v2_python_sparse | PyTorch-practice/video_gans_cnn/discriminator.py | TheIllusion/TheIllusionsLibraries | train | 1 | |
879dfd80e872d307995bd365202b96fcf2d9affd | [
"data = jsonld.read_yaml(path)\nself = DatasetSchemaV3(client=client, commit=commit).load(data)\nself.__reference__ = path\nreturn self",
"from renku.core.management import LocalClient\ndata = DatasetSchemaV3().dump(self)\npath = path or self.__reference__ or os.path.join(self.path, LocalClient.METADATA)\njsonld.... | <|body_start_0|>
data = jsonld.read_yaml(path)
self = DatasetSchemaV3(client=client, commit=commit).load(data)
self.__reference__ = path
return self
<|end_body_0|>
<|body_start_1|>
from renku.core.management import LocalClient
data = DatasetSchemaV3().dump(self)
... | Dataset migration model. | Dataset | [
"Apache-2.0",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""Dataset migration model."""
def from_yaml(cls, path, client=None, commit=None):
"""Read content from YAML file."""
<|body_0|>
def to_yaml(self, path=None):
"""Write content to a YAML file."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_016415 | 9,656 | permissive | [
{
"docstring": "Read content from YAML file.",
"name": "from_yaml",
"signature": "def from_yaml(cls, path, client=None, commit=None)"
},
{
"docstring": "Write content to a YAML file.",
"name": "to_yaml",
"signature": "def to_yaml(self, path=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008319 | Implement the Python class `Dataset` described below.
Class description:
Dataset migration model.
Method signatures and docstrings:
- def from_yaml(cls, path, client=None, commit=None): Read content from YAML file.
- def to_yaml(self, path=None): Write content to a YAML file. | Implement the Python class `Dataset` described below.
Class description:
Dataset migration model.
Method signatures and docstrings:
- def from_yaml(cls, path, client=None, commit=None): Read content from YAML file.
- def to_yaml(self, path=None): Write content to a YAML file.
<|skeleton|>
class Dataset:
"""Datas... | 88504c2ebe2f5b052f25f073957fd4a2e1f79d11 | <|skeleton|>
class Dataset:
"""Dataset migration model."""
def from_yaml(cls, path, client=None, commit=None):
"""Read content from YAML file."""
<|body_0|>
def to_yaml(self, path=None):
"""Write content to a YAML file."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dataset:
"""Dataset migration model."""
def from_yaml(cls, path, client=None, commit=None):
"""Read content from YAML file."""
data = jsonld.read_yaml(path)
self = DatasetSchemaV3(client=client, commit=commit).load(data)
self.__reference__ = path
return self
d... | the_stack_v2_python_sparse | renku/core/management/migrations/models/v3.py | ltalirz/renku-python | train | 0 |
dc3bc04babbd05cc7879e90a99281c58e54fba3d | [
"if id is None:\n Base.__nb_objects += 1\n self.id = Base.__nb_objects\nelif id <= 0:\n raise ValueError('id must be positive integer')\nelse:\n self.id = id\n Base.__nb_objects += 1",
"if list_dictionaries is None or list_dictionaries is []:\n return '[]'\nreturn json.dumps(list_dictionaries)",... | <|body_start_0|>
if id is None:
Base.__nb_objects += 1
self.id = Base.__nb_objects
elif id <= 0:
raise ValueError('id must be positive integer')
else:
self.id = id
Base.__nb_objects += 1
<|end_body_0|>
<|body_start_1|>
if list_... | This class will be the base of all other classes in project | Base | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Base:
"""This class will be the base of all other classes in project"""
def __init__(self, id=None):
"""Assign obj id"""
<|body_0|>
def to_json_string(list_dictionaries):
"""json string rep of for instances of dicts"""
<|body_1|>
def save_to_file(cls... | stack_v2_sparse_classes_36k_train_016416 | 2,141 | no_license | [
{
"docstring": "Assign obj id",
"name": "__init__",
"signature": "def __init__(self, id=None)"
},
{
"docstring": "json string rep of for instances of dicts",
"name": "to_json_string",
"signature": "def to_json_string(list_dictionaries)"
},
{
"docstring": "writes the JSON string r... | 6 | stack_v2_sparse_classes_30k_val_000048 | Implement the Python class `Base` described below.
Class description:
This class will be the base of all other classes in project
Method signatures and docstrings:
- def __init__(self, id=None): Assign obj id
- def to_json_string(list_dictionaries): json string rep of for instances of dicts
- def save_to_file(cls, li... | Implement the Python class `Base` described below.
Class description:
This class will be the base of all other classes in project
Method signatures and docstrings:
- def __init__(self, id=None): Assign obj id
- def to_json_string(list_dictionaries): json string rep of for instances of dicts
- def save_to_file(cls, li... | 75bedbbd249be2536da5a77f6337b14c8363f1b8 | <|skeleton|>
class Base:
"""This class will be the base of all other classes in project"""
def __init__(self, id=None):
"""Assign obj id"""
<|body_0|>
def to_json_string(list_dictionaries):
"""json string rep of for instances of dicts"""
<|body_1|>
def save_to_file(cls... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Base:
"""This class will be the base of all other classes in project"""
def __init__(self, id=None):
"""Assign obj id"""
if id is None:
Base.__nb_objects += 1
self.id = Base.__nb_objects
elif id <= 0:
raise ValueError('id must be positive intege... | the_stack_v2_python_sparse | 0x0C-python-almost_a_circle/models/base.py | Sainterman/holbertonschool-higher_level_programming | train | 0 |
d38d707802ba785f70aaa93d9464269f3627902d | [
"product = Product.objects.create(name='earings', description='very beautiful', price=200, quantity=20)\nphoto = ProductPhoto.objects.create(photo_url='photo a', product=product)\nproduct_id = product.id\nbasket_id = Basket.objects.create().id\nresponse = self.client.post(path=f'/api/v1/baskets/{basket_id}/items/',... | <|body_start_0|>
product = Product.objects.create(name='earings', description='very beautiful', price=200, quantity=20)
photo = ProductPhoto.objects.create(photo_url='photo a', product=product)
product_id = product.id
basket_id = Basket.objects.create().id
response = self.client.... | BasketItemsTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasketItemsTest:
def test_return_item_info(self):
"""tests that the endpoint returns a dictionary with all the relevant information for the item."""
<|body_0|>
def test_item_can_be_added_only_once(self):
"""tests that the endpoint adds the item to the basket only one... | stack_v2_sparse_classes_36k_train_016417 | 7,771 | no_license | [
{
"docstring": "tests that the endpoint returns a dictionary with all the relevant information for the item.",
"name": "test_return_item_info",
"signature": "def test_return_item_info(self)"
},
{
"docstring": "tests that the endpoint adds the item to the basket only one time.",
"name": "test... | 5 | null | Implement the Python class `BasketItemsTest` described below.
Class description:
Implement the BasketItemsTest class.
Method signatures and docstrings:
- def test_return_item_info(self): tests that the endpoint returns a dictionary with all the relevant information for the item.
- def test_item_can_be_added_only_once... | Implement the Python class `BasketItemsTest` described below.
Class description:
Implement the BasketItemsTest class.
Method signatures and docstrings:
- def test_return_item_info(self): tests that the endpoint returns a dictionary with all the relevant information for the item.
- def test_item_can_be_added_only_once... | d84bdedc9ed011dc009cd1b6d42eed1925ccc977 | <|skeleton|>
class BasketItemsTest:
def test_return_item_info(self):
"""tests that the endpoint returns a dictionary with all the relevant information for the item."""
<|body_0|>
def test_item_can_be_added_only_once(self):
"""tests that the endpoint adds the item to the basket only one... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BasketItemsTest:
def test_return_item_info(self):
"""tests that the endpoint returns a dictionary with all the relevant information for the item."""
product = Product.objects.create(name='earings', description='very beautiful', price=200, quantity=20)
photo = ProductPhoto.objects.creat... | the_stack_v2_python_sparse | backend/basket/tests.py | Code-Institute-Submissions/vintage-earrings | train | 0 | |
80e130debf343b1ea7769e77323115739ddc4391 | [
"if not graph.is_directed():\n raise ValueError('the graph is not directed')\nself.graph = graph\nself.T = dict()\nfor source in self.graph.iternodes():\n self.T[source] = dict()\n for target in self.graph.iternodes():\n self.T[source][target] = False\n self.T[source][source] = True",
"for step... | <|body_start_0|>
if not graph.is_directed():
raise ValueError('the graph is not directed')
self.graph = graph
self.T = dict()
for source in self.graph.iternodes():
self.T[source] = dict()
for target in self.graph.iternodes():
self.T[sou... | Based on the matrix multiplication, O(V**2 E) time. | TransitiveClosureSimple | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransitiveClosureSimple:
"""Based on the matrix multiplication, O(V**2 E) time."""
def __init__(self, graph):
"""The algorithm initialization, O(V**2) time."""
<|body_0|>
def run(self):
"""Executable pseudocode."""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_016418 | 3,816 | permissive | [
{
"docstring": "The algorithm initialization, O(V**2) time.",
"name": "__init__",
"signature": "def __init__(self, graph)"
},
{
"docstring": "Executable pseudocode.",
"name": "run",
"signature": "def run(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000640 | Implement the Python class `TransitiveClosureSimple` described below.
Class description:
Based on the matrix multiplication, O(V**2 E) time.
Method signatures and docstrings:
- def __init__(self, graph): The algorithm initialization, O(V**2) time.
- def run(self): Executable pseudocode. | Implement the Python class `TransitiveClosureSimple` described below.
Class description:
Based on the matrix multiplication, O(V**2 E) time.
Method signatures and docstrings:
- def __init__(self, graph): The algorithm initialization, O(V**2) time.
- def run(self): Executable pseudocode.
<|skeleton|>
class Transitive... | 0ff4ae303e8824e6bb8474d23b29a7b3e5ed8e60 | <|skeleton|>
class TransitiveClosureSimple:
"""Based on the matrix multiplication, O(V**2 E) time."""
def __init__(self, graph):
"""The algorithm initialization, O(V**2) time."""
<|body_0|>
def run(self):
"""Executable pseudocode."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TransitiveClosureSimple:
"""Based on the matrix multiplication, O(V**2 E) time."""
def __init__(self, graph):
"""The algorithm initialization, O(V**2) time."""
if not graph.is_directed():
raise ValueError('the graph is not directed')
self.graph = graph
self.T =... | the_stack_v2_python_sparse | graphtheory/algorithms/closure.py | kgashok/graphs-dict | train | 0 |
7bd72cf3778d6311e0102715cdddfbd6c8bd130f | [
"self._flag_n = False\nself.__cached__.clear()\nBUFID = info.bufid\nFO = info.fo\nIHL = info.ihl\nMF = info.mf\nTL = info.tl\nif not FO and (not MF):\n if BUFID in self._buffer:\n self._dtgram.extend(self.submit(self._buffer.pop(BUFID), bufid=BUFID))\n return\nif BUFID not in self._buffer:\n sel... | <|body_start_0|>
self._flag_n = False
self.__cached__.clear()
BUFID = info.bufid
FO = info.fo
IHL = info.ihl
MF = info.mf
TL = info.tl
if not FO and (not MF):
if BUFID in self._buffer:
self._dtgram.extend(self.submit(self._buffe... | Reassembly for IP payload. Args: strict: if return all datagrams (including those not implemented) when submit *args: Arbitrary positional arguments. **kwargs: Arbitrary keyword arguments. Important: This class is not intended to be instantiated directly, but rather used as a base class for the protocol-aware reassembl... | IP | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IP:
"""Reassembly for IP payload. Args: strict: if return all datagrams (including those not implemented) when submit *args: Arbitrary positional arguments. **kwargs: Arbitrary keyword arguments. Important: This class is not intended to be instantiated directly, but rather used as a base class fo... | stack_v2_sparse_classes_36k_train_016419 | 6,372 | permissive | [
{
"docstring": "Reassembly procedure. Arguments: info: info dict of packets to be reassembled",
"name": "reassembly",
"signature": "def reassembly(self, info: 'Packet[AT]') -> 'None'"
},
{
"docstring": "Submit reassembled payload. Arguments: buf: buffer dict of reassembled packets bufid: buffer ... | 2 | null | Implement the Python class `IP` described below.
Class description:
Reassembly for IP payload. Args: strict: if return all datagrams (including those not implemented) when submit *args: Arbitrary positional arguments. **kwargs: Arbitrary keyword arguments. Important: This class is not intended to be instantiated direc... | Implement the Python class `IP` described below.
Class description:
Reassembly for IP payload. Args: strict: if return all datagrams (including those not implemented) when submit *args: Arbitrary positional arguments. **kwargs: Arbitrary keyword arguments. Important: This class is not intended to be instantiated direc... | a6fe49ec58f09e105bec5a00fb66d9b3f22730d9 | <|skeleton|>
class IP:
"""Reassembly for IP payload. Args: strict: if return all datagrams (including those not implemented) when submit *args: Arbitrary positional arguments. **kwargs: Arbitrary keyword arguments. Important: This class is not intended to be instantiated directly, but rather used as a base class fo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IP:
"""Reassembly for IP payload. Args: strict: if return all datagrams (including those not implemented) when submit *args: Arbitrary positional arguments. **kwargs: Arbitrary keyword arguments. Important: This class is not intended to be instantiated directly, but rather used as a base class for the protoco... | the_stack_v2_python_sparse | pcapkit/foundation/reassembly/ip.py | JarryShaw/PyPCAPKit | train | 204 |
fbb4838432e3b26b59c11c186a608911a5b9c8a1 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn Trending()",
"from .entity import Entity\nfrom .resource_reference import ResourceReference\nfrom .resource_visualization import ResourceVisualization\nfrom .entity import Entity\nfrom .resource_reference import ResourceReference\nfrom... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return Trending()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .resource_reference import ResourceReference
from .resource_visualization import ResourceVisualization
... | Trending | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Trending:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Trending:
"""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: Trending... | stack_v2_sparse_classes_36k_train_016420 | 3,591 | 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: Trending",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(pars... | 3 | null | Implement the Python class `Trending` described below.
Class description:
Implement the Trending class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Trending: Creates a new instance of the appropriate class based on discriminator value Args: parse_no... | Implement the Python class `Trending` described below.
Class description:
Implement the Trending class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Trending: Creates a new instance of the appropriate class based on discriminator value Args: parse_no... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class Trending:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Trending:
"""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: Trending... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Trending:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Trending:
"""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: Trending"""
if... | the_stack_v2_python_sparse | msgraph/generated/models/trending.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
8f5a313b35770f6ebb2c1c0045db312e8147b070 | [
"if site_admin_email is None and reject_instructions is None:\n raise ValueError('Either reject_instructions or site_admin_email must be provided.')\nself.privacy_policy_url = privacy_policy_url\nself.terms_of_service_url = terms_of_service_url\nself.site_admin_email = site_admin_email\nself.reject_instructions ... | <|body_start_0|>
if site_admin_email is None and reject_instructions is None:
raise ValueError('Either reject_instructions or site_admin_email must be provided.')
self.privacy_policy_url = privacy_policy_url
self.terms_of_service_url = terms_of_service_url
self.site_admin_ema... | A consent requirement for asking users to acknowledge policies. | PolicyConsentRequirement | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PolicyConsentRequirement:
"""A consent requirement for asking users to acknowledge policies."""
def __init__(self, privacy_policy_url, terms_of_service_url, site_admin_email=None, reject_instructions=None):
"""Initialize the consent requirement. Args: privacy_policy_url (unicode): Th... | stack_v2_sparse_classes_36k_train_016421 | 5,387 | no_license | [
{
"docstring": "Initialize the consent requirement. Args: privacy_policy_url (unicode): The URL to the privacy policy, if applicable. terms_of_service (unicode): The URL to the terms of service, if applicable. site_admin_email (unicode, optional): The e-mail address of the site admin. This is only used if ``rej... | 4 | stack_v2_sparse_classes_30k_train_018712 | Implement the Python class `PolicyConsentRequirement` described below.
Class description:
A consent requirement for asking users to acknowledge policies.
Method signatures and docstrings:
- def __init__(self, privacy_policy_url, terms_of_service_url, site_admin_email=None, reject_instructions=None): Initialize the co... | Implement the Python class `PolicyConsentRequirement` described below.
Class description:
A consent requirement for asking users to acknowledge policies.
Method signatures and docstrings:
- def __init__(self, privacy_policy_url, terms_of_service_url, site_admin_email=None, reject_instructions=None): Initialize the co... | 99ea69d80a3a393b0da4da3152ef26e808dd8487 | <|skeleton|>
class PolicyConsentRequirement:
"""A consent requirement for asking users to acknowledge policies."""
def __init__(self, privacy_policy_url, terms_of_service_url, site_admin_email=None, reject_instructions=None):
"""Initialize the consent requirement. Args: privacy_policy_url (unicode): Th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PolicyConsentRequirement:
"""A consent requirement for asking users to acknowledge policies."""
def __init__(self, privacy_policy_url, terms_of_service_url, site_admin_email=None, reject_instructions=None):
"""Initialize the consent requirement. Args: privacy_policy_url (unicode): The URL to the ... | the_stack_v2_python_sparse | djblets/privacy/consent/common.py | chipx86/djblets | train | 2 |
8b66b5892f8d31c0a5405b9f2066fb0627f91ee8 | [
"self.dev = dev\nself.metadata = metadata\nself.fs_type = get_filesystem_type(fs_stream)\nif self.fs_type == 'NTFS':\n self.fs = NTFSMftSlack(dev, fs_stream)\n self.fs.domirr = domirr\n self.metadata.set_module('ntfs-mft-slack')\nelse:\n raise NotImplementedError()",
"LOGGER.info('Write')\nif filename... | <|body_start_0|>
self.dev = dev
self.metadata = metadata
self.fs_type = get_filesystem_type(fs_stream)
if self.fs_type == 'NTFS':
self.fs = NTFSMftSlack(dev, fs_stream)
self.fs.domirr = domirr
self.metadata.set_module('ntfs-mft-slack')
else:
... | This class wrapps the mft slack implementations usage examples: >>> f = open('/dev/sdb1', 'rb+') >>> ms = MftSlack(f) >>> m = Metadata("MftSlack") to write something from stdin into slack: >>> fs.write(sys.stdin.buffer, m) to write something from stdin into slack with offset: >>> fs.write(sys.stdin.buffer, m, 36) to re... | MftSlack | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MftSlack:
"""This class wrapps the mft slack implementations usage examples: >>> f = open('/dev/sdb1', 'rb+') >>> ms = MftSlack(f) >>> m = Metadata("MftSlack") to write something from stdin into slack: >>> fs.write(sys.stdin.buffer, m) to write something from stdin into slack with offset: >>> fs.... | stack_v2_sparse_classes_36k_train_016422 | 4,776 | permissive | [
{
"docstring": ":param fs_stream: Stream of filesystem :param metadata: Metadata object :param dev: device to use :param domirr: write copy of data to $MFTMirr",
"name": "__init__",
"signature": "def __init__(self, fs_stream: typ.BinaryIO, metadata: Metadata, dev: str=None, domirr=False)"
},
{
"... | 6 | null | Implement the Python class `MftSlack` described below.
Class description:
This class wrapps the mft slack implementations usage examples: >>> f = open('/dev/sdb1', 'rb+') >>> ms = MftSlack(f) >>> m = Metadata("MftSlack") to write something from stdin into slack: >>> fs.write(sys.stdin.buffer, m) to write something fro... | Implement the Python class `MftSlack` described below.
Class description:
This class wrapps the mft slack implementations usage examples: >>> f = open('/dev/sdb1', 'rb+') >>> ms = MftSlack(f) >>> m = Metadata("MftSlack") to write something from stdin into slack: >>> fs.write(sys.stdin.buffer, m) to write something fro... | b602e90ddecb8e469a28e092da3ca7fec514e3dc | <|skeleton|>
class MftSlack:
"""This class wrapps the mft slack implementations usage examples: >>> f = open('/dev/sdb1', 'rb+') >>> ms = MftSlack(f) >>> m = Metadata("MftSlack") to write something from stdin into slack: >>> fs.write(sys.stdin.buffer, m) to write something from stdin into slack with offset: >>> fs.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MftSlack:
"""This class wrapps the mft slack implementations usage examples: >>> f = open('/dev/sdb1', 'rb+') >>> ms = MftSlack(f) >>> m = Metadata("MftSlack") to write something from stdin into slack: >>> fs.write(sys.stdin.buffer, m) to write something from stdin into slack with offset: >>> fs.write(sys.std... | the_stack_v2_python_sparse | src/wrapper/mft_slack.py | VanirLab/weever | train | 3 |
14ffef7b8d46d353529df670c2c4fa0c97c348bb | [
"seo = SiteSeo.objects.get(choices='Password Reset')\ntemplate_name = 'registration/password_reset_form.html'\nreset_form = PasswordResetForm()\ncontext = {'reset_form': reset_form, 'seo': seo}\nreturn render(request, template_name, context)",
"template_name = 'registration/password_reset_form.html'\nseo = SiteSe... | <|body_start_0|>
seo = SiteSeo.objects.get(choices='Password Reset')
template_name = 'registration/password_reset_form.html'
reset_form = PasswordResetForm()
context = {'reset_form': reset_form, 'seo': seo}
return render(request, template_name, context)
<|end_body_0|>
<|body_sta... | PasswordResetView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PasswordResetView:
def get(self, request):
"""get method to display the password reset form"""
<|body_0|>
def post(self, request):
"""post method to receive the email id entered by the customer, create token,id to send the password reset link to entered email"""
... | stack_v2_sparse_classes_36k_train_016423 | 36,770 | permissive | [
{
"docstring": "get method to display the password reset form",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "post method to receive the email id entered by the customer, create token,id to send the password reset link to entered email",
"name": "post",
"signatu... | 2 | stack_v2_sparse_classes_30k_train_016911 | Implement the Python class `PasswordResetView` described below.
Class description:
Implement the PasswordResetView class.
Method signatures and docstrings:
- def get(self, request): get method to display the password reset form
- def post(self, request): post method to receive the email id entered by the customer, cr... | Implement the Python class `PasswordResetView` described below.
Class description:
Implement the PasswordResetView class.
Method signatures and docstrings:
- def get(self, request): get method to display the password reset form
- def post(self, request): post method to receive the email id entered by the customer, cr... | ab828ca95571c6dffef2b2392522e6a4160a2304 | <|skeleton|>
class PasswordResetView:
def get(self, request):
"""get method to display the password reset form"""
<|body_0|>
def post(self, request):
"""post method to receive the email id entered by the customer, create token,id to send the password reset link to entered email"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PasswordResetView:
def get(self, request):
"""get method to display the password reset form"""
seo = SiteSeo.objects.get(choices='Password Reset')
template_name = 'registration/password_reset_form.html'
reset_form = PasswordResetForm()
context = {'reset_form': reset_for... | the_stack_v2_python_sparse | login/views.py | Quanscendence/braynai | train | 0 | |
f55f034d334cfd5b2e18df4c9f7146f6f08939c0 | [
"if rng is None:\n rng = numpy.random.RandomState(1234)\nUpwardModel.__init__(self, nDim, nRela, rng)\nself.nDim, self.nRela = (nDim, nRela)\nself.U = initU(nDim, nRela, rng)\nself.b = numpy.zeros((nRela,))\nself.USqSum = {}\nfor nrela in range(nRela):\n self.USqSum[nrela] = numpy.ones(self.U[nrela].shape)\ns... | <|body_start_0|>
if rng is None:
rng = numpy.random.RandomState(1234)
UpwardModel.__init__(self, nDim, nRela, rng)
self.nDim, self.nRela = (nDim, nRela)
self.U = initU(nDim, nRela, rng)
self.b = numpy.zeros((nRela,))
self.USqSum = {}
for nrela in range... | UpwardModelMV | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpwardModelMV:
def __init__(self, nDim, nRela, rng=None):
"""Initialize the basic model, which is the standard RNN based semantic composition model :type nDim: int :param nDim: latent units :type nRela: int :param nRela: number of discourse relations"""
<|body_0|>
def combin... | stack_v2_sparse_classes_36k_train_016424 | 4,123 | no_license | [
{
"docstring": "Initialize the basic model, which is the standard RNN based semantic composition model :type nDim: int :param nDim: latent units :type nRela: int :param nRela: number of discourse relations",
"name": "__init__",
"signature": "def __init__(self, nDim, nRela, rng=None)"
},
{
"docst... | 3 | stack_v2_sparse_classes_30k_train_016157 | Implement the Python class `UpwardModelMV` described below.
Class description:
Implement the UpwardModelMV class.
Method signatures and docstrings:
- def __init__(self, nDim, nRela, rng=None): Initialize the basic model, which is the standard RNN based semantic composition model :type nDim: int :param nDim: latent un... | Implement the Python class `UpwardModelMV` described below.
Class description:
Implement the UpwardModelMV class.
Method signatures and docstrings:
- def __init__(self, nDim, nRela, rng=None): Initialize the basic model, which is the standard RNN based semantic composition model :type nDim: int :param nDim: latent un... | b4703ef7af94034bb336d596c2914b51d82c4e4b | <|skeleton|>
class UpwardModelMV:
def __init__(self, nDim, nRela, rng=None):
"""Initialize the basic model, which is the standard RNN based semantic composition model :type nDim: int :param nDim: latent units :type nRela: int :param nRela: number of discourse relations"""
<|body_0|>
def combin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UpwardModelMV:
def __init__(self, nDim, nRela, rng=None):
"""Initialize the basic model, which is the standard RNN based semantic composition model :type nDim: int :param nDim: latent units :type nRela: int :param nRela: number of discourse relations"""
if rng is None:
rng = numpy.... | the_stack_v2_python_sparse | UpwardModel/upwardmodelmv.py | qileio/updown | train | 0 | |
43e5e226a83acc990de5062936b4ecee34d405e1 | [
"url = f'https://graph.microsoft.com/v1.0/users/{user_oid}/getMemberObjects'\nheaders = {'Authorization': f'Bearer {graph_api_access_token}'}\ndata = {'securityEnabledOnly': False}\nresponse = requests.post(url, json=data, headers=headers)\nresponse.raise_for_status()\ngroups = response.json()['value']\ntry:\n f... | <|body_start_0|>
url = f'https://graph.microsoft.com/v1.0/users/{user_oid}/getMemberObjects'
headers = {'Authorization': f'Bearer {graph_api_access_token}'}
data = {'securityEnabledOnly': False}
response = requests.post(url, json=data, headers=headers)
response.raise_for_status()... | HelsinkiAdfsAuthCodeBackend | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HelsinkiAdfsAuthCodeBackend:
def get_member_objects_from_graph_api(self, user_oid: str, graph_api_access_token: str):
"""Fetches user groups from MS Graph API, validates the group names and adds "adfs-" prefix for them. :returns A list of group names"""
<|body_0|>
def update... | stack_v2_sparse_classes_36k_train_016425 | 6,014 | permissive | [
{
"docstring": "Fetches user groups from MS Graph API, validates the group names and adds \"adfs-\" prefix for them. :returns A list of group names",
"name": "get_member_objects_from_graph_api",
"signature": "def get_member_objects_from_graph_api(self, user_oid: str, graph_api_access_token: str)"
},
... | 4 | stack_v2_sparse_classes_30k_train_015172 | Implement the Python class `HelsinkiAdfsAuthCodeBackend` described below.
Class description:
Implement the HelsinkiAdfsAuthCodeBackend class.
Method signatures and docstrings:
- def get_member_objects_from_graph_api(self, user_oid: str, graph_api_access_token: str): Fetches user groups from MS Graph API, validates th... | Implement the Python class `HelsinkiAdfsAuthCodeBackend` described below.
Class description:
Implement the HelsinkiAdfsAuthCodeBackend class.
Method signatures and docstrings:
- def get_member_objects_from_graph_api(self, user_oid: str, graph_api_access_token: str): Fetches user groups from MS Graph API, validates th... | 90f477f31f7ffb4a9c2a13fb7fa55908303cccce | <|skeleton|>
class HelsinkiAdfsAuthCodeBackend:
def get_member_objects_from_graph_api(self, user_oid: str, graph_api_access_token: str):
"""Fetches user groups from MS Graph API, validates the group names and adds "adfs-" prefix for them. :returns A list of group names"""
<|body_0|>
def update... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HelsinkiAdfsAuthCodeBackend:
def get_member_objects_from_graph_api(self, user_oid: str, graph_api_access_token: str):
"""Fetches user groups from MS Graph API, validates the group names and adds "adfs-" prefix for them. :returns A list of group names"""
url = f'https://graph.microsoft.com/v1.0... | the_stack_v2_python_sparse | backend/shared/shared/azure_adfs/auth.py | jannetasa/yjdh | train | 0 | |
12033b48fdd9011707e972f6aedc5bc3fbcdb7d2 | [
"user = get_object_or_404(DashUser, uuid=request.data['userID'])\nscenario = get_object_or_404(Scenario, uuid=request.data['scenarioID'])\nuser_scenario = UserScenario.objects.filter(userID=user, scenarioID=scenario)\nif user_scenario:\n msg = scenario.name + ' scenario is already subscribed'\n return Respons... | <|body_start_0|>
user = get_object_or_404(DashUser, uuid=request.data['userID'])
scenario = get_object_or_404(Scenario, uuid=request.data['scenarioID'])
user_scenario = UserScenario.objects.filter(userID=user, scenarioID=scenario)
if user_scenario:
msg = scenario.name + ' sce... | SubscribeScenario | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubscribeScenario:
def post(self, request):
"""To subscribe a new scenario # Format { "userID": "<USER UUID>", "scenarioID": "<SCENARIO UUID>" }"""
<|body_0|>
def delete(self, request):
"""To unsubscribe existing scenario of the user # Format { "uuid": "<USERSCENARIO... | stack_v2_sparse_classes_36k_train_016426 | 16,902 | no_license | [
{
"docstring": "To subscribe a new scenario # Format { \"userID\": \"<USER UUID>\", \"scenarioID\": \"<SCENARIO UUID>\" }",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "To unsubscribe existing scenario of the user # Format { \"uuid\": \"<USERSCENARIO UUID>\" }",
... | 2 | stack_v2_sparse_classes_30k_test_000542 | Implement the Python class `SubscribeScenario` described below.
Class description:
Implement the SubscribeScenario class.
Method signatures and docstrings:
- def post(self, request): To subscribe a new scenario # Format { "userID": "<USER UUID>", "scenarioID": "<SCENARIO UUID>" }
- def delete(self, request): To unsub... | Implement the Python class `SubscribeScenario` described below.
Class description:
Implement the SubscribeScenario class.
Method signatures and docstrings:
- def post(self, request): To subscribe a new scenario # Format { "userID": "<USER UUID>", "scenarioID": "<SCENARIO UUID>" }
- def delete(self, request): To unsub... | 9cb5303df8c54f1d26e72557680c35aa4d0b74d0 | <|skeleton|>
class SubscribeScenario:
def post(self, request):
"""To subscribe a new scenario # Format { "userID": "<USER UUID>", "scenarioID": "<SCENARIO UUID>" }"""
<|body_0|>
def delete(self, request):
"""To unsubscribe existing scenario of the user # Format { "uuid": "<USERSCENARIO... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubscribeScenario:
def post(self, request):
"""To subscribe a new scenario # Format { "userID": "<USER UUID>", "scenarioID": "<SCENARIO UUID>" }"""
user = get_object_or_404(DashUser, uuid=request.data['userID'])
scenario = get_object_or_404(Scenario, uuid=request.data['scenarioID'])
... | the_stack_v2_python_sparse | apps/apis/views.py | guyandtheworld/news-serve-api | train | 2 | |
a6af09a5c1317191a7c65188261706f32c29a367 | [
"deq1 = collections.deque()\ndeq2 = collections.deque()\nfor i in range(len(start)):\n a, b = (start[i], end[i])\n if a != 'X':\n deq1.append((a, i))\n if b != 'X':\n deq2.append((b, i))\n while deq1 and deq2:\n if deq1[0][0] != deq2[0][0]:\n return False\n elif de... | <|body_start_0|>
deq1 = collections.deque()
deq2 = collections.deque()
for i in range(len(start)):
a, b = (start[i], end[i])
if a != 'X':
deq1.append((a, i))
if b != 'X':
deq2.append((b, i))
while deq1 and deq2:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canTransform(self, start: str, end: str) -> bool:
"""Intuition: we can ignore all "X" and only check for "L" & "R". 1. The order of "L", "R" in start, end should be the same. 2. For "L", the index in start should be no less than that of end. For "R", the index in start shou... | stack_v2_sparse_classes_36k_train_016427 | 2,729 | no_license | [
{
"docstring": "Intuition: we can ignore all \"X\" and only check for \"L\" & \"R\". 1. The order of \"L\", \"R\" in start, end should be the same. 2. For \"L\", the index in start should be no less than that of end. For \"R\", the index in start should be no more than that of start. time/space O(N)",
"name... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canTransform(self, start: str, end: str) -> bool: Intuition: we can ignore all "X" and only check for "L" & "R". 1. The order of "L", "R" in start, end should be the same. 2.... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canTransform(self, start: str, end: str) -> bool: Intuition: we can ignore all "X" and only check for "L" & "R". 1. The order of "L", "R" in start, end should be the same. 2.... | 6ff1941ff213a843013100ac7033e2d4f90fbd6a | <|skeleton|>
class Solution:
def canTransform(self, start: str, end: str) -> bool:
"""Intuition: we can ignore all "X" and only check for "L" & "R". 1. The order of "L", "R" in start, end should be the same. 2. For "L", the index in start should be no less than that of end. For "R", the index in start shou... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def canTransform(self, start: str, end: str) -> bool:
"""Intuition: we can ignore all "X" and only check for "L" & "R". 1. The order of "L", "R" in start, end should be the same. 2. For "L", the index in start should be no less than that of end. For "R", the index in start should be no more ... | the_stack_v2_python_sparse | Leetcode 0777. Swap Adjacent in LR String.py | Chaoran-sjsu/leetcode | train | 0 | |
b2a45618b02c9babe1661231eefdf1d309e6fe6d | [
"assert isinstance(response, scrapy.http.response.html.HtmlResponse)\ncurboard = response.selector.xpath('//div[contains(@class, \"titleBar\")]/h1/text()').extract()\nlast_page = MAX_PAGE[curboard[0].lower()]\n'try:\\n last_page = int(response.selector.xpath(\\'//nav/a[@class=\"PageNavNext\"]/following::... | <|body_start_0|>
assert isinstance(response, scrapy.http.response.html.HtmlResponse)
curboard = response.selector.xpath('//div[contains(@class, "titleBar")]/h1/text()').extract()
last_page = MAX_PAGE[curboard[0].lower()]
'try:\n last_page = int(response.selector.xpath(\'//nav/... | scrape reports from angling addicts forum | worldseafishingAfloatSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class worldseafishingAfloatSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, ..."""
<|body_0|>
def crawl_board_threads(sel... | stack_v2_sparse_classes_36k_train_016428 | 9,045 | no_license | [
{
"docstring": "generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, ...",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "crawl",
"name": "crawl_board_threads",
"signature": "def crawl_board_t... | 3 | stack_v2_sparse_classes_30k_train_018107 | Implement the Python class `worldseafishingAfloatSpider` described below.
Class description:
scrape reports from angling addicts forum
Method signatures and docstrings:
- def parse(self, response): generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, .... | Implement the Python class `worldseafishingAfloatSpider` described below.
Class description:
scrape reports from angling addicts forum
Method signatures and docstrings:
- def parse(self, response): generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, .... | 9123aa6baf538b662143b9098d963d55165e8409 | <|skeleton|>
class worldseafishingAfloatSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, ..."""
<|body_0|>
def crawl_board_threads(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class worldseafishingAfloatSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, ..."""
assert isinstance(response, scrapy.http.response.html.Ht... | the_stack_v2_python_sparse | imgscrape/spiders/worldseafishing_reports.py | gmonkman/python | train | 0 |
ed94486254899116b94770c0259e0fb6dc50c06d | [
"data_list = []\nresults = self.query.all()\nformatter = date.getLocaleFormatter(self.request, 'date', 'long')\nfor result in results:\n data = {}\n data['qid'] = 'i-' + str(result.parliamentary_item_id)\n if type(result) == domain.AgendaItem:\n g = u' ' + result.group.type + u' ' + result.group.sho... | <|body_start_0|>
data_list = []
results = self.query.all()
formatter = date.getLocaleFormatter(self.request, 'date', 'long')
for result in results:
data = {}
data['qid'] = 'i-' + str(result.parliamentary_item_id)
if type(result) == domain.AgendaItem:
... | Group parliamentary items per stage e.g. action required, in progress, answered/debated, "dead" (withdrawn, elapsed, inadmissible, dropped). | OwnedItemsInStageViewlet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OwnedItemsInStageViewlet:
"""Group parliamentary items per stage e.g. action required, in progress, answered/debated, "dead" (withdrawn, elapsed, inadmissible, dropped)."""
def _setData(self):
"""Return the data of the query."""
<|body_0|>
def update(self):
"""Re... | stack_v2_sparse_classes_36k_train_016429 | 27,657 | no_license | [
{
"docstring": "Return the data of the query.",
"name": "_setData",
"signature": "def _setData(self)"
},
{
"docstring": "Refresh the query.",
"name": "update",
"signature": "def update(self)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000903 | Implement the Python class `OwnedItemsInStageViewlet` described below.
Class description:
Group parliamentary items per stage e.g. action required, in progress, answered/debated, "dead" (withdrawn, elapsed, inadmissible, dropped).
Method signatures and docstrings:
- def _setData(self): Return the data of the query.
-... | Implement the Python class `OwnedItemsInStageViewlet` described below.
Class description:
Group parliamentary items per stage e.g. action required, in progress, answered/debated, "dead" (withdrawn, elapsed, inadmissible, dropped).
Method signatures and docstrings:
- def _setData(self): Return the data of the query.
-... | 5cf0ba31dfbff8d2c1b4aa8ab6f69c7a0ae9870d | <|skeleton|>
class OwnedItemsInStageViewlet:
"""Group parliamentary items per stage e.g. action required, in progress, answered/debated, "dead" (withdrawn, elapsed, inadmissible, dropped)."""
def _setData(self):
"""Return the data of the query."""
<|body_0|>
def update(self):
"""Re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OwnedItemsInStageViewlet:
"""Group parliamentary items per stage e.g. action required, in progress, answered/debated, "dead" (withdrawn, elapsed, inadmissible, dropped)."""
def _setData(self):
"""Return the data of the query."""
data_list = []
results = self.query.all()
fo... | the_stack_v2_python_sparse | bungeni.main/branches/mr/bungeni/ui/viewlets/workspace.py | malangalanga/bungeni-portal | train | 0 |
818de0542b7448a1b819c641c5203ddd5bfd35a2 | [
"if len(temp_result) == n:\n result.append(temp_result.copy())\nelse:\n for num in range(1, n + 1):\n if num in temp_result:\n continue\n temp_result.append(num)\n self.genPermutation(result, n, temp_result)\n temp_result.pop()\nreturn result",
"ordered_permutations = ... | <|body_start_0|>
if len(temp_result) == n:
result.append(temp_result.copy())
else:
for num in range(1, n + 1):
if num in temp_result:
continue
temp_result.append(num)
self.genPermutation(result, n, temp_result)
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def genPermutation(self, result, n, temp_result):
"""time limit exceeded!!!!"""
<|body_0|>
def getPermutation(self, n, k):
""":type n: int :type k: int :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(temp_result) == n:
... | stack_v2_sparse_classes_36k_train_016430 | 1,399 | permissive | [
{
"docstring": "time limit exceeded!!!!",
"name": "genPermutation",
"signature": "def genPermutation(self, result, n, temp_result)"
},
{
"docstring": ":type n: int :type k: int :rtype: str",
"name": "getPermutation",
"signature": "def getPermutation(self, n, k)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def genPermutation(self, result, n, temp_result): time limit exceeded!!!!
- def getPermutation(self, n, k): :type n: int :type k: int :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def genPermutation(self, result, n, temp_result): time limit exceeded!!!!
- def getPermutation(self, n, k): :type n: int :type k: int :rtype: str
<|skeleton|>
class Solution:
... | 1ed22267156fb968671731c2e983b0e65f670750 | <|skeleton|>
class Solution:
def genPermutation(self, result, n, temp_result):
"""time limit exceeded!!!!"""
<|body_0|>
def getPermutation(self, n, k):
""":type n: int :type k: int :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def genPermutation(self, result, n, temp_result):
"""time limit exceeded!!!!"""
if len(temp_result) == n:
result.append(temp_result.copy())
else:
for num in range(1, n + 1):
if num in temp_result:
continue
... | the_stack_v2_python_sparse | leetcode/60.py | pingrunhuang/CodeChallenge | train | 0 | |
e206688cd0b613637d148461689d31f87a5e44dc | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn WindowsMalwareSeverityCount()",
"from .windows_malware_severity import WindowsMalwareSeverity\nfrom .windows_malware_severity import WindowsMalwareSeverity\nfields: Dict[str, Callable[[Any], None]] = {'distinctMalwareCount': lambda n: ... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return WindowsMalwareSeverityCount()
<|end_body_0|>
<|body_start_1|>
from .windows_malware_severity import WindowsMalwareSeverity
from .windows_malware_severity import WindowsMalwareSeverity
... | Windows Malware Severity Count Summary | WindowsMalwareSeverityCount | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WindowsMalwareSeverityCount:
"""Windows Malware Severity Count Summary"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsMalwareSeverityCount:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The pa... | stack_v2_sparse_classes_36k_train_016431 | 3,844 | 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: WindowsMalwareSeverityCount",
"name": "create_from_discriminator_value",
"signature": "def create_from_discr... | 3 | null | Implement the Python class `WindowsMalwareSeverityCount` described below.
Class description:
Windows Malware Severity Count Summary
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsMalwareSeverityCount: Creates a new instance of the appropriate cl... | Implement the Python class `WindowsMalwareSeverityCount` described below.
Class description:
Windows Malware Severity Count Summary
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsMalwareSeverityCount: Creates a new instance of the appropriate cl... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class WindowsMalwareSeverityCount:
"""Windows Malware Severity Count Summary"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsMalwareSeverityCount:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The pa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WindowsMalwareSeverityCount:
"""Windows Malware Severity Count Summary"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsMalwareSeverityCount:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to u... | the_stack_v2_python_sparse | msgraph/generated/models/windows_malware_severity_count.py | microsoftgraph/msgraph-sdk-python | train | 135 |
a70f58e352d3e052b4ce5c1066e27e8ccd298312 | [
"retcode = 201\npost = TaxonomyPost(self.get_connection())\ntaxa = post.post(taxonomy)\nreturn (taxa, retcode)",
"retcode = 200\nlog_items = None\nget = History(self.get_connection())\ntry:\n log_items = get.get(record_type, record_id, action_types)\nexcept MissingKeyException as dme:\n logging.getLogger(__... | <|body_start_0|>
retcode = 201
post = TaxonomyPost(self.get_connection())
taxa = post.post(taxonomy)
return (taxa, retcode)
<|end_body_0|>
<|body_start_1|>
retcode = 200
log_items = None
get = History(self.get_connection())
try:
log_items = ge... | MetadataController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MetadataController:
def create_taxonomy(self, taxonomy, studies=None, user=None, auths=None):
"""create_taxonomy Create a Taxonomy :param taxonomy: :type taxonomy: dict | bytes :rtype: Taxonomy"""
<|body_0|>
def download_history(self, record_type, record_id, action_types=Non... | stack_v2_sparse_classes_36k_train_016432 | 3,641 | no_license | [
{
"docstring": "create_taxonomy Create a Taxonomy :param taxonomy: :type taxonomy: dict | bytes :rtype: Taxonomy",
"name": "create_taxonomy",
"signature": "def create_taxonomy(self, taxonomy, studies=None, user=None, auths=None)"
},
{
"docstring": "fetches the history of a record # noqa: E501 :p... | 6 | stack_v2_sparse_classes_30k_train_006658 | Implement the Python class `MetadataController` described below.
Class description:
Implement the MetadataController class.
Method signatures and docstrings:
- def create_taxonomy(self, taxonomy, studies=None, user=None, auths=None): create_taxonomy Create a Taxonomy :param taxonomy: :type taxonomy: dict | bytes :rty... | Implement the Python class `MetadataController` described below.
Class description:
Implement the MetadataController class.
Method signatures and docstrings:
- def create_taxonomy(self, taxonomy, studies=None, user=None, auths=None): create_taxonomy Create a Taxonomy :param taxonomy: :type taxonomy: dict | bytes :rty... | 69884943f6e0afa2d371e78b02ab7ce3542e32c4 | <|skeleton|>
class MetadataController:
def create_taxonomy(self, taxonomy, studies=None, user=None, auths=None):
"""create_taxonomy Create a Taxonomy :param taxonomy: :type taxonomy: dict | bytes :rtype: Taxonomy"""
<|body_0|>
def download_history(self, record_type, record_id, action_types=Non... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MetadataController:
def create_taxonomy(self, taxonomy, studies=None, user=None, auths=None):
"""create_taxonomy Create a Taxonomy :param taxonomy: :type taxonomy: dict | bytes :rtype: Taxonomy"""
retcode = 201
post = TaxonomyPost(self.get_connection())
taxa = post.post(taxonom... | the_stack_v2_python_sparse | server/backbone_server/controllers/metadata_controller.py | malariagen/sims-backbone | train | 1 | |
2e94d2f2d4931cbdecd564c290af1f08df494eed | [
"params = super().get_default_params()\nparams['optimizer'] = 'adam'\nparams['input_shapes'] = [(None, 900), (None, 900)]\nparams.add(engine.Param('w_initializer', 'glorot_normal'))\nparams.add(engine.Param('b_initializer', 'zeros'))\nparams.add(engine.Param('dim_fan_out', 128))\nparams.add(engine.Param('dim_hidden... | <|body_start_0|>
params = super().get_default_params()
params['optimizer'] = 'adam'
params['input_shapes'] = [(None, 900), (None, 900)]
params.add(engine.Param('w_initializer', 'glorot_normal'))
params.add(engine.Param('b_initializer', 'zeros'))
params.add(engine.Param('d... | CDSSM Model implementation. A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. Learning Semantic Representations Using Convolutional Neural Networks for Web Search. Examples: >>> model = CDSSMModel() >>> model.guess_and_fill_missing_params() >>> model.build() | CDSSMModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CDSSMModel:
"""CDSSM Model implementation. A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. Learning Semantic Representations Using Convolutional Neural Networks for Web Search. Examples: >>> model = CDSSMModel() >>> model.guess_and_fill_missing_params() >>>... | stack_v2_sparse_classes_36k_train_016433 | 3,995 | permissive | [
{
"docstring": ":return: model default parameters.",
"name": "get_default_params",
"signature": "def get_default_params(cls) -> engine.ParamTable"
},
{
"docstring": "Apply conv and maxpooling operation towards to each tri-letter. The input shape is `num_word_ngrams`*(`contextual_window`* `dim_tr... | 3 | stack_v2_sparse_classes_30k_train_008012 | Implement the Python class `CDSSMModel` described below.
Class description:
CDSSM Model implementation. A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. Learning Semantic Representations Using Convolutional Neural Networks for Web Search. Examples: >>> model = CDSSMModel() >>> mo... | Implement the Python class `CDSSMModel` described below.
Class description:
CDSSM Model implementation. A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. Learning Semantic Representations Using Convolutional Neural Networks for Web Search. Examples: >>> model = CDSSMModel() >>> mo... | e49d619a52b2e96b6f0e8e76164d76f623210198 | <|skeleton|>
class CDSSMModel:
"""CDSSM Model implementation. A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. Learning Semantic Representations Using Convolutional Neural Networks for Web Search. Examples: >>> model = CDSSMModel() >>> model.guess_and_fill_missing_params() >>>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CDSSMModel:
"""CDSSM Model implementation. A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. Learning Semantic Representations Using Convolutional Neural Networks for Web Search. Examples: >>> model = CDSSMModel() >>> model.guess_and_fill_missing_params() >>> model.build(... | the_stack_v2_python_sparse | matchzoo/models/cdssm_model.py | JacobPolloreno/MatchZoo | train | 0 |
a77270dbcc8b177b11735b92e0c953ca640d7e77 | [
"self.n_elements = n_elements\nself.x_axis = x_axis\nself.unique_locations = set()\nself._sample_rate = sample_rate\nself.appends = []",
"if start is None and end is None:\n start = 'Start'\n if isinstance(pulse, list):\n if isinstance(pulse[0], list):\n og_start, end = self.append(pulse[0... | <|body_start_0|>
self.n_elements = n_elements
self.x_axis = x_axis
self.unique_locations = set()
self._sample_rate = sample_rate
self.appends = []
<|end_body_0|>
<|body_start_1|>
if start is None and end is None:
start = 'Start'
if isinstance(puls... | SequenceWrapper | [
"BSD-3-Clause-LBNL"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SequenceWrapper:
def __init__(self, n_elements, x_axis=None, sample_rate=None):
"""This is a wrapper around a standard Sequence. It records the absolute minimum in order to rebuild a full sequence. It is almost a direct replacement for the Sequence object. The primary change is that the ... | stack_v2_sparse_classes_36k_train_016434 | 5,553 | permissive | [
{
"docstring": "This is a wrapper around a standard Sequence. It records the absolute minimum in order to rebuild a full sequence. It is almost a direct replacement for the Sequence object. The primary change is that the pulses which are appended should be the keys in the pulse dictionary which is specified dur... | 3 | null | Implement the Python class `SequenceWrapper` described below.
Class description:
Implement the SequenceWrapper class.
Method signatures and docstrings:
- def __init__(self, n_elements, x_axis=None, sample_rate=None): This is a wrapper around a standard Sequence. It records the absolute minimum in order to rebuild a f... | Implement the Python class `SequenceWrapper` described below.
Class description:
Implement the SequenceWrapper class.
Method signatures and docstrings:
- def __init__(self, n_elements, x_axis=None, sample_rate=None): This is a wrapper around a standard Sequence. It records the absolute minimum in order to rebuild a f... | ea2ecae8fb3315ec220a165b39f4003b4fb12c0b | <|skeleton|>
class SequenceWrapper:
def __init__(self, n_elements, x_axis=None, sample_rate=None):
"""This is a wrapper around a standard Sequence. It records the absolute minimum in order to rebuild a full sequence. It is almost a direct replacement for the Sequence object. The primary change is that the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SequenceWrapper:
def __init__(self, n_elements, x_axis=None, sample_rate=None):
"""This is a wrapper around a standard Sequence. It records the absolute minimum in order to rebuild a full sequence. It is almost a direct replacement for the Sequence object. The primary change is that the pulses which a... | the_stack_v2_python_sparse | qtrl/qtrl/sequencer/wrapper.py | ana-tudor/openqasm_to_qtrl | train | 1 | |
c7bdd9a54bc9288d40edad0da10c9efe01dea791 | [
"fields = super(RelationSerializer, self).get_fields()\nif self.request.method == 'GET':\n fields['type'] = serializers.CharField(source='type.name')\nelse:\n fields['type'] = serializers.PrimaryKeyRelatedField(queryset=RelationType.objects.all())\nreturn fields",
"entities = validated_data.pop('positioninr... | <|body_start_0|>
fields = super(RelationSerializer, self).get_fields()
if self.request.method == 'GET':
fields['type'] = serializers.CharField(source='type.name')
else:
fields['type'] = serializers.PrimaryKeyRelatedField(queryset=RelationType.objects.all())
return... | Serializer for Relation objects. | RelationSerializer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelationSerializer:
"""Serializer for Relation objects."""
def get_fields(self):
"""Dynamically adapt fields based on the current request."""
<|body_0|>
def create(self, validated_data):
"""Create ``Relation`` object and add ``Entities``."""
<|body_1|>
<... | stack_v2_sparse_classes_36k_train_016435 | 2,651 | permissive | [
{
"docstring": "Dynamically adapt fields based on the current request.",
"name": "get_fields",
"signature": "def get_fields(self)"
},
{
"docstring": "Create ``Relation`` object and add ``Entities``.",
"name": "create",
"signature": "def create(self, validated_data)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010652 | Implement the Python class `RelationSerializer` described below.
Class description:
Serializer for Relation objects.
Method signatures and docstrings:
- def get_fields(self): Dynamically adapt fields based on the current request.
- def create(self, validated_data): Create ``Relation`` object and add ``Entities``. | Implement the Python class `RelationSerializer` described below.
Class description:
Serializer for Relation objects.
Method signatures and docstrings:
- def get_fields(self): Dynamically adapt fields based on the current request.
- def create(self, validated_data): Create ``Relation`` object and add ``Entities``.
<|... | cf7b0d63a3cf9bd6d85ab891ded6aeb2208636c0 | <|skeleton|>
class RelationSerializer:
"""Serializer for Relation objects."""
def get_fields(self):
"""Dynamically adapt fields based on the current request."""
<|body_0|>
def create(self, validated_data):
"""Create ``Relation`` object and add ``Entities``."""
<|body_1|>
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RelationSerializer:
"""Serializer for Relation objects."""
def get_fields(self):
"""Dynamically adapt fields based on the current request."""
fields = super(RelationSerializer, self).get_fields()
if self.request.method == 'GET':
fields['type'] = serializers.CharField(s... | the_stack_v2_python_sparse | resolwe/flow/serializers/relation.py | mstajdohar/resolwe | train | 0 |
898844c574a735ddd0b2c73f92daf849c700c4e1 | [
"adm = ApplikationsAdministration()\nhaendler = adm.get_einzelhaendler_by_id(id)\nadm.delete_einzelhaendler(haendler)\nreturn ('', 200)",
"adm = ApplikationsAdministration()\na = Einzelhaendler.from_dict(api.payload)\nif a is not None:\n a.set_id(id)\n adm.update_einzelhaendler(a)\n return ('', 200)\nels... | <|body_start_0|>
adm = ApplikationsAdministration()
haendler = adm.get_einzelhaendler_by_id(id)
adm.delete_einzelhaendler(haendler)
return ('', 200)
<|end_body_0|>
<|body_start_1|>
adm = ApplikationsAdministration()
a = Einzelhaendler.from_dict(api.payload)
if a ... | EinzelhaendlerOperations | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EinzelhaendlerOperations:
def delete(self, id):
"""Löschen eines Einzelhändlers anhand einer id"""
<|body_0|>
def put(self, id):
"""Update eines durch eine id bestimmten Einzelhändlers"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
adm = Applikatio... | stack_v2_sparse_classes_36k_train_016436 | 23,456 | no_license | [
{
"docstring": "Löschen eines Einzelhändlers anhand einer id",
"name": "delete",
"signature": "def delete(self, id)"
},
{
"docstring": "Update eines durch eine id bestimmten Einzelhändlers",
"name": "put",
"signature": "def put(self, id)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000752 | Implement the Python class `EinzelhaendlerOperations` described below.
Class description:
Implement the EinzelhaendlerOperations class.
Method signatures and docstrings:
- def delete(self, id): Löschen eines Einzelhändlers anhand einer id
- def put(self, id): Update eines durch eine id bestimmten Einzelhändlers | Implement the Python class `EinzelhaendlerOperations` described below.
Class description:
Implement the EinzelhaendlerOperations class.
Method signatures and docstrings:
- def delete(self, id): Löschen eines Einzelhändlers anhand einer id
- def put(self, id): Update eines durch eine id bestimmten Einzelhändlers
<|sk... | d4a2b196f21a5379188cb78b31c59d69f739964f | <|skeleton|>
class EinzelhaendlerOperations:
def delete(self, id):
"""Löschen eines Einzelhändlers anhand einer id"""
<|body_0|>
def put(self, id):
"""Update eines durch eine id bestimmten Einzelhändlers"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EinzelhaendlerOperations:
def delete(self, id):
"""Löschen eines Einzelhändlers anhand einer id"""
adm = ApplikationsAdministration()
haendler = adm.get_einzelhaendler_by_id(id)
adm.delete_einzelhaendler(haendler)
return ('', 200)
def put(self, id):
"""Upda... | the_stack_v2_python_sparse | src/main.py | SvenjaHolzinger/SoftwarePraktikum | train | 0 | |
048562c223f206e16cb9dc4a6975e2ecc5cb2012 | [
"tree = []\nqueue = collections.deque([root])\nwhile queue:\n val = queue.popleft()\n if not val:\n tree.append(None)\n else:\n tree.append(str(val.val))\n queue.append(val.left)\n queue.append(val.right)\nreturn ' '.join(tree)",
"data = collections.deque(data.split(' '))\nnod... | <|body_start_0|>
tree = []
queue = collections.deque([root])
while queue:
val = queue.popleft()
if not val:
tree.append(None)
else:
tree.append(str(val.val))
queue.append(val.left)
queue.append(va... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_016437 | 2,037 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_007748 | 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:... | c7a42753b2b16c7b9c66b8d7c2e67b683a15e27d | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
tree = []
queue = collections.deque([root])
while queue:
val = queue.popleft()
if not val:
tree.append(None)
else:
... | the_stack_v2_python_sparse | hard/297.py | brandoneng000/LeetCode | train | 0 | |
0232cb2adc1232eae895e06ea96367b49961d1c5 | [
"if grid is None or len(grid) == 0 or len(grid[0]) == 0:\n return 0\nresult = 0\nfor r in range(len(grid)):\n for c in range(len(grid[0])):\n if grid[r][c] == '1':\n result += 1\n self.dfs(grid, r, c)\nreturn result",
"if r < 0 or c < 0 or r >= len(grid) or (c >= len(grid[0])):\... | <|body_start_0|>
if grid is None or len(grid) == 0 or len(grid[0]) == 0:
return 0
result = 0
for r in range(len(grid)):
for c in range(len(grid[0])):
if grid[r][c] == '1':
result += 1
self.dfs(grid, r, c)
ret... | DFSSolution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DFSSolution:
def numIslands(self, grid):
"""Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surround... | stack_v2_sparse_classes_36k_train_016438 | 3,205 | no_license | [
{
"docstring": "Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surrounded by water. Example 1: Input: 11110 11010 11000 000... | 2 | null | Implement the Python class `DFSSolution` described below.
Class description:
Implement the DFSSolution class.
Method signatures and docstrings:
- def numIslands(self, grid): Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting a... | Implement the Python class `DFSSolution` described below.
Class description:
Implement the DFSSolution class.
Method signatures and docstrings:
- def numIslands(self, grid): Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting a... | 08c6d27498e35f636045fed05a6f94b760ab69ca | <|skeleton|>
class DFSSolution:
def numIslands(self, grid):
"""Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surround... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DFSSolution:
def numIslands(self, grid):
"""Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surrounded by water. E... | the_stack_v2_python_sparse | solutions/dfs/200.Number.of.Islands.py | ljia2/leetcode.py | train | 0 | |
95a00bf540728437bb745b9e5ca5eb3cdfbb087e | [
"super(ResNet, self).__init__()\nself.conv1 = nn.Conv2d(num_channels, 16, 3, 1, 1)\nself.norm1 = nn.BatchNorm2d(16)\nself.relu1 = nn.ReLU(inplace=True)\nself.layers1 = self._make_layer(n, 16, 16, 1)\nself.layers2 = self._make_layer(n, 32, 16, 2)\nself.layers3 = self._make_layer(n, 64, 32, 2)\nself.avgpool = nn.AvgP... | <|body_start_0|>
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(num_channels, 16, 3, 1, 1)
self.norm1 = nn.BatchNorm2d(16)
self.relu1 = nn.ReLU(inplace=True)
self.layers1 = self._make_layer(n, 16, 16, 1)
self.layers2 = self._make_layer(n, 32, 16, 2)
self.la... | Class for a ResNet classifier. | ResNet | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResNet:
"""Class for a ResNet classifier."""
def __init__(self, num_channels, num_classes, n=2):
"""Class initializer."""
<|body_0|>
def _make_layer(n, num_filters, channels_in, stride):
"""Make a single layer."""
<|body_1|>
def forward(self, x):
... | stack_v2_sparse_classes_36k_train_016439 | 4,467 | permissive | [
{
"docstring": "Class initializer.",
"name": "__init__",
"signature": "def __init__(self, num_channels, num_classes, n=2)"
},
{
"docstring": "Make a single layer.",
"name": "_make_layer",
"signature": "def _make_layer(n, num_filters, channels_in, stride)"
},
{
"docstring": "Forwa... | 5 | stack_v2_sparse_classes_30k_train_007742 | Implement the Python class `ResNet` described below.
Class description:
Class for a ResNet classifier.
Method signatures and docstrings:
- def __init__(self, num_channels, num_classes, n=2): Class initializer.
- def _make_layer(n, num_filters, channels_in, stride): Make a single layer.
- def forward(self, x): Forward... | Implement the Python class `ResNet` described below.
Class description:
Class for a ResNet classifier.
Method signatures and docstrings:
- def __init__(self, num_channels, num_classes, n=2): Class initializer.
- def _make_layer(n, num_filters, channels_in, stride): Make a single layer.
- def forward(self, x): Forward... | fe5d1eb5ab5453be70c4be473fd3da71afe4b06c | <|skeleton|>
class ResNet:
"""Class for a ResNet classifier."""
def __init__(self, num_channels, num_classes, n=2):
"""Class initializer."""
<|body_0|>
def _make_layer(n, num_filters, channels_in, stride):
"""Make a single layer."""
<|body_1|>
def forward(self, x):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResNet:
"""Class for a ResNet classifier."""
def __init__(self, num_channels, num_classes, n=2):
"""Class initializer."""
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(num_channels, 16, 3, 1, 1)
self.norm1 = nn.BatchNorm2d(16)
self.relu1 = nn.ReLU(inplace=T... | the_stack_v2_python_sparse | src/kegnet/classifier/models/resnet.py | videoturingtest/KegNet | train | 0 |
d2872ffd05b8c027136a2652a720b7c32cb7a4ff | [
"self.converterWindow = converterWindow\nself.inFile = inFile\nself.outLocation = outLocation\nself.outName = outName\nself.outPath = os.path.join(self.outLocation, 'latools_temp_data.csv')\ntry:\n if getattr(sys, 'frozen', False):\n infoFile = os.path.join(os.path.dirname(sys.executable), inFile)\n ... | <|body_start_0|>
self.converterWindow = converterWindow
self.inFile = inFile
self.outLocation = outLocation
self.outName = outName
self.outPath = os.path.join(self.outLocation, 'latools_temp_data.csv')
try:
if getattr(sys, 'frozen', False):
inf... | A parser that converts a text file into a csv file that can be processed by Parser_csv. Currently this is a bit more specific than the csv parser, due to a lack of example txt files to work with, but it works on general principles. | Parser_txt | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Parser_txt:
"""A parser that converts a text file into a csv file that can be processed by Parser_csv. Currently this is a bit more specific than the csv parser, due to a lack of example txt files to work with, but it works on general principles."""
def __init__(self, converterWindow, inFile... | stack_v2_sparse_classes_36k_train_016440 | 32,016 | no_license | [
{
"docstring": "Parameters ---------- converterWindow : ConverterWindow A reference to the window running the show, so that responses to questions can be gathered. inFile : str The filepath of the data file to process outLocation : str The directory to save the converted file to outName : str The name of the co... | 6 | stack_v2_sparse_classes_30k_train_017731 | Implement the Python class `Parser_txt` described below.
Class description:
A parser that converts a text file into a csv file that can be processed by Parser_csv. Currently this is a bit more specific than the csv parser, due to a lack of example txt files to work with, but it works on general principles.
Method sig... | Implement the Python class `Parser_txt` described below.
Class description:
A parser that converts a text file into a csv file that can be processed by Parser_csv. Currently this is a bit more specific than the csv parser, due to a lack of example txt files to work with, but it works on general principles.
Method sig... | ab59d0a5655d514246fa23a1110e0279254ea5d2 | <|skeleton|>
class Parser_txt:
"""A parser that converts a text file into a csv file that can be processed by Parser_csv. Currently this is a bit more specific than the csv parser, due to a lack of example txt files to work with, but it works on general principles."""
def __init__(self, converterWindow, inFile... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Parser_txt:
"""A parser that converts a text file into a csv file that can be processed by Parser_csv. Currently this is a bit more specific than the csv parser, due to a lack of example txt files to work with, but it works on general principles."""
def __init__(self, converterWindow, inFile, outLocation... | the_stack_v2_python_sparse | latools_gui/templates/converterWindow.py | ch-king/latools_gui | train | 0 |
5ca359631eab5a5205457dba67ca43f49785e9c1 | [
"if not string:\n return ''\nm, M = (min(string), max(string))\nfor i, letter in enumerate(m):\n if letter != M[i]:\n return m[:i]\nreturn m",
"if not string:\n return ''\nm = min(string, key=len)\nleft, right = (0, len(m))\nwhile left <= right:\n pivot = (left + right) // 2\n prefix = strin... | <|body_start_0|>
if not string:
return ''
m, M = (min(string), max(string))
for i, letter in enumerate(m):
if letter != M[i]:
return m[:i]
return m
<|end_body_0|>
<|body_start_1|>
if not string:
return ''
m = min(string... | LongestCommonPrefix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LongestCommonPrefix:
def longest_common_prefix_1(self, string: str) -> str:
"""Approach: Using min and max function. :param string: :return:"""
<|body_0|>
def longest_common_prefix_2(self, string: str) -> str:
"""Approach: Binary Search :param string: :return:"""
... | stack_v2_sparse_classes_36k_train_016441 | 1,164 | no_license | [
{
"docstring": "Approach: Using min and max function. :param string: :return:",
"name": "longest_common_prefix_1",
"signature": "def longest_common_prefix_1(self, string: str) -> str"
},
{
"docstring": "Approach: Binary Search :param string: :return:",
"name": "longest_common_prefix_2",
... | 2 | stack_v2_sparse_classes_30k_train_002469 | Implement the Python class `LongestCommonPrefix` described below.
Class description:
Implement the LongestCommonPrefix class.
Method signatures and docstrings:
- def longest_common_prefix_1(self, string: str) -> str: Approach: Using min and max function. :param string: :return:
- def longest_common_prefix_2(self, str... | Implement the Python class `LongestCommonPrefix` described below.
Class description:
Implement the LongestCommonPrefix class.
Method signatures and docstrings:
- def longest_common_prefix_1(self, string: str) -> str: Approach: Using min and max function. :param string: :return:
- def longest_common_prefix_2(self, str... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class LongestCommonPrefix:
def longest_common_prefix_1(self, string: str) -> str:
"""Approach: Using min and max function. :param string: :return:"""
<|body_0|>
def longest_common_prefix_2(self, string: str) -> str:
"""Approach: Binary Search :param string: :return:"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LongestCommonPrefix:
def longest_common_prefix_1(self, string: str) -> str:
"""Approach: Using min and max function. :param string: :return:"""
if not string:
return ''
m, M = (min(string), max(string))
for i, letter in enumerate(m):
if letter != M[i]:
... | the_stack_v2_python_sparse | math_and_srings/longestcommonprefix.py | Shiv2157k/leet_code | train | 1 | |
cfe8803fb0d0878cc21ec74d87d8fab115e7ecf7 | [
"response_mock = Response()\nresponse_mock.url = url\nif redirect_url is not None:\n response_mock.status_code = 301\n response_mock.headers['Location'] = redirect_url\nelse:\n response_mock.status_code = response_code\nreturn response_mock",
"def redirect_side_effect() -> Response:\n for response in ... | <|body_start_0|>
response_mock = Response()
response_mock.url = url
if redirect_url is not None:
response_mock.status_code = 301
response_mock.headers['Location'] = redirect_url
else:
response_mock.status_code = response_code
return response_mo... | TestBuiltInService | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestBuiltInService:
def get_response_mock(url: str, redirect_url: str | None=None, response_code: int | None=200) -> Response:
"""Create a mock requests.Response object. Args: url (str): URL to set as the `url` attribute (should be the last URL in the redirect chain). redirect_url (str |... | stack_v2_sparse_classes_36k_train_016442 | 12,600 | permissive | [
{
"docstring": "Create a mock requests.Response object. Args: url (str): URL to set as the `url` attribute (should be the last URL in the redirect chain). redirect_url (str | None, optional): URL to redirect to. Defaults to None. response_code (int | None, optional): Response code to set as the `status_code` at... | 3 | null | Implement the Python class `TestBuiltInService` described below.
Class description:
Implement the TestBuiltInService class.
Method signatures and docstrings:
- def get_response_mock(url: str, redirect_url: str | None=None, response_code: int | None=200) -> Response: Create a mock requests.Response object. Args: url (... | Implement the Python class `TestBuiltInService` described below.
Class description:
Implement the TestBuiltInService class.
Method signatures and docstrings:
- def get_response_mock(url: str, redirect_url: str | None=None, response_code: int | None=200) -> Response: Create a mock requests.Response object. Args: url (... | 890def5a0e0ae8d6eaa538148249ddbc851dbb6b | <|skeleton|>
class TestBuiltInService:
def get_response_mock(url: str, redirect_url: str | None=None, response_code: int | None=200) -> Response:
"""Create a mock requests.Response object. Args: url (str): URL to set as the `url` attribute (should be the last URL in the redirect chain). redirect_url (str |... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestBuiltInService:
def get_response_mock(url: str, redirect_url: str | None=None, response_code: int | None=200) -> Response:
"""Create a mock requests.Response object. Args: url (str): URL to set as the `url` attribute (should be the last URL in the redirect chain). redirect_url (str | None, optiona... | the_stack_v2_python_sparse | Packs/CommonScripts/Scripts/ResolveShortenedURL/ResolveShortenedURL_test.py | demisto/content | train | 1,023 | |
f50e14b4613a5249702012a19ab22828da5c608c | [
"try:\n self.sqlite_obj = sqlite.Database('report.db')\n with open(file_name) as file:\n self.contents = yaml.load(file)\n self.root_string = [i for i in self.contents.keys()][0]\nexcept Exception as err:\n print(err)",
"test_config_query = '\\n CREATE TABLE `test_configuration` (\\n... | <|body_start_0|>
try:
self.sqlite_obj = sqlite.Database('report.db')
with open(file_name) as file:
self.contents = yaml.load(file)
self.root_string = [i for i in self.contents.keys()][0]
except Exception as err:
print(err)
<|end_body_0|... | Class to represent a YAML Parser and creates database Methods: create_table: Creates sqlite db table with necessary fields. parse_file: Parses the yaml file to obtain necessary data for the test result table and updates it. update_test_config_table: Parses the yaml file to obtain necessary data fot the test config tabl... | YAMLParser | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class YAMLParser:
"""Class to represent a YAML Parser and creates database Methods: create_table: Creates sqlite db table with necessary fields. parse_file: Parses the yaml file to obtain necessary data for the test result table and updates it. update_test_config_table: Parses the yaml file to obtain n... | stack_v2_sparse_classes_36k_train_016443 | 4,235 | permissive | [
{
"docstring": "Creates an instance for sqlite_obj and loads the contents of the yamlfile to be parsed",
"name": "__init__",
"signature": "def __init__(self, file_name='')"
},
{
"docstring": "Creates empty tables in the sqlite database from the contents of test_config_table and test_result_table... | 4 | stack_v2_sparse_classes_30k_train_017605 | Implement the Python class `YAMLParser` described below.
Class description:
Class to represent a YAML Parser and creates database Methods: create_table: Creates sqlite db table with necessary fields. parse_file: Parses the yaml file to obtain necessary data for the test result table and updates it. update_test_config_... | Implement the Python class `YAMLParser` described below.
Class description:
Class to represent a YAML Parser and creates database Methods: create_table: Creates sqlite db table with necessary fields. parse_file: Parses the yaml file to obtain necessary data for the test result table and updates it. update_test_config_... | f98052a6e824dbefac257b4e327f3ab36e3ed5c9 | <|skeleton|>
class YAMLParser:
"""Class to represent a YAML Parser and creates database Methods: create_table: Creates sqlite db table with necessary fields. parse_file: Parses the yaml file to obtain necessary data for the test result table and updates it. update_test_config_table: Parses the yaml file to obtain n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class YAMLParser:
"""Class to represent a YAML Parser and creates database Methods: create_table: Creates sqlite db table with necessary fields. parse_file: Parses the yaml file to obtain necessary data for the test result table and updates it. update_test_config_table: Parses the yaml file to obtain necessary data... | the_stack_v2_python_sparse | report-tools/adaptors/sql/yaml_parser.py | lsandov1/arm-qa-tools | train | 0 |
ea0565a332eb121e99f74c4d4b42866fa020cf01 | [
"B, Nt, E = q.shape\nq = q / math.sqrt(E)\nattn = torch.bmm(q, k.transpose(-2, -1))\nif attn_mask is not None:\n attn += attn_mask\nattn = attn.softmax(-1)\noutput = torch.bmm(attn, v)\nreturn (output, attn)",
"inputs = [q, k, v]\nif mask is not None:\n inputs += [mask]\nreturn g.op('mmdeploy::ScaledDotProd... | <|body_start_0|>
B, Nt, E = q.shape
q = q / math.sqrt(E)
attn = torch.bmm(q, k.transpose(-2, -1))
if attn_mask is not None:
attn += attn_mask
attn = attn.softmax(-1)
output = torch.bmm(attn, v)
return (output, attn)
<|end_body_0|>
<|body_start_1|>
... | Caller of scale dot product attention. | ScaledDotProductAttentionTRT | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScaledDotProductAttentionTRT:
"""Caller of scale dot product attention."""
def forward(ctx, q: Tensor, k: Tensor, v: Tensor, attn_mask: Optional[Tensor]=None):
"""forward function."""
<|body_0|>
def symbolic(g, q, k, v, mask):
"""Symbolic function."""
<|b... | stack_v2_sparse_classes_36k_train_016444 | 1,811 | permissive | [
{
"docstring": "forward function.",
"name": "forward",
"signature": "def forward(ctx, q: Tensor, k: Tensor, v: Tensor, attn_mask: Optional[Tensor]=None)"
},
{
"docstring": "Symbolic function.",
"name": "symbolic",
"signature": "def symbolic(g, q, k, v, mask)"
}
] | 2 | null | Implement the Python class `ScaledDotProductAttentionTRT` described below.
Class description:
Caller of scale dot product attention.
Method signatures and docstrings:
- def forward(ctx, q: Tensor, k: Tensor, v: Tensor, attn_mask: Optional[Tensor]=None): forward function.
- def symbolic(g, q, k, v, mask): Symbolic fun... | Implement the Python class `ScaledDotProductAttentionTRT` described below.
Class description:
Caller of scale dot product attention.
Method signatures and docstrings:
- def forward(ctx, q: Tensor, k: Tensor, v: Tensor, attn_mask: Optional[Tensor]=None): forward function.
- def symbolic(g, q, k, v, mask): Symbolic fun... | 5479c8774f5b88d7ed9d399d4e305cb42cc2e73a | <|skeleton|>
class ScaledDotProductAttentionTRT:
"""Caller of scale dot product attention."""
def forward(ctx, q: Tensor, k: Tensor, v: Tensor, attn_mask: Optional[Tensor]=None):
"""forward function."""
<|body_0|>
def symbolic(g, q, k, v, mask):
"""Symbolic function."""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScaledDotProductAttentionTRT:
"""Caller of scale dot product attention."""
def forward(ctx, q: Tensor, k: Tensor, v: Tensor, attn_mask: Optional[Tensor]=None):
"""forward function."""
B, Nt, E = q.shape
q = q / math.sqrt(E)
attn = torch.bmm(q, k.transpose(-2, -1))
... | the_stack_v2_python_sparse | mmdeploy/pytorch/functions/multi_head_attention_forward.py | open-mmlab/mmdeploy | train | 2,164 |
6b00a9bf98504e604ea482a9cddc3674ce47d7cf | [
"hex_addr = _HexAddressRegexpFor(android_abi)\nself._re_stack_line = re.compile('\\\\s+(?P<frame_number>#[0-9]+)?\\\\s*' + '(?P<stack_addr>' + hex_addr + ')\\\\s+' + '(?P<stack_value>' + hex_addr + ')' + '(\\\\s+(?P<location>[^ \\\\t]+))?')\nself._re_stack_abbrev = re.compile('\\\\s+[.]+\\\\s+[.]+')\nself._memory_m... | <|body_start_0|>
hex_addr = _HexAddressRegexpFor(android_abi)
self._re_stack_line = re.compile('\\s+(?P<frame_number>#[0-9]+)?\\s*' + '(?P<stack_addr>' + hex_addr + ')\\s+' + '(?P<stack_value>' + hex_addr + ')' + '(\\s+(?P<location>[^ \\t]+))?')
self._re_stack_abbrev = re.compile('\\s+[.]+\\s+[.... | Translates stack-related lines in a tombstone or crash report. | StackTranslator | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StackTranslator:
"""Translates stack-related lines in a tombstone or crash report."""
def __init__(self, android_abi, memory_map, apk_translator):
"""Initialize instance."""
<|body_0|>
def _ParseLine(self, line):
"""Check a given input line for a relevant _re_sta... | stack_v2_sparse_classes_36k_train_016445 | 28,535 | permissive | [
{
"docstring": "Initialize instance.",
"name": "__init__",
"signature": "def __init__(self, android_abi, memory_map, apk_translator)"
},
{
"docstring": "Check a given input line for a relevant _re_stack_line match. Args: line: input tombstone line. Returns: A LineTuple instance on success, None ... | 4 | null | Implement the Python class `StackTranslator` described below.
Class description:
Translates stack-related lines in a tombstone or crash report.
Method signatures and docstrings:
- def __init__(self, android_abi, memory_map, apk_translator): Initialize instance.
- def _ParseLine(self, line): Check a given input line f... | Implement the Python class `StackTranslator` described below.
Class description:
Translates stack-related lines in a tombstone or crash report.
Method signatures and docstrings:
- def __init__(self, android_abi, memory_map, apk_translator): Initialize instance.
- def _ParseLine(self, line): Check a given input line f... | acefdaaadd3ef46f10f63d1acae2259e4024d383 | <|skeleton|>
class StackTranslator:
"""Translates stack-related lines in a tombstone or crash report."""
def __init__(self, android_abi, memory_map, apk_translator):
"""Initialize instance."""
<|body_0|>
def _ParseLine(self, line):
"""Check a given input line for a relevant _re_sta... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StackTranslator:
"""Translates stack-related lines in a tombstone or crash report."""
def __init__(self, android_abi, memory_map, apk_translator):
"""Initialize instance."""
hex_addr = _HexAddressRegexpFor(android_abi)
self._re_stack_line = re.compile('\\s+(?P<frame_number>#[0-9]+... | the_stack_v2_python_sparse | build/android/pylib/symbols/symbol_utils.py | youtube/cobalt | train | 169 |
f9cfaad71e09c10311e6cc27634f3c7249b79f33 | [
"self.URL_VARIABLES = {'client_id': kwargs.get('client_id'), 'client_location_id': kwargs.get('client_location_id')}\nresults = ClientLocationMeal.objects.prefetch_related('meal').prefetch_related('client_location').filter(client_location__pk=kwargs.get('client_location_id'), client_location__client__pk=kwargs.get(... | <|body_start_0|>
self.URL_VARIABLES = {'client_id': kwargs.get('client_id'), 'client_location_id': kwargs.get('client_location_id')}
results = ClientLocationMeal.objects.prefetch_related('meal').prefetch_related('client_location').filter(client_location__pk=kwargs.get('client_location_id'), client_locat... | Client Location Meal List API Class Example URLs: /api/v1/clients/<client_id>/locations/<client_location_id>/meals/ | ClientLocationMealList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClientLocationMealList:
"""Client Location Meal List API Class Example URLs: /api/v1/clients/<client_id>/locations/<client_location_id>/meals/"""
def _handle_get(self, request, *args, **kwargs):
"""GET handler for Client Location Meal List :request - HTTP request from the api call :c... | stack_v2_sparse_classes_36k_train_016446 | 2,376 | no_license | [
{
"docstring": "GET handler for Client Location Meal List :request - HTTP request from the api call :client_id - The ID of the client that we want to get locations for",
"name": "_handle_get",
"signature": "def _handle_get(self, request, *args, **kwargs)"
},
{
"docstring": "POST handler for Clie... | 2 | null | Implement the Python class `ClientLocationMealList` described below.
Class description:
Client Location Meal List API Class Example URLs: /api/v1/clients/<client_id>/locations/<client_location_id>/meals/
Method signatures and docstrings:
- def _handle_get(self, request, *args, **kwargs): GET handler for Client Locati... | Implement the Python class `ClientLocationMealList` described below.
Class description:
Client Location Meal List API Class Example URLs: /api/v1/clients/<client_id>/locations/<client_location_id>/meals/
Method signatures and docstrings:
- def _handle_get(self, request, *args, **kwargs): GET handler for Client Locati... | 9769e1a96730b02511d25af8828b075dff5c35b5 | <|skeleton|>
class ClientLocationMealList:
"""Client Location Meal List API Class Example URLs: /api/v1/clients/<client_id>/locations/<client_location_id>/meals/"""
def _handle_get(self, request, *args, **kwargs):
"""GET handler for Client Location Meal List :request - HTTP request from the api call :c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClientLocationMealList:
"""Client Location Meal List API Class Example URLs: /api/v1/clients/<client_id>/locations/<client_location_id>/meals/"""
def _handle_get(self, request, *args, **kwargs):
"""GET handler for Client Location Meal List :request - HTTP request from the api call :client_id - Th... | the_stack_v2_python_sparse | clients/api/client_location_meal_list.py | doubleclickdetroit/dindintonight | train | 0 |
c69c633ed0d4cc30bc8b89190e2c5e9ed0f706b4 | [
"self.rtol = rtol\nself.atol = atol\nsuper(WeightedDiGraphMatcher, self).__init__(G1, G2)",
"G1_succ = self.G1.succ\nG1_pred = self.G1.pred\nG2_succ = self.G2.succ\nG2_pred = self.G2.pred\ncore_1 = self.core_1\nrtol, atol = (self.rtol, self.atol)\nfor successor in G1_succ[G1_node]:\n if successor is G1_node:\n... | <|body_start_0|>
self.rtol = rtol
self.atol = atol
super(WeightedDiGraphMatcher, self).__init__(G1, G2)
<|end_body_0|>
<|body_start_1|>
G1_succ = self.G1.succ
G1_pred = self.G1.pred
G2_succ = self.G2.succ
G2_pred = self.G2.pred
core_1 = self.core_1
... | Implementation of VF2 algorithm for directed, weighted graphs. | WeightedDiGraphMatcher | [
"BSD-3-Clause",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeightedDiGraphMatcher:
"""Implementation of VF2 algorithm for directed, weighted graphs."""
def __init__(self, G1, G2, rtol=1e-06, atol=1e-09):
"""Initialize WeightedGraphMatcher. Parameters ---------- G1, G2 : nx.DiGraph instances G1 and G2 must be weighted graphs. rtol : float, op... | stack_v2_sparse_classes_36k_train_016447 | 9,804 | permissive | [
{
"docstring": "Initialize WeightedGraphMatcher. Parameters ---------- G1, G2 : nx.DiGraph instances G1 and G2 must be weighted graphs. rtol : float, optional The relative tolerance used to compare weights. atol : float, optional The absolute tolerance used to compare weights.",
"name": "__init__",
"sig... | 2 | null | Implement the Python class `WeightedDiGraphMatcher` described below.
Class description:
Implementation of VF2 algorithm for directed, weighted graphs.
Method signatures and docstrings:
- def __init__(self, G1, G2, rtol=1e-06, atol=1e-09): Initialize WeightedGraphMatcher. Parameters ---------- G1, G2 : nx.DiGraph inst... | Implement the Python class `WeightedDiGraphMatcher` described below.
Class description:
Implementation of VF2 algorithm for directed, weighted graphs.
Method signatures and docstrings:
- def __init__(self, G1, G2, rtol=1e-06, atol=1e-09): Initialize WeightedGraphMatcher. Parameters ---------- G1, G2 : nx.DiGraph inst... | de0cdb26248f6d0d8bea594124c1dd7a155d406d | <|skeleton|>
class WeightedDiGraphMatcher:
"""Implementation of VF2 algorithm for directed, weighted graphs."""
def __init__(self, G1, G2, rtol=1e-06, atol=1e-09):
"""Initialize WeightedGraphMatcher. Parameters ---------- G1, G2 : nx.DiGraph instances G1 and G2 must be weighted graphs. rtol : float, op... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WeightedDiGraphMatcher:
"""Implementation of VF2 algorithm for directed, weighted graphs."""
def __init__(self, G1, G2, rtol=1e-06, atol=1e-09):
"""Initialize WeightedGraphMatcher. Parameters ---------- G1, G2 : nx.DiGraph instances G1 and G2 must be weighted graphs. rtol : float, optional The re... | the_stack_v2_python_sparse | Source/lib/CrossPlatform/networkx/algorithms/isomorphism/vf2weighted.py | JaneliaSciComp/Neuroptikon | train | 9 |
037d5658e5e85f09b18e9b0cab84902de5aed6fe | [
"super().__init__()\nself.fft_size = fft_size\nself.shift_size = shift_size\nself.win_length = win_length\nself.window = window\nself.spectral_convergence_loss = SpectralConvergenceLoss()\nself.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()",
"x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, sel... | <|body_start_0|>
super().__init__()
self.fft_size = fft_size
self.shift_size = shift_size
self.win_length = win_length
self.window = window
self.spectral_convergence_loss = SpectralConvergenceLoss()
self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
<|end_body_... | STFT loss module. | STFTLoss | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class STFTLoss:
"""STFT loss module."""
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window='hann'):
"""Initialize STFT loss module."""
<|body_0|>
def forward(self, x, y):
"""Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T).... | stack_v2_sparse_classes_36k_train_016448 | 46,210 | permissive | [
{
"docstring": "Initialize STFT loss module.",
"name": "__init__",
"signature": "def __init__(self, fft_size=1024, shift_size=120, win_length=600, window='hann')"
},
{
"docstring": "Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T). R... | 2 | stack_v2_sparse_classes_30k_train_005085 | Implement the Python class `STFTLoss` described below.
Class description:
STFT loss module.
Method signatures and docstrings:
- def __init__(self, fft_size=1024, shift_size=120, win_length=600, window='hann'): Initialize STFT loss module.
- def forward(self, x, y): Calculate forward propagation. Args: x (Tensor): Pre... | Implement the Python class `STFTLoss` described below.
Class description:
STFT loss module.
Method signatures and docstrings:
- def __init__(self, fft_size=1024, shift_size=120, win_length=600, window='hann'): Initialize STFT loss module.
- def forward(self, x, y): Calculate forward propagation. Args: x (Tensor): Pre... | 17854a04d43c231eff66bfed9d6aa55e94a29e79 | <|skeleton|>
class STFTLoss:
"""STFT loss module."""
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window='hann'):
"""Initialize STFT loss module."""
<|body_0|>
def forward(self, x, y):
"""Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T).... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class STFTLoss:
"""STFT loss module."""
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window='hann'):
"""Initialize STFT loss module."""
super().__init__()
self.fft_size = fft_size
self.shift_size = shift_size
self.win_length = win_length
self... | the_stack_v2_python_sparse | paddlespeech/t2s/modules/losses.py | anniyanvr/DeepSpeech-1 | train | 0 |
6e6d6ade4ff5207f3f27e89817678565d30012dd | [
"index_i = 0\nindex_j = 0\nloop_i = 0\nwhile loop_i < m and index_j < n:\n if nums1[index_i] <= nums2[index_j]:\n index_i += 1\n else:\n nums1.insert(index_i, nums2[index_j])\n nums1.pop()\n index_i += 1\n index_j += 1\n loop_i -= 1\n loop_i += 1\nwhile index_j < n... | <|body_start_0|>
index_i = 0
index_j = 0
loop_i = 0
while loop_i < m and index_j < n:
if nums1[index_i] <= nums2[index_j]:
index_i += 1
else:
nums1.insert(index_i, nums2[index_j])
nums1.pop()
index_i ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def merge(self, nums1, m, nums2, n):
"""Do not return anything, modify nums1 in-place instead."""
<|body_0|>
def merge_2(self, nums1, m, nums2, n):
"""Do not return anything, modify nums1 in-place instead."""
<|body_1|>
def merge_LC(self, nums1... | stack_v2_sparse_classes_36k_train_016449 | 3,309 | no_license | [
{
"docstring": "Do not return anything, modify nums1 in-place instead.",
"name": "merge",
"signature": "def merge(self, nums1, m, nums2, n)"
},
{
"docstring": "Do not return anything, modify nums1 in-place instead.",
"name": "merge_2",
"signature": "def merge_2(self, nums1, m, nums2, n)"... | 3 | stack_v2_sparse_classes_30k_train_005653 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def merge(self, nums1, m, nums2, n): Do not return anything, modify nums1 in-place instead.
- def merge_2(self, nums1, m, nums2, n): Do not return anything, modify nums1 in-place... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def merge(self, nums1, m, nums2, n): Do not return anything, modify nums1 in-place instead.
- def merge_2(self, nums1, m, nums2, n): Do not return anything, modify nums1 in-place... | ec48cbde4356208afac226d41752daffe674be2c | <|skeleton|>
class Solution:
def merge(self, nums1, m, nums2, n):
"""Do not return anything, modify nums1 in-place instead."""
<|body_0|>
def merge_2(self, nums1, m, nums2, n):
"""Do not return anything, modify nums1 in-place instead."""
<|body_1|>
def merge_LC(self, nums1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def merge(self, nums1, m, nums2, n):
"""Do not return anything, modify nums1 in-place instead."""
index_i = 0
index_j = 0
loop_i = 0
while loop_i < m and index_j < n:
if nums1[index_i] <= nums2[index_j]:
index_i += 1
els... | the_stack_v2_python_sparse | Leetcode/Python/Easy/Sorting/merge_sorted_array.py | librar127/PythonDS | train | 0 | |
7ada38ce3f9e547a2bbc91c707b9c16f68211b33 | [
"with patch.object(DailyIndexDocType, 'get_field_mapping') as gfm:\n field = 'field'\n gfm.return_value = {'index': {'mappings': {'syslog': {field: {'mapping': {field: {'fields': {'raw': True}}}}}}}}\n result = DailyIndexDocType.field_has_raw('field')\n self.assertTrue(gfm.called)\n self.assertTrue(r... | <|body_start_0|>
with patch.object(DailyIndexDocType, 'get_field_mapping') as gfm:
field = 'field'
gfm.return_value = {'index': {'mappings': {'syslog': {field: {'mapping': {field: {'fields': {'raw': True}}}}}}}}
result = DailyIndexDocType.field_has_raw('field')
se... | Tests for the LogData model | DailyIndexDocTypeTests | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DailyIndexDocTypeTests:
"""Tests for the LogData model"""
def test_field_has_raw_true(self):
"""field_has_raw returns True if mapping has a raw field."""
<|body_0|>
def test_field_has_raw_false(self):
"""field_has_raw returns False if mapping doesn't have a raw f... | stack_v2_sparse_classes_36k_train_016450 | 12,045 | permissive | [
{
"docstring": "field_has_raw returns True if mapping has a raw field.",
"name": "test_field_has_raw_true",
"signature": "def test_field_has_raw_true(self)"
},
{
"docstring": "field_has_raw returns False if mapping doesn't have a raw field.",
"name": "test_field_has_raw_false",
"signatur... | 4 | stack_v2_sparse_classes_30k_train_017708 | Implement the Python class `DailyIndexDocTypeTests` described below.
Class description:
Tests for the LogData model
Method signatures and docstrings:
- def test_field_has_raw_true(self): field_has_raw returns True if mapping has a raw field.
- def test_field_has_raw_false(self): field_has_raw returns False if mapping... | Implement the Python class `DailyIndexDocTypeTests` described below.
Class description:
Tests for the LogData model
Method signatures and docstrings:
- def test_field_has_raw_true(self): field_has_raw returns True if mapping has a raw field.
- def test_field_has_raw_false(self): field_has_raw returns False if mapping... | 73d334a9f0df7c044c06989977a9a22dd2ff9b7a | <|skeleton|>
class DailyIndexDocTypeTests:
"""Tests for the LogData model"""
def test_field_has_raw_true(self):
"""field_has_raw returns True if mapping has a raw field."""
<|body_0|>
def test_field_has_raw_false(self):
"""field_has_raw returns False if mapping doesn't have a raw f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DailyIndexDocTypeTests:
"""Tests for the LogData model"""
def test_field_has_raw_true(self):
"""field_has_raw returns True if mapping has a raw field."""
with patch.object(DailyIndexDocType, 'get_field_mapping') as gfm:
field = 'field'
gfm.return_value = {'index': ... | the_stack_v2_python_sparse | goldstone/drfes/tests.py | bhuvan-rk/goldstone-server | train | 0 |
df67df348f50c1e76ef37b8b857dae16d7453aa8 | [
"if left == 0 and right == 0:\n self.results.append(pattern_str)\n return\nif left > 0:\n self._gen(left - 1, right, pattern_str + '(')\nif right > left and right > 0:\n self._gen(left, right - 1, pattern_str + ')')",
"self.results = []\nself._gen(n, n, '')\nreturn self.results"
] | <|body_start_0|>
if left == 0 and right == 0:
self.results.append(pattern_str)
return
if left > 0:
self._gen(left - 1, right, pattern_str + '(')
if right > left and right > 0:
self._gen(left, right - 1, pattern_str + ')')
<|end_body_0|>
<|body_sta... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def _gen(self, left: int, right: int, pattern_str: str):
"""Args: left: right: pattern_str: Returns:"""
<|body_0|>
def generateParenthesis(self, n: int) -> List[str]:
"""DFS 递归 Args: n: Returns:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_016451 | 1,028 | no_license | [
{
"docstring": "Args: left: right: pattern_str: Returns:",
"name": "_gen",
"signature": "def _gen(self, left: int, right: int, pattern_str: str)"
},
{
"docstring": "DFS 递归 Args: n: Returns:",
"name": "generateParenthesis",
"signature": "def generateParenthesis(self, n: int) -> List[str]"... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _gen(self, left: int, right: int, pattern_str: str): Args: left: right: pattern_str: Returns:
- def generateParenthesis(self, n: int) -> List[str]: DFS 递归 Args: n: Returns: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _gen(self, left: int, right: int, pattern_str: str): Args: left: right: pattern_str: Returns:
- def generateParenthesis(self, n: int) -> List[str]: DFS 递归 Args: n: Returns:
... | c0dd577481b46129d950354d567d332a4d091137 | <|skeleton|>
class Solution:
def _gen(self, left: int, right: int, pattern_str: str):
"""Args: left: right: pattern_str: Returns:"""
<|body_0|>
def generateParenthesis(self, n: int) -> List[str]:
"""DFS 递归 Args: n: Returns:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def _gen(self, left: int, right: int, pattern_str: str):
"""Args: left: right: pattern_str: Returns:"""
if left == 0 and right == 0:
self.results.append(pattern_str)
return
if left > 0:
self._gen(left - 1, right, pattern_str + '(')
... | the_stack_v2_python_sparse | leetcode/22_括号生成.py | tenqaz/crazy_arithmetic | train | 0 | |
62f96a6d2ff09586914263ebfbdb2827d8ad5e38 | [
"view = cls.as_view('periodic_expenses')\napp.add_url_rule('/api/budgets/<int:budget_id>/periodic-expenses', defaults={'expense_id': None}, view_func=view, methods=['GET'])\napp.add_url_rule('/api/budgets/<int:budget_id>/periodic-expenses', view_func=view, methods=['POST'])\napp.add_url_rule('/api/budget-periodic-e... | <|body_start_0|>
view = cls.as_view('periodic_expenses')
app.add_url_rule('/api/budgets/<int:budget_id>/periodic-expenses', defaults={'expense_id': None}, view_func=view, methods=['GET'])
app.add_url_rule('/api/budgets/<int:budget_id>/periodic-expenses', view_func=view, methods=['POST'])
... | Periodic expense REST resource view | PeriodicExpensesView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PeriodicExpensesView:
"""Periodic expense REST resource view"""
def register(cls, app: Flask):
"""Registers routes for this view"""
<|body_0|>
def get(budget_id: Optional[int], expense_id: Optional[int]):
"""Gets a specific periodic expense or all expenses in the... | stack_v2_sparse_classes_36k_train_016452 | 17,779 | permissive | [
{
"docstring": "Registers routes for this view",
"name": "register",
"signature": "def register(cls, app: Flask)"
},
{
"docstring": "Gets a specific periodic expense or all expenses in the specified budget",
"name": "get",
"signature": "def get(budget_id: Optional[int], expense_id: Optio... | 5 | stack_v2_sparse_classes_30k_test_001026 | Implement the Python class `PeriodicExpensesView` described below.
Class description:
Periodic expense REST resource view
Method signatures and docstrings:
- def register(cls, app: Flask): Registers routes for this view
- def get(budget_id: Optional[int], expense_id: Optional[int]): Gets a specific periodic expense o... | Implement the Python class `PeriodicExpensesView` described below.
Class description:
Periodic expense REST resource view
Method signatures and docstrings:
- def register(cls, app: Flask): Registers routes for this view
- def get(budget_id: Optional[int], expense_id: Optional[int]): Gets a specific periodic expense o... | 20d992356952542fd79aab69849a04129fa22de2 | <|skeleton|>
class PeriodicExpensesView:
"""Periodic expense REST resource view"""
def register(cls, app: Flask):
"""Registers routes for this view"""
<|body_0|>
def get(budget_id: Optional[int], expense_id: Optional[int]):
"""Gets a specific periodic expense or all expenses in the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PeriodicExpensesView:
"""Periodic expense REST resource view"""
def register(cls, app: Flask):
"""Registers routes for this view"""
view = cls.as_view('periodic_expenses')
app.add_url_rule('/api/budgets/<int:budget_id>/periodic-expenses', defaults={'expense_id': None}, view_func=v... | the_stack_v2_python_sparse | backend/underbudget/views/budgets.py | vimofthevine/underbudget4 | train | 0 |
831f99a16fff6da946db1b337de2ca1436969f8a | [
"self._cfg_pins = cfg_pins\nself._program_pin = program_pin\nself._done_pin = done_pin\nself._init_pin = init_pin\nif self._cfg_pins:\n self._cfg_pins.all_output()\nif self._program_pin:\n self._program_pin.high()\nif self._done_pin:\n self._done_pin.input()\nif self._init_pin:\n self._init_pin.input()\... | <|body_start_0|>
self._cfg_pins = cfg_pins
self._program_pin = program_pin
self._done_pin = done_pin
self._init_pin = init_pin
if self._cfg_pins:
self._cfg_pins.all_output()
if self._program_pin:
self._program_pin.high()
if self._done_pin:
... | Abstract base class for programming ECP5 FPGAs. | ECP5Programmer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ECP5Programmer:
"""Abstract base class for programming ECP5 FPGAs."""
def __init__(self, cfg_pins=None, init_pin=None, program_pin=None, done_pin=None, verbose_function=None):
"""Captures the common fields for all ECP5 programmers. Paramters, all optional: cfg_pins -- A (potentially ... | stack_v2_sparse_classes_36k_train_016453 | 22,746 | permissive | [
{
"docstring": "Captures the common fields for all ECP5 programmers. Paramters, all optional: cfg_pins -- A (potentially virtual) port containing the three CFG pins that select the device's configuration mode. See VirtualGPIOPort for an example of a class that might be used for this. init_pin -- A GPIOPin objec... | 3 | null | Implement the Python class `ECP5Programmer` described below.
Class description:
Abstract base class for programming ECP5 FPGAs.
Method signatures and docstrings:
- def __init__(self, cfg_pins=None, init_pin=None, program_pin=None, done_pin=None, verbose_function=None): Captures the common fields for all ECP5 programm... | Implement the Python class `ECP5Programmer` described below.
Class description:
Abstract base class for programming ECP5 FPGAs.
Method signatures and docstrings:
- def __init__(self, cfg_pins=None, init_pin=None, program_pin=None, done_pin=None, verbose_function=None): Captures the common fields for all ECP5 programm... | 2409575d28fc7c9cae44c9085c7457ddfb54f893 | <|skeleton|>
class ECP5Programmer:
"""Abstract base class for programming ECP5 FPGAs."""
def __init__(self, cfg_pins=None, init_pin=None, program_pin=None, done_pin=None, verbose_function=None):
"""Captures the common fields for all ECP5 programmers. Paramters, all optional: cfg_pins -- A (potentially ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ECP5Programmer:
"""Abstract base class for programming ECP5 FPGAs."""
def __init__(self, cfg_pins=None, init_pin=None, program_pin=None, done_pin=None, verbose_function=None):
"""Captures the common fields for all ECP5 programmers. Paramters, all optional: cfg_pins -- A (potentially virtual) port... | the_stack_v2_python_sparse | host/greatfet/programmers/ecp5.py | greatscottgadgets/greatfet | train | 273 |
d929855fd46453dfb44b845cb863131983683675 | [
"self.id = id\nself.title = title\nself.authentication_flows = authentication_flows\nself.links = links",
"for key, value in (('id', self.id), ('title', self.title)):\n if not value:\n raise ValueError(\"'%s' is required in an Authentication For OPDS document.\" % key)\nfor key, value in [('authenticati... | <|body_start_0|>
self.id = id
self.title = title
self.authentication_flows = authentication_flows
self.links = links
<|end_body_0|>
<|body_start_1|>
for key, value in (('id', self.id), ('title', self.title)):
if not value:
raise ValueError("'%s' is re... | A data structure that can become an Authentication For OPDS document. | AuthenticationForOPDSDocument | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthenticationForOPDSDocument:
"""A data structure that can become an Authentication For OPDS document."""
def __init__(self, id=None, title=None, authentication_flows=[], links=[]):
"""Initialize an Authentication For OPDS document. :param id: URL to use as the 'id' of the Authentic... | stack_v2_sparse_classes_36k_train_016454 | 3,847 | permissive | [
{
"docstring": "Initialize an Authentication For OPDS document. :param id: URL to use as the 'id' of the Authentication For OPDS document. :param title: String to use as the 'title' of the Authentication For OPDS document. :param authentication_flows: A list of `OPDSAuthenticationFlow` objects, used to construc... | 2 | null | Implement the Python class `AuthenticationForOPDSDocument` described below.
Class description:
A data structure that can become an Authentication For OPDS document.
Method signatures and docstrings:
- def __init__(self, id=None, title=None, authentication_flows=[], links=[]): Initialize an Authentication For OPDS doc... | Implement the Python class `AuthenticationForOPDSDocument` described below.
Class description:
A data structure that can become an Authentication For OPDS document.
Method signatures and docstrings:
- def __init__(self, id=None, title=None, authentication_flows=[], links=[]): Initialize an Authentication For OPDS doc... | 662cc7e0721d0153857c8c17a37e2a6df86f8ce6 | <|skeleton|>
class AuthenticationForOPDSDocument:
"""A data structure that can become an Authentication For OPDS document."""
def __init__(self, id=None, title=None, authentication_flows=[], links=[]):
"""Initialize an Authentication For OPDS document. :param id: URL to use as the 'id' of the Authentic... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AuthenticationForOPDSDocument:
"""A data structure that can become an Authentication For OPDS document."""
def __init__(self, id=None, title=None, authentication_flows=[], links=[]):
"""Initialize an Authentication For OPDS document. :param id: URL to use as the 'id' of the Authentication For OPD... | the_stack_v2_python_sparse | core/util/authentication_for_opds.py | NYPL-Simplified/circulation | train | 20 |
e9c4f08fb41979f14ea0af5a35afa726eb233112 | [
"self.game = game\nself.nnet = nnet\nself.mcts = MCTS(game, nnet, args)\nself.playerId = playerId",
"probs = self.mcts.getActionProb(board, temp=0, curPlayer=self.playerId)\nvalids = self.game.getValidMoves(board, 1, self.playerId)\naction = np.argmax(valids * probs)\nreturn action"
] | <|body_start_0|>
self.game = game
self.nnet = nnet
self.mcts = MCTS(game, nnet, args)
self.playerId = playerId
<|end_body_0|>
<|body_start_1|>
probs = self.mcts.getActionProb(board, temp=0, curPlayer=self.playerId)
valids = self.game.getValidMoves(board, 1, self.playerId... | CentaurPlayer | [
"MIT",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CentaurPlayer:
def __init__(self, game, nnet, playerId, args):
""":type game: HexagonGame :type nnet: HexagonModel"""
<|body_0|>
def play(self, board):
"""xx :type board: ndarray :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.game = g... | stack_v2_sparse_classes_36k_train_016455 | 28,513 | permissive | [
{
"docstring": ":type game: HexagonGame :type nnet: HexagonModel",
"name": "__init__",
"signature": "def __init__(self, game, nnet, playerId, args)"
},
{
"docstring": "xx :type board: ndarray :return:",
"name": "play",
"signature": "def play(self, board)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014512 | Implement the Python class `CentaurPlayer` described below.
Class description:
Implement the CentaurPlayer class.
Method signatures and docstrings:
- def __init__(self, game, nnet, playerId, args): :type game: HexagonGame :type nnet: HexagonModel
- def play(self, board): xx :type board: ndarray :return: | Implement the Python class `CentaurPlayer` described below.
Class description:
Implement the CentaurPlayer class.
Method signatures and docstrings:
- def __init__(self, game, nnet, playerId, args): :type game: HexagonGame :type nnet: HexagonModel
- def play(self, board): xx :type board: ndarray :return:
<|skeleton|>... | cd4180bda26f92c0bde11a08aa13c825cd151a10 | <|skeleton|>
class CentaurPlayer:
def __init__(self, game, nnet, playerId, args):
""":type game: HexagonGame :type nnet: HexagonModel"""
<|body_0|>
def play(self, board):
"""xx :type board: ndarray :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CentaurPlayer:
def __init__(self, game, nnet, playerId, args):
""":type game: HexagonGame :type nnet: HexagonModel"""
self.game = game
self.nnet = nnet
self.mcts = MCTS(game, nnet, args)
self.playerId = playerId
def play(self, board):
"""xx :type board: nda... | the_stack_v2_python_sparse | hexagon_alphazero.py | aliostad/hexagon-rl | train | 7 | |
76899410cc97ac4d1debfb613e384d52e2217a6f | [
"self.spy_on(TrelloCardSearchView.get, owner=TrelloCardSearchView, call_fake=lambda self, request, **kwargs: HttpResponse('{}', content_type='application/json'))\nreview_request = self.create_review_request(public=True)\nrsp = self.client.get(local_site_reverse('trello-card-search', kwargs={'review_request_id': rev... | <|body_start_0|>
self.spy_on(TrelloCardSearchView.get, owner=TrelloCardSearchView, call_fake=lambda self, request, **kwargs: HttpResponse('{}', content_type='application/json'))
review_request = self.create_review_request(public=True)
rsp = self.client.get(local_site_reverse('trello-card-search'... | Tests for Trello. | TrelloIntegrationTests | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrelloIntegrationTests:
"""Tests for Trello."""
def test_card_search(self):
"""Testing TrelloCardSearchView"""
<|body_0|>
def test_card_search_unpublished(self):
"""Testing TrelloCardSearchView with an unpublished review request"""
<|body_1|>
def tes... | stack_v2_sparse_classes_36k_train_016456 | 3,505 | permissive | [
{
"docstring": "Testing TrelloCardSearchView",
"name": "test_card_search",
"signature": "def test_card_search(self)"
},
{
"docstring": "Testing TrelloCardSearchView with an unpublished review request",
"name": "test_card_search_unpublished",
"signature": "def test_card_search_unpublished... | 4 | stack_v2_sparse_classes_30k_train_006498 | Implement the Python class `TrelloIntegrationTests` described below.
Class description:
Tests for Trello.
Method signatures and docstrings:
- def test_card_search(self): Testing TrelloCardSearchView
- def test_card_search_unpublished(self): Testing TrelloCardSearchView with an unpublished review request
- def test_ca... | Implement the Python class `TrelloIntegrationTests` described below.
Class description:
Tests for Trello.
Method signatures and docstrings:
- def test_card_search(self): Testing TrelloCardSearchView
- def test_card_search_unpublished(self): Testing TrelloCardSearchView with an unpublished review request
- def test_ca... | 52bbaecc1227764f3e9a66f03226e0013f2b0c48 | <|skeleton|>
class TrelloIntegrationTests:
"""Tests for Trello."""
def test_card_search(self):
"""Testing TrelloCardSearchView"""
<|body_0|>
def test_card_search_unpublished(self):
"""Testing TrelloCardSearchView with an unpublished review request"""
<|body_1|>
def tes... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrelloIntegrationTests:
"""Tests for Trello."""
def test_card_search(self):
"""Testing TrelloCardSearchView"""
self.spy_on(TrelloCardSearchView.get, owner=TrelloCardSearchView, call_fake=lambda self, request, **kwargs: HttpResponse('{}', content_type='application/json'))
review_re... | the_stack_v2_python_sparse | rbintegrations/trello/tests.py | reviewboard/rbintegrations | train | 0 |
572c2c2259bc5aa72c2603c3b9f0be17a64a2359 | [
"inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])\nfor pretrained_model_name in XLNetEncoder.available_checkpoints():\n encoder = XLNetEncoder(pretrained_model_name=pretrained_model_name)\n _ = encoder(inputs)",
"inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])\nencoder = XLNetEncoder(... | <|body_start_0|>
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])
for pretrained_model_name in XLNetEncoder.available_checkpoints():
encoder = XLNetEncoder(pretrained_model_name=pretrained_model_name)
_ = encoder(inputs)
<|end_body_0|>
<|body_start_1|>
inputs ... | Tests :class:`~texar.tf.modules.XLNetEncoder` class. | XLNetEncoderTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XLNetEncoderTest:
"""Tests :class:`~texar.tf.modules.XLNetEncoder` class."""
def test_model_loading(self):
"""Tests model loading functionality."""
<|body_0|>
def test_hparams(self):
"""Tests the priority of the encoder architecture parameter."""
<|body_1... | stack_v2_sparse_classes_36k_train_016457 | 5,625 | permissive | [
{
"docstring": "Tests model loading functionality.",
"name": "test_model_loading",
"signature": "def test_model_loading(self)"
},
{
"docstring": "Tests the priority of the encoder architecture parameter.",
"name": "test_hparams",
"signature": "def test_hparams(self)"
},
{
"docstr... | 4 | stack_v2_sparse_classes_30k_train_007047 | Implement the Python class `XLNetEncoderTest` described below.
Class description:
Tests :class:`~texar.tf.modules.XLNetEncoder` class.
Method signatures and docstrings:
- def test_model_loading(self): Tests model loading functionality.
- def test_hparams(self): Tests the priority of the encoder architecture parameter... | Implement the Python class `XLNetEncoderTest` described below.
Class description:
Tests :class:`~texar.tf.modules.XLNetEncoder` class.
Method signatures and docstrings:
- def test_model_loading(self): Tests model loading functionality.
- def test_hparams(self): Tests the priority of the encoder architecture parameter... | 0704b3d4c93915b9a6f96b725e49ae20bf5d1e90 | <|skeleton|>
class XLNetEncoderTest:
"""Tests :class:`~texar.tf.modules.XLNetEncoder` class."""
def test_model_loading(self):
"""Tests model loading functionality."""
<|body_0|>
def test_hparams(self):
"""Tests the priority of the encoder architecture parameter."""
<|body_1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XLNetEncoderTest:
"""Tests :class:`~texar.tf.modules.XLNetEncoder` class."""
def test_model_loading(self):
"""Tests model loading functionality."""
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])
for pretrained_model_name in XLNetEncoder.available_checkpoints():
... | the_stack_v2_python_sparse | texar/tf/modules/encoders/xlnet_encoder_test.py | arita37/texar | train | 2 |
ab473e48183d95ef12932b7c1151ce6c242a0d49 | [
"if not devices:\n devices = [0]\nself._device_queue = Queue()\nself._done_queue = Queue()\nfor d in devices:\n self._device_queue.put(str(d))\n_LOGGER.info(f'Initialized profiler runner with devices: {devices}')\nself._timeout = timeout\nself._executor = concurrent.futures.ThreadPoolExecutor(max_workers=len(... | <|body_start_0|>
if not devices:
devices = [0]
self._device_queue = Queue()
self._done_queue = Queue()
for d in devices:
self._device_queue.put(str(d))
_LOGGER.info(f'Initialized profiler runner with devices: {devices}')
self._timeout = timeout
... | Another parallel runner to execute profilers on multiple GPUs in parallel It uses a process pool for implementation, avoiding process creation overhead The size of the process pool is equal to the number of provided GPUs, so ~ideally~ each process should execute a profiler on its dedicated GPU. This property hasn't bee... | ProfilerRunner | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProfilerRunner:
"""Another parallel runner to execute profilers on multiple GPUs in parallel It uses a process pool for implementation, avoiding process creation overhead The size of the process pool is equal to the number of provided GPUs, so ~ideally~ each process should execute a profiler on i... | stack_v2_sparse_classes_36k_train_016458 | 12,702 | permissive | [
{
"docstring": "Parameters ---------- devices : List[str] device identifiers (contents of {CUDA,HIP}_VISIBLE_DEVICES) postprocessing_delegate : object responsible for postprocessing results after futures completion timeout : int timeout to wait for all profilers completion in seconds",
"name": "__init__",
... | 3 | stack_v2_sparse_classes_30k_train_017065 | Implement the Python class `ProfilerRunner` described below.
Class description:
Another parallel runner to execute profilers on multiple GPUs in parallel It uses a process pool for implementation, avoiding process creation overhead The size of the process pool is equal to the number of provided GPUs, so ~ideally~ each... | Implement the Python class `ProfilerRunner` described below.
Class description:
Another parallel runner to execute profilers on multiple GPUs in parallel It uses a process pool for implementation, avoiding process creation overhead The size of the process pool is equal to the number of provided GPUs, so ~ideally~ each... | c60dc19788217556ba12ea378c02b9fd0aea9ffe | <|skeleton|>
class ProfilerRunner:
"""Another parallel runner to execute profilers on multiple GPUs in parallel It uses a process pool for implementation, avoiding process creation overhead The size of the process pool is equal to the number of provided GPUs, so ~ideally~ each process should execute a profiler on i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProfilerRunner:
"""Another parallel runner to execute profilers on multiple GPUs in parallel It uses a process pool for implementation, avoiding process creation overhead The size of the process pool is equal to the number of provided GPUs, so ~ideally~ each process should execute a profiler on its dedicated ... | the_stack_v2_python_sparse | python/aitemplate/backend/profiler_runner.py | facebookincubator/AITemplate | train | 4,065 |
eb02282443b4919f4245f332e019d276eb806544 | [
"self.options.declare('in_name', types=str)\nself.options.declare('out_name', types=str)\nself.options.declare('shape', types=tuple)\nself.options.declare('constraint_size', types=int, default=1)\nself.options.declare('lower_flag', types=bool, default=False)\nself.options.declare('rho', 50.0, desc='Constraint Aggre... | <|body_start_0|>
self.options.declare('in_name', types=str)
self.options.declare('out_name', types=str)
self.options.declare('shape', types=tuple)
self.options.declare('constraint_size', types=int, default=1)
self.options.declare('lower_flag', types=bool, default=False)
s... | KSComp | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KSComp:
def initialize(self):
"""Declare options."""
<|body_0|>
def setup(self):
"""Declare inputs, outputs, and derivatives for the KS component."""
<|body_1|>
def compute(self, inputs, outputs):
"""Compute the output of the KS function. Paramet... | stack_v2_sparse_classes_36k_train_016459 | 4,368 | no_license | [
{
"docstring": "Declare options.",
"name": "initialize",
"signature": "def initialize(self)"
},
{
"docstring": "Declare inputs, outputs, and derivatives for the KS component.",
"name": "setup",
"signature": "def setup(self)"
},
{
"docstring": "Compute the output of the KS functio... | 4 | stack_v2_sparse_classes_30k_train_002074 | Implement the Python class `KSComp` described below.
Class description:
Implement the KSComp class.
Method signatures and docstrings:
- def initialize(self): Declare options.
- def setup(self): Declare inputs, outputs, and derivatives for the KS component.
- def compute(self, inputs, outputs): Compute the output of t... | Implement the Python class `KSComp` described below.
Class description:
Implement the KSComp class.
Method signatures and docstrings:
- def initialize(self): Declare options.
- def setup(self): Declare inputs, outputs, and derivatives for the KS component.
- def compute(self, inputs, outputs): Compute the output of t... | 439a6f68f37b69f19d859e775a78c6c1eb16b7a1 | <|skeleton|>
class KSComp:
def initialize(self):
"""Declare options."""
<|body_0|>
def setup(self):
"""Declare inputs, outputs, and derivatives for the KS component."""
<|body_1|>
def compute(self, inputs, outputs):
"""Compute the output of the KS function. Paramet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KSComp:
def initialize(self):
"""Declare options."""
self.options.declare('in_name', types=str)
self.options.declare('out_name', types=str)
self.options.declare('shape', types=tuple)
self.options.declare('constraint_size', types=int, default=1)
self.options.decl... | the_stack_v2_python_sparse | lsdo_cubesat/utils/ks_comp.py | vgucsd/cubesat | train | 0 | |
111c9845685a67f03f3fb81282a62e7b2956ee17 | [
"self.que = collections.deque(maxlen=size)\nself.size = size\nself.s = 0",
"que = self.que\nif len(que) == self.size:\n self.s -= que.popleft()\nque.append(val)\nself.s += val\nreturn float(self.s) / len(que)"
] | <|body_start_0|>
self.que = collections.deque(maxlen=size)
self.size = size
self.s = 0
<|end_body_0|>
<|body_start_1|>
que = self.que
if len(que) == self.size:
self.s -= que.popleft()
que.append(val)
self.s += val
return float(self.s) / len(qu... | MovingAverage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type val: int :rtype: float"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.que = collections.deque(maxlen=s... | stack_v2_sparse_classes_36k_train_016460 | 733 | no_license | [
{
"docstring": "Initialize your data structure here. :type size: int",
"name": "__init__",
"signature": "def __init__(self, size)"
},
{
"docstring": ":type val: int :rtype: float",
"name": "next",
"signature": "def next(self, val)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005752 | Implement the Python class `MovingAverage` described below.
Class description:
Implement the MovingAverage class.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float | Implement the Python class `MovingAverage` described below.
Class description:
Implement the MovingAverage class.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float
<|skeleton|>
class MovingAverage:
... | 4e1f2fd08316354d8ef2babac6bb355f8acdeaef | <|skeleton|>
class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type val: int :rtype: float"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
self.que = collections.deque(maxlen=size)
self.size = size
self.s = 0
def next(self, val):
""":type val: int :rtype: float"""
que = self.que
if len(q... | the_stack_v2_python_sparse | leetcode/346_Moving_Average_from_Data_Stream.py | JinXJinX/practice | train | 0 | |
a6b67a05aa54125d2195b5b30c293370bc4e10cc | [
"base_options = _BaseOptions(model_asset_path=model_path)\noptions = InteractiveSegmenterOptions(base_options=base_options)\nreturn cls.create_from_options(options)",
"output_streams = [':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])]\nif options.output_confidence_masks:\n output_streams.append(':'.join([_CONFI... | <|body_start_0|>
base_options = _BaseOptions(model_asset_path=model_path)
options = InteractiveSegmenterOptions(base_options=base_options)
return cls.create_from_options(options)
<|end_body_0|>
<|body_start_1|>
output_streams = [':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])]
if... | Class that performs interactive segmentation on images. Users can represent user interaction through `RegionOfInterest`, which gives a hint to InteractiveSegmenter to perform segmentation focusing on the given region of interest. The API expects a TFLite model with mandatory TFLite Model Metadata. Input tensor: (kTfLit... | InteractiveSegmenter | [
"Apache-2.0",
"dtoa"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InteractiveSegmenter:
"""Class that performs interactive segmentation on images. Users can represent user interaction through `RegionOfInterest`, which gives a hint to InteractiveSegmenter to perform segmentation focusing on the given region of interest. The API expects a TFLite model with mandat... | stack_v2_sparse_classes_36k_train_016461 | 10,462 | permissive | [
{
"docstring": "Creates an `InteractiveSegmenter` object from a TensorFlow Lite model and the default `InteractiveSegmenterOptions`. Note that the created `InteractiveSegmenter` instance is in image mode, for performing image segmentation on single image inputs. Args: model_path: Path to the model. Returns: `In... | 3 | null | Implement the Python class `InteractiveSegmenter` described below.
Class description:
Class that performs interactive segmentation on images. Users can represent user interaction through `RegionOfInterest`, which gives a hint to InteractiveSegmenter to perform segmentation focusing on the given region of interest. The... | Implement the Python class `InteractiveSegmenter` described below.
Class description:
Class that performs interactive segmentation on images. Users can represent user interaction through `RegionOfInterest`, which gives a hint to InteractiveSegmenter to perform segmentation focusing on the given region of interest. The... | 007824594bf1d07c7c1467df03a43886f8a4b3ad | <|skeleton|>
class InteractiveSegmenter:
"""Class that performs interactive segmentation on images. Users can represent user interaction through `RegionOfInterest`, which gives a hint to InteractiveSegmenter to perform segmentation focusing on the given region of interest. The API expects a TFLite model with mandat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InteractiveSegmenter:
"""Class that performs interactive segmentation on images. Users can represent user interaction through `RegionOfInterest`, which gives a hint to InteractiveSegmenter to perform segmentation focusing on the given region of interest. The API expects a TFLite model with mandatory TFLite Mo... | the_stack_v2_python_sparse | mediapipe/tasks/python/vision/interactive_segmenter.py | google/mediapipe | train | 23,940 |
927227f189fbf77b4efee31e0086986f19a31976 | [
"self.block_rule_type_one = {'icon': constants.RULE_NODE_ICON, 'text': constants.BLOCK_RULE_TREE_KIND_ONE_NAME, 'data': {'rule_id': 0, 'is_root': True, 'rule_type': 'block_rule_1'}, 'state': {'opened': True}, 'children': []}\nself.block_rule_type_two = {'icon': constants.RULE_NODE_ICON, 'text': constants.BLOCK_RULE... | <|body_start_0|>
self.block_rule_type_one = {'icon': constants.RULE_NODE_ICON, 'text': constants.BLOCK_RULE_TREE_KIND_ONE_NAME, 'data': {'rule_id': 0, 'is_root': True, 'rule_type': 'block_rule_1'}, 'state': {'opened': True}, 'children': []}
self.block_rule_type_two = {'icon': constants.RULE_NODE_ICON, '... | Rule_Tree | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rule_Tree:
def __init__(self):
"""!@brief init parent node of rules @param @pre @post @note @return @author Gin Chen @date 2017/12/20"""
<|body_0|>
def toReturn(self):
"""!@brief return data rule tree and block rule tree @param @pre @post @note @return data rule tree... | stack_v2_sparse_classes_36k_train_016462 | 14,409 | no_license | [
{
"docstring": "!@brief init parent node of rules @param @pre @post @note @return @author Gin Chen @date 2017/12/20",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "!@brief return data rule tree and block rule tree @param @pre @post @note @return data rule tree and bloc... | 2 | null | Implement the Python class `Rule_Tree` described below.
Class description:
Implement the Rule_Tree class.
Method signatures and docstrings:
- def __init__(self): !@brief init parent node of rules @param @pre @post @note @return @author Gin Chen @date 2017/12/20
- def toReturn(self): !@brief return data rule tree and ... | Implement the Python class `Rule_Tree` described below.
Class description:
Implement the Rule_Tree class.
Method signatures and docstrings:
- def __init__(self): !@brief init parent node of rules @param @pre @post @note @return @author Gin Chen @date 2017/12/20
- def toReturn(self): !@brief return data rule tree and ... | 10d1307609f96eee99e3cd220135dc3b73936451 | <|skeleton|>
class Rule_Tree:
def __init__(self):
"""!@brief init parent node of rules @param @pre @post @note @return @author Gin Chen @date 2017/12/20"""
<|body_0|>
def toReturn(self):
"""!@brief return data rule tree and block rule tree @param @pre @post @note @return data rule tree... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Rule_Tree:
def __init__(self):
"""!@brief init parent node of rules @param @pre @post @note @return @author Gin Chen @date 2017/12/20"""
self.block_rule_type_one = {'icon': constants.RULE_NODE_ICON, 'text': constants.BLOCK_RULE_TREE_KIND_ONE_NAME, 'data': {'rule_id': 0, 'is_root': True, 'rule_... | the_stack_v2_python_sparse | backend/apolo/apolomgr/resource/common/common_policy_tree/policy_tree.py | renxchen/asuka | train | 0 | |
a0d76895e28f09417d36b0ef92499a4a098f0cc9 | [
"def cal(indexes: List[int]) -> int:\n res, left = (0, 0)\n for right in range(len(indexes)):\n while left <= right and indexes[right] - indexes[left] + 1 > k + right - left + 1:\n left += 1\n res = max(res, right - left + 1)\n return res\nmp = defaultdict(list)\nfor i, v in enumer... | <|body_start_0|>
def cal(indexes: List[int]) -> int:
res, left = (0, 0)
for right in range(len(indexes)):
while left <= right and indexes[right] - indexes[left] + 1 > k + right - left + 1:
left += 1
res = max(res, right - left + 1)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestEqualSubarray(self, nums: List[int], k: int) -> int:
"""哈希表+不定长滑动窗口"""
<|body_0|>
def longestEqualSubarray2(self, nums: List[int], k: int) -> int:
"""二分+定长滑动窗口"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def cal(indexes: Lis... | stack_v2_sparse_classes_36k_train_016463 | 2,316 | no_license | [
{
"docstring": "哈希表+不定长滑动窗口",
"name": "longestEqualSubarray",
"signature": "def longestEqualSubarray(self, nums: List[int], k: int) -> int"
},
{
"docstring": "二分+定长滑动窗口",
"name": "longestEqualSubarray2",
"signature": "def longestEqualSubarray2(self, nums: List[int], k: int) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_014402 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestEqualSubarray(self, nums: List[int], k: int) -> int: 哈希表+不定长滑动窗口
- def longestEqualSubarray2(self, nums: List[int], k: int) -> int: 二分+定长滑动窗口 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestEqualSubarray(self, nums: List[int], k: int) -> int: 哈希表+不定长滑动窗口
- def longestEqualSubarray2(self, nums: List[int], k: int) -> int: 二分+定长滑动窗口
<|skeleton|>
class Solut... | 7e79e26bb8f641868561b186e34c1127ed63c9e0 | <|skeleton|>
class Solution:
def longestEqualSubarray(self, nums: List[int], k: int) -> int:
"""哈希表+不定长滑动窗口"""
<|body_0|>
def longestEqualSubarray2(self, nums: List[int], k: int) -> int:
"""二分+定长滑动窗口"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestEqualSubarray(self, nums: List[int], k: int) -> int:
"""哈希表+不定长滑动窗口"""
def cal(indexes: List[int]) -> int:
res, left = (0, 0)
for right in range(len(indexes)):
while left <= right and indexes[right] - indexes[left] + 1 > k + right - ... | the_stack_v2_python_sparse | 16_滑动窗口/2831. 找出最长等值子数组.py | 981377660LMT/algorithm-study | train | 225 | |
f899d4e2c3627bdd3816386c0e610bf62e9e3bb1 | [
"self.capacity = capacity\nself.values = {}\nself.freq_time = {}\nself.least_used = []\nself.update = set()\nself.time = 0",
"self.time += 1\nif key in self.values:\n self.update.add(key)\n self.freq_time[key] = (self.freq_time[key][0] + 1, self.time)\n return self.values[key]\nreturn -1",
"if self.cap... | <|body_start_0|>
self.capacity = capacity
self.values = {}
self.freq_time = {}
self.least_used = []
self.update = set()
self.time = 0
<|end_body_0|>
<|body_start_1|>
self.time += 1
if key in self.values:
self.update.add(key)
self.f... | LFUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k_train_016464 | 4,992 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: None",
"name": "pu... | 3 | stack_v2_sparse_classes_30k_train_005952 | Implement the Python class `LFUCache` described below.
Class description:
Implement the LFUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None | Implement the Python class `LFUCache` described below.
Class description:
Implement the LFUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None
<|sk... | 36d7f9e967a62db77622e0888f61999d7f37579a | <|skeleton|>
class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.values = {}
self.freq_time = {}
self.least_used = []
self.update = set()
self.time = 0
def get(self, key):
""":type key: int :rtype: int"""
... | the_stack_v2_python_sparse | P0460.py | westgate458/LeetCode | train | 0 | |
72cd529cee2c41262ac730a775a1704dae8c85ba | [
"if not head or not head.next:\n return head\ncur = self.reverseList(head.next)\nhead.next.next = head\nhead.next = None\nreturn cur",
"if not head or not head.next:\n return head\ncur, pre = (None, head)\nwhile pre:\n t = pre.next\n pre.next = cur\n cur, pre = (pre, t)\nreturn cur"
] | <|body_start_0|>
if not head or not head.next:
return head
cur = self.reverseList(head.next)
head.next.next = head
head.next = None
return cur
<|end_body_0|>
<|body_start_1|>
if not head or not head.next:
return head
cur, pre = (None, head... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseList(self, head: ListNode) -> ListNode:
"""递归方法 :param head: :return:"""
<|body_0|>
def reverseList2(self, head: ListNode) -> ListNode:
"""双指针 使用中间节点存储以前的值， 把当前节点的next指向前面，然后把两个指针依次后移 :param head: :return:"""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_016465 | 1,212 | no_license | [
{
"docstring": "递归方法 :param head: :return:",
"name": "reverseList",
"signature": "def reverseList(self, head: ListNode) -> ListNode"
},
{
"docstring": "双指针 使用中间节点存储以前的值， 把当前节点的next指向前面，然后把两个指针依次后移 :param head: :return:",
"name": "reverseList2",
"signature": "def reverseList2(self, head: ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head: ListNode) -> ListNode: 递归方法 :param head: :return:
- def reverseList2(self, head: ListNode) -> ListNode: 双指针 使用中间节点存储以前的值， 把当前节点的next指向前面，然后把两个指针依次后移 :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head: ListNode) -> ListNode: 递归方法 :param head: :return:
- def reverseList2(self, head: ListNode) -> ListNode: 双指针 使用中间节点存储以前的值， 把当前节点的next指向前面，然后把两个指针依次后移 :... | 578cacff5851c5c2522981693c34e3c318002d30 | <|skeleton|>
class Solution:
def reverseList(self, head: ListNode) -> ListNode:
"""递归方法 :param head: :return:"""
<|body_0|>
def reverseList2(self, head: ListNode) -> ListNode:
"""双指针 使用中间节点存储以前的值， 把当前节点的next指向前面，然后把两个指针依次后移 :param head: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def reverseList(self, head: ListNode) -> ListNode:
"""递归方法 :param head: :return:"""
if not head or not head.next:
return head
cur = self.reverseList(head.next)
head.next.next = head
head.next = None
return cur
def reverseList2(self, he... | the_stack_v2_python_sparse | 剑指offer/反转链表.py | cjrzs/MyLeetCode | train | 8 | |
52e4e25fe8965a2eeb15cd600a31e3fa831cfbb3 | [
"super(LinearTransformFineCoattention, self).__init__()\nwith self.init_scope():\n self.energy_layer = links.Bilinear(hidden_dim, hidden_dim, 1)\n self.j_layer = GraphLinear(hidden_dim, out_dim)\nself.hidden_dim = hidden_dim\nself.out_dim = out_dim\nself.activation = activation",
"mb = atoms_1.shape[0]\nN_1... | <|body_start_0|>
super(LinearTransformFineCoattention, self).__init__()
with self.init_scope():
self.energy_layer = links.Bilinear(hidden_dim, hidden_dim, 1)
self.j_layer = GraphLinear(hidden_dim, out_dim)
self.hidden_dim = hidden_dim
self.out_dim = out_dim
... | TODO | LinearTransformFineCoattention | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinearTransformFineCoattention:
"""TODO"""
def __init__(self, hidden_dim, out_dim, activation=functions.tanh):
""":param hidden_dim: dimension of atom representation :param out_dim: dimension of molecular representation"""
<|body_0|>
def __call__(self, atoms_1, g_1, atom... | stack_v2_sparse_classes_36k_train_016466 | 4,044 | permissive | [
{
"docstring": ":param hidden_dim: dimension of atom representation :param out_dim: dimension of molecular representation",
"name": "__init__",
"signature": "def __init__(self, hidden_dim, out_dim, activation=functions.tanh)"
},
{
"docstring": ":param atoms_1: atomic representation of molecule 1... | 3 | stack_v2_sparse_classes_30k_train_012861 | Implement the Python class `LinearTransformFineCoattention` described below.
Class description:
TODO
Method signatures and docstrings:
- def __init__(self, hidden_dim, out_dim, activation=functions.tanh): :param hidden_dim: dimension of atom representation :param out_dim: dimension of molecular representation
- def _... | Implement the Python class `LinearTransformFineCoattention` described below.
Class description:
TODO
Method signatures and docstrings:
- def __init__(self, hidden_dim, out_dim, activation=functions.tanh): :param hidden_dim: dimension of atom representation :param out_dim: dimension of molecular representation
- def _... | 21b64a3c8cc9bc33718ae09c65aa917e575132eb | <|skeleton|>
class LinearTransformFineCoattention:
"""TODO"""
def __init__(self, hidden_dim, out_dim, activation=functions.tanh):
""":param hidden_dim: dimension of atom representation :param out_dim: dimension of molecular representation"""
<|body_0|>
def __call__(self, atoms_1, g_1, atom... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinearTransformFineCoattention:
"""TODO"""
def __init__(self, hidden_dim, out_dim, activation=functions.tanh):
""":param hidden_dim: dimension of atom representation :param out_dim: dimension of molecular representation"""
super(LinearTransformFineCoattention, self).__init__()
wit... | the_stack_v2_python_sparse | models/coattention/lt_fine_coattention.py | Minys233/GCN-BMP | train | 1 |
ed7b0b9e14418a4cf83d91b1cd44a4ad3018d8df | [
"super().__init__(title, group_name)\nself.compound = compound\nrt_ref: metob.RtReference = compound['identification'].rt_references[0]\nself.rt_range: Tuple[float, float] = (rt_ref.rt_min, rt_ref.rt_max)\nself.rt_peak: float = rt_ref.rt_peak\nself.rt_buffer = rt_buffer",
"super().plot(ax, back_color)\nself._draw... | <|body_start_0|>
super().__init__(title, group_name)
self.compound = compound
rt_ref: metob.RtReference = compound['identification'].rt_references[0]
self.rt_range: Tuple[float, float] = (rt_ref.rt_min, rt_ref.rt_max)
self.rt_peak: float = rt_ref.rt_peak
self.rt_buffer = ... | EIC for one compound within a single sample | CompoundEic | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompoundEic:
"""EIC for one compound within a single sample"""
def __init__(self, title: str, group_name: str, compound: Dict[str, Any], rt_buffer: float=0.5):
"""compound: Compound instance rt_buffer: amount of time in minutes to show to each side of rt_min/rt_max/rt_peak"""
... | stack_v2_sparse_classes_36k_train_016467 | 3,948 | permissive | [
{
"docstring": "compound: Compound instance rt_buffer: amount of time in minutes to show to each side of rt_min/rt_max/rt_peak",
"name": "__init__",
"signature": "def __init__(self, title: str, group_name: str, compound: Dict[str, Any], rt_buffer: float=0.5)"
},
{
"docstring": "Draw plot of EIC ... | 4 | stack_v2_sparse_classes_30k_train_020867 | Implement the Python class `CompoundEic` described below.
Class description:
EIC for one compound within a single sample
Method signatures and docstrings:
- def __init__(self, title: str, group_name: str, compound: Dict[str, Any], rt_buffer: float=0.5): compound: Compound instance rt_buffer: amount of time in minutes... | Implement the Python class `CompoundEic` described below.
Class description:
EIC for one compound within a single sample
Method signatures and docstrings:
- def __init__(self, title: str, group_name: str, compound: Dict[str, Any], rt_buffer: float=0.5): compound: Compound instance rt_buffer: amount of time in minutes... | 909ede3d1fe75fa5d64c6ff1b4c6016dc3df6746 | <|skeleton|>
class CompoundEic:
"""EIC for one compound within a single sample"""
def __init__(self, title: str, group_name: str, compound: Dict[str, Any], rt_buffer: float=0.5):
"""compound: Compound instance rt_buffer: amount of time in minutes to show to each side of rt_min/rt_max/rt_peak"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CompoundEic:
"""EIC for one compound within a single sample"""
def __init__(self, title: str, group_name: str, compound: Dict[str, Any], rt_buffer: float=0.5):
"""compound: Compound instance rt_buffer: amount of time in minutes to show to each side of rt_min/rt_max/rt_peak"""
super().__in... | the_stack_v2_python_sparse | metatlas/plots/compound_eic.py | biorack/metatlas | train | 10 |
79fe33879daaf7191e44567c3d43c9c598e32f2b | [
"media = self.media_info(media_pk)\nassert media.media_type == 8, 'Must been album'\npaths = []\nfor resource in media.resources:\n filename = f'{media.user.username}_{resource.pk}'\n if resource.media_type == 1:\n paths.append(self.photo_download_by_url(resource.thumbnail_url, filename, folder))\n ... | <|body_start_0|>
media = self.media_info(media_pk)
assert media.media_type == 8, 'Must been album'
paths = []
for resource in media.resources:
filename = f'{media.user.username}_{resource.pk}'
if resource.media_type == 1:
paths.append(self.photo_do... | Helper class to download album | DownloadAlbumMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DownloadAlbumMixin:
"""Helper class to download album"""
def album_download(self, media_pk: int, folder: Path='') -> List[Path]:
"""Download your album Parameters ---------- media_pk: int PK for the album you want to download folder: Path, optional Directory in which you want to down... | stack_v2_sparse_classes_36k_train_016468 | 8,777 | permissive | [
{
"docstring": "Download your album Parameters ---------- media_pk: int PK for the album you want to download folder: Path, optional Directory in which you want to download the album, default is \"\" and will download the files to working directory. Returns ------- List[Path] List of path for all the files down... | 2 | stack_v2_sparse_classes_30k_train_017273 | Implement the Python class `DownloadAlbumMixin` described below.
Class description:
Helper class to download album
Method signatures and docstrings:
- def album_download(self, media_pk: int, folder: Path='') -> List[Path]: Download your album Parameters ---------- media_pk: int PK for the album you want to download f... | Implement the Python class `DownloadAlbumMixin` described below.
Class description:
Helper class to download album
Method signatures and docstrings:
- def album_download(self, media_pk: int, folder: Path='') -> List[Path]: Download your album Parameters ---------- media_pk: int PK for the album you want to download f... | 14922b4038de0b693c6dd396c7ee0b57e626f32e | <|skeleton|>
class DownloadAlbumMixin:
"""Helper class to download album"""
def album_download(self, media_pk: int, folder: Path='') -> List[Path]:
"""Download your album Parameters ---------- media_pk: int PK for the album you want to download folder: Path, optional Directory in which you want to down... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DownloadAlbumMixin:
"""Helper class to download album"""
def album_download(self, media_pk: int, folder: Path='') -> List[Path]:
"""Download your album Parameters ---------- media_pk: int PK for the album you want to download folder: Path, optional Directory in which you want to download the albu... | the_stack_v2_python_sparse | instagrapi/mixins/album.py | bedefaced/instagrapi | train | 1 |
5b200bed84a3c38dbb7e90eeb52bb2e1ad1a36b7 | [
"if not s:\n return 0\ndp = [0] * (len(s) + 1)\ndp[0] = 1\nfor i in range(1, len(s) + 1):\n if s[i - 1] != '0':\n dp[i] += dp[i - 1]\n if i != 1 and s[i - 2:i] > '09' and (s[i - 2:i] < '27'):\n dp[i] += dp[i - 2]\nreturn dp[len(s)]",
"if s.startswith('0'):\n return 0\nn = len(s)\ndp = [1... | <|body_start_0|>
if not s:
return 0
dp = [0] * (len(s) + 1)
dp[0] = 1
for i in range(1, len(s) + 1):
if s[i - 1] != '0':
dp[i] += dp[i - 1]
if i != 1 and s[i - 2:i] > '09' and (s[i - 2:i] < '27'):
dp[i] += dp[i - 2]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numDecodings_20211226(self, s: str) -> int:
"""状态表示： - 集合： 所有前 i 个数字解码得到的字符串 - 属性： 数量 状态计算： - 最后一个字母是一位数 f[i-1] - 最后一个字母是两位数 f[i-2]"""
<|body_0|>
def numDecodings(self, s: str) -> int:
"""状态： dp[i]: 若前 i 个字符可以解码， 表示前 i 个字符可以解码的方法数 若前 i 个字符不能解码，那整个字符串肯定不... | stack_v2_sparse_classes_36k_train_016469 | 1,633 | no_license | [
{
"docstring": "状态表示： - 集合： 所有前 i 个数字解码得到的字符串 - 属性： 数量 状态计算： - 最后一个字母是一位数 f[i-1] - 最后一个字母是两位数 f[i-2]",
"name": "numDecodings_20211226",
"signature": "def numDecodings_20211226(self, s: str) -> int"
},
{
"docstring": "状态： dp[i]: 若前 i 个字符可以解码， 表示前 i 个字符可以解码的方法数 若前 i 个字符不能解码，那整个字符串肯定不能解码，我们无需再进行下去了... | 2 | stack_v2_sparse_classes_30k_train_021062 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numDecodings_20211226(self, s: str) -> int: 状态表示： - 集合： 所有前 i 个数字解码得到的字符串 - 属性： 数量 状态计算： - 最后一个字母是一位数 f[i-1] - 最后一个字母是两位数 f[i-2]
- def numDecodings(self, s: str) -> int: 状态： ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numDecodings_20211226(self, s: str) -> int: 状态表示： - 集合： 所有前 i 个数字解码得到的字符串 - 属性： 数量 状态计算： - 最后一个字母是一位数 f[i-1] - 最后一个字母是两位数 f[i-2]
- def numDecodings(self, s: str) -> int: 状态： ... | f350b3d6e59fd5771e11ec0b466f9ba5eeb8e927 | <|skeleton|>
class Solution:
def numDecodings_20211226(self, s: str) -> int:
"""状态表示： - 集合： 所有前 i 个数字解码得到的字符串 - 属性： 数量 状态计算： - 最后一个字母是一位数 f[i-1] - 最后一个字母是两位数 f[i-2]"""
<|body_0|>
def numDecodings(self, s: str) -> int:
"""状态： dp[i]: 若前 i 个字符可以解码， 表示前 i 个字符可以解码的方法数 若前 i 个字符不能解码，那整个字符串肯定不... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def numDecodings_20211226(self, s: str) -> int:
"""状态表示： - 集合： 所有前 i 个数字解码得到的字符串 - 属性： 数量 状态计算： - 最后一个字母是一位数 f[i-1] - 最后一个字母是两位数 f[i-2]"""
if not s:
return 0
dp = [0] * (len(s) + 1)
dp[0] = 1
for i in range(1, len(s) + 1):
if s[i - 1] !... | the_stack_v2_python_sparse | leetcode/python/91.py | ShawnDong98/Algorithm-Book | train | 0 | |
79a6fc90ebd8d2a827cbc40eecab2f38f78fecf6 | [
"super(SentimentRNN, self).__init__()\nself.output_size = output_size\nself.n_layers = n_layers\nself.hidden_dim = hidden_dim\nself.embedding = nn.Embedding(vocab_size, embedding_dim)\nself.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=drop_prob, batch_first=True)\nself.dropout = nn.Dropout(0.3)\nself... | <|body_start_0|>
super(SentimentRNN, self).__init__()
self.output_size = output_size
self.n_layers = n_layers
self.hidden_dim = hidden_dim
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=drop_prob, ... | The RNN model that will be used to perform Sentiment analysis. vocab_size: Size of our vocabulary or the range of values for our input, word tokens. output_size: Size of our desired output; the number of class scores we want to output (pos/neg). embedding_dim: Number of columns in the embedding lookup table; size of ou... | SentimentRNN | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SentimentRNN:
"""The RNN model that will be used to perform Sentiment analysis. vocab_size: Size of our vocabulary or the range of values for our input, word tokens. output_size: Size of our desired output; the number of class scores we want to output (pos/neg). embedding_dim: Number of columns i... | stack_v2_sparse_classes_36k_train_016470 | 14,084 | permissive | [
{
"docstring": "Initialize the model by setting up the layers.",
"name": "__init__",
"signature": "def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5)"
},
{
"docstring": "Perform a forward pass of our model on some input and hidden state.",
"name":... | 3 | stack_v2_sparse_classes_30k_train_016834 | Implement the Python class `SentimentRNN` described below.
Class description:
The RNN model that will be used to perform Sentiment analysis. vocab_size: Size of our vocabulary or the range of values for our input, word tokens. output_size: Size of our desired output; the number of class scores we want to output (pos/n... | Implement the Python class `SentimentRNN` described below.
Class description:
The RNN model that will be used to perform Sentiment analysis. vocab_size: Size of our vocabulary or the range of values for our input, word tokens. output_size: Size of our desired output; the number of class scores we want to output (pos/n... | b9b54564f94aadfc3c71ff513da0f05ef85d22a8 | <|skeleton|>
class SentimentRNN:
"""The RNN model that will be used to perform Sentiment analysis. vocab_size: Size of our vocabulary or the range of values for our input, word tokens. output_size: Size of our desired output; the number of class scores we want to output (pos/neg). embedding_dim: Number of columns i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SentimentRNN:
"""The RNN model that will be used to perform Sentiment analysis. vocab_size: Size of our vocabulary or the range of values for our input, word tokens. output_size: Size of our desired output; the number of class scores we want to output (pos/neg). embedding_dim: Number of columns in the embeddi... | the_stack_v2_python_sparse | dl/pytorch/rnn/sentiment.py | xta0/Python-Playground | train | 0 |
82538a4576d08100583c46aa593b218dbe36934e | [
"self._resolver = resolver\nself._port = port\nself._lock_monitor_cb = lock_monitor_cb",
"results = {}\nrequests = {}\nassert isinstance(body, dict)\nassert len(body) == len(hosts)\nassert compat.all((isinstance(v, (str, bytes)) for v in body.values()))\nassert frozenset((h[2] for h in hosts)) == frozenset(body),... | <|body_start_0|>
self._resolver = resolver
self._port = port
self._lock_monitor_cb = lock_monitor_cb
<|end_body_0|>
<|body_start_1|>
results = {}
requests = {}
assert isinstance(body, dict)
assert len(body) == len(hosts)
assert compat.all((isinstance(v, (... | _RpcProcessor | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _RpcProcessor:
def __init__(self, resolver, port, lock_monitor_cb=None):
"""Initializes this class. @param resolver: callable accepting a list of node UUIDs or hostnames, returning a list of tuples containing name, IP address and original name of the resolved node. IP address can be the ... | stack_v2_sparse_classes_36k_train_016471 | 33,673 | permissive | [
{
"docstring": "Initializes this class. @param resolver: callable accepting a list of node UUIDs or hostnames, returning a list of tuples containing name, IP address and original name of the resolved node. IP address can be the name or the special value L{_OFFLINE} to mark offline machines. @type port: int @par... | 4 | null | Implement the Python class `_RpcProcessor` described below.
Class description:
Implement the _RpcProcessor class.
Method signatures and docstrings:
- def __init__(self, resolver, port, lock_monitor_cb=None): Initializes this class. @param resolver: callable accepting a list of node UUIDs or hostnames, returning a lis... | Implement the Python class `_RpcProcessor` described below.
Class description:
Implement the _RpcProcessor class.
Method signatures and docstrings:
- def __init__(self, resolver, port, lock_monitor_cb=None): Initializes this class. @param resolver: callable accepting a list of node UUIDs or hostnames, returning a lis... | 456ea285a7583183c2c8e5bcffe9006ec8a9d658 | <|skeleton|>
class _RpcProcessor:
def __init__(self, resolver, port, lock_monitor_cb=None):
"""Initializes this class. @param resolver: callable accepting a list of node UUIDs or hostnames, returning a list of tuples containing name, IP address and original name of the resolved node. IP address can be the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _RpcProcessor:
def __init__(self, resolver, port, lock_monitor_cb=None):
"""Initializes this class. @param resolver: callable accepting a list of node UUIDs or hostnames, returning a list of tuples containing name, IP address and original name of the resolved node. IP address can be the name or the sp... | the_stack_v2_python_sparse | lib/rpc/node.py | ganeti/ganeti | train | 465 | |
c211c64f6568d18d2cd296e515ff53f1bfb117ef | [
"production_multiplier = 1\nstart_years = Incentives._data['start_year']\nif len(start_years[start_years <= vehicle.model_year]) > 0:\n cache_key = max(start_years[start_years <= vehicle.model_year])\n if cache_key in Incentives._data:\n calcs = Incentives._data[cache_key]\n for calc, multiplier... | <|body_start_0|>
production_multiplier = 1
start_years = Incentives._data['start_year']
if len(start_years[start_years <= vehicle.model_year]) > 0:
cache_key = max(start_years[start_years <= vehicle.model_year])
if cache_key in Incentives._data:
calcs = In... | **Loads and provides access to GHG incentives.** | Incentives | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Incentives:
"""**Loads and provides access to GHG incentives.**"""
def get_production_multiplier(vehicle):
"""Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier for Returns: The production multiplier, if applicable, or... | stack_v2_sparse_classes_36k_train_016472 | 5,113 | no_license | [
{
"docstring": "Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier for Returns: The production multiplier, if applicable, or 1.0",
"name": "get_production_multiplier",
"signature": "def get_production_multiplier(vehicle)"
},
{
"do... | 2 | null | Implement the Python class `Incentives` described below.
Class description:
**Loads and provides access to GHG incentives.**
Method signatures and docstrings:
- def get_production_multiplier(vehicle): Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier ... | Implement the Python class `Incentives` described below.
Class description:
**Loads and provides access to GHG incentives.**
Method signatures and docstrings:
- def get_production_multiplier(vehicle): Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier ... | afe912c57383b9de90ef30820f7977c3367a30c4 | <|skeleton|>
class Incentives:
"""**Loads and provides access to GHG incentives.**"""
def get_production_multiplier(vehicle):
"""Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier for Returns: The production multiplier, if applicable, or... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Incentives:
"""**Loads and provides access to GHG incentives.**"""
def get_production_multiplier(vehicle):
"""Get production multiplier (if any) for the given vehicle. Args: vehicle (Vehicle): the vehicle to get the multiplier for Returns: The production multiplier, if applicable, or 1.0"""
... | the_stack_v2_python_sparse | omega_model/policy/incentives.py | USEPA/EPA_OMEGA_Model | train | 17 |
9a13a641f8ec42e9ed6e1dc4fca9529199bc50fe | [
"res = 0\nn = len(prices)\nif n <= 1:\n return 0\nminpre = prices[0]\nfor i in range(1, n):\n res = max(prices[i] - minpre, res)\n minpre = min(prices[i], minpre)\nreturn res",
"res = 0\nn = len(prices)\nif n <= 1:\n return 0\ndp = [[0 for i in range(2)] for j in range(n)]\ndp[0][0] = 0\ndp[0][1] = -p... | <|body_start_0|>
res = 0
n = len(prices)
if n <= 1:
return 0
minpre = prices[0]
for i in range(1, n):
res = max(prices[i] - minpre, res)
minpre = min(prices[i], minpre)
return res
<|end_body_0|>
<|body_start_1|>
res = 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""只交易一次"""
<|body_0|>
def maxProfit(self, prices: List[int]) -> int:
"""给定一个数组，它的第 i 个元素是一支给定股票第 i 天的价格。 设计一个算法来计算你所能获取的最大利润。你可以尽可能地完成更多的交易（多次买卖一支股票）。 注意：你不能同时参与多笔交易（你必须在再次购买前出售掉之前的股票）。"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_016473 | 1,188 | no_license | [
{
"docstring": "只交易一次",
"name": "maxProfit",
"signature": "def maxProfit(self, prices: List[int]) -> int"
},
{
"docstring": "给定一个数组，它的第 i 个元素是一支给定股票第 i 天的价格。 设计一个算法来计算你所能获取的最大利润。你可以尽可能地完成更多的交易（多次买卖一支股票）。 注意：你不能同时参与多笔交易（你必须在再次购买前出售掉之前的股票）。",
"name": "maxProfit",
"signature": "def maxProfi... | 2 | stack_v2_sparse_classes_30k_train_010663 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices: List[int]) -> int: 只交易一次
- def maxProfit(self, prices: List[int]) -> int: 给定一个数组，它的第 i 个元素是一支给定股票第 i 天的价格。 设计一个算法来计算你所能获取的最大利润。你可以尽可能地完成更多的交易（多次买卖一支股票... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices: List[int]) -> int: 只交易一次
- def maxProfit(self, prices: List[int]) -> int: 给定一个数组，它的第 i 个元素是一支给定股票第 i 天的价格。 设计一个算法来计算你所能获取的最大利润。你可以尽可能地完成更多的交易（多次买卖一支股票... | cb3587242195bb3f2626231af2da13b90945a4d5 | <|skeleton|>
class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""只交易一次"""
<|body_0|>
def maxProfit(self, prices: List[int]) -> int:
"""给定一个数组，它的第 i 个元素是一支给定股票第 i 天的价格。 设计一个算法来计算你所能获取的最大利润。你可以尽可能地完成更多的交易（多次买卖一支股票）。 注意：你不能同时参与多笔交易（你必须在再次购买前出售掉之前的股票）。"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""只交易一次"""
res = 0
n = len(prices)
if n <= 1:
return 0
minpre = prices[0]
for i in range(1, n):
res = max(prices[i] - minpre, res)
minpre = min(prices[i], minpre)
... | the_stack_v2_python_sparse | MianShi/zijie/买卖股票的最佳时机N121.py | lionheartStark/sword_towards_offer | train | 0 | |
731a6d470386933676630447a2b9fbc42ca67924 | [
"self.busbars = busbars\nself.entries = dict()\nself.columns = constants.GUI.busbar_columns\nself.vertical_busbars = constants.GUI.vertical_busbars\nself.master = master\nself.master.protocol('WM_DELETE_WINDOW', self.on_closing)\nself.master.title = 'Identified Busbars'\nlbl = Tk.Label(self.master, text='Edit busba... | <|body_start_0|>
self.busbars = busbars
self.entries = dict()
self.columns = constants.GUI.busbar_columns
self.vertical_busbars = constants.GUI.vertical_busbars
self.master = master
self.master.protocol('WM_DELETE_WINDOW', self.on_closing)
self.master.title = 'Ide... | Produces a new window which is used to contain a list of busbars which can be edited by the user and then adjusted to produce the required output. | BusbarsWindow | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BusbarsWindow:
"""Produces a new window which is used to contain a list of busbars which can be edited by the user and then adjusted to produce the required output."""
def __init__(self, master, busbars=list()):
"""Produce window which contains details of all the busbars that will be... | stack_v2_sparse_classes_36k_train_016474 | 27,259 | no_license | [
{
"docstring": "Produce window which contains details of all the busbars that will be faulted and the ability to edit the busbars lists :param Tk.Tk() master: This is the main window master which this one will be popped up on-top of :param list busbars: List of busbars to be faulted will initially be populated ... | 5 | stack_v2_sparse_classes_30k_train_018222 | Implement the Python class `BusbarsWindow` described below.
Class description:
Produces a new window which is used to contain a list of busbars which can be edited by the user and then adjusted to produce the required output.
Method signatures and docstrings:
- def __init__(self, master, busbars=list()): Produce wind... | Implement the Python class `BusbarsWindow` described below.
Class description:
Produces a new window which is used to contain a list of busbars which can be edited by the user and then adjusted to produce the required output.
Method signatures and docstrings:
- def __init__(self, master, busbars=list()): Produce wind... | 16c479e31e1d02eadb64bd0452324c9d3af4ca9e | <|skeleton|>
class BusbarsWindow:
"""Produces a new window which is used to contain a list of busbars which can be edited by the user and then adjusted to produce the required output."""
def __init__(self, master, busbars=list()):
"""Produce window which contains details of all the busbars that will be... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BusbarsWindow:
"""Produces a new window which is used to contain a list of busbars which can be edited by the user and then adjusted to produce the required output."""
def __init__(self, master, busbars=list()):
"""Produce window which contains details of all the busbars that will be faulted and ... | the_stack_v2_python_sparse | g74/gui.py | NegarShams/JK7938_SHEPD_FaultLevels | train | 0 |
6a97151cbeabba5790948417ac96426ba1e8480c | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn DefenderDetectedMalwareActions()",
"from .defender_threat_action import DefenderThreatAction\nfrom .defender_threat_action import DefenderThreatAction\nfields: Dict[str, Callable[[Any], None]] = {'highSeverity': lambda n: setattr(self,... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return DefenderDetectedMalwareActions()
<|end_body_0|>
<|body_start_1|>
from .defender_threat_action import DefenderThreatAction
from .defender_threat_action import DefenderThreatAction
... | Specify Defender’s actions to take on detected Malware per threat level. | DefenderDetectedMalwareActions | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefenderDetectedMalwareActions:
"""Specify Defender’s actions to take on detected Malware per threat level."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DefenderDetectedMalwareActions:
"""Creates a new instance of the appropriate class based on disc... | stack_v2_sparse_classes_36k_train_016475 | 3,857 | 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: DefenderDetectedMalwareActions",
"name": "create_from_discriminator_value",
"signature": "def create_from_di... | 3 | null | Implement the Python class `DefenderDetectedMalwareActions` described below.
Class description:
Specify Defender’s actions to take on detected Malware per threat level.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DefenderDetectedMalwareActions: Crea... | Implement the Python class `DefenderDetectedMalwareActions` described below.
Class description:
Specify Defender’s actions to take on detected Malware per threat level.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DefenderDetectedMalwareActions: Crea... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class DefenderDetectedMalwareActions:
"""Specify Defender’s actions to take on detected Malware per threat level."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DefenderDetectedMalwareActions:
"""Creates a new instance of the appropriate class based on disc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DefenderDetectedMalwareActions:
"""Specify Defender’s actions to take on detected Malware per threat level."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DefenderDetectedMalwareActions:
"""Creates a new instance of the appropriate class based on discriminator val... | the_stack_v2_python_sparse | msgraph/generated/models/defender_detected_malware_actions.py | microsoftgraph/msgraph-sdk-python | train | 135 |
f2aed85036073f6f73e79d026b88aeb6163f0b08 | [
"if final_structure.formula != initial_structure.formula:\n raise ValueError('Initial and final structures have different ' + 'formulas!')\nself.initial = initial_structure\nself.final = final_structure",
"initial_vol = self.initial.lattice.volume\nfinal_vol = self.final.lattice.volume\nreturn final_vol / init... | <|body_start_0|>
if final_structure.formula != initial_structure.formula:
raise ValueError('Initial and final structures have different ' + 'formulas!')
self.initial = initial_structure
self.final = final_structure
<|end_body_0|>
<|body_start_1|>
initial_vol = self.initial.l... | This class analyzes the relaxation in a calculation. | RelaxationAnalyzer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelaxationAnalyzer:
"""This class analyzes the relaxation in a calculation."""
def __init__(self, initial_structure, final_structure):
"""Please note that the input and final structures should have the same ordering of sites. This is typically the case for most computational codes. A... | stack_v2_sparse_classes_36k_train_016476 | 24,388 | permissive | [
{
"docstring": "Please note that the input and final structures should have the same ordering of sites. This is typically the case for most computational codes. Args: initial_structure (Structure): Initial input structure to calculation. final_structure (Structure): Final output structure from calculation.",
... | 4 | null | Implement the Python class `RelaxationAnalyzer` described below.
Class description:
This class analyzes the relaxation in a calculation.
Method signatures and docstrings:
- def __init__(self, initial_structure, final_structure): Please note that the input and final structures should have the same ordering of sites. T... | Implement the Python class `RelaxationAnalyzer` described below.
Class description:
This class analyzes the relaxation in a calculation.
Method signatures and docstrings:
- def __init__(self, initial_structure, final_structure): Please note that the input and final structures should have the same ordering of sites. T... | 62ecae1c7382a41861e3a5d9b9c8dd1207472409 | <|skeleton|>
class RelaxationAnalyzer:
"""This class analyzes the relaxation in a calculation."""
def __init__(self, initial_structure, final_structure):
"""Please note that the input and final structures should have the same ordering of sites. This is typically the case for most computational codes. A... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RelaxationAnalyzer:
"""This class analyzes the relaxation in a calculation."""
def __init__(self, initial_structure, final_structure):
"""Please note that the input and final structures should have the same ordering of sites. This is typically the case for most computational codes. Args: initial_... | the_stack_v2_python_sparse | pymatgen/analysis/structure_analyzer.py | montoyjh/pymatgen | train | 2 |
14d17d6a2ffa10a70eef465f3a0027e703359b52 | [
"user = self.personalize_page_and_get_user()\nif not user:\n self.redirect(users.create_login_url(self.request.uri), normalize=False)\n return\nstudent = Student.get_enrolled_student_by_email(user.email())\nif student:\n self.redirect('/course')\n return\ncan_register = self.app_context.get_environ()['r... | <|body_start_0|>
user = self.personalize_page_and_get_user()
if not user:
self.redirect(users.create_login_url(self.request.uri), normalize=False)
return
student = Student.get_enrolled_student_by_email(user.email())
if student:
self.redirect('/course')... | Handler for course registration. | RegisterHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegisterHandler:
"""Handler for course registration."""
def get(self):
"""Handles GET request."""
<|body_0|>
def post(self):
"""Handles POST requests."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
user = self.personalize_page_and_get_user()
... | stack_v2_sparse_classes_36k_train_016477 | 24,555 | permissive | [
{
"docstring": "Handles GET request.",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Handles POST requests.",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004445 | Implement the Python class `RegisterHandler` described below.
Class description:
Handler for course registration.
Method signatures and docstrings:
- def get(self): Handles GET request.
- def post(self): Handles POST requests. | Implement the Python class `RegisterHandler` described below.
Class description:
Handler for course registration.
Method signatures and docstrings:
- def get(self): Handles GET request.
- def post(self): Handles POST requests.
<|skeleton|>
class RegisterHandler:
"""Handler for course registration."""
def ge... | 1bd1eeb41ad77f31c95897916efdb4a5a9cef2a8 | <|skeleton|>
class RegisterHandler:
"""Handler for course registration."""
def get(self):
"""Handles GET request."""
<|body_0|>
def post(self):
"""Handles POST requests."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegisterHandler:
"""Handler for course registration."""
def get(self):
"""Handles GET request."""
user = self.personalize_page_and_get_user()
if not user:
self.redirect(users.create_login_url(self.request.uri), normalize=False)
return
student = Stud... | the_stack_v2_python_sparse | coursebuilder/controllers/utils.py | xaferima/Malware-MetasploitLab | train | 1 |
0771dde3c88db872a004e7b7d169c40014b13677 | [
"self.dataframe = df\nself.rows_count = None\nself.variables_count = None\nself.has_error = False\nself.error_messages = []\nself.final_output = OrderedDict()\nself.set_values()",
"if self.dataframe is not None:\n self.rows_count = self.dataframe.shape[0]\n self.variables_count = len(self.dataframe.columns)... | <|body_start_0|>
self.dataframe = df
self.rows_count = None
self.variables_count = None
self.has_error = False
self.error_messages = []
self.final_output = OrderedDict()
self.set_values()
<|end_body_0|>
<|body_start_1|>
if self.dataframe is not None:
... | DatasetLevelInfo | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatasetLevelInfo:
def __init__(self, df):
"""This class sets the dataset level info of the preprocess file."""
<|body_0|>
def set_values(self):
""""dataset": { "row_cnt": 1000, "variable_cnt": 25 }"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
sel... | stack_v2_sparse_classes_36k_train_016478 | 1,809 | permissive | [
{
"docstring": "This class sets the dataset level info of the preprocess file.",
"name": "__init__",
"signature": "def __init__(self, df)"
},
{
"docstring": "\"dataset\": { \"row_cnt\": 1000, \"variable_cnt\": 25 }",
"name": "set_values",
"signature": "def set_values(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020692 | Implement the Python class `DatasetLevelInfo` described below.
Class description:
Implement the DatasetLevelInfo class.
Method signatures and docstrings:
- def __init__(self, df): This class sets the dataset level info of the preprocess file.
- def set_values(self): "dataset": { "row_cnt": 1000, "variable_cnt": 25 } | Implement the Python class `DatasetLevelInfo` described below.
Class description:
Implement the DatasetLevelInfo class.
Method signatures and docstrings:
- def __init__(self, df): This class sets the dataset level info of the preprocess file.
- def set_values(self): "dataset": { "row_cnt": 1000, "variable_cnt": 25 }
... | 9461522219f5ef0f4877f24c8f5923e462bd9557 | <|skeleton|>
class DatasetLevelInfo:
def __init__(self, df):
"""This class sets the dataset level info of the preprocess file."""
<|body_0|>
def set_values(self):
""""dataset": { "row_cnt": 1000, "variable_cnt": 25 }"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatasetLevelInfo:
def __init__(self, df):
"""This class sets the dataset level info of the preprocess file."""
self.dataframe = df
self.rows_count = None
self.variables_count = None
self.has_error = False
self.error_messages = []
self.final_output = Orde... | the_stack_v2_python_sparse | preprocess/raven_preprocess/dataset_level_info_util.py | TwoRavens/raven-metadata-service | train | 0 | |
bc93425ccf2ea0bf2112737c247253ba64b772e4 | [
"paginator, page, qs, has_other_pages = self.paginate_queryset(queryset, self.paginate_by)\nprev = paginator.previous_page_number if paginator.has_previous() else None\nnext = paginator.next_page_number if paginator.has_other_pages() else None\nreturn {'count': len(queryset), 'total_pages': paginator.num_pages, 'pr... | <|body_start_0|>
paginator, page, qs, has_other_pages = self.paginate_queryset(queryset, self.paginate_by)
prev = paginator.previous_page_number if paginator.has_previous() else None
next = paginator.next_page_number if paginator.has_other_pages() else None
return {'count': len(queryset)... | Movies | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Movies:
def custom_paginate_queryset(self, queryset):
"""Дополнение встроенного пагинатора"""
<|body_0|>
def get_context_data(self, *, object_list=None, **kwargs):
"""Возвращает постраничную информацию о фильмах"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_36k_train_016479 | 3,990 | no_license | [
{
"docstring": "Дополнение встроенного пагинатора",
"name": "custom_paginate_queryset",
"signature": "def custom_paginate_queryset(self, queryset)"
},
{
"docstring": "Возвращает постраничную информацию о фильмах",
"name": "get_context_data",
"signature": "def get_context_data(self, *, ob... | 2 | stack_v2_sparse_classes_30k_train_019266 | Implement the Python class `Movies` described below.
Class description:
Implement the Movies class.
Method signatures and docstrings:
- def custom_paginate_queryset(self, queryset): Дополнение встроенного пагинатора
- def get_context_data(self, *, object_list=None, **kwargs): Возвращает постраничную информацию о филь... | Implement the Python class `Movies` described below.
Class description:
Implement the Movies class.
Method signatures and docstrings:
- def custom_paginate_queryset(self, queryset): Дополнение встроенного пагинатора
- def get_context_data(self, *, object_list=None, **kwargs): Возвращает постраничную информацию о филь... | 95f6520de331cb762a4351acf3bbea10e570f040 | <|skeleton|>
class Movies:
def custom_paginate_queryset(self, queryset):
"""Дополнение встроенного пагинатора"""
<|body_0|>
def get_context_data(self, *, object_list=None, **kwargs):
"""Возвращает постраничную информацию о фильмах"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Movies:
def custom_paginate_queryset(self, queryset):
"""Дополнение встроенного пагинатора"""
paginator, page, qs, has_other_pages = self.paginate_queryset(queryset, self.paginate_by)
prev = paginator.previous_page_number if paginator.has_previous() else None
next = paginator.n... | the_stack_v2_python_sparse | admin_service/api/v1/views.py | frbgd/Auth_sprint_2-1 | train | 0 | |
94950b9d0d2420b80d50b149182f0325d557d4d9 | [
"l = 0\nr = len(List) - 1\nif l > r:\n return None\nif l == r:\n return TreeNode(List[l])\nmid = int((l + r) / 2)\nroot = TreeNode(List[mid])\nroot.left = self.build_tree(List[:mid])\nroot.right = self.build_tree(List[mid + 1:])\nreturn root",
"if not root:\n return []\nqueue = []\nresult = []\nqueue.app... | <|body_start_0|>
l = 0
r = len(List) - 1
if l > r:
return None
if l == r:
return TreeNode(List[l])
mid = int((l + r) / 2)
root = TreeNode(List[mid])
root.left = self.build_tree(List[:mid])
root.right = self.build_tree(List[mid + 1:]... | 二叉树结构类 | BinaryTree | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinaryTree:
"""二叉树结构类"""
def build_tree(self, List):
"""构建一棵平衡二叉树，数组必须为排序好地数组，才能使得是平衡二叉树 前提：输入中序遍历，该列表必须满足一棵满二叉树，才能取中间结点为根结点，然后左右子树递归"""
<|body_0|>
def PrintFromTopToBottom(self, root):
"""从上往下打印二叉树——层序遍历"""
<|body_1|>
def Mirror(self, root):
... | stack_v2_sparse_classes_36k_train_016480 | 4,754 | no_license | [
{
"docstring": "构建一棵平衡二叉树，数组必须为排序好地数组，才能使得是平衡二叉树 前提：输入中序遍历，该列表必须满足一棵满二叉树，才能取中间结点为根结点，然后左右子树递归",
"name": "build_tree",
"signature": "def build_tree(self, List)"
},
{
"docstring": "从上往下打印二叉树——层序遍历",
"name": "PrintFromTopToBottom",
"signature": "def PrintFromTopToBottom(self, root)"
},
... | 3 | stack_v2_sparse_classes_30k_train_021478 | Implement the Python class `BinaryTree` described below.
Class description:
二叉树结构类
Method signatures and docstrings:
- def build_tree(self, List): 构建一棵平衡二叉树，数组必须为排序好地数组，才能使得是平衡二叉树 前提：输入中序遍历，该列表必须满足一棵满二叉树，才能取中间结点为根结点，然后左右子树递归
- def PrintFromTopToBottom(self, root): 从上往下打印二叉树——层序遍历
- def Mirror(self, root): 构造二叉树镜像 | Implement the Python class `BinaryTree` described below.
Class description:
二叉树结构类
Method signatures and docstrings:
- def build_tree(self, List): 构建一棵平衡二叉树，数组必须为排序好地数组，才能使得是平衡二叉树 前提：输入中序遍历，该列表必须满足一棵满二叉树，才能取中间结点为根结点，然后左右子树递归
- def PrintFromTopToBottom(self, root): 从上往下打印二叉树——层序遍历
- def Mirror(self, root): 构造二叉树镜像
<|... | 4e4f739402b95691f6c91411da26d7d3bfe042b6 | <|skeleton|>
class BinaryTree:
"""二叉树结构类"""
def build_tree(self, List):
"""构建一棵平衡二叉树，数组必须为排序好地数组，才能使得是平衡二叉树 前提：输入中序遍历，该列表必须满足一棵满二叉树，才能取中间结点为根结点，然后左右子树递归"""
<|body_0|>
def PrintFromTopToBottom(self, root):
"""从上往下打印二叉树——层序遍历"""
<|body_1|>
def Mirror(self, root):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BinaryTree:
"""二叉树结构类"""
def build_tree(self, List):
"""构建一棵平衡二叉树，数组必须为排序好地数组，才能使得是平衡二叉树 前提：输入中序遍历，该列表必须满足一棵满二叉树，才能取中间结点为根结点，然后左右子树递归"""
l = 0
r = len(List) - 1
if l > r:
return None
if l == r:
return TreeNode(List[l])
mid = int((l +... | the_stack_v2_python_sparse | 剑指offer/58.对称的二叉树.py | hugechuanqi/Algorithms-and-Data-Structures | train | 3 |
1b78c7d9d8a926db4786577ea8ebd650eecd1d3c | [
"super(LandmarkGeneratorMultipleHeatmap, self).__init__(dim, output_size, landmark_indizes, landmark_flip_pairs, data_format, pre_transformation, post_transformation)\nself.output_size_np = list(reversed(self.output_size))\nself.sigma = sigma\nself.scale_factor = scale_factor\nself.normalize_center = normalize_cent... | <|body_start_0|>
super(LandmarkGeneratorMultipleHeatmap, self).__init__(dim, output_size, landmark_indizes, landmark_flip_pairs, data_format, pre_transformation, post_transformation)
self.output_size_np = list(reversed(self.output_size))
self.sigma = sigma
self.scale_factor = scale_facto... | Generates heatmap images with multiple Gaussian peaks | LandmarkGeneratorMultipleHeatmap | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LandmarkGeneratorMultipleHeatmap:
"""Generates heatmap images with multiple Gaussian peaks"""
def __init__(self, dim, output_size, sigma, scale_factor, normalize_center, landmark_indizes=None, landmark_flip_pairs=None, data_format='channels_first', pre_transformation=None, post_transformatio... | stack_v2_sparse_classes_36k_train_016481 | 16,690 | no_license | [
{
"docstring": "Initializer :param output_size: output image size :param sigma: Gaussian sigma :param scale_factor: heatmap scale factor, each value of the Gaussian will be multiplied with this value :param normalize_center: if True, the value on the center is set to scale_factor otherwise, the default gaussian... | 2 | stack_v2_sparse_classes_30k_train_019255 | Implement the Python class `LandmarkGeneratorMultipleHeatmap` described below.
Class description:
Generates heatmap images with multiple Gaussian peaks
Method signatures and docstrings:
- def __init__(self, dim, output_size, sigma, scale_factor, normalize_center, landmark_indizes=None, landmark_flip_pairs=None, data_... | Implement the Python class `LandmarkGeneratorMultipleHeatmap` described below.
Class description:
Generates heatmap images with multiple Gaussian peaks
Method signatures and docstrings:
- def __init__(self, dim, output_size, sigma, scale_factor, normalize_center, landmark_indizes=None, landmark_flip_pairs=None, data_... | ef6cee91264ba1fe6b40d9823a07647b95bcc2c4 | <|skeleton|>
class LandmarkGeneratorMultipleHeatmap:
"""Generates heatmap images with multiple Gaussian peaks"""
def __init__(self, dim, output_size, sigma, scale_factor, normalize_center, landmark_indizes=None, landmark_flip_pairs=None, data_format='channels_first', pre_transformation=None, post_transformatio... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LandmarkGeneratorMultipleHeatmap:
"""Generates heatmap images with multiple Gaussian peaks"""
def __init__(self, dim, output_size, sigma, scale_factor, normalize_center, landmark_indizes=None, landmark_flip_pairs=None, data_format='channels_first', pre_transformation=None, post_transformation=None):
... | the_stack_v2_python_sparse | generators/landmark_generator.py | XiaoweiXu/MedicalDataAugmentationTool | train | 1 |
67e9754a63d7c48778858b96bf7967510c6daf8f | [
"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... | [Google Service Control API](/service-control/overview) Lets clients check and report operations against a [managed service][google.api.servicemanagement.v1.ManagedService]. | ServiceControllerServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ServiceControllerServicer:
"""[Google Service Control API](/service-control/overview) Lets clients check and report operations against a [managed service][google.api.servicemanagement.v1.ManagedService]."""
def Check(self, request, context):
"""Checks an operation with Google Service... | stack_v2_sparse_classes_36k_train_016482 | 4,241 | no_license | [
{
"docstring": "Checks an operation with Google Service Control to decide whether the given operation should proceed. It should be called before the operation is executed. If feasible, the client should cache the check results and reuse them for 60 seconds. In case of server errors, the client can rely on the c... | 2 | stack_v2_sparse_classes_30k_train_010513 | Implement the Python class `ServiceControllerServicer` described below.
Class description:
[Google Service Control API](/service-control/overview) Lets clients check and report operations against a [managed service][google.api.servicemanagement.v1.ManagedService].
Method signatures and docstrings:
- def Check(self, r... | Implement the Python class `ServiceControllerServicer` described below.
Class description:
[Google Service Control API](/service-control/overview) Lets clients check and report operations against a [managed service][google.api.servicemanagement.v1.ManagedService].
Method signatures and docstrings:
- def Check(self, r... | d7424d21aa0dc121acc4d64b427ba365a3581a20 | <|skeleton|>
class ServiceControllerServicer:
"""[Google Service Control API](/service-control/overview) Lets clients check and report operations against a [managed service][google.api.servicemanagement.v1.ManagedService]."""
def Check(self, request, context):
"""Checks an operation with Google Service... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ServiceControllerServicer:
"""[Google Service Control API](/service-control/overview) Lets clients check and report operations against a [managed service][google.api.servicemanagement.v1.ManagedService]."""
def Check(self, request, context):
"""Checks an operation with Google Service Control to d... | the_stack_v2_python_sparse | google/api/servicecontrol/v1/service_controller_pb2_grpc.py | msachtler/bazel-event-protocol-parser | train | 1 |
73e4cba34ad5d0d2fb6c6a1b912917176c9c1390 | [
"self.register('M1', torch.randn(hdim, idim))\nself.register('b1', torch.randn(hdim))\nself.register('M2', torch.randn(odim, hdim))\nself.register('b2', torch.randn(odim))",
"assert x.shape[0] == self.M1.shape[1]\nh = torch.mv(self.M1, x) + self.b1\nreturn torch.mv(self.M2, torch.sigmoid(h)) + self.b2"
] | <|body_start_0|>
self.register('M1', torch.randn(hdim, idim))
self.register('b1', torch.randn(hdim))
self.register('M2', torch.randn(odim, hdim))
self.register('b2', torch.randn(odim))
<|end_body_0|>
<|body_start_1|>
assert x.shape[0] == self.M1.shape[1]
h = torch.mv(sel... | Feed-forward network as a network module. | FFN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FFN:
"""Feed-forward network as a network module."""
def __init__(self, idim: int, hdim: int, odim: int):
"""Create a feed-forward network. Args: idim: size of the input vector hdim: size of the hidden vector odim: size of the output vector"""
<|body_0|>
def forward(self... | stack_v2_sparse_classes_36k_train_016483 | 1,267 | no_license | [
{
"docstring": "Create a feed-forward network. Args: idim: size of the input vector hdim: size of the hidden vector odim: size of the output vector",
"name": "__init__",
"signature": "def __init__(self, idim: int, hdim: int, odim: int)"
},
{
"docstring": "Transform the input vector using the und... | 2 | stack_v2_sparse_classes_30k_train_016921 | Implement the Python class `FFN` described below.
Class description:
Feed-forward network as a network module.
Method signatures and docstrings:
- def __init__(self, idim: int, hdim: int, odim: int): Create a feed-forward network. Args: idim: size of the input vector hdim: size of the hidden vector odim: size of the ... | Implement the Python class `FFN` described below.
Class description:
Feed-forward network as a network module.
Method signatures and docstrings:
- def __init__(self, idim: int, hdim: int, odim: int): Create a feed-forward network. Args: idim: size of the input vector hdim: size of the hidden vector odim: size of the ... | 80911390bf23bba71435c4318d181c87b26f293d | <|skeleton|>
class FFN:
"""Feed-forward network as a network module."""
def __init__(self, idim: int, hdim: int, odim: int):
"""Create a feed-forward network. Args: idim: size of the input vector hdim: size of the hidden vector odim: size of the output vector"""
<|body_0|>
def forward(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FFN:
"""Feed-forward network as a network module."""
def __init__(self, idim: int, hdim: int, odim: int):
"""Create a feed-forward network. Args: idim: size of the input vector hdim: size of the hidden vector odim: size of the output vector"""
self.register('M1', torch.randn(hdim, idim))
... | the_stack_v2_python_sparse | lang-rec/code/ffn.py | javierdelcampo/hhu-dl-materials-2019 | train | 0 |
2cf2edd25c0f87c671c3af1bca5be7385563ec60 | [
"super().__init__()\nself.vocab_size = vocab_size\nself.embed_size = embed_size\nself.lstm_size = lstm_size\nself.output_size = output_size\nself.lstm_layers = lstm_layers\nself.dropout = dropout\nself.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=embed_size)\nself.lstm = nn.LSTM(embed_size, lst... | <|body_start_0|>
super().__init__()
self.vocab_size = vocab_size
self.embed_size = embed_size
self.lstm_size = lstm_size
self.output_size = output_size
self.lstm_layers = lstm_layers
self.dropout = dropout
self.embedding = nn.Embedding(num_embeddings=vocab... | BidirectionalLSTM | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BidirectionalLSTM:
def __init__(self, vocab_size, embed_size, lstm_size, output_size, lstm_layers=1, dropout=0.1):
"""Initialize the model by setting up the layers. Parameters ---------- vocab_size : The vocabulary size. embed_size : The embedding layer size. embedding_dim lstm_size : Th... | stack_v2_sparse_classes_36k_train_016484 | 11,718 | no_license | [
{
"docstring": "Initialize the model by setting up the layers. Parameters ---------- vocab_size : The vocabulary size. embed_size : The embedding layer size. embedding_dim lstm_size : The LSTM layer size. hidden_dim output_size : The output size. lstm_layers : The number of LSTM layers. n_layers dropout : The d... | 3 | stack_v2_sparse_classes_30k_train_015731 | Implement the Python class `BidirectionalLSTM` described below.
Class description:
Implement the BidirectionalLSTM class.
Method signatures and docstrings:
- def __init__(self, vocab_size, embed_size, lstm_size, output_size, lstm_layers=1, dropout=0.1): Initialize the model by setting up the layers. Parameters ------... | Implement the Python class `BidirectionalLSTM` described below.
Class description:
Implement the BidirectionalLSTM class.
Method signatures and docstrings:
- def __init__(self, vocab_size, embed_size, lstm_size, output_size, lstm_layers=1, dropout=0.1): Initialize the model by setting up the layers. Parameters ------... | 0cdabea4afb58af71b909fbde7a9260eaa2b7849 | <|skeleton|>
class BidirectionalLSTM:
def __init__(self, vocab_size, embed_size, lstm_size, output_size, lstm_layers=1, dropout=0.1):
"""Initialize the model by setting up the layers. Parameters ---------- vocab_size : The vocabulary size. embed_size : The embedding layer size. embedding_dim lstm_size : Th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BidirectionalLSTM:
def __init__(self, vocab_size, embed_size, lstm_size, output_size, lstm_layers=1, dropout=0.1):
"""Initialize the model by setting up the layers. Parameters ---------- vocab_size : The vocabulary size. embed_size : The embedding layer size. embedding_dim lstm_size : The LSTM layer s... | the_stack_v2_python_sparse | trial_calculations_2/LSTMs.py | ThesisNegatif/Thesis | train | 2 | |
3332b6d13b909c30ff5df8fca65e4657cb0d06f9 | [
"super(CurlDiffusion, self).__init__(variables=[velocity, vorticity], **kwds)\nself.velocity = velocity\nself.vorticity = vorticity\nraise ValueError('This operator is obsolete and must be reviewed. Do not use it.')",
"if self._comm is None:\n from hysop.mpi.main_var import main_comm a... | <|body_start_0|>
super(CurlDiffusion, self).__init__(variables=[velocity, vorticity], **kwds)
self.velocity = velocity
self.vorticity = vorticity
raise ValueError('This operator is obsolete and must be reviewed. Do not use it.')
<|end_body_0|>
<|body_start_1|>
... | Diffusion operator {eqnarray*} \\omega = Op(\\omega) } with : {eqnarray*} rac{\\partial \\omega}{\\partial t} &=& u\\Delta\\omega } | CurlDiffusion | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CurlDiffusion:
"""Diffusion operator {eqnarray*} \\omega = Op(\\omega) } with : {eqnarray*} rac{\\partial \\omega}{\\partial t} &=& u\\Delta\\omega }"""
def __init__(self, velocity, vorticity, **kwds):
"""Constructor. Create a Diffusion operator using FFT. @param velocity ContinuousV... | stack_v2_sparse_classes_36k_train_016485 | 2,499 | no_license | [
{
"docstring": "Constructor. Create a Diffusion operator using FFT. @param velocity ContinuousVectorField : velocity variable. @param vorticity ContinuousVectorField : vorticity variable. @param viscosity : viscosity of the considered medium.",
"name": "__init__",
"signature": "def __init__(self, veloci... | 2 | null | Implement the Python class `CurlDiffusion` described below.
Class description:
Diffusion operator {eqnarray*} \\omega = Op(\\omega) } with : {eqnarray*} rac{\\partial \\omega}{\\partial t} &=& u\\Delta\\omega }
Method signatures and docstrings:
- def __init__(self, velocity, vorticity, **kwds): Constructor. Create a ... | Implement the Python class `CurlDiffusion` described below.
Class description:
Diffusion operator {eqnarray*} \\omega = Op(\\omega) } with : {eqnarray*} rac{\\partial \\omega}{\\partial t} &=& u\\Delta\\omega }
Method signatures and docstrings:
- def __init__(self, velocity, vorticity, **kwds): Constructor. Create a ... | 60e9535ece75321367b19f1daf13e1ae014d6e81 | <|skeleton|>
class CurlDiffusion:
"""Diffusion operator {eqnarray*} \\omega = Op(\\omega) } with : {eqnarray*} rac{\\partial \\omega}{\\partial t} &=& u\\Delta\\omega }"""
def __init__(self, velocity, vorticity, **kwds):
"""Constructor. Create a Diffusion operator using FFT. @param velocity ContinuousV... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CurlDiffusion:
"""Diffusion operator {eqnarray*} \\omega = Op(\\omega) } with : {eqnarray*} rac{\\partial \\omega}{\\partial t} &=& u\\Delta\\omega }"""
def __init__(self, velocity, vorticity, **kwds):
"""Constructor. Create a Diffusion operator using FFT. @param velocity ContinuousVectorField : ... | the_stack_v2_python_sparse | hysop/operator/curlAndDiffusion.py | ljktest/tmp-tests | train | 0 |
2724fd1e7bc09a955f1e5d13e87b1a834e4566ac | [
"super().__init__()\nself.tade1 = TADELayer(in_channels=in_channels, aux_channels=aux_channels, kernel_size=kernel_size, bias=bias, upsample_factor=1, upsample_mode=upsample_mode)\nself.gated_conv1 = nn.Conv1D(in_channels, in_channels * 2, kernel_size, 1, bias_attr=bias, padding=(kernel_size - 1) // 2)\nself.tade2 ... | <|body_start_0|>
super().__init__()
self.tade1 = TADELayer(in_channels=in_channels, aux_channels=aux_channels, kernel_size=kernel_size, bias=bias, upsample_factor=1, upsample_mode=upsample_mode)
self.gated_conv1 = nn.Conv1D(in_channels, in_channels * 2, kernel_size, 1, bias_attr=bias, padding=(k... | TADEResBlock module. | TADEResBlock | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TADEResBlock:
"""TADEResBlock module."""
def __init__(self, in_channels: int=64, aux_channels: int=80, kernel_size: int=9, dilation: int=2, bias: bool=True, upsample_factor: int=2, upsample_mode: str='nearest', gated_function: str='softmax'):
"""Initialize TADEResBlock module."""
... | stack_v2_sparse_classes_36k_train_016486 | 5,634 | permissive | [
{
"docstring": "Initialize TADEResBlock module.",
"name": "__init__",
"signature": "def __init__(self, in_channels: int=64, aux_channels: int=80, kernel_size: int=9, dilation: int=2, bias: bool=True, upsample_factor: int=2, upsample_mode: str='nearest', gated_function: str='softmax')"
},
{
"docs... | 2 | null | Implement the Python class `TADEResBlock` described below.
Class description:
TADEResBlock module.
Method signatures and docstrings:
- def __init__(self, in_channels: int=64, aux_channels: int=80, kernel_size: int=9, dilation: int=2, bias: bool=True, upsample_factor: int=2, upsample_mode: str='nearest', gated_functio... | Implement the Python class `TADEResBlock` described below.
Class description:
TADEResBlock module.
Method signatures and docstrings:
- def __init__(self, in_channels: int=64, aux_channels: int=80, kernel_size: int=9, dilation: int=2, bias: bool=True, upsample_factor: int=2, upsample_mode: str='nearest', gated_functio... | 17854a04d43c231eff66bfed9d6aa55e94a29e79 | <|skeleton|>
class TADEResBlock:
"""TADEResBlock module."""
def __init__(self, in_channels: int=64, aux_channels: int=80, kernel_size: int=9, dilation: int=2, bias: bool=True, upsample_factor: int=2, upsample_mode: str='nearest', gated_function: str='softmax'):
"""Initialize TADEResBlock module."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TADEResBlock:
"""TADEResBlock module."""
def __init__(self, in_channels: int=64, aux_channels: int=80, kernel_size: int=9, dilation: int=2, bias: bool=True, upsample_factor: int=2, upsample_mode: str='nearest', gated_function: str='softmax'):
"""Initialize TADEResBlock module."""
super().... | the_stack_v2_python_sparse | paddlespeech/t2s/modules/tade_res_block.py | anniyanvr/DeepSpeech-1 | train | 0 |
56e96af8c1b7f322f38209985edda0305a449f93 | [
"assert len(input_list) > 0\nsuper().__init__(self.PROBLEM_NAME)\nself.input_list = input_list",
"print('Solving {} problem ...'.format(self.PROBLEM_NAME))\nsum_dict = {}\nn = len(self.input_list)\nresult = []\nfor i in range(n - 1):\n for j in range(i + 1, n):\n total = self.input_list[i] + self.input_... | <|body_start_0|>
assert len(input_list) > 0
super().__init__(self.PROBLEM_NAME)
self.input_list = input_list
<|end_body_0|>
<|body_start_1|>
print('Solving {} problem ...'.format(self.PROBLEM_NAME))
sum_dict = {}
n = len(self.input_list)
result = []
for i... | Equal | FourEqual | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FourEqual:
"""Equal"""
def __init__(self, input_list):
"""FourEqual Args: input_list: Contains a list of integers Returns: None Raises: None"""
<|body_0|>
def solve(self):
"""Solve the problem Note: Args: Returns: list Raises: None"""
<|body_1|>
<|end_sk... | stack_v2_sparse_classes_36k_train_016487 | 1,642 | no_license | [
{
"docstring": "FourEqual Args: input_list: Contains a list of integers Returns: None Raises: None",
"name": "__init__",
"signature": "def __init__(self, input_list)"
},
{
"docstring": "Solve the problem Note: Args: Returns: list Raises: None",
"name": "solve",
"signature": "def solve(se... | 2 | stack_v2_sparse_classes_30k_train_005798 | Implement the Python class `FourEqual` described below.
Class description:
Equal
Method signatures and docstrings:
- def __init__(self, input_list): FourEqual Args: input_list: Contains a list of integers Returns: None Raises: None
- def solve(self): Solve the problem Note: Args: Returns: list Raises: None | Implement the Python class `FourEqual` described below.
Class description:
Equal
Method signatures and docstrings:
- def __init__(self, input_list): FourEqual Args: input_list: Contains a list of integers Returns: None Raises: None
- def solve(self): Solve the problem Note: Args: Returns: list Raises: None
<|skeleto... | 11f4d25cb211740514c119a60962d075a0817abd | <|skeleton|>
class FourEqual:
"""Equal"""
def __init__(self, input_list):
"""FourEqual Args: input_list: Contains a list of integers Returns: None Raises: None"""
<|body_0|>
def solve(self):
"""Solve the problem Note: Args: Returns: list Raises: None"""
<|body_1|>
<|end_sk... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FourEqual:
"""Equal"""
def __init__(self, input_list):
"""FourEqual Args: input_list: Contains a list of integers Returns: None Raises: None"""
assert len(input_list) > 0
super().__init__(self.PROBLEM_NAME)
self.input_list = input_list
def solve(self):
"""Solv... | the_stack_v2_python_sparse | python/problems/hashing/four_equal.py | santhosh-kumar/AlgorithmsAndDataStructures | train | 2 |
e8fd645788a7ad5242c7bbe4e36fd0abcfb71464 | [
"if arg == 'no_default':\n value = None\nelse:\n value = cls._get_from_registry(arg)\nreturn value",
"if value is None:\n value = cls._default_value\nif isinstance(value, str):\n if value == 'no_default':\n value = None\n else:\n value = cls._get_from_registry(value)\nelif not isinsta... | <|body_start_0|>
if arg == 'no_default':
value = None
else:
value = cls._get_from_registry(arg)
return value
<|end_body_0|>
<|body_start_1|>
if value is None:
value = cls._default_value
if isinstance(value, str):
if value == 'no_de... | The default cosmology to use. To change it:: >>> from astropy.cosmology import default_cosmology, WMAP7 >>> with default_cosmology.set(WMAP7): ... # WMAP7 cosmology in effect ... pass Or, you may use a string:: >>> with default_cosmology.set('WMAP7'): ... # WMAP7 cosmology in effect ... pass To get the default cosmolog... | default_cosmology | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class default_cosmology:
"""The default cosmology to use. To change it:: >>> from astropy.cosmology import default_cosmology, WMAP7 >>> with default_cosmology.set(WMAP7): ... # WMAP7 cosmology in effect ... pass Or, you may use a string:: >>> with default_cosmology.set('WMAP7'): ... # WMAP7 cosmology i... | stack_v2_sparse_classes_36k_train_016488 | 4,954 | permissive | [
{
"docstring": "Return a cosmology instance from a string.",
"name": "get_cosmology_from_string",
"signature": "def get_cosmology_from_string(cls, arg)"
},
{
"docstring": "Return a Cosmology given a value. Parameters ---------- value : None, str, or `~astropy.cosmology.Cosmology` Returns -------... | 3 | null | Implement the Python class `default_cosmology` described below.
Class description:
The default cosmology to use. To change it:: >>> from astropy.cosmology import default_cosmology, WMAP7 >>> with default_cosmology.set(WMAP7): ... # WMAP7 cosmology in effect ... pass Or, you may use a string:: >>> with default_cosmolog... | Implement the Python class `default_cosmology` described below.
Class description:
The default cosmology to use. To change it:: >>> from astropy.cosmology import default_cosmology, WMAP7 >>> with default_cosmology.set(WMAP7): ... # WMAP7 cosmology in effect ... pass Or, you may use a string:: >>> with default_cosmolog... | 53188c39a23c33b72df5850ec59e31886f84e29d | <|skeleton|>
class default_cosmology:
"""The default cosmology to use. To change it:: >>> from astropy.cosmology import default_cosmology, WMAP7 >>> with default_cosmology.set(WMAP7): ... # WMAP7 cosmology in effect ... pass Or, you may use a string:: >>> with default_cosmology.set('WMAP7'): ... # WMAP7 cosmology i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class default_cosmology:
"""The default cosmology to use. To change it:: >>> from astropy.cosmology import default_cosmology, WMAP7 >>> with default_cosmology.set(WMAP7): ... # WMAP7 cosmology in effect ... pass Or, you may use a string:: >>> with default_cosmology.set('WMAP7'): ... # WMAP7 cosmology in effect ... ... | the_stack_v2_python_sparse | astropy/cosmology/realizations.py | astropy/astropy | train | 3,922 |
d082b8c802214f69a5cb8e40b3826b3ca19f96f8 | [
"thresholds = thresholds[:]\nassert thresholds[0] > 0\nthresholds.insert(0, -float('inf'))\nthresholds.append(float('inf'))\nassert all([low <= high for low, high in zip(thresholds[:-1], thresholds[1:])])\nassert all([l in [-1, 0, 1] for l in labels])\nassert len(labels) == len(thresholds) - 1\nself.thresholds = th... | <|body_start_0|>
thresholds = thresholds[:]
assert thresholds[0] > 0
thresholds.insert(0, -float('inf'))
thresholds.append(float('inf'))
assert all([low <= high for low, high in zip(thresholds[:-1], thresholds[1:])])
assert all([l in [-1, 0, 1] for l in labels])
a... | This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements. The matching is determined by the MxN match_quality_matrix, that characterizes how well each (gr... | Matcher | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Matcher:
"""This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements. The matching is determined by the MxN match_quality_matrix, t... | stack_v2_sparse_classes_36k_train_016489 | 6,256 | permissive | [
{
"docstring": "Args: thresholds (list): a list of thresholds used to stratify predictions into levels. labels (list): a list of values to label predictions belonging at each level. A label can be one of {-1, 0, 1} signifying {ignore, negative class, positive class}, respectively. allow_low_quality_matches (boo... | 3 | null | Implement the Python class `Matcher` described below.
Class description:
This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements. The matching is determ... | Implement the Python class `Matcher` described below.
Class description:
This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements. The matching is determ... | 80307d2d5e06f06a8a677cc2653f23a4c56402ac | <|skeleton|>
class Matcher:
"""This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements. The matching is determined by the MxN match_quality_matrix, t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Matcher:
"""This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements. The matching is determined by the MxN match_quality_matrix, that character... | the_stack_v2_python_sparse | detectron2/modeling/matcher.py | facebookresearch/detectron2 | train | 27,469 |
8d5f1ecbf42f6cc1a97ff8ceb7e70aef366b86c7 | [
"self.all_endpoints_reachable = all_endpoints_reachable\nself.auto_register_target = auto_register_target\nself.auto_registration = auto_registration\nself.bandwidth_limit = bandwidth_limit\nself.cluster_id = cluster_id\nself.cluster_incarnation_id = cluster_incarnation_id\nself.compression_enabled = compression_en... | <|body_start_0|>
self.all_endpoints_reachable = all_endpoints_reachable
self.auto_register_target = auto_register_target
self.auto_registration = auto_registration
self.bandwidth_limit = bandwidth_limit
self.cluster_id = cluster_id
self.cluster_incarnation_id = cluster_in... | Implementation of the 'RegisterRemoteCluster' model. Specifies the settings required for registering a remote Cluster on this local Cluster. Attributes: all_endpoints_reachable (bool): Specifies whether any endpoint (such as a Node) on the remote Cluster is reachable from this local Cluster. If true, a service running ... | RegisterRemoteCluster | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegisterRemoteCluster:
"""Implementation of the 'RegisterRemoteCluster' model. Specifies the settings required for registering a remote Cluster on this local Cluster. Attributes: all_endpoints_reachable (bool): Specifies whether any endpoint (such as a Node) on the remote Cluster is reachable fro... | stack_v2_sparse_classes_36k_train_016490 | 10,649 | permissive | [
{
"docstring": "Constructor for the RegisterRemoteCluster class",
"name": "__init__",
"signature": "def __init__(self, all_endpoints_reachable=None, auto_register_target=None, auto_registration=None, bandwidth_limit=None, cluster_id=None, cluster_incarnation_id=None, compression_enabled=None, descriptio... | 2 | stack_v2_sparse_classes_30k_train_013239 | Implement the Python class `RegisterRemoteCluster` described below.
Class description:
Implementation of the 'RegisterRemoteCluster' model. Specifies the settings required for registering a remote Cluster on this local Cluster. Attributes: all_endpoints_reachable (bool): Specifies whether any endpoint (such as a Node)... | Implement the Python class `RegisterRemoteCluster` described below.
Class description:
Implementation of the 'RegisterRemoteCluster' model. Specifies the settings required for registering a remote Cluster on this local Cluster. Attributes: all_endpoints_reachable (bool): Specifies whether any endpoint (such as a Node)... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RegisterRemoteCluster:
"""Implementation of the 'RegisterRemoteCluster' model. Specifies the settings required for registering a remote Cluster on this local Cluster. Attributes: all_endpoints_reachable (bool): Specifies whether any endpoint (such as a Node) on the remote Cluster is reachable fro... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegisterRemoteCluster:
"""Implementation of the 'RegisterRemoteCluster' model. Specifies the settings required for registering a remote Cluster on this local Cluster. Attributes: all_endpoints_reachable (bool): Specifies whether any endpoint (such as a Node) on the remote Cluster is reachable from this local ... | the_stack_v2_python_sparse | cohesity_management_sdk/models/register_remote_cluster.py | cohesity/management-sdk-python | train | 24 |
4ed696d2a0a4dac761c54325ba5b9e822c0a8d50 | [
"self.dynamics_net = ForwardModel(state_dim, action_dim)\nself.rewards_net = RewardModel(state_dim, action_dim)\nself.done_net = RewardModel(state_dim, action_dim)\nself.dyn_optimizer = tfa_optimizers.AdamW(learning_rate=learning_rate, weight_decay=weight_decay)\nself.reward_optimizer = tfa_optimizers.AdamW(learnin... | <|body_start_0|>
self.dynamics_net = ForwardModel(state_dim, action_dim)
self.rewards_net = RewardModel(state_dim, action_dim)
self.done_net = RewardModel(state_dim, action_dim)
self.dyn_optimizer = tfa_optimizers.AdamW(learning_rate=learning_rate, weight_decay=weight_decay)
self... | A class that learns models and estimated returns via rollouts. | ModelBased | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelBased:
"""A class that learns models and estimated returns via rollouts."""
def __init__(self, state_dim, action_dim, learning_rate, weight_decay):
"""Creates networks and optimizers for model based policy evaluation. Args: state_dim: State size. action_dim: Action size. learnin... | stack_v2_sparse_classes_36k_train_016491 | 7,358 | permissive | [
{
"docstring": "Creates networks and optimizers for model based policy evaluation. Args: state_dim: State size. action_dim: Action size. learning_rate: Critic learning rate. weight_decay: Weight decay.",
"name": "__init__",
"signature": "def __init__(self, state_dim, action_dim, learning_rate, weight_de... | 3 | stack_v2_sparse_classes_30k_train_015455 | Implement the Python class `ModelBased` described below.
Class description:
A class that learns models and estimated returns via rollouts.
Method signatures and docstrings:
- def __init__(self, state_dim, action_dim, learning_rate, weight_decay): Creates networks and optimizers for model based policy evaluation. Args... | Implement the Python class `ModelBased` described below.
Class description:
A class that learns models and estimated returns via rollouts.
Method signatures and docstrings:
- def __init__(self, state_dim, action_dim, learning_rate, weight_decay): Creates networks and optimizers for model based policy evaluation. Args... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class ModelBased:
"""A class that learns models and estimated returns via rollouts."""
def __init__(self, state_dim, action_dim, learning_rate, weight_decay):
"""Creates networks and optimizers for model based policy evaluation. Args: state_dim: State size. action_dim: Action size. learnin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModelBased:
"""A class that learns models and estimated returns via rollouts."""
def __init__(self, state_dim, action_dim, learning_rate, weight_decay):
"""Creates networks and optimizers for model based policy evaluation. Args: state_dim: State size. action_dim: Action size. learning_rate: Criti... | the_stack_v2_python_sparse | policy_eval/model_based.py | Jimmy-INL/google-research | train | 1 |
21ef5f5f28107a6147b030dad6b0993fb04e5085 | [
"\"\"\"\n 2 pointers jump.\n \"\"\"\nslow = nums[0]\nfast = nums[nums[0]]\nwhile slow != fast:\n slow = nums[slow]\n fast = nums[nums[fast]]\nfast = 0\nwhile slow != fast:\n slow = nums[slow]\n fast = nums[fast]\nreturn fast",
"\"\"\"\n Using xor.\n \"\"\"\nxor = 0\nfor n i... | <|body_start_0|>
"""
2 pointers jump.
"""
slow = nums[0]
fast = nums[nums[0]]
while slow != fast:
slow = nums[slow]
fast = nums[nums[fast]]
fast = 0
while slow != fast:
slow = nums[slow]
fast ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rewrite(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
"""
2 pointers jump.
... | stack_v2_sparse_classes_36k_train_016492 | 3,095 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findDuplicate",
"signature": "def findDuplicate(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rewrite",
"signature": "def rewrite(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005408 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDuplicate(self, nums): :type nums: List[int] :rtype: int
- def rewrite(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 findDuplicate(self, nums): :type nums: List[int] :rtype: int
- def rewrite(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def findDuplicate... | 6350568d16b0f8c49a020f055bb6d72e2705ea56 | <|skeleton|>
class Solution:
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rewrite(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
"""
2 pointers jump.
"""
slow = nums[0]
fast = nums[nums[0]]
while slow != fast:
slow = nums[slow]
fast = nums[nums[fast]]
fa... | the_stack_v2_python_sparse | array/287_Find_the_Duplicate_Number.py | vsdrun/lc_public | train | 6 | |
635c5bee25dbb5c2f14c3e1cb6c16f805c9dc727 | [
"Company = self.old_state.apps.get_model('company', 'company')\nsupplier = Company.objects.create(name='Supplier A', description='A great supplier!', is_supplier=True, is_customer=True)\nPurchaseOrder = self.old_state.apps.get_model('order', 'purchaseorder')\nSalesOrder = self.old_state.apps.get_model('order', 'sal... | <|body_start_0|>
Company = self.old_state.apps.get_model('company', 'company')
supplier = Company.objects.create(name='Supplier A', description='A great supplier!', is_supplier=True, is_customer=True)
PurchaseOrder = self.old_state.apps.get_model('order', 'purchaseorder')
SalesOrder = se... | Test entire schema migration. | TestRefIntMigrations | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestRefIntMigrations:
"""Test entire schema migration."""
def prepare(self):
"""Create initial data set."""
<|body_0|>
def test_ref_field(self):
"""Test that the 'reference_int' field has been created and is filled out correctly."""
<|body_1|>
<|end_skel... | stack_v2_sparse_classes_36k_train_016493 | 7,688 | permissive | [
{
"docstring": "Create initial data set.",
"name": "prepare",
"signature": "def prepare(self)"
},
{
"docstring": "Test that the 'reference_int' field has been created and is filled out correctly.",
"name": "test_ref_field",
"signature": "def test_ref_field(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017478 | Implement the Python class `TestRefIntMigrations` described below.
Class description:
Test entire schema migration.
Method signatures and docstrings:
- def prepare(self): Create initial data set.
- def test_ref_field(self): Test that the 'reference_int' field has been created and is filled out correctly. | Implement the Python class `TestRefIntMigrations` described below.
Class description:
Test entire schema migration.
Method signatures and docstrings:
- def prepare(self): Create initial data set.
- def test_ref_field(self): Test that the 'reference_int' field has been created and is filled out correctly.
<|skeleton|... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class TestRefIntMigrations:
"""Test entire schema migration."""
def prepare(self):
"""Create initial data set."""
<|body_0|>
def test_ref_field(self):
"""Test that the 'reference_int' field has been created and is filled out correctly."""
<|body_1|>
<|end_skel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestRefIntMigrations:
"""Test entire schema migration."""
def prepare(self):
"""Create initial data set."""
Company = self.old_state.apps.get_model('company', 'company')
supplier = Company.objects.create(name='Supplier A', description='A great supplier!', is_supplier=True, is_cust... | the_stack_v2_python_sparse | InvenTree/order/test_migrations.py | inventree/InvenTree | train | 3,077 |
c13c0c436c5e436f594253a29b25b05189225cb8 | [
"ressource_options = default_ressource_options(request, current_app)\nif rss_name:\n RssUtils.validate_rss_name(rss_name)\n spec = {'name': {'$regex': rss_name}}\nelse:\n spec = {}\nresults = current_app.mongo.finder(cursor=current_app.mongo.rss, spec=spec)\ndata = [document for document in results]\nretur... | <|body_start_0|>
ressource_options = default_ressource_options(request, current_app)
if rss_name:
RssUtils.validate_rss_name(rss_name)
spec = {'name': {'$regex': rss_name}}
else:
spec = {}
results = current_app.mongo.finder(cursor=current_app.mongo.rss... | docstrings | Rss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rss:
"""docstrings"""
def get(self, rss_name=None):
"""retrieve the list of a rss"""
<|body_0|>
def post(self):
"""add a new rss @post data: <name> <url>"""
<|body_1|>
def patch(self, rss_name):
"""patch a rss for the givent rss_name paramete... | stack_v2_sparse_classes_36k_train_016494 | 2,828 | permissive | [
{
"docstring": "retrieve the list of a rss",
"name": "get",
"signature": "def get(self, rss_name=None)"
},
{
"docstring": "add a new rss @post data: <name> <url>",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "patch a rss for the givent rss_name parameter @patc... | 3 | stack_v2_sparse_classes_30k_train_011305 | Implement the Python class `Rss` described below.
Class description:
docstrings
Method signatures and docstrings:
- def get(self, rss_name=None): retrieve the list of a rss
- def post(self): add a new rss @post data: <name> <url>
- def patch(self, rss_name): patch a rss for the givent rss_name parameter @patch data: ... | Implement the Python class `Rss` described below.
Class description:
docstrings
Method signatures and docstrings:
- def get(self, rss_name=None): retrieve the list of a rss
- def post(self): add a new rss @post data: <name> <url>
- def patch(self, rss_name): patch a rss for the givent rss_name parameter @patch data: ... | 657304c8b017a98935de9728fc695abe8be7cc4f | <|skeleton|>
class Rss:
"""docstrings"""
def get(self, rss_name=None):
"""retrieve the list of a rss"""
<|body_0|>
def post(self):
"""add a new rss @post data: <name> <url>"""
<|body_1|>
def patch(self, rss_name):
"""patch a rss for the givent rss_name paramete... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Rss:
"""docstrings"""
def get(self, rss_name=None):
"""retrieve the list of a rss"""
ressource_options = default_ressource_options(request, current_app)
if rss_name:
RssUtils.validate_rss_name(rss_name)
spec = {'name': {'$regex': rss_name}}
else:
... | the_stack_v2_python_sparse | Observer/api/resources/rss.py | Lujeni/old-projects | train | 0 |
7c41143c55293f904525ce0bf5eb7734c14cb5f2 | [
"self.n = n\nself.discount = discount\nself.products = products\nself.f = {}\nfor i in range(len(self.products)):\n self.f[self.products[i]] = i\nself.prices = prices\nself.cnt = 1",
"ans = 0\nfor i in range(len(amount)):\n ans += self.prices[self.f[product[i]]] * amount[i]\nprint(self.cnt)\nif self.cnt % s... | <|body_start_0|>
self.n = n
self.discount = discount
self.products = products
self.f = {}
for i in range(len(self.products)):
self.f[self.products[i]] = i
self.prices = prices
self.cnt = 1
<|end_body_0|>
<|body_start_1|>
ans = 0
for i ... | Cashier | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cashier:
def __init__(self, n, discount, products, prices):
""":type n: int :type discount: int :type products: List[int] :type prices: List[int]"""
<|body_0|>
def getBill(self, product, amount):
""":type product: List[int] :type amount: List[int] :rtype: float"""
... | stack_v2_sparse_classes_36k_train_016495 | 1,021 | no_license | [
{
"docstring": ":type n: int :type discount: int :type products: List[int] :type prices: List[int]",
"name": "__init__",
"signature": "def __init__(self, n, discount, products, prices)"
},
{
"docstring": ":type product: List[int] :type amount: List[int] :rtype: float",
"name": "getBill",
... | 2 | stack_v2_sparse_classes_30k_train_018958 | Implement the Python class `Cashier` described below.
Class description:
Implement the Cashier class.
Method signatures and docstrings:
- def __init__(self, n, discount, products, prices): :type n: int :type discount: int :type products: List[int] :type prices: List[int]
- def getBill(self, product, amount): :type pr... | Implement the Python class `Cashier` described below.
Class description:
Implement the Cashier class.
Method signatures and docstrings:
- def __init__(self, n, discount, products, prices): :type n: int :type discount: int :type products: List[int] :type prices: List[int]
- def getBill(self, product, amount): :type pr... | 67e9daecb7ffd8f7bcb2f120ad892498b1219327 | <|skeleton|>
class Cashier:
def __init__(self, n, discount, products, prices):
""":type n: int :type discount: int :type products: List[int] :type prices: List[int]"""
<|body_0|>
def getBill(self, product, amount):
""":type product: List[int] :type amount: List[int] :rtype: float"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Cashier:
def __init__(self, n, discount, products, prices):
""":type n: int :type discount: int :type products: List[int] :type prices: List[int]"""
self.n = n
self.discount = discount
self.products = products
self.f = {}
for i in range(len(self.products)):
... | the_stack_v2_python_sparse | 比赛/1357. 每隔 n 个顾客打折.py | Comyn-Echo/leeCode | train | 0 | |
773f27c380e747a39780b8067c349fce92a6a19c | [
"assert isinstance(response, requests.Response)\nif response.status_code != expected:\n raise RuntimeError('Expected %s %s for GET %s. Got %s %s instead.' % (expected, responses[expected], response.url, response.status_code, responses[response.status_code]))",
"logger.debug('GET %s', url)\nresponse = cls.sessi... | <|body_start_0|>
assert isinstance(response, requests.Response)
if response.status_code != expected:
raise RuntimeError('Expected %s %s for GET %s. Got %s %s instead.' % (expected, responses[expected], response.url, response.status_code, responses[response.status_code]))
<|end_body_0|>
<|bo... | A class which acts as a provider for other APIs by sharing a single requests session Used by other APIs to construct and share a single :class:`requests.Session` as well | Session | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Session:
"""A class which acts as a provider for other APIs by sharing a single requests session Used by other APIs to construct and share a single :class:`requests.Session` as well"""
def check_code(cls, response, expected):
"""Check the HTTP response code from ``response`` against ... | stack_v2_sparse_classes_36k_train_016496 | 16,466 | permissive | [
{
"docstring": "Check the HTTP response code from ``response`` against an expected value. :param requests.Response response: The response to check the status code for :param int expected: The expected http response code :raises RuntimeError: Raised if the response's HTTP status code does not match ``expected``"... | 3 | stack_v2_sparse_classes_30k_train_006683 | Implement the Python class `Session` described below.
Class description:
A class which acts as a provider for other APIs by sharing a single requests session Used by other APIs to construct and share a single :class:`requests.Session` as well
Method signatures and docstrings:
- def check_code(cls, response, expected)... | Implement the Python class `Session` described below.
Class description:
A class which acts as a provider for other APIs by sharing a single requests session Used by other APIs to construct and share a single :class:`requests.Session` as well
Method signatures and docstrings:
- def check_code(cls, response, expected)... | 39210182a92e93c37a9f1c644fd5fcc1aa32f6d1 | <|skeleton|>
class Session:
"""A class which acts as a provider for other APIs by sharing a single requests session Used by other APIs to construct and share a single :class:`requests.Session` as well"""
def check_code(cls, response, expected):
"""Check the HTTP response code from ``response`` against ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Session:
"""A class which acts as a provider for other APIs by sharing a single requests session Used by other APIs to construct and share a single :class:`requests.Session` as well"""
def check_code(cls, response, expected):
"""Check the HTTP response code from ``response`` against an expected v... | the_stack_v2_python_sparse | pywincffi/dev/release.py | philip-h-dye/pywincffi | train | 0 |
2a2bc64625ca19cc2c277b1bf402553d7a21e846 | [
"super(RNNDecoder, self).__init__()\nself.units = units\nself.batch = batch\nself.embedding = tf.keras.layers.Embedding(vocab, embedding)\nself.gru = tf.keras.layers.GRU(units, return_state=True, return_sequences=True, recurrent_initializer='glorot_uniform')\nself.F = tf.keras.layers.Dense(vocab)",
"sa = SelfAtte... | <|body_start_0|>
super(RNNDecoder, self).__init__()
self.units = units
self.batch = batch
self.embedding = tf.keras.layers.Embedding(vocab, embedding)
self.gru = tf.keras.layers.GRU(units, return_state=True, return_sequences=True, recurrent_initializer='glorot_uniform')
s... | rnn decoder class | RNNDecoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNDecoder:
"""rnn decoder class"""
def __init__(self, vocab, embedding, units, batch):
"""initialization method for RNNdecoder object vocab: int: size of output vocabulary embedding:int: dimensionality of embedding vector units: int: number of hidden units in RNN cell batch: int: ba... | stack_v2_sparse_classes_36k_train_016497 | 1,859 | no_license | [
{
"docstring": "initialization method for RNNdecoder object vocab: int: size of output vocabulary embedding:int: dimensionality of embedding vector units: int: number of hidden units in RNN cell batch: int: batch size",
"name": "__init__",
"signature": "def __init__(self, vocab, embedding, units, batch)... | 2 | null | Implement the Python class `RNNDecoder` described below.
Class description:
rnn decoder class
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): initialization method for RNNdecoder object vocab: int: size of output vocabulary embedding:int: dimensionality of embedding vector unit... | Implement the Python class `RNNDecoder` described below.
Class description:
rnn decoder class
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): initialization method for RNNdecoder object vocab: int: size of output vocabulary embedding:int: dimensionality of embedding vector unit... | d86b0e0cae2dd07c761f84a493abc895007873ee | <|skeleton|>
class RNNDecoder:
"""rnn decoder class"""
def __init__(self, vocab, embedding, units, batch):
"""initialization method for RNNdecoder object vocab: int: size of output vocabulary embedding:int: dimensionality of embedding vector units: int: number of hidden units in RNN cell batch: int: ba... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RNNDecoder:
"""rnn decoder class"""
def __init__(self, vocab, embedding, units, batch):
"""initialization method for RNNdecoder object vocab: int: size of output vocabulary embedding:int: dimensionality of embedding vector units: int: number of hidden units in RNN cell batch: int: batch size"""
... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/2-rnn_decoder.py | mag389/holbertonschool-machine_learning | train | 2 |
258fa2d7c5873801c63264e09d57e339eeaa4b37 | [
"super().__init__()\nself.in_chans = in_chans\nself.out_chans = out_chans\nself.chans = chans\nself.num_pool_layers = num_pool_layers\nself.drop_prob = drop_prob\nself.down_sample_layers = nn.ModuleList([ConvBlock(in_chans, chans, drop_prob)])\nch = chans\nfor i in range(num_pool_layers - 1):\n self.down_sample_... | <|body_start_0|>
super().__init__()
self.in_chans = in_chans
self.out_chans = out_chans
self.chans = chans
self.num_pool_layers = num_pool_layers
self.drop_prob = drop_prob
self.down_sample_layers = nn.ModuleList([ConvBlock(in_chans, chans, drop_prob)])
ch... | PyTorch implementation of a U-Net model. This is based on: Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. | UnetModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnetModel:
"""PyTorch implementation of a U-Net model. This is based on: Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. S... | stack_v2_sparse_classes_36k_train_016498 | 30,521 | no_license | [
{
"docstring": "Args: in_chans (int): Number of channels in the input to the U-Net model. out_chans (int): Number of channels in the output to the U-Net model. chans (int): Number of output channels of the first convolution layer. num_pool_layers (int): Number of down-sampling and up-sampling layers. drop_prob ... | 2 | stack_v2_sparse_classes_30k_train_020177 | Implement the Python class `UnetModel` described below.
Class description:
PyTorch implementation of a U-Net model. This is based on: Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-... | Implement the Python class `UnetModel` described below.
Class description:
PyTorch implementation of a U-Net model. This is based on: Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-... | 219652c8a08c4f2f682acd9f95a4e1b3fd36b70b | <|skeleton|>
class UnetModel:
"""PyTorch implementation of a U-Net model. This is based on: Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. S... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UnetModel:
"""PyTorch implementation of a U-Net model. This is based on: Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015... | the_stack_v2_python_sparse | lemawarersn_t1assist/models.py | Bala93/Holistic-MRI-Reconstruction | train | 1 |
14523f4077c5ea0e661979e9f11bb409239a1e04 | [
"QItemDelegate.paint(self, painter, option, index)\ncolumn = index.column()\nrow = index.row()\nrect = option.rect\nif column in [C.COL_NAME, C.COL_DESCRIPTION, C.COL_VERSION]:\n pen = QPen()\n pen.setWidth(1)\n pen.setColor(QColor('#ddd'))\n painter.setPen(pen)\n painter.drawLine(rect.topRight(), re... | <|body_start_0|>
QItemDelegate.paint(self, painter, option, index)
column = index.column()
row = index.row()
rect = option.rect
if column in [C.COL_NAME, C.COL_DESCRIPTION, C.COL_VERSION]:
pen = QPen()
pen.setWidth(1)
pen.setColor(QColor('#ddd'... | Custom delegate to handle selected/hovered behavior of rows. | CustomDelegate | [
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomDelegate:
"""Custom delegate to handle selected/hovered behavior of rows."""
def paint(self, painter, option, index):
"""Override Qt method."""
<|body_0|>
def sizeHint(self, style, model_index):
"""Override Qt method."""
<|body_1|>
<|end_skeleton|>... | stack_v2_sparse_classes_36k_train_016499 | 31,099 | permissive | [
{
"docstring": "Override Qt method.",
"name": "paint",
"signature": "def paint(self, painter, option, index)"
},
{
"docstring": "Override Qt method.",
"name": "sizeHint",
"signature": "def sizeHint(self, style, model_index)"
}
] | 2 | null | Implement the Python class `CustomDelegate` described below.
Class description:
Custom delegate to handle selected/hovered behavior of rows.
Method signatures and docstrings:
- def paint(self, painter, option, index): Override Qt method.
- def sizeHint(self, style, model_index): Override Qt method. | Implement the Python class `CustomDelegate` described below.
Class description:
Custom delegate to handle selected/hovered behavior of rows.
Method signatures and docstrings:
- def paint(self, painter, option, index): Override Qt method.
- def sizeHint(self, style, model_index): Override Qt method.
<|skeleton|>
clas... | 74476c9f00ee43338af696da7e9cd02b273f9005 | <|skeleton|>
class CustomDelegate:
"""Custom delegate to handle selected/hovered behavior of rows."""
def paint(self, painter, option, index):
"""Override Qt method."""
<|body_0|>
def sizeHint(self, style, model_index):
"""Override Qt method."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomDelegate:
"""Custom delegate to handle selected/hovered behavior of rows."""
def paint(self, painter, option, index):
"""Override Qt method."""
QItemDelegate.paint(self, painter, option, index)
column = index.column()
row = index.row()
rect = option.rect
... | the_stack_v2_python_sparse | python/anaconda/lib/python2.7/site-packages/anaconda_navigator/widgets/manager/table.py | locolucco209/MongoScraper | train | 3 |
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