blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
611cff14b17f6cdc5846187ccf778a29da273728 | [
"if not head:\n return None\nif head.next is None:\n return head\nvals = []\ncur = head\nwhile cur:\n vals.append(cur.val)\n cur = cur.next\nvals = sorted(vals)\npHead = ListNode(None)\ncur = ListNode(vals[0])\npHead.next = cur\nfor v in vals[1:]:\n cur.next = ListNode(v)\n cur = cur.next\nreturn ... | <|body_start_0|>
if not head:
return None
if head.next is None:
return head
vals = []
cur = head
while cur:
vals.append(cur.val)
cur = cur.next
vals = sorted(vals)
pHead = ListNode(None)
cur = ListNode(vals[0... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sortList2(self, head: ListNode) -> ListNode:
"""把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)"""
<|body_0|>
def sortList(self, head: ListNode) -> ListNode:
"""归并排序解法, 代码比较丑, 但是思路没问题: 先用快慢指针找到链表的中点, 断开, 然后分别排序, 再归并"""
<|... | stack_v2_sparse_classes_36k_train_008100 | 2,838 | no_license | [
{
"docstring": "把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)",
"name": "sortList2",
"signature": "def sortList2(self, head: ListNode) -> ListNode"
},
{
"docstring": "归并排序解法, 代码比较丑, 但是思路没问题: 先用快慢指针找到链表的中点, 断开, 然后分别排序, 再归并",
"name": "sortList",
"signature": "def ... | 2 | stack_v2_sparse_classes_30k_train_004536 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortList2(self, head: ListNode) -> ListNode: 把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)
- def sortList(self, head: ListNode) -> ListNode: 归并排序解法,... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortList2(self, head: ListNode) -> ListNode: 把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)
- def sortList(self, head: ListNode) -> ListNode: 归并排序解法,... | 99a3abf1774933af73a8405f9b59e5e64906bca4 | <|skeleton|>
class Solution:
def sortList2(self, head: ListNode) -> ListNode:
"""把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)"""
<|body_0|>
def sortList(self, head: ListNode) -> ListNode:
"""归并排序解法, 代码比较丑, 但是思路没问题: 先用快慢指针找到链表的中点, 断开, 然后分别排序, 再归并"""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def sortList2(self, head: ListNode) -> ListNode:
"""把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)"""
if not head:
return None
if head.next is None:
return head
vals = []
cur = head
while cur:
... | the_stack_v2_python_sparse | 2018年力扣高频算法面试题汇总/排序链表.py | iamkissg/leetcode | train | 0 | |
adc76783d82bb519bf26570f3dce506bb09d9a5d | [
"self.v_ht = ctx.new_var('gb_ht')\nself.v_irow = self.compile_new_tuple(ctx, self.op.schema, 'gb_irow')\ninitargs = ['None', 'None', '[]']\nif self.l_i is not None:\n initargs.append('[]')\nhtinit = 'defaultdict(lambda: [%s])' % ', '.join(initargs)\nctx.declare(self.v_ht, htinit)\nctx.request_vars(dict(row=None)... | <|body_start_0|>
self.v_ht = ctx.new_var('gb_ht')
self.v_irow = self.compile_new_tuple(ctx, self.op.schema, 'gb_irow')
initargs = ['None', 'None', '[]']
if self.l_i is not None:
initargs.append('[]')
htinit = 'defaultdict(lambda: [%s])' % ', '.join(initargs)
c... | PyGroupByBottomTranslator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PyGroupByBottomTranslator:
def produce(self, ctx):
"""Produce sets up the variables and hash table so that they can be populated by calling child's produce (which eventually calls self.consume)."""
<|body_0|>
def consume(self, ctx):
"""Build hashtable For instance, i... | stack_v2_sparse_classes_36k_train_008101 | 4,883 | permissive | [
{
"docstring": "Produce sets up the variables and hash table so that they can be populated by calling child's produce (which eventually calls self.consume).",
"name": "produce",
"signature": "def produce(self, ctx)"
},
{
"docstring": "Build hashtable For instance, if the query is: SELECT a-b, co... | 2 | null | Implement the Python class `PyGroupByBottomTranslator` described below.
Class description:
Implement the PyGroupByBottomTranslator class.
Method signatures and docstrings:
- def produce(self, ctx): Produce sets up the variables and hash table so that they can be populated by calling child's produce (which eventually ... | Implement the Python class `PyGroupByBottomTranslator` described below.
Class description:
Implement the PyGroupByBottomTranslator class.
Method signatures and docstrings:
- def produce(self, ctx): Produce sets up the variables and hash table so that they can be populated by calling child's produce (which eventually ... | 9ffbd96b258e6f98f0876da5d7de0cef90a24ef0 | <|skeleton|>
class PyGroupByBottomTranslator:
def produce(self, ctx):
"""Produce sets up the variables and hash table so that they can be populated by calling child's produce (which eventually calls self.consume)."""
<|body_0|>
def consume(self, ctx):
"""Build hashtable For instance, i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PyGroupByBottomTranslator:
def produce(self, ctx):
"""Produce sets up the variables and hash table so that they can be populated by calling child's produce (which eventually calls self.consume)."""
self.v_ht = ctx.new_var('gb_ht')
self.v_irow = self.compile_new_tuple(ctx, self.op.schem... | the_stack_v2_python_sparse | databass/compile/py/agg.py | w6113/databass-public | train | 5 | |
d4a12b2857a19dda69da2475a322a692d0cc6827 | [
"super().__init__()\nself.broadcaster = make_broadcaster(broadcaster_type=broadcaster_type, filename=filename)\nself.receiver = AirmarReceiver(logger=logger, broadcaster=self.broadcaster, mock_bbio=mock_bbio, mock_port=mock_port)\nself.read_interval = read_interval()",
"while self.is_alive():\n if not self.rec... | <|body_start_0|>
super().__init__()
self.broadcaster = make_broadcaster(broadcaster_type=broadcaster_type, filename=filename)
self.receiver = AirmarReceiver(logger=logger, broadcaster=self.broadcaster, mock_bbio=mock_bbio, mock_port=mock_port)
self.read_interval = read_interval()
<|end_b... | A separate thread to manage reading the airmar inputs. | AirmarInputThread | [
"MIT",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AirmarInputThread:
"""A separate thread to manage reading the airmar inputs."""
def __init__(self, logger, mock_bbio=None, mock_port=None, broadcaster_type=BroadcasterType.Messenger, filename=None):
"""Builds a new airmar input thread."""
<|body_0|>
def run(self):
... | stack_v2_sparse_classes_36k_train_008102 | 1,291 | permissive | [
{
"docstring": "Builds a new airmar input thread.",
"name": "__init__",
"signature": "def __init__(self, logger, mock_bbio=None, mock_port=None, broadcaster_type=BroadcasterType.Messenger, filename=None)"
},
{
"docstring": "Starts a regular read interval.",
"name": "run",
"signature": "d... | 2 | stack_v2_sparse_classes_30k_train_012966 | Implement the Python class `AirmarInputThread` described below.
Class description:
A separate thread to manage reading the airmar inputs.
Method signatures and docstrings:
- def __init__(self, logger, mock_bbio=None, mock_port=None, broadcaster_type=BroadcasterType.Messenger, filename=None): Builds a new airmar input... | Implement the Python class `AirmarInputThread` described below.
Class description:
A separate thread to manage reading the airmar inputs.
Method signatures and docstrings:
- def __init__(self, logger, mock_bbio=None, mock_port=None, broadcaster_type=BroadcasterType.Messenger, filename=None): Builds a new airmar input... | b5d75cb82e4bc3e9c4e428a288c6ac98a4aa2c52 | <|skeleton|>
class AirmarInputThread:
"""A separate thread to manage reading the airmar inputs."""
def __init__(self, logger, mock_bbio=None, mock_port=None, broadcaster_type=BroadcasterType.Messenger, filename=None):
"""Builds a new airmar input thread."""
<|body_0|>
def run(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AirmarInputThread:
"""A separate thread to manage reading the airmar inputs."""
def __init__(self, logger, mock_bbio=None, mock_port=None, broadcaster_type=BroadcasterType.Messenger, filename=None):
"""Builds a new airmar input thread."""
super().__init__()
self.broadcaster = make... | the_stack_v2_python_sparse | src/airmar/airmar_input_thread.py | vt-sailbot/sailbot-21 | train | 5 |
9963cb40a1d6ab4d2fca307a9122212b9093e8e3 | [
"keys = 'ucagrymkwsbhvdn?-'\nfor k in keys:\n assert k in RnaAlphabet\nfor k in keys.upper():\n assert k in RnaAlphabet\nassert 'X' not in RnaAlphabet",
"degens = [['ucag', 'n'], ['ucag-', '?'], ['ucg', 'b'], ['uag', 'd'], ['uca', 'h'], ['ug', 'k'], ['ca', 'm'], ['ag', 'r'], ['cg', 's'], ['cag', 'v'], ['ua'... | <|body_start_0|>
keys = 'ucagrymkwsbhvdn?-'
for k in keys:
assert k in RnaAlphabet
for k in keys.upper():
assert k in RnaAlphabet
assert 'X' not in RnaAlphabet
<|end_body_0|>
<|body_start_1|>
degens = [['ucag', 'n'], ['ucag-', '?'], ['ucg', 'b'], ['uag', ... | Spot-checks of alphabet functionality applied to RNA alphabet. | RnaAlphabetTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RnaAlphabetTests:
"""Spot-checks of alphabet functionality applied to RNA alphabet."""
def test_contains(self):
"""RnaAlphabet should __contain__ the expected symbols."""
<|body_0|>
def test_InverseDegens(self):
"""RnaAlphabet should have correct inverse degenera... | stack_v2_sparse_classes_36k_train_008103 | 27,704 | no_license | [
{
"docstring": "RnaAlphabet should __contain__ the expected symbols.",
"name": "test_contains",
"signature": "def test_contains(self)"
},
{
"docstring": "RnaAlphabet should have correct inverse degenerates",
"name": "test_InverseDegens",
"signature": "def test_InverseDegens(self)"
},
... | 3 | stack_v2_sparse_classes_30k_train_020228 | Implement the Python class `RnaAlphabetTests` described below.
Class description:
Spot-checks of alphabet functionality applied to RNA alphabet.
Method signatures and docstrings:
- def test_contains(self): RnaAlphabet should __contain__ the expected symbols.
- def test_InverseDegens(self): RnaAlphabet should have cor... | Implement the Python class `RnaAlphabetTests` described below.
Class description:
Spot-checks of alphabet functionality applied to RNA alphabet.
Method signatures and docstrings:
- def test_contains(self): RnaAlphabet should __contain__ the expected symbols.
- def test_InverseDegens(self): RnaAlphabet should have cor... | b49442bd793a743188a43809903dc140512420b7 | <|skeleton|>
class RnaAlphabetTests:
"""Spot-checks of alphabet functionality applied to RNA alphabet."""
def test_contains(self):
"""RnaAlphabet should __contain__ the expected symbols."""
<|body_0|>
def test_InverseDegens(self):
"""RnaAlphabet should have correct inverse degenera... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RnaAlphabetTests:
"""Spot-checks of alphabet functionality applied to RNA alphabet."""
def test_contains(self):
"""RnaAlphabet should __contain__ the expected symbols."""
keys = 'ucagrymkwsbhvdn?-'
for k in keys:
assert k in RnaAlphabet
for k in keys.upper():
... | the_stack_v2_python_sparse | old_cogent_tests/base/test_alphabet.py | pycogent/old-cogent | train | 0 |
48725f0716b8b0d6907e48beae85d9844a9ca368 | [
"if isinstance(key, int):\n return FlowBindingAction(key)\nif key not in FlowBindingAction._member_map_:\n return extend_enum(FlowBindingAction, key, default)\nreturn FlowBindingAction[key]",
"if not (isinstance(value, int) and 0 <= value <= 255):\n raise ValueError('%r is not a valid %s' % (value, cls._... | <|body_start_0|>
if isinstance(key, int):
return FlowBindingAction(key)
if key not in FlowBindingAction._member_map_:
return extend_enum(FlowBindingAction, key, default)
return FlowBindingAction[key]
<|end_body_0|>
<|body_start_1|>
if not (isinstance(value, int) ... | [FlowBindingAction] Flow Binding Action Values | FlowBindingAction | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlowBindingAction:
"""[FlowBindingAction] Flow Binding Action Values"""
def get(key: 'int | str', default: 'int'=-1) -> 'FlowBindingAction':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
<|body_... | stack_v2_sparse_classes_36k_train_008104 | 2,081 | permissive | [
{
"docstring": "Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:",
"name": "get",
"signature": "def get(key: 'int | str', default: 'int'=-1) -> 'FlowBindingAction'"
},
{
"docstring": "Lookup function used when value is not ... | 2 | null | Implement the Python class `FlowBindingAction` described below.
Class description:
[FlowBindingAction] Flow Binding Action Values
Method signatures and docstrings:
- def get(key: 'int | str', default: 'int'=-1) -> 'FlowBindingAction': Backport support for original codes. Args: key: Key to get enum item. default: Defa... | Implement the Python class `FlowBindingAction` described below.
Class description:
[FlowBindingAction] Flow Binding Action Values
Method signatures and docstrings:
- def get(key: 'int | str', default: 'int'=-1) -> 'FlowBindingAction': Backport support for original codes. Args: key: Key to get enum item. default: Defa... | a6fe49ec58f09e105bec5a00fb66d9b3f22730d9 | <|skeleton|>
class FlowBindingAction:
"""[FlowBindingAction] Flow Binding Action Values"""
def get(key: 'int | str', default: 'int'=-1) -> 'FlowBindingAction':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FlowBindingAction:
"""[FlowBindingAction] Flow Binding Action Values"""
def get(key: 'int | str', default: 'int'=-1) -> 'FlowBindingAction':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
if isinstance(key, i... | the_stack_v2_python_sparse | pcapkit/const/mh/fb_action.py | JarryShaw/PyPCAPKit | train | 204 |
c72252db737a7f07b77a9f9649a0309ad9367ae0 | [
"year = self.get_year()\nmonth = self.get_month()\nday = self.get_day()\ndate = _date_from_string(year, self.get_year_format(), month, self.get_month_format(), day, self.get_day_format())\nreturn self._get_dated_items(date)",
"lookup_kwargs = self._make_single_date_lookup(date)\nqs = self.get_dated_queryset(**loo... | <|body_start_0|>
year = self.get_year()
month = self.get_month()
day = self.get_day()
date = _date_from_string(year, self.get_year_format(), month, self.get_month_format(), day, self.get_day_format())
return self._get_dated_items(date)
<|end_body_0|>
<|body_start_1|>
loo... | List of objects published on a given day. | BaseDayArchiveView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseDayArchiveView:
"""List of objects published on a given day."""
def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
<|body_0|>
def _get_dated_items(self, date):
"""Do the actual heavy lifting of getting the dated items;... | stack_v2_sparse_classes_36k_train_008105 | 18,709 | permissive | [
{
"docstring": "Return (date_list, items, extra_context) for this request.",
"name": "get_dated_items",
"signature": "def get_dated_items(self)"
},
{
"docstring": "Do the actual heavy lifting of getting the dated items; this accepts a date object so that TodayArchiveView can be trivial.",
"n... | 2 | stack_v2_sparse_classes_30k_train_011327 | Implement the Python class `BaseDayArchiveView` described below.
Class description:
List of objects published on a given day.
Method signatures and docstrings:
- def get_dated_items(self): Return (date_list, items, extra_context) for this request.
- def _get_dated_items(self, date): Do the actual heavy lifting of get... | Implement the Python class `BaseDayArchiveView` described below.
Class description:
List of objects published on a given day.
Method signatures and docstrings:
- def get_dated_items(self): Return (date_list, items, extra_context) for this request.
- def _get_dated_items(self, date): Do the actual heavy lifting of get... | 88059b53a10fdce960442fcfd7470fded4cabb19 | <|skeleton|>
class BaseDayArchiveView:
"""List of objects published on a given day."""
def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
<|body_0|>
def _get_dated_items(self, date):
"""Do the actual heavy lifting of getting the dated items;... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseDayArchiveView:
"""List of objects published on a given day."""
def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
year = self.get_year()
month = self.get_month()
day = self.get_day()
date = _date_from_string(year, self.... | the_stack_v2_python_sparse | shared/utils/views/daterange.py | sha-red/django-shared-utils | train | 0 |
cf223f2937e86fe317e5b3706026fddc724017fd | [
"logging.Handler.__init__(self)\nif not isinstance(database, LogMaster) and (not isinstance(database, LogReadWrite)):\n raise TypeError('A LogMaster or LogReadWrite object must be specified. A {:} was specified instead'.format(type(database)))\nself.database_name = database.database_name\nself.write_log = databa... | <|body_start_0|>
logging.Handler.__init__(self)
if not isinstance(database, LogMaster) and (not isinstance(database, LogReadWrite)):
raise TypeError('A LogMaster or LogReadWrite object must be specified. A {:} was specified instead'.format(type(database)))
self.database_name = databa... | A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package. | MongoLogHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MongoLogHandler:
"""A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package."""
def __init__(self, database):
"""A LogMaster or... | stack_v2_sparse_classes_36k_train_008106 | 47,472 | no_license | [
{
"docstring": "A LogMaster or LogReadWrite object must be specified. The resulting handler object will have a 'database_name' attribute that can be used to identify the handler's destination.",
"name": "__init__",
"signature": "def __init__(self, database)"
},
{
"docstring": "If a formatter is ... | 2 | stack_v2_sparse_classes_30k_train_013695 | Implement the Python class `MongoLogHandler` described below.
Class description:
A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package.
Method signatures and d... | Implement the Python class `MongoLogHandler` described below.
Class description:
A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package.
Method signatures and d... | aab8f9789cb6d9b824836ffa4613b4b17d7d4df6 | <|skeleton|>
class MongoLogHandler:
"""A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package."""
def __init__(self, database):
"""A LogMaster or... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MongoLogHandler:
"""A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package."""
def __init__(self, database):
"""A LogMaster or LogReadWrite... | the_stack_v2_python_sparse | Drivers/Database/MongoDB.py | cdfredrick/AstroComb_HPF | train | 1 |
7ee6386eea2a69af1484816b2f2194df7680bbe6 | [
"queryset = Article.objects.all()\nusername = self.request.query_params.get('username', None)\nif username is not None:\n queryset = queryset.filter(author__username__iexact=username)\ntag = self.request.query_params.get('tag', None)\nif tag is not None:\n queryset = queryset.filter(tags__tag_name__iexact=tag... | <|body_start_0|>
queryset = Article.objects.all()
username = self.request.query_params.get('username', None)
if username is not None:
queryset = queryset.filter(author__username__iexact=username)
tag = self.request.query_params.get('tag', None)
if tag is not None:
... | A user can post an artcle once they have an account in the application params: ['title', 'description', 'body'] | ArticleAPIView | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ArticleAPIView:
"""A user can post an artcle once they have an account in the application params: ['title', 'description', 'body']"""
def get_queryset(self):
"""Optionally restricts the returned purchases to a given user, by filtering against a `username` query parameter in the URL."... | stack_v2_sparse_classes_36k_train_008107 | 12,242 | permissive | [
{
"docstring": "Optionally restricts the returned purchases to a given user, by filtering against a `username` query parameter in the URL.",
"name": "get_queryset",
"signature": "def get_queryset(self)"
},
{
"docstring": "Create a new article in the application",
"name": "post",
"signatu... | 3 | stack_v2_sparse_classes_30k_test_000256 | Implement the Python class `ArticleAPIView` described below.
Class description:
A user can post an artcle once they have an account in the application params: ['title', 'description', 'body']
Method signatures and docstrings:
- def get_queryset(self): Optionally restricts the returned purchases to a given user, by fi... | Implement the Python class `ArticleAPIView` described below.
Class description:
A user can post an artcle once they have an account in the application params: ['title', 'description', 'body']
Method signatures and docstrings:
- def get_queryset(self): Optionally restricts the returned purchases to a given user, by fi... | e8438b78b88c52d108520429d0b67cd3d13e0824 | <|skeleton|>
class ArticleAPIView:
"""A user can post an artcle once they have an account in the application params: ['title', 'description', 'body']"""
def get_queryset(self):
"""Optionally restricts the returned purchases to a given user, by filtering against a `username` query parameter in the URL."... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ArticleAPIView:
"""A user can post an artcle once they have an account in the application params: ['title', 'description', 'body']"""
def get_queryset(self):
"""Optionally restricts the returned purchases to a given user, by filtering against a `username` query parameter in the URL."""
qu... | the_stack_v2_python_sparse | authors/apps/articles/views.py | andela/ah-sealteam | train | 1 |
56372b6003ecaffc555fca403c207cf95e64cf51 | [
"super().__init__()\nself.name = 'PillarFeatureNet'\nassert len(num_filters) > 0\nnum_input_features += 0\nif with_distance:\n num_input_features += 1\nself._with_distance = with_distance\nself.height = height\nself.width = width\nself.depth = depth\nnum_filters = [num_input_features] + list(num_filters)\npfn_la... | <|body_start_0|>
super().__init__()
self.name = 'PillarFeatureNet'
assert len(num_filters) > 0
num_input_features += 0
if with_distance:
num_input_features += 1
self._with_distance = with_distance
self.height = height
self.width = width
... | PillarFeatureNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PillarFeatureNet:
def __init__(self, num_input_features=4, use_norm=True, num_filters=(64,), height=480, width=480, depth=2, with_distance=False):
"""Pillar Feature Net. The network prepares the pillar features and performs forward pass through PFNLayers. This net performs a similar role... | stack_v2_sparse_classes_36k_train_008108 | 7,758 | no_license | [
{
"docstring": "Pillar Feature Net. The network prepares the pillar features and performs forward pass through PFNLayers. This net performs a similar role to SECOND's second.pytorch.voxelnet.VoxelFeatureExtractor. :param num_input_features: <int>. Number of input features, either x, y, z or x, y, z, r. :param u... | 2 | null | Implement the Python class `PillarFeatureNet` described below.
Class description:
Implement the PillarFeatureNet class.
Method signatures and docstrings:
- def __init__(self, num_input_features=4, use_norm=True, num_filters=(64,), height=480, width=480, depth=2, with_distance=False): Pillar Feature Net. The network p... | Implement the Python class `PillarFeatureNet` described below.
Class description:
Implement the PillarFeatureNet class.
Method signatures and docstrings:
- def __init__(self, num_input_features=4, use_norm=True, num_filters=(64,), height=480, width=480, depth=2, with_distance=False): Pillar Feature Net. The network p... | 43388efd911feecde588b27a753de353b8e28265 | <|skeleton|>
class PillarFeatureNet:
def __init__(self, num_input_features=4, use_norm=True, num_filters=(64,), height=480, width=480, depth=2, with_distance=False):
"""Pillar Feature Net. The network prepares the pillar features and performs forward pass through PFNLayers. This net performs a similar role... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PillarFeatureNet:
def __init__(self, num_input_features=4, use_norm=True, num_filters=(64,), height=480, width=480, depth=2, with_distance=False):
"""Pillar Feature Net. The network prepares the pillar features and performs forward pass through PFNLayers. This net performs a similar role to SECOND's s... | the_stack_v2_python_sparse | models/backbones/pointpillars_voxel.py | dragonlong/haoi-pose | train | 0 | |
3fb7c270348aade8d4f104fd19362e7ce91ef4a5 | [
"super().__init__()\nself.original_image = image\nself.image = image\nself.rect = self.image.get_rect()\nself.rect.x = init_x\nself.rect.y = init_y",
"my = self.rect\nits = other.rect\ndist = math.sqrt((its.x - my.x) ** 2 + (its.y - my.y) ** 2)\nreturn dist",
"my = self.rect\nits = other.rect\ndist = self.dista... | <|body_start_0|>
super().__init__()
self.original_image = image
self.image = image
self.rect = self.image.get_rect()
self.rect.x = init_x
self.rect.y = init_y
<|end_body_0|>
<|body_start_1|>
my = self.rect
its = other.rect
dist = math.sqrt((its.x ... | A base class for an entity in Nurltown | Entity | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Entity:
"""A base class for an entity in Nurltown"""
def __init__(self, image, init_x=0, init_y=0):
"""Constructor function for the Entity class :param image: The image which will be rendered to represent the entity :type image: pygame.Surface :param init_x: x coordinate of the initi... | stack_v2_sparse_classes_36k_train_008109 | 10,299 | no_license | [
{
"docstring": "Constructor function for the Entity class :param image: The image which will be rendered to represent the entity :type image: pygame.Surface :param init_x: x coordinate of the initial position in the game :type init_x: float :param init_y: y coordinate of the initial position in the game :type i... | 3 | stack_v2_sparse_classes_30k_train_005885 | Implement the Python class `Entity` described below.
Class description:
A base class for an entity in Nurltown
Method signatures and docstrings:
- def __init__(self, image, init_x=0, init_y=0): Constructor function for the Entity class :param image: The image which will be rendered to represent the entity :type image... | Implement the Python class `Entity` described below.
Class description:
A base class for an entity in Nurltown
Method signatures and docstrings:
- def __init__(self, image, init_x=0, init_y=0): Constructor function for the Entity class :param image: The image which will be rendered to represent the entity :type image... | d5cec43ac0958cfd8bcab5d841ffb13594cdcac0 | <|skeleton|>
class Entity:
"""A base class for an entity in Nurltown"""
def __init__(self, image, init_x=0, init_y=0):
"""Constructor function for the Entity class :param image: The image which will be rendered to represent the entity :type image: pygame.Surface :param init_x: x coordinate of the initi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Entity:
"""A base class for an entity in Nurltown"""
def __init__(self, image, init_x=0, init_y=0):
"""Constructor function for the Entity class :param image: The image which will be rendered to represent the entity :type image: pygame.Surface :param init_x: x coordinate of the initial position i... | the_stack_v2_python_sparse | S18/Projects/nurltown/src/entities.py | dakadabra/Lessons | train | 0 |
e7714d88306c278d3ceb8b6536ecb4a1f96c07ed | [
"self.feature_means = np.mean(X, axis=0)\nself.feature_stds = np.std(X, axis=0)\nprint('평균 : ', self.feature_means, '표준편차 : ', self.feature_stds)",
"dim = X.shape\ntransformed = np.empty(dim)\nfor row in range(dim[0]):\n for col in range(dim[1]):\n transformed[row, col] = (X[row, col] - self.feature_mea... | <|body_start_0|>
self.feature_means = np.mean(X, axis=0)
self.feature_stds = np.std(X, axis=0)
print('평균 : ', self.feature_means, '표준편차 : ', self.feature_stds)
<|end_body_0|>
<|body_start_1|>
dim = X.shape
transformed = np.empty(dim)
for row in range(dim[0]):
... | MyScaler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyScaler:
def fit(self, X):
"""x의 각 특성(컬럼)들의 평균 & 표준편차 저장 -> 평균과 표준편차는 '특성 별'로 구해져야 한다."""
<|body_0|>
def transform(self, X):
"""x의 평균을 0 / 표준편차를 1로 표준화하고 리턴"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.feature_means = np.mean(X, axis=0)
... | stack_v2_sparse_classes_36k_train_008110 | 6,624 | no_license | [
{
"docstring": "x의 각 특성(컬럼)들의 평균 & 표준편차 저장 -> 평균과 표준편차는 '특성 별'로 구해져야 한다.",
"name": "fit",
"signature": "def fit(self, X)"
},
{
"docstring": "x의 평균을 0 / 표준편차를 1로 표준화하고 리턴",
"name": "transform",
"signature": "def transform(self, X)"
}
] | 2 | null | Implement the Python class `MyScaler` described below.
Class description:
Implement the MyScaler class.
Method signatures and docstrings:
- def fit(self, X): x의 각 특성(컬럼)들의 평균 & 표준편차 저장 -> 평균과 표준편차는 '특성 별'로 구해져야 한다.
- def transform(self, X): x의 평균을 0 / 표준편차를 1로 표준화하고 리턴 | Implement the Python class `MyScaler` described below.
Class description:
Implement the MyScaler class.
Method signatures and docstrings:
- def fit(self, X): x의 각 특성(컬럼)들의 평균 & 표준편차 저장 -> 평균과 표준편차는 '특성 별'로 구해져야 한다.
- def transform(self, X): x의 평균을 0 / 표준편차를 1로 표준화하고 리턴
<|skeleton|>
class MyScaler:
def fit(self,... | da068ea62682ffa70c7d23dde4ef132c49a81364 | <|skeleton|>
class MyScaler:
def fit(self, X):
"""x의 각 특성(컬럼)들의 평균 & 표준편차 저장 -> 평균과 표준편차는 '특성 별'로 구해져야 한다."""
<|body_0|>
def transform(self, X):
"""x의 평균을 0 / 표준편차를 1로 표준화하고 리턴"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyScaler:
def fit(self, X):
"""x의 각 특성(컬럼)들의 평균 & 표준편차 저장 -> 평균과 표준편차는 '특성 별'로 구해져야 한다."""
self.feature_means = np.mean(X, axis=0)
self.feature_stds = np.std(X, axis=0)
print('평균 : ', self.feature_means, '표준편차 : ', self.feature_stds)
def transform(self, X):
"""x의 평... | the_stack_v2_python_sparse | scratch11_KNN/ex03_knn_직접구현.py | handaeho/lab_python | train | 0 | |
ff3127513310da69e8632d86476c226317618987 | [
"super().__init__()\nself.max_pool = max_pool\ntblocks = []\nfor i in range(depth):\n tblocks.append(TransformerBlock(emb=emb, heads=heads, seq_length=seq_length, mask=False, dropout=dropout, wide=wide))\nself.tblocks = nn.Sequential(*tblocks)\nself.toprobs = nn.Linear(emb, num_classes)\nself.do = nn.Dropout(dro... | <|body_start_0|>
super().__init__()
self.max_pool = max_pool
tblocks = []
for i in range(depth):
tblocks.append(TransformerBlock(emb=emb, heads=heads, seq_length=seq_length, mask=False, dropout=dropout, wide=wide))
self.tblocks = nn.Sequential(*tblocks)
self.t... | Transformer for classifying sequences | CTransformer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CTransformer:
"""Transformer for classifying sequences"""
def __init__(self, emb, heads, depth, seq_length, num_classes, max_pool=True, dropout=0.0, wide=False):
""":param emb: Embedding dimension :param heads: nr. of attention heads :param depth: Number of transformer blocks :param ... | stack_v2_sparse_classes_36k_train_008111 | 23,155 | permissive | [
{
"docstring": ":param emb: Embedding dimension :param heads: nr. of attention heads :param depth: Number of transformer blocks :param seq_length: Expected maximum sequence length :param num_tokens: Number of tokens (usually words) in the vocabulary :param num_classes: Number of classes. :param max_pool: If tru... | 2 | stack_v2_sparse_classes_30k_train_002460 | Implement the Python class `CTransformer` described below.
Class description:
Transformer for classifying sequences
Method signatures and docstrings:
- def __init__(self, emb, heads, depth, seq_length, num_classes, max_pool=True, dropout=0.0, wide=False): :param emb: Embedding dimension :param heads: nr. of attention... | Implement the Python class `CTransformer` described below.
Class description:
Transformer for classifying sequences
Method signatures and docstrings:
- def __init__(self, emb, heads, depth, seq_length, num_classes, max_pool=True, dropout=0.0, wide=False): :param emb: Embedding dimension :param heads: nr. of attention... | a68400f4918f10dde82574ad19654243c9a65024 | <|skeleton|>
class CTransformer:
"""Transformer for classifying sequences"""
def __init__(self, emb, heads, depth, seq_length, num_classes, max_pool=True, dropout=0.0, wide=False):
""":param emb: Embedding dimension :param heads: nr. of attention heads :param depth: Number of transformer blocks :param ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CTransformer:
"""Transformer for classifying sequences"""
def __init__(self, emb, heads, depth, seq_length, num_classes, max_pool=True, dropout=0.0, wide=False):
""":param emb: Embedding dimension :param heads: nr. of attention heads :param depth: Number of transformer blocks :param seq_length: E... | the_stack_v2_python_sparse | poker/models/model_layers.py | bogwero/PokerAI-2 | train | 0 |
deef330d6975a7ef77189a3b834ceeb97a8d0946 | [
"length = len(s)\nsize = [0 for i in range(length)]\nstack = [0 for i in range(length)]\nstackPtr = 0\nsumming = 0\nres = 0\nfor i, char in enumerate(s):\n if char == '(':\n stack[stackPtr] = i\n stackPtr += 1\n elif char == ')':\n if stackPtr > 0:\n prev = stack[stackPtr - 1]\... | <|body_start_0|>
length = len(s)
size = [0 for i in range(length)]
stack = [0 for i in range(length)]
stackPtr = 0
summing = 0
res = 0
for i, char in enumerate(s):
if char == '(':
stack[stackPtr] = i
stackPtr += 1
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestValidParentheses(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestValidParentheses2(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
length = len(s)
size = [0 for i in r... | stack_v2_sparse_classes_36k_train_008112 | 1,528 | permissive | [
{
"docstring": ":type s: str :rtype: int",
"name": "longestValidParentheses",
"signature": "def longestValidParentheses(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "longestValidParentheses2",
"signature": "def longestValidParentheses2(self, s)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014211 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestValidParentheses(self, s): :type s: str :rtype: int
- def longestValidParentheses2(self, s): :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestValidParentheses(self, s): :type s: str :rtype: int
- def longestValidParentheses2(self, s): :type s: str :rtype: int
<|skeleton|>
class Solution:
def longestVal... | c8bf33af30569177c5276ffcd72a8d93ba4c402a | <|skeleton|>
class Solution:
def longestValidParentheses(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestValidParentheses2(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestValidParentheses(self, s):
""":type s: str :rtype: int"""
length = len(s)
size = [0 for i in range(length)]
stack = [0 for i in range(length)]
stackPtr = 0
summing = 0
res = 0
for i, char in enumerate(s):
if char ... | the_stack_v2_python_sparse | 1-100/31-40/32-longestValidParenthesis/longestValidParenthesis.py | xuychen/Leetcode | train | 0 | |
9202d2e7929f8e5c3556ea256de476a3e1ab9cf5 | [
"kwargs['type'] = 'variable'\nif 'meta' not in kwargs:\n kwargs['meta'] = {}\nkwargs['meta']['type'] = kwargs['type']\nsuper().__init__(field_id, **kwargs)",
"if not isinstance(min_max, list):\n return -1\nif not len(min_max) == 2:\n return -2\nif not all((isinstance(x, numbers.Real) for x in min_max)):\... | <|body_start_0|>
kwargs['type'] = 'variable'
if 'meta' not in kwargs:
kwargs['meta'] = {}
kwargs['meta']['type'] = kwargs['type']
super().__init__(field_id, **kwargs)
<|end_body_0|>
<|body_start_1|>
if not isinstance(min_max, list):
return -1
if n... | Class for variable field type. | Variable | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Variable:
"""Class for variable field type."""
def __init__(self, field_id, **kwargs):
"""Init Variable class."""
<|body_0|>
def get_indices_in_range(self, min_max, invert=False):
"""Get indices for all records in range."""
<|body_1|>
def sum_values(... | stack_v2_sparse_classes_36k_train_008113 | 11,877 | permissive | [
{
"docstring": "Init Variable class.",
"name": "__init__",
"signature": "def __init__(self, field_id, **kwargs)"
},
{
"docstring": "Get indices for all records in range.",
"name": "get_indices_in_range",
"signature": "def get_indices_in_range(self, min_max, invert=False)"
},
{
"d... | 3 | stack_v2_sparse_classes_30k_train_014273 | Implement the Python class `Variable` described below.
Class description:
Class for variable field type.
Method signatures and docstrings:
- def __init__(self, field_id, **kwargs): Init Variable class.
- def get_indices_in_range(self, min_max, invert=False): Get indices for all records in range.
- def sum_values(self... | Implement the Python class `Variable` described below.
Class description:
Class for variable field type.
Method signatures and docstrings:
- def __init__(self, field_id, **kwargs): Init Variable class.
- def get_indices_in_range(self, min_max, invert=False): Get indices for all records in range.
- def sum_values(self... | 052a26316d19a48981417bf340d9f57e2cdc653a | <|skeleton|>
class Variable:
"""Class for variable field type."""
def __init__(self, field_id, **kwargs):
"""Init Variable class."""
<|body_0|>
def get_indices_in_range(self, min_max, invert=False):
"""Get indices for all records in range."""
<|body_1|>
def sum_values(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Variable:
"""Class for variable field type."""
def __init__(self, field_id, **kwargs):
"""Init Variable class."""
kwargs['type'] = 'variable'
if 'meta' not in kwargs:
kwargs['meta'] = {}
kwargs['meta']['type'] = kwargs['type']
super().__init__(field_id,... | the_stack_v2_python_sparse | src/blobtools/lib/field.py | blobtoolkit/blobtoolkit | train | 32 |
d009b74557a1af19f1ed38b28804c4b3d0fe5231 | [
"if root is None:\n return [0]\nsums = []\ncounts = []\n\ndef dfs(node, level):\n if len(sums) == level:\n sums.append(0)\n counts.append(0)\n sums[level] += node.val\n counts[level] += 1\n if node.left:\n dfs(node.left, level + 1)\n if node.right:\n dfs(node.right, lev... | <|body_start_0|>
if root is None:
return [0]
sums = []
counts = []
def dfs(node, level):
if len(sums) == level:
sums.append(0)
counts.append(0)
sums[level] += node.val
counts[level] += 1
if node.... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def averageOfLevels(self, root) -> List[float]:
"""DFS, Time: O(n), Space: O(n)"""
<|body_0|>
def averageOfLevels(self, root) -> List[float]:
"""BFS, Time: O(n), Space: O(n)"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root is None:
... | stack_v2_sparse_classes_36k_train_008114 | 1,288 | no_license | [
{
"docstring": "DFS, Time: O(n), Space: O(n)",
"name": "averageOfLevels",
"signature": "def averageOfLevels(self, root) -> List[float]"
},
{
"docstring": "BFS, Time: O(n), Space: O(n)",
"name": "averageOfLevels",
"signature": "def averageOfLevels(self, root) -> List[float]"
}
] | 2 | stack_v2_sparse_classes_30k_train_016468 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def averageOfLevels(self, root) -> List[float]: DFS, Time: O(n), Space: O(n)
- def averageOfLevels(self, root) -> List[float]: BFS, Time: O(n), Space: O(n) | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def averageOfLevels(self, root) -> List[float]: DFS, Time: O(n), Space: O(n)
- def averageOfLevels(self, root) -> List[float]: BFS, Time: O(n), Space: O(n)
<|skeleton|>
class So... | 72136e3487d239f5b37e2d6393e034262a6bf599 | <|skeleton|>
class Solution:
def averageOfLevels(self, root) -> List[float]:
"""DFS, Time: O(n), Space: O(n)"""
<|body_0|>
def averageOfLevels(self, root) -> List[float]:
"""BFS, Time: O(n), Space: O(n)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def averageOfLevels(self, root) -> List[float]:
"""DFS, Time: O(n), Space: O(n)"""
if root is None:
return [0]
sums = []
counts = []
def dfs(node, level):
if len(sums) == level:
sums.append(0)
counts.app... | the_stack_v2_python_sparse | python/637-Average of Levels in Binary Tree.py | cwza/leetcode | train | 0 | |
7d6d095ac060ef7233d74a27d31db132792097fd | [
"self.selsk_form_kode_field = selsk_form_kode_field\nself.selsk_form_tekst_field = selsk_form_tekst_field\nself.etablert_ar_field = etablert_ar_field\nself.etablert_ar_field_specified = etablert_ar_field_specified\nself.stiftet_dato_field = APIHelper.RFC3339DateTime(stiftet_dato_field) if stiftet_dato_field else No... | <|body_start_0|>
self.selsk_form_kode_field = selsk_form_kode_field
self.selsk_form_tekst_field = selsk_form_tekst_field
self.etablert_ar_field = etablert_ar_field
self.etablert_ar_field_specified = etablert_ar_field_specified
self.stiftet_dato_field = APIHelper.RFC3339DateTime(s... | Implementation of the 'Grunnfakta' model. TODO: type model description here. Attributes: selsk_form_kode_field (string): TODO: type description here. selsk_form_tekst_field (string): TODO: type description here. etablert_ar_field (int): TODO: type description here. etablert_ar_field_specified (bool): TODO: type descrip... | Grunnfakta | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Grunnfakta:
"""Implementation of the 'Grunnfakta' model. TODO: type model description here. Attributes: selsk_form_kode_field (string): TODO: type description here. selsk_form_tekst_field (string): TODO: type description here. etablert_ar_field (int): TODO: type description here. etablert_ar_fiel... | stack_v2_sparse_classes_36k_train_008115 | 7,569 | permissive | [
{
"docstring": "Constructor for the Grunnfakta class",
"name": "__init__",
"signature": "def __init__(self, selsk_form_kode_field=None, selsk_form_tekst_field=None, etablert_ar_field=None, etablert_ar_field_specified=None, stiftet_dato_field=None, aksjekapital_field=None, aksjekapital_status_field=None,... | 2 | null | Implement the Python class `Grunnfakta` described below.
Class description:
Implementation of the 'Grunnfakta' model. TODO: type model description here. Attributes: selsk_form_kode_field (string): TODO: type description here. selsk_form_tekst_field (string): TODO: type description here. etablert_ar_field (int): TODO: ... | Implement the Python class `Grunnfakta` described below.
Class description:
Implementation of the 'Grunnfakta' model. TODO: type model description here. Attributes: selsk_form_kode_field (string): TODO: type description here. selsk_form_tekst_field (string): TODO: type description here. etablert_ar_field (int): TODO: ... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class Grunnfakta:
"""Implementation of the 'Grunnfakta' model. TODO: type model description here. Attributes: selsk_form_kode_field (string): TODO: type description here. selsk_form_tekst_field (string): TODO: type description here. etablert_ar_field (int): TODO: type description here. etablert_ar_fiel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Grunnfakta:
"""Implementation of the 'Grunnfakta' model. TODO: type model description here. Attributes: selsk_form_kode_field (string): TODO: type description here. selsk_form_tekst_field (string): TODO: type description here. etablert_ar_field (int): TODO: type description here. etablert_ar_field_specified (... | the_stack_v2_python_sparse | idfy_rest_client/models/grunnfakta.py | dealflowteam/Idfy | train | 0 |
0da743e9fd2ee531cf516f7f91c5835232851ffa | [
"test, traceback = super(SetRFOnOffTask, self).check(*args, **kwargs)\nif test and self.switch:\n try:\n switch = self.format_and_eval_string(self.switch)\n except Exception:\n return (False, traceback)\n if switch not in ('Off', 'On', 0, 1):\n test = False\n traceback[self.get_... | <|body_start_0|>
test, traceback = super(SetRFOnOffTask, self).check(*args, **kwargs)
if test and self.switch:
try:
switch = self.format_and_eval_string(self.switch)
except Exception:
return (False, traceback)
if switch not in ('Off', '... | Switch on/off the output of the source. | SetRFOnOffTask | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SetRFOnOffTask:
"""Switch on/off the output of the source."""
def check(self, *args, **kwargs):
"""Validate the value of the of the switch."""
<|body_0|>
def i_perform(self, switch=None):
"""Default interface behavior."""
<|body_1|>
<|end_skeleton|>
<|b... | stack_v2_sparse_classes_36k_train_008116 | 7,840 | permissive | [
{
"docstring": "Validate the value of the of the switch.",
"name": "check",
"signature": "def check(self, *args, **kwargs)"
},
{
"docstring": "Default interface behavior.",
"name": "i_perform",
"signature": "def i_perform(self, switch=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012350 | Implement the Python class `SetRFOnOffTask` described below.
Class description:
Switch on/off the output of the source.
Method signatures and docstrings:
- def check(self, *args, **kwargs): Validate the value of the of the switch.
- def i_perform(self, switch=None): Default interface behavior. | Implement the Python class `SetRFOnOffTask` described below.
Class description:
Switch on/off the output of the source.
Method signatures and docstrings:
- def check(self, *args, **kwargs): Validate the value of the of the switch.
- def i_perform(self, switch=None): Default interface behavior.
<|skeleton|>
class Set... | b6f1f5b236c7a4e28d9a3bc8da9820c52d789309 | <|skeleton|>
class SetRFOnOffTask:
"""Switch on/off the output of the source."""
def check(self, *args, **kwargs):
"""Validate the value of the of the switch."""
<|body_0|>
def i_perform(self, switch=None):
"""Default interface behavior."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SetRFOnOffTask:
"""Switch on/off the output of the source."""
def check(self, *args, **kwargs):
"""Validate the value of the of the switch."""
test, traceback = super(SetRFOnOffTask, self).check(*args, **kwargs)
if test and self.switch:
try:
switch = se... | the_stack_v2_python_sparse | exopy_hqc_legacy/tasks/tasks/instr/rf_tasks.py | Exopy/exopy_hqc_legacy | train | 0 |
e99171cc29a3ba7a6ca9b1b4df722547a9d2527f | [
"self._lat_deg = None\nself._lat_min = None\nself._long_deg = None\nself._long_min = None\nself._bearing = None",
"self._lat_deg = sensor_package.latitude_deg\nself._lat_min = sensor_package.latitude_min\nself._long_deg = sensor_package.longitude_deg\nself._long_min = sensor_package.longitude_min\nself._bearing =... | <|body_start_0|>
self._lat_deg = None
self._lat_min = None
self._long_deg = None
self._long_min = None
self._bearing = None
<|end_body_0|>
<|body_start_1|>
self._lat_deg = sensor_package.latitude_deg
self._lat_min = sensor_package.latitude_min
self._long_... | Class for sensor package data and calculations. | RawSensorPackage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RawSensorPackage:
"""Class for sensor package data and calculations."""
def __init__(self):
"""Initialize sensor package data."""
<|body_0|>
def update(self, sensor_package):
"""Update sensor package data with new LCM data."""
<|body_1|>
<|end_skeleton|>... | stack_v2_sparse_classes_36k_train_008117 | 5,754 | no_license | [
{
"docstring": "Initialize sensor package data.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Update sensor package data with new LCM data.",
"name": "update",
"signature": "def update(self, sensor_package)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018128 | Implement the Python class `RawSensorPackage` described below.
Class description:
Class for sensor package data and calculations.
Method signatures and docstrings:
- def __init__(self): Initialize sensor package data.
- def update(self, sensor_package): Update sensor package data with new LCM data. | Implement the Python class `RawSensorPackage` described below.
Class description:
Class for sensor package data and calculations.
Method signatures and docstrings:
- def __init__(self): Initialize sensor package data.
- def update(self, sensor_package): Update sensor package data with new LCM data.
<|skeleton|>
clas... | 41230800c61b6ad782db691b12ae444d1a26ff05 | <|skeleton|>
class RawSensorPackage:
"""Class for sensor package data and calculations."""
def __init__(self):
"""Initialize sensor package data."""
<|body_0|>
def update(self, sensor_package):
"""Update sensor package data with new LCM data."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RawSensorPackage:
"""Class for sensor package data and calculations."""
def __init__(self):
"""Initialize sensor package data."""
self._lat_deg = None
self._lat_min = None
self._long_deg = None
self._long_min = None
self._bearing = None
def update(self... | the_stack_v2_python_sparse | onboard/filter/src/rawmessages.py | DavidSmith166/mrover-workspace | train | 2 |
b2c651ca1856de05b4edc60d44e18493e3cc29c9 | [
"infraction_type = get_field_value(data, 'infraction_type')\ninfraction_duration = get_field_value(data, 'infraction_duration')\nif (get_field_value(data, 'infraction_reason') or infraction_duration) and infraction_type == 'NONE':\n raise ValidationError('Infraction type is required with infraction duration or r... | <|body_start_0|>
infraction_type = get_field_value(data, 'infraction_type')
infraction_duration = get_field_value(data, 'infraction_duration')
if (get_field_value(data, 'infraction_reason') or infraction_duration) and infraction_type == 'NONE':
raise ValidationError('Infraction type ... | A class providing (de-)serialization of `Filter` instances. | FilterSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FilterSerializer:
"""A class providing (de-)serialization of `Filter` instances."""
def validate(self, data: dict) -> dict:
"""Perform infraction data + allowed and disallowed lists validation."""
<|body_0|>
def create(self, validated_data: dict) -> User:
"""Over... | stack_v2_sparse_classes_36k_train_008118 | 23,871 | permissive | [
{
"docstring": "Perform infraction data + allowed and disallowed lists validation.",
"name": "validate",
"signature": "def validate(self, data: dict) -> dict"
},
{
"docstring": "Override the create method to catch violations of the custom uniqueness constraint.",
"name": "create",
"signa... | 3 | null | Implement the Python class `FilterSerializer` described below.
Class description:
A class providing (de-)serialization of `Filter` instances.
Method signatures and docstrings:
- def validate(self, data: dict) -> dict: Perform infraction data + allowed and disallowed lists validation.
- def create(self, validated_data... | Implement the Python class `FilterSerializer` described below.
Class description:
A class providing (de-)serialization of `Filter` instances.
Method signatures and docstrings:
- def validate(self, data: dict) -> dict: Perform infraction data + allowed and disallowed lists validation.
- def create(self, validated_data... | cb6326cabee6570a5725702cb2893ae39f752279 | <|skeleton|>
class FilterSerializer:
"""A class providing (de-)serialization of `Filter` instances."""
def validate(self, data: dict) -> dict:
"""Perform infraction data + allowed and disallowed lists validation."""
<|body_0|>
def create(self, validated_data: dict) -> User:
"""Over... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FilterSerializer:
"""A class providing (de-)serialization of `Filter` instances."""
def validate(self, data: dict) -> dict:
"""Perform infraction data + allowed and disallowed lists validation."""
infraction_type = get_field_value(data, 'infraction_type')
infraction_duration = get... | the_stack_v2_python_sparse | pydis_site/apps/api/serializers.py | python-discord/site | train | 746 |
80d65e1677017085f0b2948d1b271bca4bb50404 | [
"tmpTsk = tasks[0]\nmin = 99999\nfor task in tasks:\n for machine in machines:\n if machine.get_task_duration(task) < min:\n min = machine.get_task_duration(task)\n tmpTsk = task\ntasks.remove(tmpTsk)\nreturn tmpTsk",
"beginInd = 0\nendInd = len(tasks)\nvMachines = [vMachineA, vMac... | <|body_start_0|>
tmpTsk = tasks[0]
min = 99999
for task in tasks:
for machine in machines:
if machine.get_task_duration(task) < min:
min = machine.get_task_duration(task)
tmpTsk = task
tasks.remove(tmpTsk)
return... | JohnsonVirtual | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JohnsonVirtual:
def task_w_min_duration(self, machines: list, tasks: list) -> Task:
"""Wyszukiwanie i usuwanie najkrótszego zadania Parameters ---------- machines : list Lista maszyn z ustawionymi czasami zadań; tasks : list Lista zadań; Returns ------- task Zadanie z najkrótszym czasem ... | stack_v2_sparse_classes_36k_train_008119 | 1,960 | no_license | [
{
"docstring": "Wyszukiwanie i usuwanie najkrótszego zadania Parameters ---------- machines : list Lista maszyn z ustawionymi czasami zadań; tasks : list Lista zadań; Returns ------- task Zadanie z najkrótszym czasem wykonywania (niezależnie od maszyny);",
"name": "task_w_min_duration",
"signature": "de... | 2 | stack_v2_sparse_classes_30k_test_000385 | Implement the Python class `JohnsonVirtual` described below.
Class description:
Implement the JohnsonVirtual class.
Method signatures and docstrings:
- def task_w_min_duration(self, machines: list, tasks: list) -> Task: Wyszukiwanie i usuwanie najkrótszego zadania Parameters ---------- machines : list Lista maszyn z ... | Implement the Python class `JohnsonVirtual` described below.
Class description:
Implement the JohnsonVirtual class.
Method signatures and docstrings:
- def task_w_min_duration(self, machines: list, tasks: list) -> Task: Wyszukiwanie i usuwanie najkrótszego zadania Parameters ---------- machines : list Lista maszyn z ... | 191b203143db8800759dedcb90f7b352e6e457fd | <|skeleton|>
class JohnsonVirtual:
def task_w_min_duration(self, machines: list, tasks: list) -> Task:
"""Wyszukiwanie i usuwanie najkrótszego zadania Parameters ---------- machines : list Lista maszyn z ustawionymi czasami zadań; tasks : list Lista zadań; Returns ------- task Zadanie z najkrótszym czasem ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JohnsonVirtual:
def task_w_min_duration(self, machines: list, tasks: list) -> Task:
"""Wyszukiwanie i usuwanie najkrótszego zadania Parameters ---------- machines : list Lista maszyn z ustawionymi czasami zadań; tasks : list Lista zadań; Returns ------- task Zadanie z najkrótszym czasem wykonywania (n... | the_stack_v2_python_sparse | flow_shop_scheduling/labolatorium1/johnson_virtual.py | goorkamateusz/SPD-collage-course | train | 1 | |
f605f4a95f151ae3877881c25672887055ed1e36 | [
"label_count = dict()\nlines = read_json_format_file(corpus_file)\ndata_len = list()\nfor line in lines:\n label = line['label']\n title = line['title']\n data_len.append(len(title))\n if label in label_count:\n label_count[label] += 1\n else:\n label_count[label] = 1\nprint('数据 label 分... | <|body_start_0|>
label_count = dict()
lines = read_json_format_file(corpus_file)
data_len = list()
for line in lines:
label = line['label']
title = line['title']
data_len.append(len(title))
if label in label_count:
label_cou... | AnalysisData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnalysisData:
def analysis_data(self, corpus_file):
"""分析数据分布情况, :param corpus_file: :return:"""
<|body_0|>
def plot_text_length(self, all_text_len, save_path=None):
"""绘制文本长度分布 :param all_text_len: [22,33,34,...] :param save_path: 图片保存路径 :return:"""
<|body_1... | stack_v2_sparse_classes_36k_train_008120 | 4,619 | no_license | [
{
"docstring": "分析数据分布情况, :param corpus_file: :return:",
"name": "analysis_data",
"signature": "def analysis_data(self, corpus_file)"
},
{
"docstring": "绘制文本长度分布 :param all_text_len: [22,33,34,...] :param save_path: 图片保存路径 :return:",
"name": "plot_text_length",
"signature": "def plot_tex... | 3 | null | Implement the Python class `AnalysisData` described below.
Class description:
Implement the AnalysisData class.
Method signatures and docstrings:
- def analysis_data(self, corpus_file): 分析数据分布情况, :param corpus_file: :return:
- def plot_text_length(self, all_text_len, save_path=None): 绘制文本长度分布 :param all_text_len: [22... | Implement the Python class `AnalysisData` described below.
Class description:
Implement the AnalysisData class.
Method signatures and docstrings:
- def analysis_data(self, corpus_file): 分析数据分布情况, :param corpus_file: :return:
- def plot_text_length(self, all_text_len, save_path=None): 绘制文本长度分布 :param all_text_len: [22... | ee7ecedd55ce544b127be8009e026ac2cdc3f71b | <|skeleton|>
class AnalysisData:
def analysis_data(self, corpus_file):
"""分析数据分布情况, :param corpus_file: :return:"""
<|body_0|>
def plot_text_length(self, all_text_len, save_path=None):
"""绘制文本长度分布 :param all_text_len: [22,33,34,...] :param save_path: 图片保存路径 :return:"""
<|body_1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnalysisData:
def analysis_data(self, corpus_file):
"""分析数据分布情况, :param corpus_file: :return:"""
label_count = dict()
lines = read_json_format_file(corpus_file)
data_len = list()
for line in lines:
label = line['label']
title = line['title']
... | the_stack_v2_python_sparse | nlp_tasks/text_classification/toutiao_news/preprocess_data.py | ZouJoshua/dl_project | train | 9 | |
d92854ad0bd98a095d396106757e27a091808075 | [
"MIPConstraint.__init__(self, inputs, **kwargs)\nself._choices = values\nself._internal_vars = Variable(shape=(len(values), len(self._indices)), boolean=True)",
"W, H = (self.inputs.W, self.inputs.H)\nv1, v2 = self._choices\nind = self._internal_vars\ni = self._indices\nC = [W[i] == cvx.multiply(v1[i], ind[0]) + ... | <|body_start_0|>
MIPConstraint.__init__(self, inputs, **kwargs)
self._choices = values
self._internal_vars = Variable(shape=(len(values), len(self._indices)), boolean=True)
<|end_body_0|>
<|body_start_1|>
W, H = (self.inputs.W, self.inputs.H)
v1, v2 = self._choices
ind =... | FixedDimension | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FixedDimension:
def __init__(self, inputs, values, **kwargs):
"""box.W = values[0] and box.H = values[1] or box.W = values[1] and box.H = values[0]"""
<|body_0|>
def as_constraint(self, **kwargs):
"""constrain W and H to mutually exclusive discrete values"""
... | stack_v2_sparse_classes_36k_train_008121 | 8,681 | no_license | [
{
"docstring": "box.W = values[0] and box.H = values[1] or box.W = values[1] and box.H = values[0]",
"name": "__init__",
"signature": "def __init__(self, inputs, values, **kwargs)"
},
{
"docstring": "constrain W and H to mutually exclusive discrete values",
"name": "as_constraint",
"sign... | 2 | null | Implement the Python class `FixedDimension` described below.
Class description:
Implement the FixedDimension class.
Method signatures and docstrings:
- def __init__(self, inputs, values, **kwargs): box.W = values[0] and box.H = values[1] or box.W = values[1] and box.H = values[0]
- def as_constraint(self, **kwargs): ... | Implement the Python class `FixedDimension` described below.
Class description:
Implement the FixedDimension class.
Method signatures and docstrings:
- def __init__(self, inputs, values, **kwargs): box.W = values[0] and box.H = values[1] or box.W = values[1] and box.H = values[0]
- def as_constraint(self, **kwargs): ... | 5928c1ef1eb0d60bfa0726227e690c0a66570f45 | <|skeleton|>
class FixedDimension:
def __init__(self, inputs, values, **kwargs):
"""box.W = values[0] and box.H = values[1] or box.W = values[1] and box.H = values[0]"""
<|body_0|>
def as_constraint(self, **kwargs):
"""constrain W and H to mutually exclusive discrete values"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FixedDimension:
def __init__(self, inputs, values, **kwargs):
"""box.W = values[0] and box.H = values[1] or box.W = values[1] and box.H = values[0]"""
MIPConstraint.__init__(self, inputs, **kwargs)
self._choices = values
self._internal_vars = Variable(shape=(len(values), len(se... | the_stack_v2_python_sparse | src/cvopt/formulate/mip.py | psavine42/juststuff | train | 0 | |
fb9d24b4ec005753032d52ee7f38d4e13fe5511f | [
"self._engine = create_engine('sqlite:///a.db', echo=False)\nBase.metadata.drop_all(self._engine)\nBase.metadata.create_all(self._engine)\nself.__session = None",
"if self.__session is None:\n DBSession = sessionmaker(bind=self._engine)\n self.__session = DBSession()\nreturn self.__session",
"user = User(... | <|body_start_0|>
self._engine = create_engine('sqlite:///a.db', echo=False)
Base.metadata.drop_all(self._engine)
Base.metadata.create_all(self._engine)
self.__session = None
<|end_body_0|>
<|body_start_1|>
if self.__session is None:
DBSession = sessionmaker(bind=self... | DB class | DB | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DB:
"""DB class"""
def __init__(self) -> None:
"""Initialize a new DB instance"""
<|body_0|>
def _session(self):
"""Memoized session object"""
<|body_1|>
def add_user(self, email: str, hashed_password: str) -> User:
"""add_user: returns a use... | stack_v2_sparse_classes_36k_train_008122 | 2,484 | no_license | [
{
"docstring": "Initialize a new DB instance",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Memoized session object",
"name": "_session",
"signature": "def _session(self)"
},
{
"docstring": "add_user: returns a user object and save the user to ... | 5 | stack_v2_sparse_classes_30k_train_005267 | Implement the Python class `DB` described below.
Class description:
DB class
Method signatures and docstrings:
- def __init__(self) -> None: Initialize a new DB instance
- def _session(self): Memoized session object
- def add_user(self, email: str, hashed_password: str) -> User: add_user: returns a user object and sa... | Implement the Python class `DB` described below.
Class description:
DB class
Method signatures and docstrings:
- def __init__(self) -> None: Initialize a new DB instance
- def _session(self): Memoized session object
- def add_user(self, email: str, hashed_password: str) -> User: add_user: returns a user object and sa... | 2abe59720ba72d44e363b13e56ec9efed0339c33 | <|skeleton|>
class DB:
"""DB class"""
def __init__(self) -> None:
"""Initialize a new DB instance"""
<|body_0|>
def _session(self):
"""Memoized session object"""
<|body_1|>
def add_user(self, email: str, hashed_password: str) -> User:
"""add_user: returns a use... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DB:
"""DB class"""
def __init__(self) -> None:
"""Initialize a new DB instance"""
self._engine = create_engine('sqlite:///a.db', echo=False)
Base.metadata.drop_all(self._engine)
Base.metadata.create_all(self._engine)
self.__session = None
def _session(self):
... | the_stack_v2_python_sparse | 0x08-user_authentication_service/db.py | khaldi505/holbertonschool-web_back_end | train | 1 |
13aaaab378c60e7422bccdd90dad48e34a042d03 | [
"snap = super(ImageView, self).snapshot()\nsnap['source'] = self.source\nsnap['scale_to_fit'] = self.scale_to_fit\nsnap['allow_upscaling'] = self.allow_upscaling\nsnap['preserve_aspect_ratio'] = self.preserve_aspect_ratio\nreturn snap",
"super(ImageView, self).bind()\nattrs = ('source', 'scale_to_fit', 'allow_ups... | <|body_start_0|>
snap = super(ImageView, self).snapshot()
snap['source'] = self.source
snap['scale_to_fit'] = self.scale_to_fit
snap['allow_upscaling'] = self.allow_upscaling
snap['preserve_aspect_ratio'] = self.preserve_aspect_ratio
return snap
<|end_body_0|>
<|body_sta... | A widget which can display an Image with optional scaling. | ImageView | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageView:
"""A widget which can display an Image with optional scaling."""
def snapshot(self):
"""Returns the dict of creation attribute for the control."""
<|body_0|>
def bind(self):
"""A method called after initialization which allows the widget to bind any ev... | stack_v2_sparse_classes_36k_train_008123 | 1,852 | permissive | [
{
"docstring": "Returns the dict of creation attribute for the control.",
"name": "snapshot",
"signature": "def snapshot(self)"
},
{
"docstring": "A method called after initialization which allows the widget to bind any event handlers necessary.",
"name": "bind",
"signature": "def bind(s... | 2 | null | Implement the Python class `ImageView` described below.
Class description:
A widget which can display an Image with optional scaling.
Method signatures and docstrings:
- def snapshot(self): Returns the dict of creation attribute for the control.
- def bind(self): A method called after initialization which allows the ... | Implement the Python class `ImageView` described below.
Class description:
A widget which can display an Image with optional scaling.
Method signatures and docstrings:
- def snapshot(self): Returns the dict of creation attribute for the control.
- def bind(self): A method called after initialization which allows the ... | 424bba29219de58fe9e47196de6763de8b2009f2 | <|skeleton|>
class ImageView:
"""A widget which can display an Image with optional scaling."""
def snapshot(self):
"""Returns the dict of creation attribute for the control."""
<|body_0|>
def bind(self):
"""A method called after initialization which allows the widget to bind any ev... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageView:
"""A widget which can display an Image with optional scaling."""
def snapshot(self):
"""Returns the dict of creation attribute for the control."""
snap = super(ImageView, self).snapshot()
snap['source'] = self.source
snap['scale_to_fit'] = self.scale_to_fit
... | the_stack_v2_python_sparse | enaml/widgets/image_view.py | enthought/enaml | train | 17 |
34efde1c8fe38fb5cfe4d603d1e1a693d1a02129 | [
"start = self.isodate_param(timezone.now())\nend = self.isodate_param(timezone.now() + datetime.timedelta(days=31))\nself.send_and_compare_request('getActivityStream', [start, end], None, [])\nself.send_and_compare_request('getActivityStream', [start, end], self.data['token1'], [])",
"_gen_activities(10)\nactivit... | <|body_start_0|>
start = self.isodate_param(timezone.now())
end = self.isodate_param(timezone.now() + datetime.timedelta(days=31))
self.send_and_compare_request('getActivityStream', [start, end], None, [])
self.send_and_compare_request('getActivityStream', [start, end], self.data['token1... | GetActivityStreamTest | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetActivityStreamTest:
def test_empty(self):
"""Test the getActivityStream() call without contents."""
<|body_0|>
def test_public(self):
"""Test the getActivityStream() call with public events."""
<|body_1|>
def test_private(self):
"""Test the ge... | stack_v2_sparse_classes_36k_train_008124 | 17,825 | permissive | [
{
"docstring": "Test the getActivityStream() call without contents.",
"name": "test_empty",
"signature": "def test_empty(self)"
},
{
"docstring": "Test the getActivityStream() call with public events.",
"name": "test_public",
"signature": "def test_public(self)"
},
{
"docstring":... | 4 | stack_v2_sparse_classes_30k_train_000654 | Implement the Python class `GetActivityStreamTest` described below.
Class description:
Implement the GetActivityStreamTest class.
Method signatures and docstrings:
- def test_empty(self): Test the getActivityStream() call without contents.
- def test_public(self): Test the getActivityStream() call with public events.... | Implement the Python class `GetActivityStreamTest` described below.
Class description:
Implement the GetActivityStreamTest class.
Method signatures and docstrings:
- def test_empty(self): Test the getActivityStream() call without contents.
- def test_public(self): Test the getActivityStream() call with public events.... | 5e46b82cab225b452eceffd4a5be6dadccceddd2 | <|skeleton|>
class GetActivityStreamTest:
def test_empty(self):
"""Test the getActivityStream() call without contents."""
<|body_0|>
def test_public(self):
"""Test the getActivityStream() call with public events."""
<|body_1|>
def test_private(self):
"""Test the ge... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetActivityStreamTest:
def test_empty(self):
"""Test the getActivityStream() call without contents."""
start = self.isodate_param(timezone.now())
end = self.isodate_param(timezone.now() + datetime.timedelta(days=31))
self.send_and_compare_request('getActivityStream', [start, en... | the_stack_v2_python_sparse | amelie/api/test_activitystream.py | Inter-Actief/amelie | train | 11 | |
6a8cd2950f6b94c5d9344334fede04012c162db9 | [
"xx = x / x_0\nexponent = -alpha - beta * np.log(xx)\nreturn amplitude * xx ** exponent",
"xx = x / x_0\nlog_xx = np.log(xx)\nexponent = -alpha - beta * log_xx\nd_amplitude = xx ** exponent\nd_beta = -amplitude * d_amplitude * log_xx ** 2\nd_x_0 = amplitude * d_amplitude * (beta * log_xx / x_0 - exponent / x_0)\n... | <|body_start_0|>
xx = x / x_0
exponent = -alpha - beta * np.log(xx)
return amplitude * xx ** exponent
<|end_body_0|>
<|body_start_1|>
xx = x / x_0
log_xx = np.log(xx)
exponent = -alpha - beta * log_xx
d_amplitude = xx ** exponent
d_beta = -amplitude * d_a... | One dimensional log parabola model (sometimes called curved power law). Parameters ---------- amplitude : float Model amplitude x_0 : float Reference point alpha : float Power law index beta : float Power law curvature See Also -------- PowerLaw1D, BrokenPowerLaw1D, ExponentialCutoffPowerLaw1D Notes ----- Model formula... | LogParabola1D | [
"Python-2.0",
"Apache-2.0",
"BSD-3-Clause",
"LicenseRef-scancode-unknown"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LogParabola1D:
"""One dimensional log parabola model (sometimes called curved power law). Parameters ---------- amplitude : float Model amplitude x_0 : float Reference point alpha : float Power law index beta : float Power law curvature See Also -------- PowerLaw1D, BrokenPowerLaw1D, ExponentialC... | stack_v2_sparse_classes_36k_train_008125 | 6,539 | permissive | [
{
"docstring": "One dimensional log parabola model function",
"name": "evaluate",
"signature": "def evaluate(x, amplitude, x_0, alpha, beta)"
},
{
"docstring": "One dimensional log parabola derivative with respect to parameters",
"name": "fit_deriv",
"signature": "def fit_deriv(x, amplit... | 2 | stack_v2_sparse_classes_30k_train_006023 | Implement the Python class `LogParabola1D` described below.
Class description:
One dimensional log parabola model (sometimes called curved power law). Parameters ---------- amplitude : float Model amplitude x_0 : float Reference point alpha : float Power law index beta : float Power law curvature See Also -------- Pow... | Implement the Python class `LogParabola1D` described below.
Class description:
One dimensional log parabola model (sometimes called curved power law). Parameters ---------- amplitude : float Model amplitude x_0 : float Reference point alpha : float Power law index beta : float Power law curvature See Also -------- Pow... | 2c9002f16bb5c265e0d14f4a2314c86eeaa35cb6 | <|skeleton|>
class LogParabola1D:
"""One dimensional log parabola model (sometimes called curved power law). Parameters ---------- amplitude : float Model amplitude x_0 : float Reference point alpha : float Power law index beta : float Power law curvature See Also -------- PowerLaw1D, BrokenPowerLaw1D, ExponentialC... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LogParabola1D:
"""One dimensional log parabola model (sometimes called curved power law). Parameters ---------- amplitude : float Model amplitude x_0 : float Reference point alpha : float Power law index beta : float Power law curvature See Also -------- PowerLaw1D, BrokenPowerLaw1D, ExponentialCutoffPowerLaw... | the_stack_v2_python_sparse | lib/python2.7/site-packages/astropy/modeling/powerlaws.py | wangyum/Anaconda | train | 11 |
c07f868b9868e81c6207bbaf3604c04222def86d | [
"if restore_option is None:\n restore_option = {}\nif bool(restore_option):\n if not (isinstance(overwrite, bool) and isinstance(power_on, bool)):\n raise SDKException('Subclient', '101')\nself._set_restore_inputs(restore_option, vm_to_restore=self._set_vm_to_restore(vm_to_restore), unconditional_overw... | <|body_start_0|>
if restore_option is None:
restore_option = {}
if bool(restore_option):
if not (isinstance(overwrite, bool) and isinstance(power_on, bool)):
raise SDKException('Subclient', '101')
self._set_restore_inputs(restore_option, vm_to_restore=self... | Derived class from VirtualServerSubclient Base class, representing a Azure virtual server subclient,and to perform operations on that subclient. | AzureSubclient | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AzureSubclient:
"""Derived class from VirtualServerSubclient Base class, representing a Azure virtual server subclient,and to perform operations on that subclient."""
def full_vm_restore_out_of_place(self, vm_to_restore=None, cloud_service=None, storage_account=None, proxy_client=None, resto... | stack_v2_sparse_classes_36k_train_008126 | 5,835 | permissive | [
{
"docstring": "Restores the FULL Virtual machine specified in the input list to the client, at the specified destination location. Args: cloud_service (str) -- provide the cloud service storage_account (str) -- provide the storage account vm_to_restore (list) -- provide the VM name to restore overwrite default... | 2 | stack_v2_sparse_classes_30k_train_007647 | Implement the Python class `AzureSubclient` described below.
Class description:
Derived class from VirtualServerSubclient Base class, representing a Azure virtual server subclient,and to perform operations on that subclient.
Method signatures and docstrings:
- def full_vm_restore_out_of_place(self, vm_to_restore=None... | Implement the Python class `AzureSubclient` described below.
Class description:
Derived class from VirtualServerSubclient Base class, representing a Azure virtual server subclient,and to perform operations on that subclient.
Method signatures and docstrings:
- def full_vm_restore_out_of_place(self, vm_to_restore=None... | 6aa0beb426a95de877cd531602234515723ccc94 | <|skeleton|>
class AzureSubclient:
"""Derived class from VirtualServerSubclient Base class, representing a Azure virtual server subclient,and to perform operations on that subclient."""
def full_vm_restore_out_of_place(self, vm_to_restore=None, cloud_service=None, storage_account=None, proxy_client=None, resto... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AzureSubclient:
"""Derived class from VirtualServerSubclient Base class, representing a Azure virtual server subclient,and to perform operations on that subclient."""
def full_vm_restore_out_of_place(self, vm_to_restore=None, cloud_service=None, storage_account=None, proxy_client=None, restore_new_name=N... | the_stack_v2_python_sparse | cvpysdk/subclients/virtualserver/azuresubclient.py | jack1806/cvpysdk | train | 1 |
a42227a53e0dfb62b537e0755bb28a18c9a0cf09 | [
"self.class_name = class_name\nself.klass = klass\nif not filter_models_classes(klass):\n raise NameError('Incompatible module for Models class: {0}'.format(klass.__module__))\nself.module_full_name = klass.__module__.replace('kubernetes.', 'kubernetes_typed.')\nself.module_name = klass.__module__.rpartition('.'... | <|body_start_0|>
self.class_name = class_name
self.klass = klass
if not filter_models_classes(klass):
raise NameError('Incompatible module for Models class: {0}'.format(klass.__module__))
self.module_full_name = klass.__module__.replace('kubernetes.', 'kubernetes_typed.')
... | Represents parsed state of kubernetes client model. | Model | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Model:
"""Represents parsed state of kubernetes client model."""
def __init__(self, class_name: str, klass: object) -> None:
"""Parse model parameters."""
<|body_0|>
def uniq_imports(self, import_type: str) -> List[str]:
"""Get uniq import for the model."""
... | stack_v2_sparse_classes_36k_train_008127 | 6,267 | permissive | [
{
"docstring": "Parse model parameters.",
"name": "__init__",
"signature": "def __init__(self, class_name: str, klass: object) -> None"
},
{
"docstring": "Get uniq import for the model.",
"name": "uniq_imports",
"signature": "def uniq_imports(self, import_type: str) -> List[str]"
}
] | 2 | null | Implement the Python class `Model` described below.
Class description:
Represents parsed state of kubernetes client model.
Method signatures and docstrings:
- def __init__(self, class_name: str, klass: object) -> None: Parse model parameters.
- def uniq_imports(self, import_type: str) -> List[str]: Get uniq import fo... | Implement the Python class `Model` described below.
Class description:
Represents parsed state of kubernetes client model.
Method signatures and docstrings:
- def __init__(self, class_name: str, klass: object) -> None: Parse model parameters.
- def uniq_imports(self, import_type: str) -> List[str]: Get uniq import fo... | 82995b008daf551a4fe11660018d9c08c69f9e6e | <|skeleton|>
class Model:
"""Represents parsed state of kubernetes client model."""
def __init__(self, class_name: str, klass: object) -> None:
"""Parse model parameters."""
<|body_0|>
def uniq_imports(self, import_type: str) -> List[str]:
"""Get uniq import for the model."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Model:
"""Represents parsed state of kubernetes client model."""
def __init__(self, class_name: str, klass: object) -> None:
"""Parse model parameters."""
self.class_name = class_name
self.klass = klass
if not filter_models_classes(klass):
raise NameError('Inco... | the_stack_v2_python_sparse | scripts/typeddictgen.py | gordonbondon/kubernetes-typed | train | 24 |
93965f7990b3f3f128d051b99b6e550b136ab733 | [
"if not (is_scalar(prior) or isinstance(prior, Beta) or is_any_numeric_map(prior) or is_any_beta_map(prior)):\n raise ValueError('wrong type for prior')\nif not (is_scalar(likelihood) or isinstance(likelihood, Beta) or is_any_numeric_map(likelihood) or is_any_beta_map(likelihood)):\n raise ValueError('wrong t... | <|body_start_0|>
if not (is_scalar(prior) or isinstance(prior, Beta) or is_any_numeric_map(prior) or is_any_beta_map(prior)):
raise ValueError('wrong type for prior')
if not (is_scalar(likelihood) or isinstance(likelihood, Beta) or is_any_numeric_map(likelihood) or is_any_beta_map(likelihood... | Class for testing one or more binary hypotheses using Bayes Rule. | BinaryBayesRule | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinaryBayesRule:
"""Class for testing one or more binary hypotheses using Bayes Rule."""
def __init__(self, prior: Union[float, Beta, AnyFloatMap, AnyBetaMap], likelihood: Union[float, Beta, AnyFloatMap, AnyBetaMap]):
"""Create a new Bayes Rule object from: - the prior P(A) - the lik... | stack_v2_sparse_classes_36k_train_008128 | 2,366 | permissive | [
{
"docstring": "Create a new Bayes Rule object from: - the prior P(A) - the likelihood P(B|A) - the evidence P(B) :param prior: Single figure or Beta-distributed probability representing the hypothesis. :param likelihood: Series with values of Dirichlet likelihoods.",
"name": "__init__",
"signature": "d... | 2 | stack_v2_sparse_classes_30k_train_001008 | Implement the Python class `BinaryBayesRule` described below.
Class description:
Class for testing one or more binary hypotheses using Bayes Rule.
Method signatures and docstrings:
- def __init__(self, prior: Union[float, Beta, AnyFloatMap, AnyBetaMap], likelihood: Union[float, Beta, AnyFloatMap, AnyBetaMap]): Create... | Implement the Python class `BinaryBayesRule` described below.
Class description:
Class for testing one or more binary hypotheses using Bayes Rule.
Method signatures and docstrings:
- def __init__(self, prior: Union[float, Beta, AnyFloatMap, AnyBetaMap], likelihood: Union[float, Beta, AnyFloatMap, AnyBetaMap]): Create... | ff3f5434d3da0d46b127b02cf733699e5a43c904 | <|skeleton|>
class BinaryBayesRule:
"""Class for testing one or more binary hypotheses using Bayes Rule."""
def __init__(self, prior: Union[float, Beta, AnyFloatMap, AnyBetaMap], likelihood: Union[float, Beta, AnyFloatMap, AnyBetaMap]):
"""Create a new Bayes Rule object from: - the prior P(A) - the lik... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BinaryBayesRule:
"""Class for testing one or more binary hypotheses using Bayes Rule."""
def __init__(self, prior: Union[float, Beta, AnyFloatMap, AnyBetaMap], likelihood: Union[float, Beta, AnyFloatMap, AnyBetaMap]):
"""Create a new Bayes Rule object from: - the prior P(A) - the likelihood P(B|A... | the_stack_v2_python_sparse | probability/calculations/bayes_rule/binary_bayes_rule.py | vahndi/probability | train | 3 |
77a05df04ddd22f5c609b32ceda10e6d7b02046a | [
"user = User.objects.create(username='testuser', password='qwerty12345Q!')\nrecruiter = User.objects.create(username='recruiter3', first_name='first_recruiter', last_name='last_recruiter', email='recruiter@mail.com')\ncandidate = User.objects.create(username='candidate3', first_name='first_candidate', last_name='la... | <|body_start_0|>
user = User.objects.create(username='testuser', password='qwerty12345Q!')
recruiter = User.objects.create(username='recruiter3', first_name='first_recruiter', last_name='last_recruiter', email='recruiter@mail.com')
candidate = User.objects.create(username='candidate3', first_nam... | Test GET request Answers app | AnswersGetTestCases | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnswersGetTestCases:
"""Test GET request Answers app"""
def setUp(self):
"""Create new data in in linked tables"""
<|body_0|>
def test_get_valid_answers(self):
"""Test for GET Answers with id"""
<|body_1|>
def test_get_invalid_answers(self):
... | stack_v2_sparse_classes_36k_train_008129 | 13,494 | no_license | [
{
"docstring": "Create new data in in linked tables",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test for GET Answers with id",
"name": "test_get_valid_answers",
"signature": "def test_get_valid_answers(self)"
},
{
"docstring": "Test for GET not existing A... | 3 | null | Implement the Python class `AnswersGetTestCases` described below.
Class description:
Test GET request Answers app
Method signatures and docstrings:
- def setUp(self): Create new data in in linked tables
- def test_get_valid_answers(self): Test for GET Answers with id
- def test_get_invalid_answers(self): Test for GET... | Implement the Python class `AnswersGetTestCases` described below.
Class description:
Test GET request Answers app
Method signatures and docstrings:
- def setUp(self): Create new data in in linked tables
- def test_get_valid_answers(self): Test for GET Answers with id
- def test_get_invalid_answers(self): Test for GET... | f448ec0453818d55c5c9d30aaa4f19e1d7ca5867 | <|skeleton|>
class AnswersGetTestCases:
"""Test GET request Answers app"""
def setUp(self):
"""Create new data in in linked tables"""
<|body_0|>
def test_get_valid_answers(self):
"""Test for GET Answers with id"""
<|body_1|>
def test_get_invalid_answers(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnswersGetTestCases:
"""Test GET request Answers app"""
def setUp(self):
"""Create new data in in linked tables"""
user = User.objects.create(username='testuser', password='qwerty12345Q!')
recruiter = User.objects.create(username='recruiter3', first_name='first_recruiter', last_na... | the_stack_v2_python_sparse | Portfolio/tech-interview/techinterview/feedback/test_feedback.py | HeCToR74/Python | train | 1 |
35e1bf97c8255fa3a0ea614123a4797cc33a2da6 | [
"self.products_dict_list = []\nself.clean_brands = []\nself.clean_categories = []\nself.clean_stores = []\nself.brand_of_products = []\nself.cats_of_products = []\nself.stores_of_products = []",
"complete_data = []\noff_api = OpenFoodApi()\nall_data = off_api.get_full_api_products()\ncomplete_data = [product for ... | <|body_start_0|>
self.products_dict_list = []
self.clean_brands = []
self.clean_categories = []
self.clean_stores = []
self.brand_of_products = []
self.cats_of_products = []
self.stores_of_products = []
<|end_body_0|>
<|body_start_1|>
complete_data = []
... | DataCleaner class To manage data cleaning from the Open Food Facts API | DataCleaner | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataCleaner:
"""DataCleaner class To manage data cleaning from the Open Food Facts API"""
def __init__(self):
"""Constructor"""
<|body_0|>
def create_products_dict_list(self):
"""Products are retrieved via the Open Food Facts API, but only those with all the requ... | stack_v2_sparse_classes_36k_train_008130 | 4,015 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Products are retrieved via the Open Food Facts API, but only those with all the required fields. :return: A list of dictionaries is obtained, one dictionary per product. :rtype: list()",
"n... | 5 | stack_v2_sparse_classes_30k_train_013905 | Implement the Python class `DataCleaner` described below.
Class description:
DataCleaner class To manage data cleaning from the Open Food Facts API
Method signatures and docstrings:
- def __init__(self): Constructor
- def create_products_dict_list(self): Products are retrieved via the Open Food Facts API, but only th... | Implement the Python class `DataCleaner` described below.
Class description:
DataCleaner class To manage data cleaning from the Open Food Facts API
Method signatures and docstrings:
- def __init__(self): Constructor
- def create_products_dict_list(self): Products are retrieved via the Open Food Facts API, but only th... | 377deb35afe3749c8db8d09a55d4b69f8a8238a0 | <|skeleton|>
class DataCleaner:
"""DataCleaner class To manage data cleaning from the Open Food Facts API"""
def __init__(self):
"""Constructor"""
<|body_0|>
def create_products_dict_list(self):
"""Products are retrieved via the Open Food Facts API, but only those with all the requ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataCleaner:
"""DataCleaner class To manage data cleaning from the Open Food Facts API"""
def __init__(self):
"""Constructor"""
self.products_dict_list = []
self.clean_brands = []
self.clean_categories = []
self.clean_stores = []
self.brand_of_products = []... | the_stack_v2_python_sparse | src/data/api/data_cleaner.py | Githb-usr/substitution | train | 0 |
f273cd69b5a75728a05c0bbf6687568db9542dd3 | [
"self.to_run = cmd_list[:]\nself.running = {}\nself.N = N\nself.cwd = cwd\nself.env = env",
"if self.N == 0:\n return True\nif len(self.running) < self.N:\n return True\nreturn False",
"while len(self.to_run) > 0 and self.under_process_limit():\n LOGGER.info('%d left to run', len(self.to_run))\n cmd... | <|body_start_0|>
self.to_run = cmd_list[:]
self.running = {}
self.N = N
self.cwd = cwd
self.env = env
<|end_body_0|>
<|body_start_1|>
if self.N == 0:
return True
if len(self.running) < self.N:
return True
return False
<|end_body_1|... | Queue up N commands from cmd_list, launching more jobs as the first finish. | QueueCommands | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QueueCommands:
"""Queue up N commands from cmd_list, launching more jobs as the first finish."""
def __init__(self, cmd_list, N=0, cwd=None, env=None):
"""cmd_list is a list of elements suitable for subprocess N is the number of simultanious processes to run. 0 is all of them. WARNIN... | stack_v2_sparse_classes_36k_train_008131 | 3,232 | permissive | [
{
"docstring": "cmd_list is a list of elements suitable for subprocess N is the number of simultanious processes to run. 0 is all of them. WARNING: this will not work on windows (It depends on being able to pass local file descriptors to the select call with isn't supported by the Win32 API)",
"name": "__in... | 4 | stack_v2_sparse_classes_30k_train_010179 | Implement the Python class `QueueCommands` described below.
Class description:
Queue up N commands from cmd_list, launching more jobs as the first finish.
Method signatures and docstrings:
- def __init__(self, cmd_list, N=0, cwd=None, env=None): cmd_list is a list of elements suitable for subprocess N is the number o... | Implement the Python class `QueueCommands` described below.
Class description:
Queue up N commands from cmd_list, launching more jobs as the first finish.
Method signatures and docstrings:
- def __init__(self, cmd_list, N=0, cwd=None, env=None): cmd_list is a list of elements suitable for subprocess N is the number o... | e1a246dcee157b7294030683f2d50ca0405f7095 | <|skeleton|>
class QueueCommands:
"""Queue up N commands from cmd_list, launching more jobs as the first finish."""
def __init__(self, cmd_list, N=0, cwd=None, env=None):
"""cmd_list is a list of elements suitable for subprocess N is the number of simultanious processes to run. 0 is all of them. WARNIN... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QueueCommands:
"""Queue up N commands from cmd_list, launching more jobs as the first finish."""
def __init__(self, cmd_list, N=0, cwd=None, env=None):
"""cmd_list is a list of elements suitable for subprocess N is the number of simultanious processes to run. 0 is all of them. WARNING: this will ... | the_stack_v2_python_sparse | htsworkflow/util/queuecommands.py | detrout/htsworkflow | train | 0 |
42914637a58c9b2509dc8d1e541b0a1aee3af026 | [
"self.devaddr = devaddr\nself.adr = adr\nself.adrackreq = adrackreq\nself.ack = ack\nself.fpending = fpending\nself.foptslen = foptslen\nself.fcnt = fcnt\nself.fopts = fopts\nself.fdir = fdir\nself.length = self.foptslen + 7",
"if len(data) < 7:\n raise DecodeError()\ndevaddr, fctrl, fcnt = struct.unpack('<LBH... | <|body_start_0|>
self.devaddr = devaddr
self.adr = adr
self.adrackreq = adrackreq
self.ack = ack
self.fpending = fpending
self.foptslen = foptslen
self.fcnt = fcnt
self.fopts = fopts
self.fdir = fdir
self.length = self.foptslen + 7
<|end_bo... | MAC Payload Frame Header. The frame header contains the short device address of the end device (devaddr), and frame control octet (fctrl), 2 octet frame counter (fcnt) and up to 15 octets used to transport MAC commands (fopts). Attributes: devaddr (str): Device address. adr (int): ADR bit adrackreq (int): ADR acknowled... | FrameHeader | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FrameHeader:
"""MAC Payload Frame Header. The frame header contains the short device address of the end device (devaddr), and frame control octet (fctrl), 2 octet frame counter (fcnt) and up to 15 octets used to transport MAC commands (fopts). Attributes: devaddr (str): Device address. adr (int):... | stack_v2_sparse_classes_36k_train_008132 | 26,915 | permissive | [
{
"docstring": "FrameHeader initialisation method.",
"name": "__init__",
"signature": "def __init__(self, devaddr, adr, adrackreq, ack, foptslen, fcnt, fopts, fpending=0, fdir='up')"
},
{
"docstring": "Create a FrameHeader object from binary representation. Args: data (str): MACPayload packet da... | 3 | stack_v2_sparse_classes_30k_train_009795 | Implement the Python class `FrameHeader` described below.
Class description:
MAC Payload Frame Header. The frame header contains the short device address of the end device (devaddr), and frame control octet (fctrl), 2 octet frame counter (fcnt) and up to 15 octets used to transport MAC commands (fopts). Attributes: de... | Implement the Python class `FrameHeader` described below.
Class description:
MAC Payload Frame Header. The frame header contains the short device address of the end device (devaddr), and frame control octet (fctrl), 2 octet frame counter (fcnt) and up to 15 octets used to transport MAC commands (fopts). Attributes: de... | add5a1129296dca6db55b7980c0c1091f1ca80aa | <|skeleton|>
class FrameHeader:
"""MAC Payload Frame Header. The frame header contains the short device address of the end device (devaddr), and frame control octet (fctrl), 2 octet frame counter (fcnt) and up to 15 octets used to transport MAC commands (fopts). Attributes: devaddr (str): Device address. adr (int):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FrameHeader:
"""MAC Payload Frame Header. The frame header contains the short device address of the end device (devaddr), and frame control octet (fctrl), 2 octet frame counter (fcnt) and up to 15 octets used to transport MAC commands (fopts). Attributes: devaddr (str): Device address. adr (int): ADR bit adra... | the_stack_v2_python_sparse | floranet/lora/mac.py | chengzhongkai/floranet | train | 0 |
50141ceda076ddd6e34610d2d1c1d979d7a199e1 | [
"data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)\ndata_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_supervised=True)\nself.tokenizer_pt, self.tokenizer_en = self.tokenize_dataset(data_train)\nself.data_train = data_train.map(self.tf_encode)\nself.d... | <|body_start_0|>
data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)
data_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_supervised=True)
self.tokenizer_pt, self.tokenizer_en = self.tokenize_dataset(data_train)
self.data_train... | Class | Dataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""Class"""
def __init__(self):
"""Initialize"""
<|body_0|>
def tokenize_dataset(self, data):
"""Method"""
<|body_1|>
def encode(self, pt, en):
"""Method"""
<|body_2|>
def tf_encode(self, pt, en):
"""Method"""
... | stack_v2_sparse_classes_36k_train_008133 | 2,694 | no_license | [
{
"docstring": "Initialize",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Method",
"name": "tokenize_dataset",
"signature": "def tokenize_dataset(self, data)"
},
{
"docstring": "Method",
"name": "encode",
"signature": "def encode(self, pt, en)"... | 4 | stack_v2_sparse_classes_30k_train_004780 | Implement the Python class `Dataset` described below.
Class description:
Class
Method signatures and docstrings:
- def __init__(self): Initialize
- def tokenize_dataset(self, data): Method
- def encode(self, pt, en): Method
- def tf_encode(self, pt, en): Method | Implement the Python class `Dataset` described below.
Class description:
Class
Method signatures and docstrings:
- def __init__(self): Initialize
- def tokenize_dataset(self, data): Method
- def encode(self, pt, en): Method
- def tf_encode(self, pt, en): Method
<|skeleton|>
class Dataset:
"""Class"""
def __... | b5e8f1253309567ca7be71b9575a150de1be3820 | <|skeleton|>
class Dataset:
"""Class"""
def __init__(self):
"""Initialize"""
<|body_0|>
def tokenize_dataset(self, data):
"""Method"""
<|body_1|>
def encode(self, pt, en):
"""Method"""
<|body_2|>
def tf_encode(self, pt, en):
"""Method"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dataset:
"""Class"""
def __init__(self):
"""Initialize"""
data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)
data_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_supervised=True)
self.tokenizer_pt, self.tokenize... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/3-dataset.py | jadsm98/holbertonschool-machine_learning | train | 0 |
679483262ef9e854746428f03c0310c0aae11700 | [
"if update_info.get('data_truncate', {}).get('enable', False):\n instalog_config['buffer']['args']['truncate_interval'] = 86400\nthreshold = update_info.get('input_http', {}).get('log_level_threshold', logging.NOTSET)\ninstalog_config['input']['http_in']['args']['log_level_threshold'] = threshold\nif update_info... | <|body_start_0|>
if update_info.get('data_truncate', {}).get('enable', False):
instalog_config['buffer']['args']['truncate_interval'] = 86400
threshold = update_info.get('input_http', {}).get('log_level_threshold', logging.NOTSET)
instalog_config['input']['http_in']['args']['log_leve... | Instalog service. Example: svc = GetServiceInstance('instalog') procs = svc.CreateProcesses(umpire_config_dict, umpire_env) svc.Start(procs) | InstalogService | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InstalogService:
"""Instalog service. Example: svc = GetServiceInstance('instalog') procs = svc.CreateProcesses(umpire_config_dict, umpire_env) svc.Start(procs)"""
def UpdateConfig(self, instalog_config, update_info, env):
"""Updates Instalog plugin config based on Umpire config. Arg... | stack_v2_sparse_classes_36k_train_008134 | 6,051 | permissive | [
{
"docstring": "Updates Instalog plugin config based on Umpire config. Args: instalog_config: Original Instalog configuration. update_info: The Umpire configuration used to update instalog_config. env: UmpireEnv object.",
"name": "UpdateConfig",
"signature": "def UpdateConfig(self, instalog_config, upda... | 3 | stack_v2_sparse_classes_30k_train_018930 | Implement the Python class `InstalogService` described below.
Class description:
Instalog service. Example: svc = GetServiceInstance('instalog') procs = svc.CreateProcesses(umpire_config_dict, umpire_env) svc.Start(procs)
Method signatures and docstrings:
- def UpdateConfig(self, instalog_config, update_info, env): U... | Implement the Python class `InstalogService` described below.
Class description:
Instalog service. Example: svc = GetServiceInstance('instalog') procs = svc.CreateProcesses(umpire_config_dict, umpire_env) svc.Start(procs)
Method signatures and docstrings:
- def UpdateConfig(self, instalog_config, update_info, env): U... | a1b0fccd68987d8cd9c89710adc3c04b868347ec | <|skeleton|>
class InstalogService:
"""Instalog service. Example: svc = GetServiceInstance('instalog') procs = svc.CreateProcesses(umpire_config_dict, umpire_env) svc.Start(procs)"""
def UpdateConfig(self, instalog_config, update_info, env):
"""Updates Instalog plugin config based on Umpire config. Arg... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InstalogService:
"""Instalog service. Example: svc = GetServiceInstance('instalog') procs = svc.CreateProcesses(umpire_config_dict, umpire_env) svc.Start(procs)"""
def UpdateConfig(self, instalog_config, update_info, env):
"""Updates Instalog plugin config based on Umpire config. Args: instalog_c... | the_stack_v2_python_sparse | py/umpire/server/service/instalog.py | bridder/factory | train | 0 |
70c84f975ee6d31b632aa6e6ab35d66871b98887 | [
"self.host = flags.host\nself.port = int(flags.port)\nself.user = flags.user\nwith open(os.path.expanduser(flags.password_file)) as f:\n self.password = f.readline().rstrip()\nself.connection = None",
"assert self.connection is None\nself.connection = imaplib.IMAP4_SSL(self.host, self.port)\nLOGGER.info('Opene... | <|body_start_0|>
self.host = flags.host
self.port = int(flags.port)
self.user = flags.user
with open(os.path.expanduser(flags.password_file)) as f:
self.password = f.readline().rstrip()
self.connection = None
<|end_body_0|>
<|body_start_1|>
assert self.connec... | Encapsulates access to an IMAP account. | Account | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Account:
"""Encapsulates access to an IMAP account."""
def __init__(self):
"""Constructs a new account from the command line flags."""
<|body_0|>
def open(self):
"""Opens an IMAP connection to this account."""
<|body_1|>
def list(self):
"""Re... | stack_v2_sparse_classes_36k_train_008135 | 9,251 | permissive | [
{
"docstring": "Constructs a new account from the command line flags.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Opens an IMAP connection to this account.",
"name": "open",
"signature": "def open(self)"
},
{
"docstring": "Returns a list of (datetim... | 4 | stack_v2_sparse_classes_30k_train_018063 | Implement the Python class `Account` described below.
Class description:
Encapsulates access to an IMAP account.
Method signatures and docstrings:
- def __init__(self): Constructs a new account from the command line flags.
- def open(self): Opens an IMAP connection to this account.
- def list(self): Returns a list of... | Implement the Python class `Account` described below.
Class description:
Encapsulates access to an IMAP account.
Method signatures and docstrings:
- def __init__(self): Constructs a new account from the command line flags.
- def open(self): Opens an IMAP connection to this account.
- def list(self): Returns a list of... | cec8fae0c1872bbd91b244775bee18d5db831581 | <|skeleton|>
class Account:
"""Encapsulates access to an IMAP account."""
def __init__(self):
"""Constructs a new account from the command line flags."""
<|body_0|>
def open(self):
"""Opens an IMAP connection to this account."""
<|body_1|>
def list(self):
"""Re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Account:
"""Encapsulates access to an IMAP account."""
def __init__(self):
"""Constructs a new account from the command line flags."""
self.host = flags.host
self.port = int(flags.port)
self.user = flags.user
with open(os.path.expanduser(flags.password_file)) as f:... | the_stack_v2_python_sparse | camera_event_trending.py | jodysankey/scripts | train | 0 |
58de6955ee9c4d906e62ea1f588f5c63e79e8355 | [
"super(TwoLayerNet, self).__init__()\nself.linear1 = torch.nn.Linear(D_in, H)\nself.linear2 = torch.nn.Linear(H, D_out)",
"h_relu = self.linear1(x).clamp(min=0)\ny_pred = self.linear2(h_relu).clamp(min=0)\nreturn y_pred"
] | <|body_start_0|>
super(TwoLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, D_out)
<|end_body_0|>
<|body_start_1|>
h_relu = self.linear1(x).clamp(min=0)
y_pred = self.linear2(h_relu).clamp(min=0)
return y_pred
<|end_body... | TwoLayerNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TwoLayerNet:
def __init__(self, D_in, H, D_out):
"""In the constructor we instantiate two nn.Linear modules and assign them as member variables."""
<|body_0|>
def forward(self, x):
"""In the forward function we accept a Tensor of input data and we must return a Tenso... | stack_v2_sparse_classes_36k_train_008136 | 1,096 | no_license | [
{
"docstring": "In the constructor we instantiate two nn.Linear modules and assign them as member variables.",
"name": "__init__",
"signature": "def __init__(self, D_in, H, D_out)"
},
{
"docstring": "In the forward function we accept a Tensor of input data and we must return a Tensor of output d... | 2 | stack_v2_sparse_classes_30k_train_008089 | Implement the Python class `TwoLayerNet` described below.
Class description:
Implement the TwoLayerNet class.
Method signatures and docstrings:
- def __init__(self, D_in, H, D_out): In the constructor we instantiate two nn.Linear modules and assign them as member variables.
- def forward(self, x): In the forward func... | Implement the Python class `TwoLayerNet` described below.
Class description:
Implement the TwoLayerNet class.
Method signatures and docstrings:
- def __init__(self, D_in, H, D_out): In the constructor we instantiate two nn.Linear modules and assign them as member variables.
- def forward(self, x): In the forward func... | e1b46706c09c1989513794fb9a456ebbdbae4986 | <|skeleton|>
class TwoLayerNet:
def __init__(self, D_in, H, D_out):
"""In the constructor we instantiate two nn.Linear modules and assign them as member variables."""
<|body_0|>
def forward(self, x):
"""In the forward function we accept a Tensor of input data and we must return a Tenso... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TwoLayerNet:
def __init__(self, D_in, H, D_out):
"""In the constructor we instantiate two nn.Linear modules and assign them as member variables."""
super(TwoLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, D_out)
def forw... | the_stack_v2_python_sparse | twolayernetog.py | weekend37/BG_Competition | train | 2 | |
f9ffc34d740b921314a535f472ff29896e221639 | [
"WebHttpThread.json_app_resp()\nif not name == '':\n name = 'world'\nreturn WebHttpThread.render_json({'message': 'Hello, %s!' % name})",
"json_app_resp()\nparams = WebHttpThread.get_post_json_params()\nif not name == '':\n name = 'world'\nreturn WebHttpThread.render_json({'message': 'Hello, %s!' % name})"
... | <|body_start_0|>
WebHttpThread.json_app_resp()
if not name == '':
name = 'world'
return WebHttpThread.render_json({'message': 'Hello, %s!' % name})
<|end_body_0|>
<|body_start_1|>
json_app_resp()
params = WebHttpThread.get_post_json_params()
if not name == ''... | This controller is used for the human interaction | HomeController | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HomeController:
"""This controller is used for the human interaction"""
def GET(self, name=''):
"""The get method is called for software control"""
<|body_0|>
def POST(self, name=''):
"""This post method is use for the business communication"""
<|body_1|>... | stack_v2_sparse_classes_36k_train_008137 | 5,645 | permissive | [
{
"docstring": "The get method is called for software control",
"name": "GET",
"signature": "def GET(self, name='')"
},
{
"docstring": "This post method is use for the business communication",
"name": "POST",
"signature": "def POST(self, name='')"
}
] | 2 | stack_v2_sparse_classes_30k_train_013781 | Implement the Python class `HomeController` described below.
Class description:
This controller is used for the human interaction
Method signatures and docstrings:
- def GET(self, name=''): The get method is called for software control
- def POST(self, name=''): This post method is use for the business communication | Implement the Python class `HomeController` described below.
Class description:
This controller is used for the human interaction
Method signatures and docstrings:
- def GET(self, name=''): The get method is called for software control
- def POST(self, name=''): This post method is use for the business communication
... | dc6a3d8ee8b9dc67753d5b713bfa5f81f0a20f6a | <|skeleton|>
class HomeController:
"""This controller is used for the human interaction"""
def GET(self, name=''):
"""The get method is called for software control"""
<|body_0|>
def POST(self, name=''):
"""This post method is use for the business communication"""
<|body_1|>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HomeController:
"""This controller is used for the human interaction"""
def GET(self, name=''):
"""The get method is called for software control"""
WebHttpThread.json_app_resp()
if not name == '':
name = 'world'
return WebHttpThread.render_json({'message': 'Hel... | the_stack_v2_python_sparse | src/TrainManagementWebServer.py | pierrecontri/TrainManagement | train | 0 |
cf2f4e1a5a4aa6ebc40e411493c7c6abd6f4a544 | [
"self.particleNum = particleNum\nif masses == 'random':\n masses = np.random.random(size=particleNum) + 1e-05\nelif not isinstance(masses, list) and (not isinstance(masses, np.ndarray)):\n raise Exception('Not a valid input for the masses, if not \"random\" the input has to be a list of masses!')\nelif len(ma... | <|body_start_0|>
self.particleNum = particleNum
if masses == 'random':
masses = np.random.random(size=particleNum) + 1e-05
elif not isinstance(masses, list) and (not isinstance(masses, np.ndarray)):
raise Exception('Not a valid input for the masses, if not "random" the in... | Class which simulates a system of particles. | Sistem | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Sistem:
"""Class which simulates a system of particles."""
def __init__(self, particleNum, masses='random', initPositions='random', initVelocities='random'):
"""Sistem class which contains all the particles in the ring and their interactions. Args: particleNum (integer): Number of pa... | stack_v2_sparse_classes_36k_train_008138 | 20,528 | no_license | [
{
"docstring": "Sistem class which contains all the particles in the ring and their interactions. Args: particleNum (integer): Number of particles on the ring. masses (list, optional): List of masses for the particles. Must be in given in increasing positions of the particles. Defaults to 'random'. initPosition... | 3 | stack_v2_sparse_classes_30k_train_005688 | Implement the Python class `Sistem` described below.
Class description:
Class which simulates a system of particles.
Method signatures and docstrings:
- def __init__(self, particleNum, masses='random', initPositions='random', initVelocities='random'): Sistem class which contains all the particles in the ring and thei... | Implement the Python class `Sistem` described below.
Class description:
Class which simulates a system of particles.
Method signatures and docstrings:
- def __init__(self, particleNum, masses='random', initPositions='random', initVelocities='random'): Sistem class which contains all the particles in the ring and thei... | fae485d2575c864f414719c6591bc78bb1f24166 | <|skeleton|>
class Sistem:
"""Class which simulates a system of particles."""
def __init__(self, particleNum, masses='random', initPositions='random', initVelocities='random'):
"""Sistem class which contains all the particles in the ring and their interactions. Args: particleNum (integer): Number of pa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Sistem:
"""Class which simulates a system of particles."""
def __init__(self, particleNum, masses='random', initPositions='random', initVelocities='random'):
"""Sistem class which contains all the particles in the ring and their interactions. Args: particleNum (integer): Number of particles on th... | the_stack_v2_python_sparse | simulation.py | MatejVe/SHP | train | 0 |
472a9b6b2c10807344ffe7ae929c902f64dd03eb | [
"self.model = model\nself.padding = padding\nself.key_length = key_length\nself.key = key\nself.iv = iv",
"if self.model in (MODE_ECB, MODE_CTR, MODE_CCM, MODE_EAX, MODE_SIV, MODE_GCM, MODE_OCB):\n cipher = algorithm.new(key=self.key, mode=self.model)\nelse:\n cipher = algorithm.new(key=self.key, mode=self.... | <|body_start_0|>
self.model = model
self.padding = padding
self.key_length = key_length
self.key = key
self.iv = iv
<|end_body_0|>
<|body_start_1|>
if self.model in (MODE_ECB, MODE_CTR, MODE_CCM, MODE_EAX, MODE_SIV, MODE_GCM, MODE_OCB):
cipher = algorithm.new... | Cipher | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cipher:
def __init__(self, model, padding, key_length, key, iv):
"""Encryption Class :param model: int type :param padding: str type :param key_length: int type, 16,24,32 :param key: bytes type :param iv: bytes type"""
<|body_0|>
def get_cipher_object(self, algorithm):
... | stack_v2_sparse_classes_36k_train_008139 | 3,312 | no_license | [
{
"docstring": "Encryption Class :param model: int type :param padding: str type :param key_length: int type, 16,24,32 :param key: bytes type :param iv: bytes type",
"name": "__init__",
"signature": "def __init__(self, model, padding, key_length, key, iv)"
},
{
"docstring": "Get cipher object :p... | 4 | stack_v2_sparse_classes_30k_train_012073 | Implement the Python class `Cipher` described below.
Class description:
Implement the Cipher class.
Method signatures and docstrings:
- def __init__(self, model, padding, key_length, key, iv): Encryption Class :param model: int type :param padding: str type :param key_length: int type, 16,24,32 :param key: bytes type... | Implement the Python class `Cipher` described below.
Class description:
Implement the Cipher class.
Method signatures and docstrings:
- def __init__(self, model, padding, key_length, key, iv): Encryption Class :param model: int type :param padding: str type :param key_length: int type, 16,24,32 :param key: bytes type... | 41f343dc856958060c7b58fbbf1021757f75d818 | <|skeleton|>
class Cipher:
def __init__(self, model, padding, key_length, key, iv):
"""Encryption Class :param model: int type :param padding: str type :param key_length: int type, 16,24,32 :param key: bytes type :param iv: bytes type"""
<|body_0|>
def get_cipher_object(self, algorithm):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Cipher:
def __init__(self, model, padding, key_length, key, iv):
"""Encryption Class :param model: int type :param padding: str type :param key_length: int type, 16,24,32 :param key: bytes type :param iv: bytes type"""
self.model = model
self.padding = padding
self.key_length =... | the_stack_v2_python_sparse | util/crypt.py | ChearH/ReverseWidget | train | 0 | |
1b76d9393c7aac46d57b770dd5b641808081c0dd | [
"pickle_file = PICKLE_FOLDER + os.path.sep + user + PICKLE_EXTENSION\nprint('Using ' + pickle_file + ' as the data')\nf = open(pickle_file, 'rb').read()\ndata = pickle.loads(f)\nreturn data",
"input_path = INPUT_FOLDER + os.path.sep + user + JPG_EXTENSION\nprint('Using ' + input_path + ' as input')\nimage = cv2.i... | <|body_start_0|>
pickle_file = PICKLE_FOLDER + os.path.sep + user + PICKLE_EXTENSION
print('Using ' + pickle_file + ' as the data')
f = open(pickle_file, 'rb').read()
data = pickle.loads(f)
return data
<|end_body_0|>
<|body_start_1|>
input_path = INPUT_FOLDER + os.path.s... | A class to recognize a user's face with the corresponding encoding. | RecognizeUserFace | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RecognizeUserFace:
"""A class to recognize a user's face with the corresponding encoding."""
def read_pickle(self, user):
"""Reads pickle file and returns the contents. :param user: user that's being encoded :type user: string :return: data :rtype: list"""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_008140 | 3,189 | no_license | [
{
"docstring": "Reads pickle file and returns the contents. :param user: user that's being encoded :type user: string :return: data :rtype: list",
"name": "read_pickle",
"signature": "def read_pickle(self, user)"
},
{
"docstring": "Turns the input image to encoding :param user: user that's being... | 4 | stack_v2_sparse_classes_30k_val_000770 | Implement the Python class `RecognizeUserFace` described below.
Class description:
A class to recognize a user's face with the corresponding encoding.
Method signatures and docstrings:
- def read_pickle(self, user): Reads pickle file and returns the contents. :param user: user that's being encoded :type user: string ... | Implement the Python class `RecognizeUserFace` described below.
Class description:
A class to recognize a user's face with the corresponding encoding.
Method signatures and docstrings:
- def read_pickle(self, user): Reads pickle file and returns the contents. :param user: user that's being encoded :type user: string ... | d5de98b225afb6ce07dae25e4734105432533830 | <|skeleton|>
class RecognizeUserFace:
"""A class to recognize a user's face with the corresponding encoding."""
def read_pickle(self, user):
"""Reads pickle file and returns the contents. :param user: user that's being encoded :type user: string :return: data :rtype: list"""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RecognizeUserFace:
"""A class to recognize a user's face with the corresponding encoding."""
def read_pickle(self, user):
"""Reads pickle file and returns the contents. :param user: user that's being encoded :type user: string :return: data :rtype: list"""
pickle_file = PICKLE_FOLDER + os... | the_stack_v2_python_sparse | utility/facialrecognition/recognizeuserface.py | PIoT-CSS/agent-pi | train | 0 |
2d2c89df27b33d9d85291737d6a232609f4cda4b | [
"if type(arg_index) is int:\n return {cls.CATEGORY: category, cls.ARG_INDEX: [arg_index]}\nif type(arg_index) is list and all((isinstance(i, int) for i in arg_index)):\n return {cls.CATEGORY: category, cls.ARG_INDEX: arg_index}\nreturn {cls.CATEGORY: category}",
"self.name = propertiesDict.pop('name', None)... | <|body_start_0|>
if type(arg_index) is int:
return {cls.CATEGORY: category, cls.ARG_INDEX: [arg_index]}
if type(arg_index) is list and all((isinstance(i, int) for i in arg_index)):
return {cls.CATEGORY: category, cls.ARG_INDEX: arg_index}
return {cls.CATEGORY: category}
<... | Base class for formatting log messages. This implementation delegates everything to logging.Formatter using the messagefmt and datefmt properties. Subclasses may be implemented to provide required customizations, and can be registered by specifying classname in the formatter node of the project configuration file. | BaseLogFormatter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseLogFormatter:
"""Base class for formatting log messages. This implementation delegates everything to logging.Formatter using the messagefmt and datefmt properties. Subclasses may be implemented to provide required customizations, and can be registered by specifying classname in the formatter ... | stack_v2_sparse_classes_36k_train_008141 | 8,804 | no_license | [
{
"docstring": "Return dictionary to tag a string to format with color encodings. @param category: The category, as defined in L{ColorLogFormatter.COLOR_CATEGORIES} @param arg_index: The index of argument in the string expansion to color. This can be either a single integer value representing the index, or a li... | 2 | stack_v2_sparse_classes_30k_val_000606 | Implement the Python class `BaseLogFormatter` described below.
Class description:
Base class for formatting log messages. This implementation delegates everything to logging.Formatter using the messagefmt and datefmt properties. Subclasses may be implemented to provide required customizations, and can be registered by... | Implement the Python class `BaseLogFormatter` described below.
Class description:
Base class for formatting log messages. This implementation delegates everything to logging.Formatter using the messagefmt and datefmt properties. Subclasses may be implemented to provide required customizations, and can be registered by... | 3f93cbedbb806b6c53de89358025f93c740ebdc3 | <|skeleton|>
class BaseLogFormatter:
"""Base class for formatting log messages. This implementation delegates everything to logging.Formatter using the messagefmt and datefmt properties. Subclasses may be implemented to provide required customizations, and can be registered by specifying classname in the formatter ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseLogFormatter:
"""Base class for formatting log messages. This implementation delegates everything to logging.Formatter using the messagefmt and datefmt properties. Subclasses may be implemented to provide required customizations, and can be registered by specifying classname in the formatter node of the p... | the_stack_v2_python_sparse | pysys/utils/logutils.py | moraygrieve/pysys | train | 0 |
b857f4095cf36042baa38810ad2a0995c717e324 | [
"warnings.warn('SequentialWeaveGraph is deprecated. Will be removed in DeepChem 1.4.', DeprecationWarning)\nself.graph = tf.Graph()\nself.max_atoms = max_atoms\nself.n_atom_feat = n_atom_feat\nself.n_pair_feat = n_pair_feat\nwith self.graph.as_default():\n self.graph_topology = WeaveGraphTopology(self.max_atoms,... | <|body_start_0|>
warnings.warn('SequentialWeaveGraph is deprecated. Will be removed in DeepChem 1.4.', DeprecationWarning)
self.graph = tf.Graph()
self.max_atoms = max_atoms
self.n_atom_feat = n_atom_feat
self.n_pair_feat = n_pair_feat
with self.graph.as_default():
... | SequentialGraph for Weave models | SequentialWeaveGraph | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SequentialWeaveGraph:
"""SequentialGraph for Weave models"""
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
"""Parameters ---------- max_atoms: int, optional Maximum number of atoms in a molecule, should be defined based on dataset n_atom_feat: int, optional Number... | stack_v2_sparse_classes_36k_train_008142 | 11,824 | permissive | [
{
"docstring": "Parameters ---------- max_atoms: int, optional Maximum number of atoms in a molecule, should be defined based on dataset n_atom_feat: int, optional Number of features per atom. n_pair_feat: int, optional Number of features per pair of atoms.",
"name": "__init__",
"signature": "def __init... | 2 | stack_v2_sparse_classes_30k_train_018583 | Implement the Python class `SequentialWeaveGraph` described below.
Class description:
SequentialGraph for Weave models
Method signatures and docstrings:
- def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14): Parameters ---------- max_atoms: int, optional Maximum number of atoms in a molecule, should be d... | Implement the Python class `SequentialWeaveGraph` described below.
Class description:
SequentialGraph for Weave models
Method signatures and docstrings:
- def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14): Parameters ---------- max_atoms: int, optional Maximum number of atoms in a molecule, should be d... | ee6e67ebcf7bf04259cf13aff6388e2b791fea3d | <|skeleton|>
class SequentialWeaveGraph:
"""SequentialGraph for Weave models"""
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
"""Parameters ---------- max_atoms: int, optional Maximum number of atoms in a molecule, should be defined based on dataset n_atom_feat: int, optional Number... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SequentialWeaveGraph:
"""SequentialGraph for Weave models"""
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
"""Parameters ---------- max_atoms: int, optional Maximum number of atoms in a molecule, should be defined based on dataset n_atom_feat: int, optional Number of features ... | the_stack_v2_python_sparse | contrib/one_shot_models/graph_models.py | deepchem/deepchem | train | 4,876 |
3ed4db8211e8c1267456438f74e8bb45d918f195 | [
"self.path = None\nself.end = end\nself.start = start\nself.openList = AStarList(Node(start, None, AStarObj.getDistHeur(start, end), 0))\nself.closedList = Table(len(boolMap), len(boolMap[0]))\nself.loops = 0\npath = None\nself.pathExists = self.iterate(boolMap, diagOkay)",
"hasPath = False\nwhile hasPath == Fals... | <|body_start_0|>
self.path = None
self.end = end
self.start = start
self.openList = AStarList(Node(start, None, AStarObj.getDistHeur(start, end), 0))
self.closedList = Table(len(boolMap), len(boolMap[0]))
self.loops = 0
path = None
self.pathExists = self.i... | The main class for an A* pathing function. | AStarObj | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AStarObj:
"""The main class for an A* pathing function."""
def __init__(self, boolMap, start, end, diagOkay=False):
"""Initializer. Also generates the path, if one exists."""
<|body_0|>
def iterate(self, boolMap, diagOkay):
"""Main work method. Follows the proced... | stack_v2_sparse_classes_36k_train_008143 | 5,940 | no_license | [
{
"docstring": "Initializer. Also generates the path, if one exists.",
"name": "__init__",
"signature": "def __init__(self, boolMap, start, end, diagOkay=False)"
},
{
"docstring": "Main work method. Follows the procedure outlined at top of this file.",
"name": "iterate",
"signature": "de... | 6 | stack_v2_sparse_classes_30k_train_001751 | Implement the Python class `AStarObj` described below.
Class description:
The main class for an A* pathing function.
Method signatures and docstrings:
- def __init__(self, boolMap, start, end, diagOkay=False): Initializer. Also generates the path, if one exists.
- def iterate(self, boolMap, diagOkay): Main work metho... | Implement the Python class `AStarObj` described below.
Class description:
The main class for an A* pathing function.
Method signatures and docstrings:
- def __init__(self, boolMap, start, end, diagOkay=False): Initializer. Also generates the path, if one exists.
- def iterate(self, boolMap, diagOkay): Main work metho... | 44fe3aab40959ae5829df74638037d9854eb3a91 | <|skeleton|>
class AStarObj:
"""The main class for an A* pathing function."""
def __init__(self, boolMap, start, end, diagOkay=False):
"""Initializer. Also generates the path, if one exists."""
<|body_0|>
def iterate(self, boolMap, diagOkay):
"""Main work method. Follows the proced... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AStarObj:
"""The main class for an A* pathing function."""
def __init__(self, boolMap, start, end, diagOkay=False):
"""Initializer. Also generates the path, if one exists."""
self.path = None
self.end = end
self.start = start
self.openList = AStarList(Node(start, N... | the_stack_v2_python_sparse | Side Projects/AStar/a_star_obj.py | MWidlerSchool/mwidlerschool.github.io | train | 0 |
9979c052ba7781e7a4ebe21106767b9ea2355fc0 | [
"self.agent_index = agent_index\nif fn not in dir(search_strategies):\n raise AttributeError(fn + ' is not a search function in search_strategies.py.')\nfunc = getattr(search_strategies, fn)\nif 'heuristic' not in func.__code__.co_varnames:\n sys.stderr.write('[SearchAgent] using function {} \\n'.format(fn))\... | <|body_start_0|>
self.agent_index = agent_index
if fn not in dir(search_strategies):
raise AttributeError(fn + ' is not a search function in search_strategies.py.')
func = getattr(search_strategies, fn)
if 'heuristic' not in func.__code__.co_varnames:
sys.stderr.w... | This general search agent finds a path using a supplied search algorithm for a supplied search problem, then returns actions to follow that path. This planning is done when the system calls the register_initial_state method. The system then gets actions from this plan with the get_action method. As a default, this agen... | SearchAgent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SearchAgent:
"""This general search agent finds a path using a supplied search algorithm for a supplied search problem, then returns actions to follow that path. This planning is done when the system calls the register_initial_state method. The system then gets actions from this plan with the get... | stack_v2_sparse_classes_36k_train_008144 | 10,217 | permissive | [
{
"docstring": "Set up the agent, look for the implementations of its search function, problem, and heuristic. Warning: some advanced Python magic is employed below to find the right functions and problems. (SearchAgent, str, str, str) -> None",
"name": "__init__",
"signature": "def __init__(self, agent... | 3 | stack_v2_sparse_classes_30k_train_021215 | Implement the Python class `SearchAgent` described below.
Class description:
This general search agent finds a path using a supplied search algorithm for a supplied search problem, then returns actions to follow that path. This planning is done when the system calls the register_initial_state method. The system then g... | Implement the Python class `SearchAgent` described below.
Class description:
This general search agent finds a path using a supplied search algorithm for a supplied search problem, then returns actions to follow that path. This planning is done when the system calls the register_initial_state method. The system then g... | f562f4e4bf0093da13b3fb4675c97ea8e02b0ed1 | <|skeleton|>
class SearchAgent:
"""This general search agent finds a path using a supplied search algorithm for a supplied search problem, then returns actions to follow that path. This planning is done when the system calls the register_initial_state method. The system then gets actions from this plan with the get... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SearchAgent:
"""This general search agent finds a path using a supplied search algorithm for a supplied search problem, then returns actions to follow that path. This planning is done when the system calls the register_initial_state method. The system then gets actions from this plan with the get_action metho... | the_stack_v2_python_sparse | assignment-1/agents.py | ybhan/ANU-Artificial-Intelligence | train | 0 |
01a883a37cc5faa3c886a0edfef78b578aeb14a3 | [
"self._check_params()\nself._warn_for_unused_params()\nif any((isinstance(el, list) for el in raw_documents)):\n self._tfidf.n_sent_per_doc = []\n for i in range(len(raw_documents)):\n self._tfidf.n_sent_per_doc.append(len(raw_documents[i]))\n self._tfidf.n_doc = len(raw_documents)\n raw_document... | <|body_start_0|>
self._check_params()
self._warn_for_unused_params()
if any((isinstance(el, list) for el in raw_documents)):
self._tfidf.n_sent_per_doc = []
for i in range(len(raw_documents)):
self._tfidf.n_sent_per_doc.append(len(raw_documents[i]))
... | NewTfidfVectorizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NewTfidfVectorizer:
def fit(self, raw_documents, y=None):
"""Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which yields either str, unicode or file objects. y : None This parameter is not needed to compute tfidf. Returns ------- se... | stack_v2_sparse_classes_36k_train_008145 | 6,911 | permissive | [
{
"docstring": "Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which yields either str, unicode or file objects. y : None This parameter is not needed to compute tfidf. Returns ------- self : object Fitted vectorizer.",
"name": "fit",
"signature": ... | 3 | null | Implement the Python class `NewTfidfVectorizer` described below.
Class description:
Implement the NewTfidfVectorizer class.
Method signatures and docstrings:
- def fit(self, raw_documents, y=None): Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which yields eith... | Implement the Python class `NewTfidfVectorizer` described below.
Class description:
Implement the NewTfidfVectorizer class.
Method signatures and docstrings:
- def fit(self, raw_documents, y=None): Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which yields eith... | ca748a227acd446cd9259056c6a9d6ae261aeaea | <|skeleton|>
class NewTfidfVectorizer:
def fit(self, raw_documents, y=None):
"""Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which yields either str, unicode or file objects. y : None This parameter is not needed to compute tfidf. Returns ------- se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NewTfidfVectorizer:
def fit(self, raw_documents, y=None):
"""Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which yields either str, unicode or file objects. y : None This parameter is not needed to compute tfidf. Returns ------- self : object Fi... | the_stack_v2_python_sparse | tfidf_test.py | filiparente/Predtweet | train | 3 | |
aa73ec0205c0773c6afec7ca4caf6ce923b1f557 | [
"self.screen_width = 1200\nself.screen_height = 800\nself.bg_color = (230, 230, 230)\nself.bullet_width = 30\nself.bullet_height = 6\nself.bullet_color = (60, 60, 60)\nself.bullet_allowed = 1\nself.rectangle_width = 30\nself.rectangle_height = 300\nself.rectangle_color = (120, 120, 120)\nself.hits_limit = 3\nself.s... | <|body_start_0|>
self.screen_width = 1200
self.screen_height = 800
self.bg_color = (230, 230, 230)
self.bullet_width = 30
self.bullet_height = 6
self.bullet_color = (60, 60, 60)
self.bullet_allowed = 1
self.rectangle_width = 30
self.rectangle_heigh... | 存储《外星人入侵》的所有设置的类 | Settings | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Settings:
"""存储《外星人入侵》的所有设置的类"""
def __init__(self):
"""初始化游戏设置"""
<|body_0|>
def initialize_dynamic_settings(self):
"""初始化随游戏进行而变化的设置"""
<|body_1|>
def increase_speed(self):
"""提高速度设置"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_36k_train_008146 | 1,447 | no_license | [
{
"docstring": "初始化游戏设置",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "初始化随游戏进行而变化的设置",
"name": "initialize_dynamic_settings",
"signature": "def initialize_dynamic_settings(self)"
},
{
"docstring": "提高速度设置",
"name": "increase_speed",
"signature... | 3 | null | Implement the Python class `Settings` described below.
Class description:
存储《外星人入侵》的所有设置的类
Method signatures and docstrings:
- def __init__(self): 初始化游戏设置
- def initialize_dynamic_settings(self): 初始化随游戏进行而变化的设置
- def increase_speed(self): 提高速度设置 | Implement the Python class `Settings` described below.
Class description:
存储《外星人入侵》的所有设置的类
Method signatures and docstrings:
- def __init__(self): 初始化游戏设置
- def initialize_dynamic_settings(self): 初始化随游戏进行而变化的设置
- def increase_speed(self): 提高速度设置
<|skeleton|>
class Settings:
"""存储《外星人入侵》的所有设置的类"""
def __init... | e08f63616aabe609ff1ac53b8e0ab32eaf2a472b | <|skeleton|>
class Settings:
"""存储《外星人入侵》的所有设置的类"""
def __init__(self):
"""初始化游戏设置"""
<|body_0|>
def initialize_dynamic_settings(self):
"""初始化随游戏进行而变化的设置"""
<|body_1|>
def increase_speed(self):
"""提高速度设置"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Settings:
"""存储《外星人入侵》的所有设置的类"""
def __init__(self):
"""初始化游戏设置"""
self.screen_width = 1200
self.screen_height = 800
self.bg_color = (230, 230, 230)
self.bullet_width = 30
self.bullet_height = 6
self.bullet_color = (60, 60, 60)
self.bullet_a... | the_stack_v2_python_sparse | section_ii/project_a/chapter_14/example_3/settings.py | xieqing0428/python_helloworld | train | 0 |
a760e513eaede15f1d9940484f0b20f8219c4400 | [
"parser.add_argument('experiment', help='experiment to conclude')\nmutex_group = parser.add_mutually_exclusive_group(required=True)\nmutex_group.add_argument('branch', help='branch to promote to default', nargs='?')\nmutex_group.add_argument('--no-promote-branch', help='do not promote a branch to default', action='... | <|body_start_0|>
parser.add_argument('experiment', help='experiment to conclude')
mutex_group = parser.add_mutually_exclusive_group(required=True)
mutex_group.add_argument('branch', help='branch to promote to default', nargs='?')
mutex_group.add_argument('--no-promote-branch', help='do n... | Conclude a given experiment. This is one of the main user commands. It demotes an experiment to no longer being live, records a conclusion date, and (optionally but strongly advised) promotes the settings from one of its branches into the defaults. | Conclude | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Conclude:
"""Conclude a given experiment. This is one of the main user commands. It demotes an experiment to no longer being live, records a conclusion date, and (optionally but strongly advised) promotes the settings from one of its branches into the defaults."""
def add_arguments(self, par... | stack_v2_sparse_classes_36k_train_008147 | 13,324 | permissive | [
{
"docstring": "Add argparse arguments.",
"name": "add_arguments",
"signature": "def add_arguments(self, parser)"
},
{
"docstring": "Run command.",
"name": "handle",
"signature": "def handle(self, config, options)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018139 | Implement the Python class `Conclude` described below.
Class description:
Conclude a given experiment. This is one of the main user commands. It demotes an experiment to no longer being live, records a conclusion date, and (optionally but strongly advised) promotes the settings from one of its branches into the defaul... | Implement the Python class `Conclude` described below.
Class description:
Conclude a given experiment. This is one of the main user commands. It demotes an experiment to no longer being live, records a conclusion date, and (optionally but strongly advised) promotes the settings from one of its branches into the defaul... | a06b781a81139ed578bcbef3fb521b8f712ab77d | <|skeleton|>
class Conclude:
"""Conclude a given experiment. This is one of the main user commands. It demotes an experiment to no longer being live, records a conclusion date, and (optionally but strongly advised) promotes the settings from one of its branches into the defaults."""
def add_arguments(self, par... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Conclude:
"""Conclude a given experiment. This is one of the main user commands. It demotes an experiment to no longer being live, records a conclusion date, and (optionally but strongly advised) promotes the settings from one of its branches into the defaults."""
def add_arguments(self, parser):
... | the_stack_v2_python_sparse | jacquard/experiments/commands.py | prophile/jacquard | train | 8 |
ed51b8dcd482ef63c2cd4d00d0c388d60bd18217 | [
"super(ASRModel, self).__init__()\nself.input_size = input_size\nself.hidden_size = hidden_size\nself.num_layers = num_layers\nself.dropout = dropout\nself.conv1d_layer = nn.Conv1d(in_channels=input_size, out_channels=hidden_size, kernel_size=5, stride=2, padding=2)\nself.lstm_block = nn.LSTM(input_size=hidden_size... | <|body_start_0|>
super(ASRModel, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.dropout = dropout
self.conv1d_layer = nn.Conv1d(in_channels=input_size, out_channels=hidden_size, kernel_size=5, stride=2, paddi... | ASRModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ASRModel:
def __init__(self, input_size=80, hidden_size=256, output_size=29, num_layers=2, dropout=0.4):
"""Implements a 1D-convolutional (kernel_size=5, stride=2) layer for downsampling the temporal dimension, followed by multiple bidirectional LSTM layers. Dropout is applied to the out... | stack_v2_sparse_classes_36k_train_008148 | 2,424 | no_license | [
{
"docstring": "Implements a 1D-convolutional (kernel_size=5, stride=2) layer for downsampling the temporal dimension, followed by multiple bidirectional LSTM layers. Dropout is applied to the output of each hidden layer. Args: input_size (int): Size of the input feature dimension. hidden_size (int): Size of th... | 2 | stack_v2_sparse_classes_30k_train_007935 | Implement the Python class `ASRModel` described below.
Class description:
Implement the ASRModel class.
Method signatures and docstrings:
- def __init__(self, input_size=80, hidden_size=256, output_size=29, num_layers=2, dropout=0.4): Implements a 1D-convolutional (kernel_size=5, stride=2) layer for downsampling the ... | Implement the Python class `ASRModel` described below.
Class description:
Implement the ASRModel class.
Method signatures and docstrings:
- def __init__(self, input_size=80, hidden_size=256, output_size=29, num_layers=2, dropout=0.4): Implements a 1D-convolutional (kernel_size=5, stride=2) layer for downsampling the ... | 3b7452342e5eb4eea5a8ca3e24f86a57bcd20ff1 | <|skeleton|>
class ASRModel:
def __init__(self, input_size=80, hidden_size=256, output_size=29, num_layers=2, dropout=0.4):
"""Implements a 1D-convolutional (kernel_size=5, stride=2) layer for downsampling the temporal dimension, followed by multiple bidirectional LSTM layers. Dropout is applied to the out... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ASRModel:
def __init__(self, input_size=80, hidden_size=256, output_size=29, num_layers=2, dropout=0.4):
"""Implements a 1D-convolutional (kernel_size=5, stride=2) layer for downsampling the temporal dimension, followed by multiple bidirectional LSTM layers. Dropout is applied to the output of each hi... | the_stack_v2_python_sparse | asr/modules/asr_model.py | LouisThygesen/02466_fagprojekt_done | train | 0 | |
bd39fc9a4057bde8a8fb00b5cf810ba0f1086681 | [
"def cmp(a, b):\n if str(a) > str(b):\n return 1\n return -1\nans = [i for i in range(1, n + 1)]\nans.sort(cmp=cmp)\nprint(ans)\nreturn ans",
"curr = 1\nans = []\nfor _ in range(1, n + 1):\n ans.append(curr)\n if curr * 10 <= n:\n curr *= 10\n elif curr % 10 != 9 and curr + 1 <= n:\n ... | <|body_start_0|>
def cmp(a, b):
if str(a) > str(b):
return 1
return -1
ans = [i for i in range(1, n + 1)]
ans.sort(cmp=cmp)
print(ans)
return ans
<|end_body_0|>
<|body_start_1|>
curr = 1
ans = []
for _ in range(1, n... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lexicalOrder(self, n):
""":type n: int :rtype: List[int]"""
<|body_0|>
def lexicalOrder2(self, n):
""":type n: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def cmp(a, b):
if str(a) > str(b):
... | stack_v2_sparse_classes_36k_train_008149 | 1,618 | no_license | [
{
"docstring": ":type n: int :rtype: List[int]",
"name": "lexicalOrder",
"signature": "def lexicalOrder(self, n)"
},
{
"docstring": ":type n: int :rtype: List[int]",
"name": "lexicalOrder2",
"signature": "def lexicalOrder2(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000515 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lexicalOrder(self, n): :type n: int :rtype: List[int]
- def lexicalOrder2(self, n): :type n: int :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lexicalOrder(self, n): :type n: int :rtype: List[int]
- def lexicalOrder2(self, n): :type n: int :rtype: List[int]
<|skeleton|>
class Solution:
def lexicalOrder(self, n... | 2d5fa4cd696d5035ea8859befeadc5cc436959c9 | <|skeleton|>
class Solution:
def lexicalOrder(self, n):
""":type n: int :rtype: List[int]"""
<|body_0|>
def lexicalOrder2(self, n):
""":type n: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def lexicalOrder(self, n):
""":type n: int :rtype: List[int]"""
def cmp(a, b):
if str(a) > str(b):
return 1
return -1
ans = [i for i in range(1, n + 1)]
ans.sort(cmp=cmp)
print(ans)
return ans
def lexicalOrd... | the_stack_v2_python_sparse | SourceCode/Python/Problem/00386.Lexicographical Numbers.py | roger6blog/LeetCode | train | 0 | |
dc7e72c152bd5ced1ab531901b06be698d8eddb0 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn PasswordAuthenticationMethod()",
"from .authentication_method import AuthenticationMethod\nfrom .authentication_method import AuthenticationMethod\nfields: Dict[str, Callable[[Any], None]] = {'createdDateTime': lambda n: setattr(self, ... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return PasswordAuthenticationMethod()
<|end_body_0|>
<|body_start_1|>
from .authentication_method import AuthenticationMethod
from .authentication_method import AuthenticationMethod
fie... | PasswordAuthenticationMethod | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PasswordAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PasswordAuthenticationMethod:
"""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... | stack_v2_sparse_classes_36k_train_008150 | 2,774 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: PasswordAuthenticationMethod",
"name": "create_from_discriminator_value",
"signature": "def create_from_disc... | 3 | null | Implement the Python class `PasswordAuthenticationMethod` described below.
Class description:
Implement the PasswordAuthenticationMethod class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PasswordAuthenticationMethod: Creates a new instance of the a... | Implement the Python class `PasswordAuthenticationMethod` described below.
Class description:
Implement the PasswordAuthenticationMethod class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PasswordAuthenticationMethod: Creates a new instance of the a... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class PasswordAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PasswordAuthenticationMethod:
"""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... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PasswordAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PasswordAuthenticationMethod:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create th... | the_stack_v2_python_sparse | msgraph/generated/models/password_authentication_method.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
39641c5bb9bdbcec5e2f12438ccd960298c77dc5 | [
"from m3gnet.models import M3GNet\nif model_path:\n self.describer_model = M3GNet.from_dir(model_path)\nelse:\n self.describer_model = M3GNet.from_dir(DEFAULT_MODEL)\nself.model_path = model_path\nsuper().__init__(**kwargs)",
"from m3gnet.graph import Index, tf_compute_distance_angle\nfrom m3gnet.layers imp... | <|body_start_0|>
from m3gnet.models import M3GNet
if model_path:
self.describer_model = M3GNet.from_dir(model_path)
else:
self.describer_model = M3GNet.from_dir(DEFAULT_MODEL)
self.model_path = model_path
super().__init__(**kwargs)
<|end_body_0|>
<|body_s... | M3GNetStructure | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class M3GNetStructure:
def __init__(self, model_path: str | None=None, **kwargs):
"""Args: model_path (str): m3gnet models path. If no path is provided, the models will be M3GNet formation energy model on figshare: https://figshare.com/articles/software/m3gnet_property_model_weights/20099465 P... | stack_v2_sparse_classes_36k_train_008151 | 2,220 | permissive | [
{
"docstring": "Args: model_path (str): m3gnet models path. If no path is provided, the models will be M3GNet formation energy model on figshare: https://figshare.com/articles/software/m3gnet_property_model_weights/20099465 Please refer to the M3GNet paper: https://doi.org/10.1038/s43588-022-00349-3.",
"nam... | 2 | stack_v2_sparse_classes_30k_train_018506 | Implement the Python class `M3GNetStructure` described below.
Class description:
Implement the M3GNetStructure class.
Method signatures and docstrings:
- def __init__(self, model_path: str | None=None, **kwargs): Args: model_path (str): m3gnet models path. If no path is provided, the models will be M3GNet formation e... | Implement the Python class `M3GNetStructure` described below.
Class description:
Implement the M3GNetStructure class.
Method signatures and docstrings:
- def __init__(self, model_path: str | None=None, **kwargs): Args: model_path (str): m3gnet models path. If no path is provided, the models will be M3GNet formation e... | 6ae3c7029b939e1183684358a3ae2fef41053be5 | <|skeleton|>
class M3GNetStructure:
def __init__(self, model_path: str | None=None, **kwargs):
"""Args: model_path (str): m3gnet models path. If no path is provided, the models will be M3GNet formation energy model on figshare: https://figshare.com/articles/software/m3gnet_property_model_weights/20099465 P... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class M3GNetStructure:
def __init__(self, model_path: str | None=None, **kwargs):
"""Args: model_path (str): m3gnet models path. If no path is provided, the models will be M3GNet formation energy model on figshare: https://figshare.com/articles/software/m3gnet_property_model_weights/20099465 Please refer to... | the_stack_v2_python_sparse | maml/describers/_m3gnet.py | materialsvirtuallab/maml | train | 266 | |
97421b1a9d8db2b24f66e72effb56c021431a0d4 | [
"if hasattr(request, 'user_rbac'):\n if request.user_rbac is not None:\n return True\nreturn False",
"if hasattr(request, 'user_rbac'):\n user_rbac = request.user_rbac\n if user_rbac is not None:\n user = user_rbac.user\n if hasattr(user, 'userinfo'):\n return user.userinf... | <|body_start_0|>
if hasattr(request, 'user_rbac'):
if request.user_rbac is not None:
return True
return False
<|end_body_0|>
<|body_start_1|>
if hasattr(request, 'user_rbac'):
user_rbac = request.user_rbac
if user_rbac is not None:
... | UserAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserAPI:
def is_login(request):
"""连接是否登录 :param request: :return:"""
<|body_0|>
def get_per_page(request):
"""获取用户设置的页信息 :param request: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if hasattr(request, 'user_rbac'):
if reque... | stack_v2_sparse_classes_36k_train_008152 | 1,407 | permissive | [
{
"docstring": "连接是否登录 :param request: :return:",
"name": "is_login",
"signature": "def is_login(request)"
},
{
"docstring": "获取用户设置的页信息 :param request: :return:",
"name": "get_per_page",
"signature": "def get_per_page(request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019166 | Implement the Python class `UserAPI` described below.
Class description:
Implement the UserAPI class.
Method signatures and docstrings:
- def is_login(request): 连接是否登录 :param request: :return:
- def get_per_page(request): 获取用户设置的页信息 :param request: :return: | Implement the Python class `UserAPI` described below.
Class description:
Implement the UserAPI class.
Method signatures and docstrings:
- def is_login(request): 连接是否登录 :param request: :return:
- def get_per_page(request): 获取用户设置的页信息 :param request: :return:
<|skeleton|>
class UserAPI:
def is_login(request):
... | 8b97efdc9287645ea6b99dcf3a99fbe3f6ba6862 | <|skeleton|>
class UserAPI:
def is_login(request):
"""连接是否登录 :param request: :return:"""
<|body_0|>
def get_per_page(request):
"""获取用户设置的页信息 :param request: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserAPI:
def is_login(request):
"""连接是否登录 :param request: :return:"""
if hasattr(request, 'user_rbac'):
if request.user_rbac is not None:
return True
return False
def get_per_page(request):
"""获取用户设置的页信息 :param request: :return:"""
if ha... | the_stack_v2_python_sparse | natrix/common/user_api.py | creditease-natrix/natrix | train | 4 | |
dfa62238e0acea51caeedd8fe80fd21ac6b59383 | [
"url = self.build_url('/datastores', limit=limit, marker=marker)\nif response_key:\n return self._get(url, 'datastores', **kwargs)\nelse:\n return self._get(url, **kwargs)",
"if response_key:\n return self._get('/datastores/%s' % datastore, 'datastore', **kwargs)\nelse:\n return self._get('/datastores... | <|body_start_0|>
url = self.build_url('/datastores', limit=limit, marker=marker)
if response_key:
return self._get(url, 'datastores', **kwargs)
else:
return self._get(url, **kwargs)
<|end_body_0|>
<|body_start_1|>
if response_key:
return self._get('/d... | DatastoreManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatastoreManager:
def list(self, limit=None, marker=None, response_key=True, **kwargs):
"""Get a list of all datastores. :rtype: list of :class:`Datastore`."""
<|body_0|>
def get(self, datastore, response_key=True, **kwargs):
"""Get a specific datastore. :rtype: :cla... | stack_v2_sparse_classes_36k_train_008153 | 862 | no_license | [
{
"docstring": "Get a list of all datastores. :rtype: list of :class:`Datastore`.",
"name": "list",
"signature": "def list(self, limit=None, marker=None, response_key=True, **kwargs)"
},
{
"docstring": "Get a specific datastore. :rtype: :class:`Datastore`",
"name": "get",
"signature": "d... | 2 | null | Implement the Python class `DatastoreManager` described below.
Class description:
Implement the DatastoreManager class.
Method signatures and docstrings:
- def list(self, limit=None, marker=None, response_key=True, **kwargs): Get a list of all datastores. :rtype: list of :class:`Datastore`.
- def get(self, datastore,... | Implement the Python class `DatastoreManager` described below.
Class description:
Implement the DatastoreManager class.
Method signatures and docstrings:
- def list(self, limit=None, marker=None, response_key=True, **kwargs): Get a list of all datastores. :rtype: list of :class:`Datastore`.
- def get(self, datastore,... | 42f9197ba26ffb6b9dd336a524639ecbbf194365 | <|skeleton|>
class DatastoreManager:
def list(self, limit=None, marker=None, response_key=True, **kwargs):
"""Get a list of all datastores. :rtype: list of :class:`Datastore`."""
<|body_0|>
def get(self, datastore, response_key=True, **kwargs):
"""Get a specific datastore. :rtype: :cla... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatastoreManager:
def list(self, limit=None, marker=None, response_key=True, **kwargs):
"""Get a list of all datastores. :rtype: list of :class:`Datastore`."""
url = self.build_url('/datastores', limit=limit, marker=marker)
if response_key:
return self._get(url, 'datastores... | the_stack_v2_python_sparse | ops_client/project/trove/datastores.py | tokuzfunpi/ops_client | train | 0 | |
82a41c88b4d63de6023b42384d6335f1a5215a93 | [
"max_data = []\nif len(nums) == 0:\n return max_data\nfor i in range(len(nums) - k + 1):\n max_data.append(max(nums[i:i + k]))\nreturn max_data",
"win = []\nres = []\nfor i, v in enumerate(nums):\n if i >= k and i - win[0] >= k:\n win.pop(0)\n while win and v >= nums[win[-1]]:\n win.pop(... | <|body_start_0|>
max_data = []
if len(nums) == 0:
return max_data
for i in range(len(nums) - k + 1):
max_data.append(max(nums[i:i + k]))
return max_data
<|end_body_0|>
<|body_start_1|>
win = []
res = []
for i, v in enumerate(nums):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxSlidingWindow(self, nums, k):
""":type nums: List[int] :type k: int :rtype: List[int]"""
<|body_0|>
def maxSlidingWindow2(self, nums, k):
""":type nums: List[int] :type k: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_36k_train_008154 | 1,291 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :rtype: List[int]",
"name": "maxSlidingWindow",
"signature": "def maxSlidingWindow(self, nums, k)"
},
{
"docstring": ":type nums: List[int] :type k: int :rtype: List[int]",
"name": "maxSlidingWindow2",
"signature": "def maxSlidingWindow2... | 2 | stack_v2_sparse_classes_30k_train_003623 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow(self, nums, k): :type nums: List[int] :type k: int :rtype: List[int]
- def maxSlidingWindow2(self, nums, k): :type nums: List[int] :type k: int :rtype: List[... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow(self, nums, k): :type nums: List[int] :type k: int :rtype: List[int]
- def maxSlidingWindow2(self, nums, k): :type nums: List[int] :type k: int :rtype: List[... | 013f6f222c6c2a617787b258f8a37003a9f51526 | <|skeleton|>
class Solution:
def maxSlidingWindow(self, nums, k):
""":type nums: List[int] :type k: int :rtype: List[int]"""
<|body_0|>
def maxSlidingWindow2(self, nums, k):
""":type nums: List[int] :type k: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxSlidingWindow(self, nums, k):
""":type nums: List[int] :type k: int :rtype: List[int]"""
max_data = []
if len(nums) == 0:
return max_data
for i in range(len(nums) - k + 1):
max_data.append(max(nums[i:i + k]))
return max_data
... | the_stack_v2_python_sparse | other/slid_window.py | terrifyzhao/leetcode | train | 0 | |
696164931bbfb8c814ac834384e12ad62a10f0b3 | [
"self._forward = {}\nself._backward = {}\nself._last = 0",
"if val in self._forward:\n return False\nself._forward[val] = self._last\nself._backward[self._last] = val\nself._last += 1\nreturn True",
"if val not in self._forward:\n return False\nx = self._forward.pop(val)\nprev_last = self._last - 1\nif x ... | <|body_start_0|>
self._forward = {}
self._backward = {}
self._last = 0
<|end_body_0|>
<|body_start_1|>
if val in self._forward:
return False
self._forward[val] = self._last
self._backward[self._last] = val
self._last += 1
return True
<|end_bod... | RandomizedSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomizedSet:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def insert(self, val):
"""Inserts a value to the set. Returns true if the set did not already contain the specified element. :type val: int :rtype: bool"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_008155 | 1,710 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Inserts a value to the set. Returns true if the set did not already contain the specified element. :type val: int :rtype: bool",
"name": "insert",
"signature": ... | 4 | stack_v2_sparse_classes_30k_train_002519 | Implement the Python class `RandomizedSet` described below.
Class description:
Implement the RandomizedSet class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def insert(self, val): Inserts a value to the set. Returns true if the set did not already contain the specif... | Implement the Python class `RandomizedSet` described below.
Class description:
Implement the RandomizedSet class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def insert(self, val): Inserts a value to the set. Returns true if the set did not already contain the specif... | 33e24e4be8d30ec294211024dd0eab52d3bccbb7 | <|skeleton|>
class RandomizedSet:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def insert(self, val):
"""Inserts a value to the set. Returns true if the set did not already contain the specified element. :type val: int :rtype: bool"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomizedSet:
def __init__(self):
"""Initialize your data structure here."""
self._forward = {}
self._backward = {}
self._last = 0
def insert(self, val):
"""Inserts a value to the set. Returns true if the set did not already contain the specified element. :type va... | the_stack_v2_python_sparse | leetcode/380-insert-delete-getrandom-o1/380.py | clchiou/uva-problem-set | train | 1 | |
e6f54d8145f0a6b7bfe44224c669bafa2915e324 | [
"self.__screen = screen\nself.__dns = DNS()\nself.__hostnameLabel = Label(DNSSETUP_HOSTNAME_LABEL.localize())\nself.__primaryDNSLabel = Label(DNSSETUP_FST_DNS_LABEL.localize())\nself.__secondaryDNSLabel = Label(DNSSETUP_SND_DNS_LABEL.localize())\nself.__searchListLabel = Label(DNSSETUP_SEARCH_LABEL.localize())\ndat... | <|body_start_0|>
self.__screen = screen
self.__dns = DNS()
self.__hostnameLabel = Label(DNSSETUP_HOSTNAME_LABEL.localize())
self.__primaryDNSLabel = Label(DNSSETUP_FST_DNS_LABEL.localize())
self.__secondaryDNSLabel = Label(DNSSETUP_SND_DNS_LABEL.localize())
self.__searchL... | Represents the DNS configuration screen | DNSSetup | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DNSSetup:
"""Represents the DNS configuration screen"""
def __init__(self, screen, data):
"""Constructor @type screen: SnackScreen @param screen: SnackScreen instance"""
<|body_0|>
def run(self):
"""Draws the screen @rtype: integer @returns: status of operation""... | stack_v2_sparse_classes_36k_train_008156 | 3,215 | no_license | [
{
"docstring": "Constructor @type screen: SnackScreen @param screen: SnackScreen instance",
"name": "__init__",
"signature": "def __init__(self, screen, data)"
},
{
"docstring": "Draws the screen @rtype: integer @returns: status of operation",
"name": "run",
"signature": "def run(self)"
... | 2 | null | Implement the Python class `DNSSetup` described below.
Class description:
Represents the DNS configuration screen
Method signatures and docstrings:
- def __init__(self, screen, data): Constructor @type screen: SnackScreen @param screen: SnackScreen instance
- def run(self): Draws the screen @rtype: integer @returns: ... | Implement the Python class `DNSSetup` described below.
Class description:
Represents the DNS configuration screen
Method signatures and docstrings:
- def __init__(self, screen, data): Constructor @type screen: SnackScreen @param screen: SnackScreen instance
- def run(self): Draws the screen @rtype: integer @returns: ... | 1c738fd5e6ee3f8fd4f47acf2207038f20868212 | <|skeleton|>
class DNSSetup:
"""Represents the DNS configuration screen"""
def __init__(self, screen, data):
"""Constructor @type screen: SnackScreen @param screen: SnackScreen instance"""
<|body_0|>
def run(self):
"""Draws the screen @rtype: integer @returns: status of operation""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DNSSetup:
"""Represents the DNS configuration screen"""
def __init__(self, screen, data):
"""Constructor @type screen: SnackScreen @param screen: SnackScreen instance"""
self.__screen = screen
self.__dns = DNS()
self.__hostnameLabel = Label(DNSSETUP_HOSTNAME_LABEL.localize... | the_stack_v2_python_sparse | zfrobisher-installer/src/viewer/newt/dnssetup.py | fedosu85nce/work | train | 2 |
683859b8ebbb5d83222e3e406b7333fa266a277e | [
"t1 = [0.6, 0.1, 0.6]\nt2 = np.array([0.1, 0.2, 0.3])\nt3 = onp.array([5.0, 8.0, 101.0])\nres = fn.concatenate([t1, t2, t3])\nassert isinstance(res, np.ndarray)\nassert np.all(res == np.concatenate([t1, t2, t3]))",
"t1 = jnp.array([5.0, 8.0, 101.0])\nt2 = jnp.array([0.6, 0.1, 0.6])\nt3 = jnp.array([0.1, 0.2, 0.3]... | <|body_start_0|>
t1 = [0.6, 0.1, 0.6]
t2 = np.array([0.1, 0.2, 0.3])
t3 = onp.array([5.0, 8.0, 101.0])
res = fn.concatenate([t1, t2, t3])
assert isinstance(res, np.ndarray)
assert np.all(res == np.concatenate([t1, t2, t3]))
<|end_body_0|>
<|body_start_1|>
t1 = jn... | Tests for the concatenate function | TestConcatenate | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestConcatenate:
"""Tests for the concatenate function"""
def test_concatenate_array(self):
"""Test that concatenate, called without the axis arguments, concatenates across the 0th dimension"""
<|body_0|>
def test_concatenate_jax(self):
"""Test that concatenate, ... | stack_v2_sparse_classes_36k_train_008157 | 47,600 | permissive | [
{
"docstring": "Test that concatenate, called without the axis arguments, concatenates across the 0th dimension",
"name": "test_concatenate_array",
"signature": "def test_concatenate_array(self)"
},
{
"docstring": "Test that concatenate, called without the axis arguments, concatenates across the... | 6 | null | Implement the Python class `TestConcatenate` described below.
Class description:
Tests for the concatenate function
Method signatures and docstrings:
- def test_concatenate_array(self): Test that concatenate, called without the axis arguments, concatenates across the 0th dimension
- def test_concatenate_jax(self): Te... | Implement the Python class `TestConcatenate` described below.
Class description:
Tests for the concatenate function
Method signatures and docstrings:
- def test_concatenate_array(self): Test that concatenate, called without the axis arguments, concatenates across the 0th dimension
- def test_concatenate_jax(self): Te... | 0c1c805fd5dfce465a8955ee3faf81037023a23e | <|skeleton|>
class TestConcatenate:
"""Tests for the concatenate function"""
def test_concatenate_array(self):
"""Test that concatenate, called without the axis arguments, concatenates across the 0th dimension"""
<|body_0|>
def test_concatenate_jax(self):
"""Test that concatenate, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestConcatenate:
"""Tests for the concatenate function"""
def test_concatenate_array(self):
"""Test that concatenate, called without the axis arguments, concatenates across the 0th dimension"""
t1 = [0.6, 0.1, 0.6]
t2 = np.array([0.1, 0.2, 0.3])
t3 = onp.array([5.0, 8.0, 1... | the_stack_v2_python_sparse | artifacts/old_dataset_versions/original_commits_backup/pennylane/pennylane#1081/before/test_functions.py | MattePalte/Bugs-Quantum-Computing-Platforms | train | 4 |
0afd4d6d8b8c52b30b8fe331a2ff48e628a88e87 | [
"bsscs = BSSCS_CLASSIFIER(l2_reg=tf.contrib.layers.l2_regularizer(scale=0.001), learning_rate=0.001, steps=250, batch=250)\nself.assertTrue(bsscs != None)\nsingle_layer = bsscs.create_layer(512, activation=tf.nn.relu)\nself.assertTrue(single_layer != None)",
"bsscs = BSSCS_CLASSIFIER(l2_reg=tf.contrib.layers.l2_r... | <|body_start_0|>
bsscs = BSSCS_CLASSIFIER(l2_reg=tf.contrib.layers.l2_regularizer(scale=0.001), learning_rate=0.001, steps=250, batch=250)
self.assertTrue(bsscs != None)
single_layer = bsscs.create_layer(512, activation=tf.nn.relu)
self.assertTrue(single_layer != None)
<|end_body_0|>
<|... | ClassifierNetworkTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassifierNetworkTests:
def test_layer_creation(self):
"""Tests the creation of a default layer preconditions: No layer has been created postconditions: layer with default placeholder has been created"""
<|body_0|>
def test_loss_creation(self):
"""Tests the creation ... | stack_v2_sparse_classes_36k_train_008158 | 3,035 | no_license | [
{
"docstring": "Tests the creation of a default layer preconditions: No layer has been created postconditions: layer with default placeholder has been created",
"name": "test_layer_creation",
"signature": "def test_layer_creation(self)"
},
{
"docstring": "Tests the creation of a loss function pr... | 4 | stack_v2_sparse_classes_30k_train_015968 | Implement the Python class `ClassifierNetworkTests` described below.
Class description:
Implement the ClassifierNetworkTests class.
Method signatures and docstrings:
- def test_layer_creation(self): Tests the creation of a default layer preconditions: No layer has been created postconditions: layer with default place... | Implement the Python class `ClassifierNetworkTests` described below.
Class description:
Implement the ClassifierNetworkTests class.
Method signatures and docstrings:
- def test_layer_creation(self): Tests the creation of a default layer preconditions: No layer has been created postconditions: layer with default place... | d665ca405bdf35fdb57f8149a10b90be82d8de22 | <|skeleton|>
class ClassifierNetworkTests:
def test_layer_creation(self):
"""Tests the creation of a default layer preconditions: No layer has been created postconditions: layer with default placeholder has been created"""
<|body_0|>
def test_loss_creation(self):
"""Tests the creation ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassifierNetworkTests:
def test_layer_creation(self):
"""Tests the creation of a default layer preconditions: No layer has been created postconditions: layer with default placeholder has been created"""
bsscs = BSSCS_CLASSIFIER(l2_reg=tf.contrib.layers.l2_regularizer(scale=0.001), learning_ra... | the_stack_v2_python_sparse | BSSCSFramework/classifier_tests.py | wezleysherman/TBI-NN-421 | train | 3 | |
ae8d7deb1bfec087e73dc3265c2347aec6e39df2 | [
"self.logging = Logging()\nself.file_path = rootPath + '\\\\config\\\\test_data.yaml'\nself.config = YamlManage(self.file_path)\nself.conf = {}",
"self.conf['is_id'] = self.config.read_yaml('IS', 'is_id')\nself.conf['token'] = self.config.read_yaml('IS', 'token')\nself.conf['cookie'] = self.config.read_yaml('IS',... | <|body_start_0|>
self.logging = Logging()
self.file_path = rootPath + '\\config\\test_data.yaml'
self.config = YamlManage(self.file_path)
self.conf = {}
<|end_body_0|>
<|body_start_1|>
self.conf['is_id'] = self.config.read_yaml('IS', 'is_id')
self.conf['token'] = self.co... | GetCollectTestData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetCollectTestData:
def __init__(self):
"""初始化config，读取config文件"""
<|body_0|>
def get_is_test_data(self):
"""获取email的各种参数配置值"""
<|body_1|>
def set_is_test_data(self, title, key, value):
"""修改yaml数据"""
<|body_2|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_008159 | 1,389 | no_license | [
{
"docstring": "初始化config，读取config文件",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "获取email的各种参数配置值",
"name": "get_is_test_data",
"signature": "def get_is_test_data(self)"
},
{
"docstring": "修改yaml数据",
"name": "set_is_test_data",
"signature": "... | 3 | stack_v2_sparse_classes_30k_train_015277 | Implement the Python class `GetCollectTestData` described below.
Class description:
Implement the GetCollectTestData class.
Method signatures and docstrings:
- def __init__(self): 初始化config，读取config文件
- def get_is_test_data(self): 获取email的各种参数配置值
- def set_is_test_data(self, title, key, value): 修改yaml数据 | Implement the Python class `GetCollectTestData` described below.
Class description:
Implement the GetCollectTestData class.
Method signatures and docstrings:
- def __init__(self): 初始化config，读取config文件
- def get_is_test_data(self): 获取email的各种参数配置值
- def set_is_test_data(self, title, key, value): 修改yaml数据
<|skeleton|>... | 931179680d2c0bf9187060711c9f6dab94119024 | <|skeleton|>
class GetCollectTestData:
def __init__(self):
"""初始化config，读取config文件"""
<|body_0|>
def get_is_test_data(self):
"""获取email的各种参数配置值"""
<|body_1|>
def set_is_test_data(self, title, key, value):
"""修改yaml数据"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetCollectTestData:
def __init__(self):
"""初始化config，读取config文件"""
self.logging = Logging()
self.file_path = rootPath + '\\config\\test_data.yaml'
self.config = YamlManage(self.file_path)
self.conf = {}
def get_is_test_data(self):
"""获取email的各种参数配置值"""
... | the_stack_v2_python_sparse | config/get_collect_test_data.py | zhoululululu/Automation | train | 0 | |
d9fc0d6d5d99ddfbc7a0008ea777587b6b0222ad | [
"l = len(nums)\ndp = [[0] * l for _ in range(l)]\nfor i in range(l):\n dp[i][i] = nums[i]\n for j in range(i + 1, l):\n dp[i][j] = dp[i][j - 1] + nums[j]\nprint(np.array(dp))\nself.dicts = {}\n\ndef dfs(start, end, m):\n if (start, end, m) in self.dicts:\n return self.dicts[start, end, m]\n ... | <|body_start_0|>
l = len(nums)
dp = [[0] * l for _ in range(l)]
for i in range(l):
dp[i][i] = nums[i]
for j in range(i + 1, l):
dp[i][j] = dp[i][j - 1] + nums[j]
print(np.array(dp))
self.dicts = {}
def dfs(start, end, m):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def splitArray(self, nums, m):
""":type nums: List[int] :type m: int :rtype: int"""
<|body_0|>
def splitArray_1(self, nums, m):
""":type nums: List[int] :type m: int :rtype: int 7185 ms"""
<|body_1|>
def splitArray_1(self, nums, m):
"""... | stack_v2_sparse_classes_36k_train_008160 | 3,238 | no_license | [
{
"docstring": ":type nums: List[int] :type m: int :rtype: int",
"name": "splitArray",
"signature": "def splitArray(self, nums, m)"
},
{
"docstring": ":type nums: List[int] :type m: int :rtype: int 7185 ms",
"name": "splitArray_1",
"signature": "def splitArray_1(self, nums, m)"
},
{
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def splitArray(self, nums, m): :type nums: List[int] :type m: int :rtype: int
- def splitArray_1(self, nums, m): :type nums: List[int] :type m: int :rtype: int 7185 ms
- def spli... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def splitArray(self, nums, m): :type nums: List[int] :type m: int :rtype: int
- def splitArray_1(self, nums, m): :type nums: List[int] :type m: int :rtype: int 7185 ms
- def spli... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def splitArray(self, nums, m):
""":type nums: List[int] :type m: int :rtype: int"""
<|body_0|>
def splitArray_1(self, nums, m):
""":type nums: List[int] :type m: int :rtype: int 7185 ms"""
<|body_1|>
def splitArray_1(self, nums, m):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def splitArray(self, nums, m):
""":type nums: List[int] :type m: int :rtype: int"""
l = len(nums)
dp = [[0] * l for _ in range(l)]
for i in range(l):
dp[i][i] = nums[i]
for j in range(i + 1, l):
dp[i][j] = dp[i][j - 1] + nums[j]... | the_stack_v2_python_sparse | SplitArrayLargestSum_HARD_410.py | 953250587/leetcode-python | train | 2 | |
9aef17d931bfd23878bbfde96a718598428b59ca | [
"nums.sort()\nwant = 1\nfor i in range(0, len(nums)):\n if nums[i] == want:\n want += 1\n elif want < nums[i]:\n return want\nreturn want",
"len_nums = len(nums)\nfor i in range(len_nums):\n while 1 <= nums[i] <= len_nums and nums[i] != nums[nums[i] - 1]:\n c = nums[i]\n nums[... | <|body_start_0|>
nums.sort()
want = 1
for i in range(0, len(nums)):
if nums[i] == want:
want += 1
elif want < nums[i]:
return want
return want
<|end_body_0|>
<|body_start_1|>
len_nums = len(nums)
for i in range(len_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def firstMissingPositive(self, nums: list) -> int:
"""不满足题目要求"""
<|body_0|>
def firstMissingPositive(self, nums: list) -> int:
"""置换方法"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
nums.sort()
want = 1
for i in range(0, l... | stack_v2_sparse_classes_36k_train_008161 | 859 | no_license | [
{
"docstring": "不满足题目要求",
"name": "firstMissingPositive",
"signature": "def firstMissingPositive(self, nums: list) -> int"
},
{
"docstring": "置换方法",
"name": "firstMissingPositive",
"signature": "def firstMissingPositive(self, nums: list) -> int"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def firstMissingPositive(self, nums: list) -> int: 不满足题目要求
- def firstMissingPositive(self, nums: list) -> int: 置换方法 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def firstMissingPositive(self, nums: list) -> int: 不满足题目要求
- def firstMissingPositive(self, nums: list) -> int: 置换方法
<|skeleton|>
class Solution:
def firstMissingPositive(s... | cb3587242195bb3f2626231af2da13b90945a4d5 | <|skeleton|>
class Solution:
def firstMissingPositive(self, nums: list) -> int:
"""不满足题目要求"""
<|body_0|>
def firstMissingPositive(self, nums: list) -> int:
"""置换方法"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def firstMissingPositive(self, nums: list) -> int:
"""不满足题目要求"""
nums.sort()
want = 1
for i in range(0, len(nums)):
if nums[i] == want:
want += 1
elif want < nums[i]:
return want
return want
def firs... | the_stack_v2_python_sparse | leetcode/py36/缺失的第一个正数.py | lionheartStark/sword_towards_offer | train | 0 | |
536f92fbd41caf1fe602b81c53763013c7bb1888 | [
"try:\n natController = NatController()\n json_data = json.dumps(natController.get_floating_ip_private_address(id))\n resp = Response(json_data, status=200, mimetype='application/json')\n return resp\nexcept ValueError as ve:\n return Response(json.dumps(str(ve)), status=404, mimetype='application/js... | <|body_start_0|>
try:
natController = NatController()
json_data = json.dumps(natController.get_floating_ip_private_address(id))
resp = Response(json_data, status=200, mimetype='application/json')
return resp
except ValueError as ve:
return Resp... | Nat_FloatingIP_PrivateAddress | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Nat_FloatingIP_PrivateAddress:
def get(self, id=None):
"""Gets the Floating IP private address parameter"""
<|body_0|>
def put(self, id):
"""Update the Floating IP private address parameter"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
... | stack_v2_sparse_classes_36k_train_008162 | 7,153 | no_license | [
{
"docstring": "Gets the Floating IP private address parameter",
"name": "get",
"signature": "def get(self, id=None)"
},
{
"docstring": "Update the Floating IP private address parameter",
"name": "put",
"signature": "def put(self, id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010502 | Implement the Python class `Nat_FloatingIP_PrivateAddress` described below.
Class description:
Implement the Nat_FloatingIP_PrivateAddress class.
Method signatures and docstrings:
- def get(self, id=None): Gets the Floating IP private address parameter
- def put(self, id): Update the Floating IP private address param... | Implement the Python class `Nat_FloatingIP_PrivateAddress` described below.
Class description:
Implement the Nat_FloatingIP_PrivateAddress class.
Method signatures and docstrings:
- def get(self, id=None): Gets the Floating IP private address parameter
- def put(self, id): Update the Floating IP private address param... | 6070e3cb6bf957e04f5d8267db11f3296410e18e | <|skeleton|>
class Nat_FloatingIP_PrivateAddress:
def get(self, id=None):
"""Gets the Floating IP private address parameter"""
<|body_0|>
def put(self, id):
"""Update the Floating IP private address parameter"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Nat_FloatingIP_PrivateAddress:
def get(self, id=None):
"""Gets the Floating IP private address parameter"""
try:
natController = NatController()
json_data = json.dumps(natController.get_floating_ip_private_address(id))
resp = Response(json_data, status=200, ... | the_stack_v2_python_sparse | configuration-agent/nat/rest_api/resources/floating_ip.py | ReliableLion/frog4-configurable-vnf | train | 0 | |
bddfd93148b32af0286fd9c6bebc644a703a21d4 | [
"res = [0] * length\nfor op in updates:\n res[op[0]] += op[2]\n if op[1] + 1 < length:\n res[op[1] + 1] -= op[2]\ntemp = 0\nfor i in xrange(len(res)):\n temp += res[i]\n res[i] = temp\nreturn res",
"ls, arr = ([0] * length, array.array('l'))\narr.fromlist(ls)\nfor op in updates:\n for i in x... | <|body_start_0|>
res = [0] * length
for op in updates:
res[op[0]] += op[2]
if op[1] + 1 < length:
res[op[1] + 1] -= op[2]
temp = 0
for i in xrange(len(res)):
temp += res[i]
res[i] = temp
return res
<|end_body_0|>
<|... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getModifiedArray(self, length, updates):
""":type length: int :type updates: List[List[int]] :rtype: List[int]"""
<|body_0|>
def getModifiedArray2(self, length, updates):
""":type length: int :type updates: List[List[int]] :rtype: List[int]"""
<... | stack_v2_sparse_classes_36k_train_008163 | 1,409 | no_license | [
{
"docstring": ":type length: int :type updates: List[List[int]] :rtype: List[int]",
"name": "getModifiedArray",
"signature": "def getModifiedArray(self, length, updates)"
},
{
"docstring": ":type length: int :type updates: List[List[int]] :rtype: List[int]",
"name": "getModifiedArray2",
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getModifiedArray(self, length, updates): :type length: int :type updates: List[List[int]] :rtype: List[int]
- def getModifiedArray2(self, length, updates): :type length: int ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getModifiedArray(self, length, updates): :type length: int :type updates: List[List[int]] :rtype: List[int]
- def getModifiedArray2(self, length, updates): :type length: int ... | b4da922c4e8406c486760639b71e3ec50283ca43 | <|skeleton|>
class Solution:
def getModifiedArray(self, length, updates):
""":type length: int :type updates: List[List[int]] :rtype: List[int]"""
<|body_0|>
def getModifiedArray2(self, length, updates):
""":type length: int :type updates: List[List[int]] :rtype: List[int]"""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def getModifiedArray(self, length, updates):
""":type length: int :type updates: List[List[int]] :rtype: List[int]"""
res = [0] * length
for op in updates:
res[op[0]] += op[2]
if op[1] + 1 < length:
res[op[1] + 1] -= op[2]
temp ... | the_stack_v2_python_sparse | old_session/session_1/_370/_370_range_addition.py | YJL33/LeetCode | train | 3 | |
05c3f4ce92dd0f6f9403bcad31ca149d5642373e | [
"task_configs = find_all_files(path, extension=name_task_config)\nassert len(task_configs) == 1\nwith open(task_configs[0]) as config_file:\n config = json.load(config_file)\n gene = config.get('{cls_network}.gene')\n gene = split(gene, int)\n data_set = get_dataset_from_json(task_configs[0], fake=True)... | <|body_start_0|>
task_configs = find_all_files(path, extension=name_task_config)
assert len(task_configs) == 1
with open(task_configs[0]) as config_file:
config = json.load(config_file)
gene = config.get('{cls_network}.gene')
gene = split(gene, int)
... | go through a directory of training save-dirs, parsing the results of each single training run and its used architecture assumptions: - all networks are created as RetrainFromSearchUninasNetwork, so that their genes can be easily read | MiniNASParsedTabularBenchmark | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MiniNASParsedTabularBenchmark:
"""go through a directory of training save-dirs, parsing the results of each single training run and its used architecture assumptions: - all networks are created as RetrainFromSearchUninasNetwork, so that their genes can be easily read"""
def make_from_single_... | stack_v2_sparse_classes_36k_train_008164 | 7,134 | permissive | [
{
"docstring": "creating a mini result by parsing a training process",
"name": "make_from_single_dir",
"signature": "def make_from_single_dir(cls, path: str, space_name: str, arch_index: int) -> MiniResult"
},
{
"docstring": "creating a mini bench dataset by parsing multiple training processes",... | 2 | null | Implement the Python class `MiniNASParsedTabularBenchmark` described below.
Class description:
go through a directory of training save-dirs, parsing the results of each single training run and its used architecture assumptions: - all networks are created as RetrainFromSearchUninasNetwork, so that their genes can be ea... | Implement the Python class `MiniNASParsedTabularBenchmark` described below.
Class description:
go through a directory of training save-dirs, parsing the results of each single training run and its used architecture assumptions: - all networks are created as RetrainFromSearchUninasNetwork, so that their genes can be ea... | 06729b9cf517ec416fb798ae387c5bd9c3a278ac | <|skeleton|>
class MiniNASParsedTabularBenchmark:
"""go through a directory of training save-dirs, parsing the results of each single training run and its used architecture assumptions: - all networks are created as RetrainFromSearchUninasNetwork, so that their genes can be easily read"""
def make_from_single_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MiniNASParsedTabularBenchmark:
"""go through a directory of training save-dirs, parsing the results of each single training run and its used architecture assumptions: - all networks are created as RetrainFromSearchUninasNetwork, so that their genes can be easily read"""
def make_from_single_dir(cls, path... | the_stack_v2_python_sparse | uninas/optimization/benchmarks/mini/tabular_parsed.py | MLDL/uninas | train | 0 |
d0c44c99119ac2e02260ff2f0b0d23a3c6d45be4 | [
"super().__init__()\ntotal_size = input_size + prev_input_size\nself.proj = nn.Linear(total_size, input_size)\nself.transform = nn.Linear(total_size, input_size)\nself.transform.bias.data.fill_(-2.0)",
"concat_inputs = torch.cat((current, previous), 1)\nproj_result = F.relu(self.proj(concat_inputs))\nproj_gate = ... | <|body_start_0|>
super().__init__()
total_size = input_size + prev_input_size
self.proj = nn.Linear(total_size, input_size)
self.transform = nn.Linear(total_size, input_size)
self.transform.bias.data.fill_(-2.0)
<|end_body_0|>
<|body_start_1|>
concat_inputs = torch.cat((... | The Highway update layer from [srivastava2015]_. .. [srivastava2015] Srivastava, R. K., *et al.* (2015). `Highway Networks <http://arxiv.org/abs/1505.00387>`_. *arXiv*, 1505.00387. | Highway | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Highway:
"""The Highway update layer from [srivastava2015]_. .. [srivastava2015] Srivastava, R. K., *et al.* (2015). `Highway Networks <http://arxiv.org/abs/1505.00387>`_. *arXiv*, 1505.00387."""
def __init__(self, input_size: int, prev_input_size: int):
"""Instantiate the Highway up... | stack_v2_sparse_classes_36k_train_008165 | 25,672 | no_license | [
{
"docstring": "Instantiate the Highway update layer. :param input_size: Current representation size. :param prev_input_size: Size of the representation obtained by the previous convolutional layer.",
"name": "__init__",
"signature": "def __init__(self, input_size: int, prev_input_size: int)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_019883 | Implement the Python class `Highway` described below.
Class description:
The Highway update layer from [srivastava2015]_. .. [srivastava2015] Srivastava, R. K., *et al.* (2015). `Highway Networks <http://arxiv.org/abs/1505.00387>`_. *arXiv*, 1505.00387.
Method signatures and docstrings:
- def __init__(self, input_siz... | Implement the Python class `Highway` described below.
Class description:
The Highway update layer from [srivastava2015]_. .. [srivastava2015] Srivastava, R. K., *et al.* (2015). `Highway Networks <http://arxiv.org/abs/1505.00387>`_. *arXiv*, 1505.00387.
Method signatures and docstrings:
- def __init__(self, input_siz... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class Highway:
"""The Highway update layer from [srivastava2015]_. .. [srivastava2015] Srivastava, R. K., *et al.* (2015). `Highway Networks <http://arxiv.org/abs/1505.00387>`_. *arXiv*, 1505.00387."""
def __init__(self, input_size: int, prev_input_size: int):
"""Instantiate the Highway up... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Highway:
"""The Highway update layer from [srivastava2015]_. .. [srivastava2015] Srivastava, R. K., *et al.* (2015). `Highway Networks <http://arxiv.org/abs/1505.00387>`_. *arXiv*, 1505.00387."""
def __init__(self, input_size: int, prev_input_size: int):
"""Instantiate the Highway update layer. :... | the_stack_v2_python_sparse | generated/test_AstraZeneca_chemicalx.py | jansel/pytorch-jit-paritybench | train | 35 |
c0b879709e1cc761c4a4db22824405de09a147cf | [
"self.k = k\nself.nums = nums\nheapq.heapify(self.nums)\nwhile len(self.nums) > self.k:\n heapq.heappop(self.nums)",
"if len(self.nums) < self.k:\n heapq.heappush(self.nums, val)\nelif val > self.nums[0]:\n heapq.heappushpop(self.nums, val)\nreturn self.nums[0]"
] | <|body_start_0|>
self.k = k
self.nums = nums
heapq.heapify(self.nums)
while len(self.nums) > self.k:
heapq.heappop(self.nums)
<|end_body_0|>
<|body_start_1|>
if len(self.nums) < self.k:
heapq.heappush(self.nums, val)
elif val > self.nums[0]:
... | KthLargest3 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KthLargest3:
def __init__(self, k: int, nums: List[int]):
"""Time complexity: O(N), where N is the number of elements in nums"""
<|body_0|>
def add(self, val: int) -> int:
"""Time complexity: O(logN), where N is the number of elements in nums"""
<|body_1|>
<... | stack_v2_sparse_classes_36k_train_008166 | 6,443 | no_license | [
{
"docstring": "Time complexity: O(N), where N is the number of elements in nums",
"name": "__init__",
"signature": "def __init__(self, k: int, nums: List[int])"
},
{
"docstring": "Time complexity: O(logN), where N is the number of elements in nums",
"name": "add",
"signature": "def add(... | 2 | stack_v2_sparse_classes_30k_test_000054 | Implement the Python class `KthLargest3` described below.
Class description:
Implement the KthLargest3 class.
Method signatures and docstrings:
- def __init__(self, k: int, nums: List[int]): Time complexity: O(N), where N is the number of elements in nums
- def add(self, val: int) -> int: Time complexity: O(logN), wh... | Implement the Python class `KthLargest3` described below.
Class description:
Implement the KthLargest3 class.
Method signatures and docstrings:
- def __init__(self, k: int, nums: List[int]): Time complexity: O(N), where N is the number of elements in nums
- def add(self, val: int) -> int: Time complexity: O(logN), wh... | 642e6dd2c3cd65704c90d6e06a392bdae2ddd644 | <|skeleton|>
class KthLargest3:
def __init__(self, k: int, nums: List[int]):
"""Time complexity: O(N), where N is the number of elements in nums"""
<|body_0|>
def add(self, val: int) -> int:
"""Time complexity: O(logN), where N is the number of elements in nums"""
<|body_1|>
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KthLargest3:
def __init__(self, k: int, nums: List[int]):
"""Time complexity: O(N), where N is the number of elements in nums"""
self.k = k
self.nums = nums
heapq.heapify(self.nums)
while len(self.nums) > self.k:
heapq.heappop(self.nums)
def add(self, v... | the_stack_v2_python_sparse | LeetCode/703.Kth_Largest_Element_in_a_Stream.py | viiicky/Problem-Solving | train | 0 | |
adf2414bf67dd17b494b72bb24106ec1192b8ae6 | [
"self.compare_angle = None\nself._centroid_vectors = None\nself.ref_curve = self._calc_dist_curve(training_mask)\nself._ref_angle = np.arctan2(*self._centroid_vectors[0])",
"input_curve = self._calc_dist_curve(query_mask)\ncost_landscape = np.abs(self.ref_curve[:, None] - input_curve[None, :])\nn_eval = min(500, ... | <|body_start_0|>
self.compare_angle = None
self._centroid_vectors = None
self.ref_curve = self._calc_dist_curve(training_mask)
self._ref_angle = np.arctan2(*self._centroid_vectors[0])
<|end_body_0|>
<|body_start_1|>
input_curve = self._calc_dist_curve(query_mask)
cost_la... | OutlineComparer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OutlineComparer:
def __init__(self, training_mask):
"""Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then the outline distance will be small. :param training_mask: Binary mask with a single blob to use as the ref... | stack_v2_sparse_classes_36k_train_008167 | 22,952 | no_license | [
{
"docstring": "Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then the outline distance will be small. :param training_mask: Binary mask with a single blob to use as the reference/training object. :type training_mask: np.ndarray",
"... | 3 | stack_v2_sparse_classes_30k_train_014773 | Implement the Python class `OutlineComparer` described below.
Class description:
Implement the OutlineComparer class.
Method signatures and docstrings:
- def __init__(self, training_mask): Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then th... | Implement the Python class `OutlineComparer` described below.
Class description:
Implement the OutlineComparer class.
Method signatures and docstrings:
- def __init__(self, training_mask): Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then th... | 457d839a4ae5401c76a46616c622f2728f56f25b | <|skeleton|>
class OutlineComparer:
def __init__(self, training_mask):
"""Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then the outline distance will be small. :param training_mask: Binary mask with a single blob to use as the ref... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OutlineComparer:
def __init__(self, training_mask):
"""Create object for comparing blobs/connected components outlines, meaning if same geometrical outline, including rotation, then the outline distance will be small. :param training_mask: Binary mask with a single blob to use as the reference/trainin... | the_stack_v2_python_sparse | AdvancedVisionTools.py | AndersDHenriksen/Vision | train | 0 | |
d9c2eb175d70fb3920a078fc581b3fa19f51beac | [
"self.copy_task_uid = copy_task_uid\nself.job_run_id = job_run_id\nself.task_id_list = task_id_list",
"if dictionary is None:\n return None\ncopy_task_uid = cohesity_management_sdk.models.universal_id.UniversalId.from_dictionary(dictionary.get('copyTaskUid')) if dictionary.get('copyTaskUid') else None\njob_run... | <|body_start_0|>
self.copy_task_uid = copy_task_uid
self.job_run_id = job_run_id
self.task_id_list = task_id_list
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
copy_task_uid = cohesity_management_sdk.models.universal_id.UniversalId.from_dictionar... | Implementation of the 'CancelProtectionJobRunParam' model. TODO: type description here. Attributes: copy_task_uid (UniversalId): CopyTaskUid is the Uid of a copy task. If a particular copy task is to be cancelled, this field should be set to the id of that particular copy task. For example, if replication task is to be... | CancelProtectionJobRunParam | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CancelProtectionJobRunParam:
"""Implementation of the 'CancelProtectionJobRunParam' model. TODO: type description here. Attributes: copy_task_uid (UniversalId): CopyTaskUid is the Uid of a copy task. If a particular copy task is to be cancelled, this field should be set to the id of that particul... | stack_v2_sparse_classes_36k_train_008168 | 2,888 | permissive | [
{
"docstring": "Constructor for the CancelProtectionJobRunParam class",
"name": "__init__",
"signature": "def __init__(self, copy_task_uid=None, job_run_id=None, task_id_list=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary... | 2 | stack_v2_sparse_classes_30k_train_020498 | Implement the Python class `CancelProtectionJobRunParam` described below.
Class description:
Implementation of the 'CancelProtectionJobRunParam' model. TODO: type description here. Attributes: copy_task_uid (UniversalId): CopyTaskUid is the Uid of a copy task. If a particular copy task is to be cancelled, this field s... | Implement the Python class `CancelProtectionJobRunParam` described below.
Class description:
Implementation of the 'CancelProtectionJobRunParam' model. TODO: type description here. Attributes: copy_task_uid (UniversalId): CopyTaskUid is the Uid of a copy task. If a particular copy task is to be cancelled, this field s... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class CancelProtectionJobRunParam:
"""Implementation of the 'CancelProtectionJobRunParam' model. TODO: type description here. Attributes: copy_task_uid (UniversalId): CopyTaskUid is the Uid of a copy task. If a particular copy task is to be cancelled, this field should be set to the id of that particul... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CancelProtectionJobRunParam:
"""Implementation of the 'CancelProtectionJobRunParam' model. TODO: type description here. Attributes: copy_task_uid (UniversalId): CopyTaskUid is the Uid of a copy task. If a particular copy task is to be cancelled, this field should be set to the id of that particular copy task.... | the_stack_v2_python_sparse | cohesity_management_sdk/models/cancel_protection_job_run_param.py | cohesity/management-sdk-python | train | 24 |
59c06aa50a5ab697676be284b760258ba33eca33 | [
"super(Encoder, self).__init__()\nself.N = N\nself.dm = dm\nself.embedding = tf.keras.layers.Embedding(input_vocab, dm)\nself.positional_encoding = positional_encoding(max_seq_len, self.dm)\nself.blocks = [EncoderBlock(dm, h, hidden, drop_rate) for _ in range(N)]\nself.dropout = tf.keras.layers.Dropout(drop_rate)",... | <|body_start_0|>
super(Encoder, self).__init__()
self.N = N
self.dm = dm
self.embedding = tf.keras.layers.Embedding(input_vocab, dm)
self.positional_encoding = positional_encoding(max_seq_len, self.dm)
self.blocks = [EncoderBlock(dm, h, hidden, drop_rate) for _ in range(N... | [summary] Args: tf ([type]): [description] | Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
"""[summary] Args: tf ([type]): [description]"""
def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1):
"""[summary] Args: N ([type]): [description] dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] input_vocab ([type... | stack_v2_sparse_classes_36k_train_008169 | 1,989 | no_license | [
{
"docstring": "[summary] Args: N ([type]): [description] dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] input_vocab ([type]): [description] max_seq_len ([type]): [description] drop_rate (float, optional): [description]. Defaults to 0.1.",
"name": "__init__",
"signat... | 2 | stack_v2_sparse_classes_30k_train_012554 | Implement the Python class `Encoder` described below.
Class description:
[summary] Args: tf ([type]): [description]
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1): [summary] Args: N ([type]): [description] dm ([type]): [description] h ([type]): [descr... | Implement the Python class `Encoder` described below.
Class description:
[summary] Args: tf ([type]): [description]
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1): [summary] Args: N ([type]): [description] dm ([type]): [description] h ([type]): [descr... | 5f86dee95f4d1c32014d0d74a368f342ff3ce6f7 | <|skeleton|>
class Encoder:
"""[summary] Args: tf ([type]): [description]"""
def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1):
"""[summary] Args: N ([type]): [description] dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] input_vocab ([type... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Encoder:
"""[summary] Args: tf ([type]): [description]"""
def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1):
"""[summary] Args: N ([type]): [description] dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] input_vocab ([type]): [descript... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/9-transformer_encoder.py | d1sd41n/holbertonschool-machine_learning | train | 0 |
4c23cf231898a7466e100634ac91ab1337b588f5 | [
"frame_id = 1\nfor img in images:\n img_name = folderName + '{}'.format(frame_id) + imgType\n frame_id = frame_id + 1\n cv2.imwrite(img_name, img)",
"files = []\nimages = []\nfor r, d, f in os.walk(folderName):\n for file in f:\n if imgType in file:\n files.append(os.path.join(r, fil... | <|body_start_0|>
frame_id = 1
for img in images:
img_name = folderName + '{}'.format(frame_id) + imgType
frame_id = frame_id + 1
cv2.imwrite(img_name, img)
<|end_body_0|>
<|body_start_1|>
files = []
images = []
for r, d, f in os.walk(folderNam... | ImageIO | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageIO:
def WriteImagesToFolder(images, folderName, imgType='.jpg'):
"""Writes a list of images to folder Parameters ---------- images : list list of images folderName : str location for the images to be saved imgType : str, optional type of image format)"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_008170 | 1,716 | permissive | [
{
"docstring": "Writes a list of images to folder Parameters ---------- images : list list of images folderName : str location for the images to be saved imgType : str, optional type of image format)",
"name": "WriteImagesToFolder",
"signature": "def WriteImagesToFolder(images, folderName, imgType='.jpg... | 2 | null | Implement the Python class `ImageIO` described below.
Class description:
Implement the ImageIO class.
Method signatures and docstrings:
- def WriteImagesToFolder(images, folderName, imgType='.jpg'): Writes a list of images to folder Parameters ---------- images : list list of images folderName : str location for the ... | Implement the Python class `ImageIO` described below.
Class description:
Implement the ImageIO class.
Method signatures and docstrings:
- def WriteImagesToFolder(images, folderName, imgType='.jpg'): Writes a list of images to folder Parameters ---------- images : list list of images folderName : str location for the ... | 8d4b9c3e058765c3d633038e9d6954094347d202 | <|skeleton|>
class ImageIO:
def WriteImagesToFolder(images, folderName, imgType='.jpg'):
"""Writes a list of images to folder Parameters ---------- images : list list of images folderName : str location for the images to be saved imgType : str, optional type of image format)"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageIO:
def WriteImagesToFolder(images, folderName, imgType='.jpg'):
"""Writes a list of images to folder Parameters ---------- images : list list of images folderName : str location for the images to be saved imgType : str, optional type of image format)"""
frame_id = 1
for img in im... | the_stack_v2_python_sparse | DataProcessing/ImageIO.py | abhishekmadhu/FrontNetPorting | train | 1 | |
dfd72a4fd51a6d709e15c38a33b68236d16102c4 | [
"stack = []\nres = 0\nfor i, h in enumerate(height):\n while stack and height[stack[-1]] < h:\n buttom = stack.pop()\n if not stack:\n break\n left = stack[-1]\n res += (i - left - 1) * (min(h, height[left]) - height[buttom])\n stack.append(i)\nreturn res",
"n = len(he... | <|body_start_0|>
stack = []
res = 0
for i, h in enumerate(height):
while stack and height[stack[-1]] < h:
buttom = stack.pop()
if not stack:
break
left = stack[-1]
res += (i - left - 1) * (min(h, heig... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def trap1(self, height: List[int]) -> int:
"""思路：单调栈 @param height: @return:"""
<|body_0|>
def trap2(self, height: List[int]) -> int:
"""思路：动态规划法 :param height: :return:"""
<|body_1|>
def trap3(self, height: List[int]) -> int:
"""思路：双指针... | stack_v2_sparse_classes_36k_train_008171 | 4,311 | no_license | [
{
"docstring": "思路：单调栈 @param height: @return:",
"name": "trap1",
"signature": "def trap1(self, height: List[int]) -> int"
},
{
"docstring": "思路：动态规划法 :param height: :return:",
"name": "trap2",
"signature": "def trap2(self, height: List[int]) -> int"
},
{
"docstring": "思路：双指针 :pa... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def trap1(self, height: List[int]) -> int: 思路：单调栈 @param height: @return:
- def trap2(self, height: List[int]) -> int: 思路：动态规划法 :param height: :return:
- def trap3(self, height: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def trap1(self, height: List[int]) -> int: 思路：单调栈 @param height: @return:
- def trap2(self, height: List[int]) -> int: 思路：动态规划法 :param height: :return:
- def trap3(self, height: ... | e43ee86c5a8cdb808da09b4b6138e10275abadb5 | <|skeleton|>
class Solution:
def trap1(self, height: List[int]) -> int:
"""思路：单调栈 @param height: @return:"""
<|body_0|>
def trap2(self, height: List[int]) -> int:
"""思路：动态规划法 :param height: :return:"""
<|body_1|>
def trap3(self, height: List[int]) -> int:
"""思路：双指针... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def trap1(self, height: List[int]) -> int:
"""思路：单调栈 @param height: @return:"""
stack = []
res = 0
for i, h in enumerate(height):
while stack and height[stack[-1]] < h:
buttom = stack.pop()
if not stack:
... | the_stack_v2_python_sparse | LeetCode/栈/单调栈(Monotone Stack)/42. 接雨水.py | yiming1012/MyLeetCode | train | 2 | |
14d3b3aaa387ccf601f709d393e28467833fc526 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn TodoTask()",
"from .attachment_base import AttachmentBase\nfrom .attachment_session import AttachmentSession\nfrom .checklist_item import ChecklistItem\nfrom .date_time_time_zone import DateTimeTimeZone\nfrom .entity import Entity\nfro... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return TodoTask()
<|end_body_0|>
<|body_start_1|>
from .attachment_base import AttachmentBase
from .attachment_session import AttachmentSession
from .checklist_item import ChecklistItem... | TodoTask | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TodoTask:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TodoTask:
"""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: TodoTask... | stack_v2_sparse_classes_36k_train_008172 | 9,869 | 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: TodoTask",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(pars... | 3 | null | Implement the Python class `TodoTask` described below.
Class description:
Implement the TodoTask class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TodoTask: Creates a new instance of the appropriate class based on discriminator value Args: parse_no... | Implement the Python class `TodoTask` described below.
Class description:
Implement the TodoTask class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TodoTask: Creates a new instance of the appropriate class based on discriminator value Args: parse_no... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class TodoTask:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TodoTask:
"""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: TodoTask... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TodoTask:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TodoTask:
"""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: TodoTask"""
if... | the_stack_v2_python_sparse | msgraph/generated/models/todo_task.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
fd6fa49d49e33477589e8e0c933ffc3972c52073 | [
"i = 0\ndigits = ''\nret = NestedInteger()\nwhile i < len(s):\n if '0' <= s[i] <= '9' or s[i] == '-':\n digits += s[i]\n else:\n if digits:\n num = int(digits)\n buf = NestedInteger(num)\n ret.add(buf)\n if s[i] == '[':\n start = i\n ... | <|body_start_0|>
i = 0
digits = ''
ret = NestedInteger()
while i < len(s):
if '0' <= s[i] <= '9' or s[i] == '-':
digits += s[i]
else:
if digits:
num = int(digits)
buf = NestedInteger(num)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def buff(self, s):
"""# using DFS # find all single integers # as for the list in it , call self to manage :type s: str :rtype: NestedInteger"""
<|body_0|>
def deserialize(self, s):
""":type s: str :rtype: NestedInteger"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k_train_008173 | 2,855 | no_license | [
{
"docstring": "# using DFS # find all single integers # as for the list in it , call self to manage :type s: str :rtype: NestedInteger",
"name": "buff",
"signature": "def buff(self, s)"
},
{
"docstring": ":type s: str :rtype: NestedInteger",
"name": "deserialize",
"signature": "def dese... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def buff(self, s): # using DFS # find all single integers # as for the list in it , call self to manage :type s: str :rtype: NestedInteger
- def deserialize(self, s): :type s: st... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def buff(self, s): # using DFS # find all single integers # as for the list in it , call self to manage :type s: str :rtype: NestedInteger
- def deserialize(self, s): :type s: st... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def buff(self, s):
"""# using DFS # find all single integers # as for the list in it , call self to manage :type s: str :rtype: NestedInteger"""
<|body_0|>
def deserialize(self, s):
""":type s: str :rtype: NestedInteger"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def buff(self, s):
"""# using DFS # find all single integers # as for the list in it , call self to manage :type s: str :rtype: NestedInteger"""
i = 0
digits = ''
ret = NestedInteger()
while i < len(s):
if '0' <= s[i] <= '9' or s[i] == '-':
... | the_stack_v2_python_sparse | python/leetcode/385_Mini_Parser.py | bobcaoge/my-code | train | 0 | |
83dad2e11cc4c94614bdff547c2beef2e1b4d848 | [
"try:\n params = request._serialize()\n headers = request.headers\n body = self.call('CreatePrefetchTask', params, headers=headers)\n response = json.loads(body)\n model = models.CreatePrefetchTaskResponse()\n model._deserialize(response['Response'])\n return model\nexcept Exception as e:\n ... | <|body_start_0|>
try:
params = request._serialize()
headers = request.headers
body = self.call('CreatePrefetchTask', params, headers=headers)
response = json.loads(body)
model = models.CreatePrefetchTaskResponse()
model._deserialize(respons... | TeoClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TeoClient:
def CreatePrefetchTask(self, request):
"""创建预热任务 :param request: Request instance for CreatePrefetchTask. :type request: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTaskRequest` :rtype: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTaskResponse`"""
... | stack_v2_sparse_classes_36k_train_008174 | 5,399 | permissive | [
{
"docstring": "创建预热任务 :param request: Request instance for CreatePrefetchTask. :type request: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTaskRequest` :rtype: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTaskResponse`",
"name": "CreatePrefetchTask",
"signature": "def CreatePrefet... | 5 | stack_v2_sparse_classes_30k_train_015883 | Implement the Python class `TeoClient` described below.
Class description:
Implement the TeoClient class.
Method signatures and docstrings:
- def CreatePrefetchTask(self, request): 创建预热任务 :param request: Request instance for CreatePrefetchTask. :type request: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTa... | Implement the Python class `TeoClient` described below.
Class description:
Implement the TeoClient class.
Method signatures and docstrings:
- def CreatePrefetchTask(self, request): 创建预热任务 :param request: Request instance for CreatePrefetchTask. :type request: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTa... | 6baf00a5a56ba58b6a1123423e0a1422d17a0201 | <|skeleton|>
class TeoClient:
def CreatePrefetchTask(self, request):
"""创建预热任务 :param request: Request instance for CreatePrefetchTask. :type request: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTaskRequest` :rtype: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTaskResponse`"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TeoClient:
def CreatePrefetchTask(self, request):
"""创建预热任务 :param request: Request instance for CreatePrefetchTask. :type request: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTaskRequest` :rtype: :class:`tencentcloud.teo.v20220106.models.CreatePrefetchTaskResponse`"""
try:
... | the_stack_v2_python_sparse | tencentcloud/teo/v20220106/teo_client.py | TencentCloud/tencentcloud-sdk-python | train | 594 | |
d4ec8f8729eecd0b281f940eee5246c88529ae16 | [
"ret = ''\nif root is None:\n return '#'\nret += str(root.val)\nret += ' ' + str(len(root.children))\nfor i in range(len(root.children)):\n ret += ' ' + self.serialize(root.children[i])\nreturn ret",
"def dfs(it):\n val = next(it)\n if val == '#':\n return None\n val = int(val)\n children... | <|body_start_0|>
ret = ''
if root is None:
return '#'
ret += str(root.val)
ret += ' ' + str(len(root.children))
for i in range(len(root.children)):
ret += ' ' + self.serialize(root.children[i])
return ret
<|end_body_0|>
<|body_start_1|>
de... | Codec3 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec3:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: Node"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_36k_train_008175 | 4,629 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: Node :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: Node",
"name": "deserialize",
"signature": "def deserialize(self, ... | 2 | null | Implement the Python class `Codec3` described below.
Class description:
Implement the Codec3 class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: Node :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: N... | Implement the Python class `Codec3` described below.
Class description:
Implement the Codec3 class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: Node :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: N... | 9190d3d178f1733aa226973757ee7e045b7bab00 | <|skeleton|>
class Codec3:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: Node"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec3:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
ret = ''
if root is None:
return '#'
ret += str(root.val)
ret += ' ' + str(len(root.children))
for i in range(len(root.children)):
re... | the_stack_v2_python_sparse | SerializeAndDeserializeN-aryTree.py | ellinx/LC-python | train | 1 | |
e9433dda7638aade544bd5e12915d806c35cff27 | [
"error_messages = {'incomplete': _('Must fill both latitude and longitude')}\nfields = [forms.FloatField(), forms.FloatField()]\nfor field in fields:\n field.error_messages = {}\nkwargs['widget'] = LocationWidget\nif 'max_length' in kwargs:\n del kwargs['max_length']\nsuper(LocationFormField, self).__init__(*... | <|body_start_0|>
error_messages = {'incomplete': _('Must fill both latitude and longitude')}
fields = [forms.FloatField(), forms.FloatField()]
for field in fields:
field.error_messages = {}
kwargs['widget'] = LocationWidget
if 'max_length' in kwargs:
del k... | Location field | LocationFormField | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LocationFormField:
"""Location field"""
def __init__(self, *args, **kwargs):
"""Class initialization method"""
<|body_0|>
def compress(self, data_list):
"""Data compression method"""
<|body_1|>
def clean(self, value):
"""Cleaning method"""
... | stack_v2_sparse_classes_36k_train_008176 | 6,288 | permissive | [
{
"docstring": "Class initialization method",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Data compression method",
"name": "compress",
"signature": "def compress(self, data_list)"
},
{
"docstring": "Cleaning method",
"name": "cle... | 3 | stack_v2_sparse_classes_30k_val_000822 | Implement the Python class `LocationFormField` described below.
Class description:
Location field
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Class initialization method
- def compress(self, data_list): Data compression method
- def clean(self, value): Cleaning method | Implement the Python class `LocationFormField` described below.
Class description:
Location field
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Class initialization method
- def compress(self, data_list): Data compression method
- def clean(self, value): Cleaning method
<|skeleton|>
class ... | be9d747b8ca4c5d18f9725b2dad08dba6119d810 | <|skeleton|>
class LocationFormField:
"""Location field"""
def __init__(self, *args, **kwargs):
"""Class initialization method"""
<|body_0|>
def compress(self, data_list):
"""Data compression method"""
<|body_1|>
def clean(self, value):
"""Cleaning method"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LocationFormField:
"""Location field"""
def __init__(self, *args, **kwargs):
"""Class initialization method"""
error_messages = {'incomplete': _('Must fill both latitude and longitude')}
fields = [forms.FloatField(), forms.FloatField()]
for field in fields:
fie... | the_stack_v2_python_sparse | sitetools/forms/fields.py | olivergs/django-sitetools | train | 0 |
89583538a29b38c97ca5d769ef8b17902833fb79 | [
"self.verify_mco_parameters(params)\ntfunc = partial(self.translated_function, func=func, params=params)\nx0, bounds = self.get_initial_and_bounds(params)\noptimization_result = scipy_optimize.minimize(tfunc, x0, method=self.algorithms, bounds=bounds)\noptimal_point = self.translate_array_to_mco(optimization_result... | <|body_start_0|>
self.verify_mco_parameters(params)
tfunc = partial(self.translated_function, func=func, params=params)
x0, bounds = self.get_initial_and_bounds(params)
optimization_result = scipy_optimize.minimize(tfunc, x0, method=self.algorithms, bounds=bounds)
optimal_point =... | Optimization of an objective function using scipy. | ScipyOptimizer | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScipyOptimizer:
"""Optimization of an objective function using scipy."""
def optimize_function(self, func, params):
"""Minimize the passed function. Parameters ---------- func: Callable The MCO function to optimize Takes a list of MCO parameter values. Should return a scalar (i.e. a ... | stack_v2_sparse_classes_36k_train_008177 | 9,020 | permissive | [
{
"docstring": "Minimize the passed function. Parameters ---------- func: Callable The MCO function to optimize Takes a list of MCO parameter values. Should return a scalar (i.e. a single-objective). If not the return (objectives) will be summed. params: list of MCOParameter The MCO parameter objects correspond... | 6 | stack_v2_sparse_classes_30k_train_003466 | Implement the Python class `ScipyOptimizer` described below.
Class description:
Optimization of an objective function using scipy.
Method signatures and docstrings:
- def optimize_function(self, func, params): Minimize the passed function. Parameters ---------- func: Callable The MCO function to optimize Takes a list... | Implement the Python class `ScipyOptimizer` described below.
Class description:
Optimization of an objective function using scipy.
Method signatures and docstrings:
- def optimize_function(self, func, params): Minimize the passed function. Parameters ---------- func: Callable The MCO function to optimize Takes a list... | 6106bec35d6ad2383138a35205cea44fe529a229 | <|skeleton|>
class ScipyOptimizer:
"""Optimization of an objective function using scipy."""
def optimize_function(self, func, params):
"""Minimize the passed function. Parameters ---------- func: Callable The MCO function to optimize Takes a list of MCO parameter values. Should return a scalar (i.e. a ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScipyOptimizer:
"""Optimization of an objective function using scipy."""
def optimize_function(self, func, params):
"""Minimize the passed function. Parameters ---------- func: Callable The MCO function to optimize Takes a list of MCO parameter values. Should return a scalar (i.e. a single-object... | the_stack_v2_python_sparse | force_bdss/mco/optimizers/scipy_optimizer.py | force-h2020/force-bdss | train | 2 |
f7829bf4c540f1c02bc6e0d73f3bd8422b888847 | [
"super().__init__(force_update=force_update, sleeping_time=sleeping_time)\nassert self.entity_provider is not None\nassert self.entity_schema is not None\nself.exchanges = exchanges\nif codes is None and code is not None:\n self.codes = [code]\nelse:\n self.codes = codes\nself.day_data = day_data\nself.entity... | <|body_start_0|>
super().__init__(force_update=force_update, sleeping_time=sleeping_time)
assert self.entity_provider is not None
assert self.entity_schema is not None
self.exchanges = exchanges
if codes is None and code is not None:
self.codes = [code]
else:
... | EntityEventRecorder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntityEventRecorder:
def __init__(self, force_update=False, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True) -> None:
""":param code: :param ignore_failed: :param entity_filters: :param exch... | stack_v2_sparse_classes_36k_train_008178 | 24,497 | permissive | [
{
"docstring": ":param code: :param ignore_failed: :param entity_filters: :param exchanges: :param entity_id: for record single entity :param entity_ids: set entity_ids or (entity_type,exchanges,codes) :param codes: :param day_data: one record per day,set to True if you want skip recording it when data of today... | 2 | stack_v2_sparse_classes_30k_test_000288 | Implement the Python class `EntityEventRecorder` described below.
Class description:
Implement the EntityEventRecorder class.
Method signatures and docstrings:
- def __init__(self, force_update=False, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filt... | Implement the Python class `EntityEventRecorder` described below.
Class description:
Implement the EntityEventRecorder class.
Method signatures and docstrings:
- def __init__(self, force_update=False, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filt... | 03aee869fd432bb933d59ba419401cfc11501392 | <|skeleton|>
class EntityEventRecorder:
def __init__(self, force_update=False, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True) -> None:
""":param code: :param ignore_failed: :param entity_filters: :param exch... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EntityEventRecorder:
def __init__(self, force_update=False, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True) -> None:
""":param code: :param ignore_failed: :param entity_filters: :param exchanges: :param ... | the_stack_v2_python_sparse | src/zvt/contract/recorder.py | zvtvz/zvt | train | 2,782 | |
1e7c09fbab53137d472dc26fb752c5fe458a4540 | [
"BaseResource.__init__(self, *args, **kw)\nself._rsc = eval(open('%s.txt' % self.basesourcefile, 'r').read().replace('\\r\\n', '\\n'))\nself._code = open('%s.py' % self.basesourcefile, 'r').read()",
"fle = open(os.path.join(basedir, self.name) + '.rsrc.py', 'w')\nlog.info(\"Writing '%s'\" % os.path.join(basedir, ... | <|body_start_0|>
BaseResource.__init__(self, *args, **kw)
self._rsc = eval(open('%s.txt' % self.basesourcefile, 'r').read().replace('\r\n', '\n'))
self._code = open('%s.py' % self.basesourcefile, 'r').read()
<|end_body_0|>
<|body_start_1|>
fle = open(os.path.join(basedir, self.name) + '... | Represents a Python Card resource object | Resource | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Resource:
"""Represents a Python Card resource object"""
def __init__(self, *args, **kw):
"""Initialize the PythonCard resource"""
<|body_0|>
def writeToFile(self, basedir, write_code=0):
"""Write ourselves out to a directory"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k_train_008179 | 2,155 | permissive | [
{
"docstring": "Initialize the PythonCard resource",
"name": "__init__",
"signature": "def __init__(self, *args, **kw)"
},
{
"docstring": "Write ourselves out to a directory",
"name": "writeToFile",
"signature": "def writeToFile(self, basedir, write_code=0)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007791 | Implement the Python class `Resource` described below.
Class description:
Represents a Python Card resource object
Method signatures and docstrings:
- def __init__(self, *args, **kw): Initialize the PythonCard resource
- def writeToFile(self, basedir, write_code=0): Write ourselves out to a directory | Implement the Python class `Resource` described below.
Class description:
Represents a Python Card resource object
Method signatures and docstrings:
- def __init__(self, *args, **kw): Initialize the PythonCard resource
- def writeToFile(self, basedir, write_code=0): Write ourselves out to a directory
<|skeleton|>
cl... | 847ce71e85093ea5ee668ec61dbfba760ffa6bbd | <|skeleton|>
class Resource:
"""Represents a Python Card resource object"""
def __init__(self, *args, **kw):
"""Initialize the PythonCard resource"""
<|body_0|>
def writeToFile(self, basedir, write_code=0):
"""Write ourselves out to a directory"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Resource:
"""Represents a Python Card resource object"""
def __init__(self, *args, **kw):
"""Initialize the PythonCard resource"""
BaseResource.__init__(self, *args, **kw)
self._rsc = eval(open('%s.txt' % self.basesourcefile, 'r').read().replace('\r\n', '\n'))
self._code =... | the_stack_v2_python_sparse | vb2py/targets/pythoncard/resource.py | rayzamgh/sumurProjection | train | 1 |
de87c056d08750b240a3d876316ea1ec4c6b5b5b | [
"super().__init__(**kwargs)\nself.blocks_1 = []\nself.blocks_2 = []\nfor i in range(len(dilation_rate)):\n self.blocks_1.append([TFReflectionPad1d((kernel_size - 1) // 2 * dilation_rate[i]), tf.keras.layers.Conv1D(filters=filters, kernel_size=kernel_size, dilation_rate=dilation_rate[i], use_bias=use_bias)])\n ... | <|body_start_0|>
super().__init__(**kwargs)
self.blocks_1 = []
self.blocks_2 = []
for i in range(len(dilation_rate)):
self.blocks_1.append([TFReflectionPad1d((kernel_size - 1) // 2 * dilation_rate[i]), tf.keras.layers.Conv1D(filters=filters, kernel_size=kernel_size, dilation_... | Tensorflow Hifigan resblock 1 module. | TFHifiResBlock | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TFHifiResBlock:
"""Tensorflow Hifigan resblock 1 module."""
def __init__(self, kernel_size, filters, dilation_rate, use_bias, nonlinear_activation, nonlinear_activation_params, is_weight_norm, initializer_seed, **kwargs):
"""Initialize TFHifiResBlock module. Args: kernel_size (int): ... | stack_v2_sparse_classes_36k_train_008180 | 13,272 | permissive | [
{
"docstring": "Initialize TFHifiResBlock module. Args: kernel_size (int): Kernel size. filters (int): Number of filters. dilation_rate (list): List dilation rate. use_bias (bool): Whether to add bias parameter in convolution layers. nonlinear_activation (str): Activation function module name. nonlinear_activat... | 3 | stack_v2_sparse_classes_30k_train_005200 | Implement the Python class `TFHifiResBlock` described below.
Class description:
Tensorflow Hifigan resblock 1 module.
Method signatures and docstrings:
- def __init__(self, kernel_size, filters, dilation_rate, use_bias, nonlinear_activation, nonlinear_activation_params, is_weight_norm, initializer_seed, **kwargs): In... | Implement the Python class `TFHifiResBlock` described below.
Class description:
Tensorflow Hifigan resblock 1 module.
Method signatures and docstrings:
- def __init__(self, kernel_size, filters, dilation_rate, use_bias, nonlinear_activation, nonlinear_activation_params, is_weight_norm, initializer_seed, **kwargs): In... | 136877136355c82d7ba474ceb7a8f133bd84767e | <|skeleton|>
class TFHifiResBlock:
"""Tensorflow Hifigan resblock 1 module."""
def __init__(self, kernel_size, filters, dilation_rate, use_bias, nonlinear_activation, nonlinear_activation_params, is_weight_norm, initializer_seed, **kwargs):
"""Initialize TFHifiResBlock module. Args: kernel_size (int): ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TFHifiResBlock:
"""Tensorflow Hifigan resblock 1 module."""
def __init__(self, kernel_size, filters, dilation_rate, use_bias, nonlinear_activation, nonlinear_activation_params, is_weight_norm, initializer_seed, **kwargs):
"""Initialize TFHifiResBlock module. Args: kernel_size (int): Kernel size. ... | the_stack_v2_python_sparse | tensorflow_tts/models/hifigan.py | TensorSpeech/TensorFlowTTS | train | 2,889 |
27337149f46daba9c9cddc430b4d32699d4ddfd8 | [
"super(Net, self).__init__()\nself.conv1 = nn.Conv2d(c_in, 128, 3, 1)\nself.conv2 = nn.Conv2d(self.conv1.out_channels, 128, 3, 1)\nself.relu = nn.ReLU(inplace=True)\nself.flatten = nn.Flatten(1)\nself.fc = nn.Linear((h_in - 4) * (w_in - 4) * 128, classes)\nnn.init.kaiming_normal_(self.conv1.weight, mode='fan_out')\... | <|body_start_0|>
super(Net, self).__init__()
self.conv1 = nn.Conv2d(c_in, 128, 3, 1)
self.conv2 = nn.Conv2d(self.conv1.out_channels, 128, 3, 1)
self.relu = nn.ReLU(inplace=True)
self.flatten = nn.Flatten(1)
self.fc = nn.Linear((h_in - 4) * (w_in - 4) * 128, classes)
... | Network definition. | Net | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Net:
"""Network definition."""
def __init__(self, c_in=1, h_in=28, w_in=28, classes=10):
"""Network initialize method. Args: c_in: Input tensor channels. h_in: Input tensor height. w_in: Input tensor width. classes: Number of classes."""
<|body_0|>
def forward(self, x):
... | stack_v2_sparse_classes_36k_train_008181 | 1,999 | permissive | [
{
"docstring": "Network initialize method. Args: c_in: Input tensor channels. h_in: Input tensor height. w_in: Input tensor width. classes: Number of classes.",
"name": "__init__",
"signature": "def __init__(self, c_in=1, h_in=28, w_in=28, classes=10)"
},
{
"docstring": "Network forward method. ... | 2 | null | Implement the Python class `Net` described below.
Class description:
Network definition.
Method signatures and docstrings:
- def __init__(self, c_in=1, h_in=28, w_in=28, classes=10): Network initialize method. Args: c_in: Input tensor channels. h_in: Input tensor height. w_in: Input tensor width. classes: Number of c... | Implement the Python class `Net` described below.
Class description:
Network definition.
Method signatures and docstrings:
- def __init__(self, c_in=1, h_in=28, w_in=28, classes=10): Network initialize method. Args: c_in: Input tensor channels. h_in: Input tensor height. w_in: Input tensor width. classes: Number of c... | 757aac75a0f3921c6d1b4d98599bd7d4ffda936b | <|skeleton|>
class Net:
"""Network definition."""
def __init__(self, c_in=1, h_in=28, w_in=28, classes=10):
"""Network initialize method. Args: c_in: Input tensor channels. h_in: Input tensor height. w_in: Input tensor width. classes: Number of classes."""
<|body_0|>
def forward(self, x):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Net:
"""Network definition."""
def __init__(self, c_in=1, h_in=28, w_in=28, classes=10):
"""Network initialize method. Args: c_in: Input tensor channels. h_in: Input tensor height. w_in: Input tensor width. classes: Number of classes."""
super(Net, self).__init__()
self.conv1 = nn... | the_stack_v2_python_sparse | python/level1_single_api/9_amct/amct_pytorch/tensor_decompose/src/common/model.py | RomanGaraev/samples | train | 0 |
cc8ad53b6ab4e33f96eead9a3c58683ea5e3ec44 | [
"triplets = []\nlength = len(nums)\nif length < 3:\n return triplets\nnums.sort()\nfor i in range(length):\n target = 0 - nums[i]\n hashmap = {}\n for j in range(i + 1, length):\n item_j = nums[j]\n if target - item_j in hashmap:\n triplet = [nums[i], target - item_j, item_j]\n ... | <|body_start_0|>
triplets = []
length = len(nums)
if length < 3:
return triplets
nums.sort()
for i in range(length):
target = 0 - nums[i]
hashmap = {}
for j in range(i + 1, length):
item_j = nums[j]
i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def threeSum(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_0|>
def threeSum2(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
triplets = []
leng... | stack_v2_sparse_classes_36k_train_008182 | 1,433 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[List[int]]",
"name": "threeSum",
"signature": "def threeSum(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: List[List[int]]",
"name": "threeSum2",
"signature": "def threeSum2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013658 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSum(self, nums): :type nums: List[int] :rtype: List[List[int]]
- def threeSum2(self, nums): :type nums: List[int] :rtype: List[List[int]] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSum(self, nums): :type nums: List[int] :rtype: List[List[int]]
- def threeSum2(self, nums): :type nums: List[int] :rtype: List[List[int]]
<|skeleton|>
class Solution:
... | 3f8a1acc28520e65714296b337f817148ec3e0af | <|skeleton|>
class Solution:
def threeSum(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_0|>
def threeSum2(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def threeSum(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
triplets = []
length = len(nums)
if length < 3:
return triplets
nums.sort()
for i in range(length):
target = 0 - nums[i]
hashmap = {}
... | the_stack_v2_python_sparse | lintcode/TwoPointers/3Sum.py | sassyst/leetcode-python | train | 0 | |
0d1594b8d9cdbc151a3dde812fd9047ea008b13f | [
"self.logger = logging.getLogger(base_logger + '.' + self.__class__.__name__)\nself.mount_point = mount_point\nself.local_conf = auth_config\nself.vault_client = vault_client",
"string = str(string)\nself.logger.debug('Hashing ' + string)\nsha256_hash = hashlib.sha256(string.encode()).hexdigest()\nself.logger.deb... | <|body_start_0|>
self.logger = logging.getLogger(base_logger + '.' + self.__class__.__name__)
self.mount_point = mount_point
self.local_conf = auth_config
self.vault_client = vault_client
<|end_body_0|>
<|body_start_1|>
string = str(string)
self.logger.debug('Hashing ' +... | LDAP configuration specific class | AuthMethodLDAP | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthMethodLDAP:
"""LDAP configuration specific class"""
def __init__(self, base_logger, mount_point, auth_config, vault_client):
""":param base_logger: main class name :type base_logger: string :param mount_point: auth method mount point :type mount_point: str :param auth_config: aut... | stack_v2_sparse_classes_36k_train_008183 | 2,828 | permissive | [
{
"docstring": ":param base_logger: main class name :type base_logger: string :param mount_point: auth method mount point :type mount_point: str :param auth_config: auth method specific configuration :type auth_config: dict",
"name": "__init__",
"signature": "def __init__(self, base_logger, mount_point,... | 5 | stack_v2_sparse_classes_30k_train_001887 | Implement the Python class `AuthMethodLDAP` described below.
Class description:
LDAP configuration specific class
Method signatures and docstrings:
- def __init__(self, base_logger, mount_point, auth_config, vault_client): :param base_logger: main class name :type base_logger: string :param mount_point: auth method m... | Implement the Python class `AuthMethodLDAP` described below.
Class description:
LDAP configuration specific class
Method signatures and docstrings:
- def __init__(self, base_logger, mount_point, auth_config, vault_client): :param base_logger: main class name :type base_logger: string :param mount_point: auth method m... | 3457e8c2487a0d9cba8362208df91b77024d7782 | <|skeleton|>
class AuthMethodLDAP:
"""LDAP configuration specific class"""
def __init__(self, base_logger, mount_point, auth_config, vault_client):
""":param base_logger: main class name :type base_logger: string :param mount_point: auth method mount point :type mount_point: str :param auth_config: aut... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AuthMethodLDAP:
"""LDAP configuration specific class"""
def __init__(self, base_logger, mount_point, auth_config, vault_client):
""":param base_logger: main class name :type base_logger: string :param mount_point: auth method mount point :type mount_point: str :param auth_config: auth method spec... | the_stack_v2_python_sparse | vaultmanager/lib/AuthMethods/AuthMethodLDAP.py | manuelaguilar/vault-manager | train | 0 |
eb4ed989f04dcdce30a03f0b2cce08868ac1a1de | [
"super(Inception, self).__init__()\nself.branch1 = ConvBNLayer(num_channels=num_channels, num_filters=ch1x1, filter_size=1, stride=1, padding=0)\nself.branch2 = paddle.nn.Sequential(ConvBNLayer(num_channels=num_channels, num_filters=ch3x3reduced, filter_size=1, stride=1, padding=0), ConvBNLayer(num_channels=ch3x3re... | <|body_start_0|>
super(Inception, self).__init__()
self.branch1 = ConvBNLayer(num_channels=num_channels, num_filters=ch1x1, filter_size=1, stride=1, padding=0)
self.branch2 = paddle.nn.Sequential(ConvBNLayer(num_channels=num_channels, num_filters=ch3x3reduced, filter_size=1, stride=1, padding=0)... | Inception | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Inception:
def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj):
"""@Brief `Inception` @Parameters num_channels : channel numbers of input tensor ch1x1 : output channel numbers of 1x1 conv ch3x3reduced : channel numbers... | stack_v2_sparse_classes_36k_train_008184 | 23,805 | permissive | [
{
"docstring": "@Brief `Inception` @Parameters num_channels : channel numbers of input tensor ch1x1 : output channel numbers of 1x1 conv ch3x3reduced : channel numbers of 1x1 conv before 3x3 conv ch3x3 : output channel numbers of 3x3 conv doublech3x3reduced : channel numbers of 1x1 conv before the double 3x3 co... | 2 | null | Implement the Python class `Inception` described below.
Class description:
Implement the Inception class.
Method signatures and docstrings:
- def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj): @Brief `Inception` @Parameters num_channels : channel... | Implement the Python class `Inception` described below.
Class description:
Implement the Inception class.
Method signatures and docstrings:
- def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj): @Brief `Inception` @Parameters num_channels : channel... | 78ff3c3ab3906012a0f4a612251347632aa493a7 | <|skeleton|>
class Inception:
def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj):
"""@Brief `Inception` @Parameters num_channels : channel numbers of input tensor ch1x1 : output channel numbers of 1x1 conv ch3x3reduced : channel numbers... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Inception:
def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj):
"""@Brief `Inception` @Parameters num_channels : channel numbers of input tensor ch1x1 : output channel numbers of 1x1 conv ch3x3reduced : channel numbers of 1x1 conv b... | the_stack_v2_python_sparse | ECO/paddle2.0/model/ECO.py | thinkall/Contrib | train | 1 | |
aa0930de16646b3dce4f78458371976f904e4ad5 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | ResourceServiceServicer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResourceServiceServicer:
"""Missing associated documentation comment in .proto file."""
def List(self, request, context):
"""List resources: [Compute Cloud instances](/docs/backup/concepts/vm-connection#os)."""
<|body_0|>
def Get(self, request, context):
"""Get s... | stack_v2_sparse_classes_36k_train_008185 | 12,259 | permissive | [
{
"docstring": "List resources: [Compute Cloud instances](/docs/backup/concepts/vm-connection#os).",
"name": "List",
"signature": "def List(self, request, context)"
},
{
"docstring": "Get specific Compute Cloud instance.",
"name": "Get",
"signature": "def Get(self, request, context)"
}... | 6 | stack_v2_sparse_classes_30k_test_001073 | Implement the Python class `ResourceServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def List(self, request, context): List resources: [Compute Cloud instances](/docs/backup/concepts/vm-connection#os).
- def Get(self, req... | Implement the Python class `ResourceServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def List(self, request, context): List resources: [Compute Cloud instances](/docs/backup/concepts/vm-connection#os).
- def Get(self, req... | b906a014dd893e2697864e1e48e814a8d9fbc48c | <|skeleton|>
class ResourceServiceServicer:
"""Missing associated documentation comment in .proto file."""
def List(self, request, context):
"""List resources: [Compute Cloud instances](/docs/backup/concepts/vm-connection#os)."""
<|body_0|>
def Get(self, request, context):
"""Get s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResourceServiceServicer:
"""Missing associated documentation comment in .proto file."""
def List(self, request, context):
"""List resources: [Compute Cloud instances](/docs/backup/concepts/vm-connection#os)."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('M... | the_stack_v2_python_sparse | yandex/cloud/backup/v1/resource_service_pb2_grpc.py | yandex-cloud/python-sdk | train | 63 |
561be3b4151813e52f42fdc7871bf5ef4e69fca8 | [
"envs = data.get('env', {})\np = data.get('with', {})\na = p.pop('args') if 'args' in p else ()\nk = p.pop('kwargs') if 'kwargs' in p else {}\ntmp_a = (expand_env_var(v) for v in a)\ntmp_p = {kk: expand_env_var(vv) for kk, vv in {**k, **p}.items()}\nobj = cls(*tmp_a, env=envs, **tmp_p)\npp = data.get('pods', [])\nf... | <|body_start_0|>
envs = data.get('env', {})
p = data.get('with', {})
a = p.pop('args') if 'args' in p else ()
k = p.pop('kwargs') if 'kwargs' in p else {}
tmp_a = (expand_env_var(v) for v in a)
tmp_p = {kk: expand_env_var(vv) for kk, vv in {**k, **p}.items()}
obj ... | V1Parser introduces new syntax and features: - It has a top-level field ``version`` - ``pods`` is now a List of Dict (rather than a Dict as prev.) - ``name`` is now optional - new field ``method`` can be used to specify how to add this Pod into the Flow, availables are: - ``add``: (default) equal to `Flow.add(...)` - `... | V1Parser | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class V1Parser:
"""V1Parser introduces new syntax and features: - It has a top-level field ``version`` - ``pods`` is now a List of Dict (rather than a Dict as prev.) - ``name`` is now optional - new field ``method`` can be used to specify how to add this Pod into the Flow, availables are: - ``add``: (d... | stack_v2_sparse_classes_36k_train_008186 | 4,335 | permissive | [
{
"docstring": ":param cls: the class registered for dumping/loading :param data: flow yaml file loaded as python dict :return: the Flow YAML parser given the syntax version number",
"name": "parse",
"signature": "def parse(self, cls: type, data: Dict) -> 'Flow'"
},
{
"docstring": ":param data: ... | 2 | null | Implement the Python class `V1Parser` described below.
Class description:
V1Parser introduces new syntax and features: - It has a top-level field ``version`` - ``pods`` is now a List of Dict (rather than a Dict as prev.) - ``name`` is now optional - new field ``method`` can be used to specify how to add this Pod into ... | Implement the Python class `V1Parser` described below.
Class description:
V1Parser introduces new syntax and features: - It has a top-level field ``version`` - ``pods`` is now a List of Dict (rather than a Dict as prev.) - ``name`` is now optional - new field ``method`` can be used to specify how to add this Pod into ... | 97f9e97a4a678a28bdeacbc7346eaf7bbd2aeb89 | <|skeleton|>
class V1Parser:
"""V1Parser introduces new syntax and features: - It has a top-level field ``version`` - ``pods`` is now a List of Dict (rather than a Dict as prev.) - ``name`` is now optional - new field ``method`` can be used to specify how to add this Pod into the Flow, availables are: - ``add``: (d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class V1Parser:
"""V1Parser introduces new syntax and features: - It has a top-level field ``version`` - ``pods`` is now a List of Dict (rather than a Dict as prev.) - ``name`` is now optional - new field ``method`` can be used to specify how to add this Pod into the Flow, availables are: - ``add``: (default) equal... | the_stack_v2_python_sparse | jina/jaml/parsers/flow/v1.py | deepampatel/jina | train | 2 |
603d7a72acdde774ad8a9edd696c4992b5624dd1 | [
"for p in [region_name, cluster_name]:\n if p is None or p == '' or p == ' ':\n exit(1)\nelse:\n self.region_name = region_name\n self.cluster_name = cluster_name\n self.err = 0",
"self.err = 0\nerrInfo = None\ntry:\n client = boto3.Session(region_name=self.region_name)\n self.msk_client ... | <|body_start_0|>
for p in [region_name, cluster_name]:
if p is None or p == '' or p == ' ':
exit(1)
else:
self.region_name = region_name
self.cluster_name = cluster_name
self.err = 0
<|end_body_0|>
<|body_start_1|>
self.err = 0
... | Class for exporting MSK Connection information - Brokers - Zookeepers - Connection Protocol - Trustostore | BackendConfig | [
"JSON"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BackendConfig:
"""Class for exporting MSK Connection information - Brokers - Zookeepers - Connection Protocol - Trustostore"""
def __init__(self, cluster_name, region_name):
""":param region_name": Region Name for the AWS MSK Cluster :type region_name": string :param cluster_name: AW... | stack_v2_sparse_classes_36k_train_008187 | 4,994 | permissive | [
{
"docstring": ":param region_name\": Region Name for the AWS MSK Cluster :type region_name\": string :param cluster_name: AWS MSK Cluster Name :type cluster_name: string",
"name": "__init__",
"signature": "def __init__(self, cluster_name, region_name)"
},
{
"docstring": "Method that creates a M... | 3 | stack_v2_sparse_classes_30k_train_005577 | Implement the Python class `BackendConfig` described below.
Class description:
Class for exporting MSK Connection information - Brokers - Zookeepers - Connection Protocol - Trustostore
Method signatures and docstrings:
- def __init__(self, cluster_name, region_name): :param region_name": Region Name for the AWS MSK C... | Implement the Python class `BackendConfig` described below.
Class description:
Class for exporting MSK Connection information - Brokers - Zookeepers - Connection Protocol - Trustostore
Method signatures and docstrings:
- def __init__(self, cluster_name, region_name): :param region_name": Region Name for the AWS MSK C... | a327a56d8722bc039a7250903098f4ac7613e5a2 | <|skeleton|>
class BackendConfig:
"""Class for exporting MSK Connection information - Brokers - Zookeepers - Connection Protocol - Trustostore"""
def __init__(self, cluster_name, region_name):
""":param region_name": Region Name for the AWS MSK Cluster :type region_name": string :param cluster_name: AW... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BackendConfig:
"""Class for exporting MSK Connection information - Brokers - Zookeepers - Connection Protocol - Trustostore"""
def __init__(self, cluster_name, region_name):
""":param region_name": Region Name for the AWS MSK Cluster :type region_name": string :param cluster_name: AWS MSK Cluster... | the_stack_v2_python_sparse | aws/eks/lambdas/backend_config.py | christafford/lenses-cloud-templates | train | 0 |
70caee432b859b430060bb4db68db62227cb7ddb | [
"form = CommentForm()\nis_liked = Like.is_liked_by_user(self.user, self.post)\nself.render_viewpost(form, is_liked)",
"form = CommentForm(self.request.POST)\nis_liked = Like.is_liked_by_user(self.user, self.post)\nif form.validate():\n comment_key = Comment.new_comment(form.content.data, self.user, self.post)\... | <|body_start_0|>
form = CommentForm()
is_liked = Like.is_liked_by_user(self.user, self.post)
self.render_viewpost(form, is_liked)
<|end_body_0|>
<|body_start_1|>
form = CommentForm(self.request.POST)
is_liked = Like.is_liked_by_user(self.user, self.post)
if form.validate... | ViewPost | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ViewPost:
def get(self, post_key):
"""Display a post and its comments Generate a form to post comments :param post_key: Key of the post to display"""
<|body_0|>
def post(self, post_key):
"""Create a new comment and redirect user to the post page If error, display for... | stack_v2_sparse_classes_36k_train_008188 | 1,426 | no_license | [
{
"docstring": "Display a post and its comments Generate a form to post comments :param post_key: Key of the post to display",
"name": "get",
"signature": "def get(self, post_key)"
},
{
"docstring": "Create a new comment and redirect user to the post page If error, display form with error detail... | 2 | stack_v2_sparse_classes_30k_train_001630 | Implement the Python class `ViewPost` described below.
Class description:
Implement the ViewPost class.
Method signatures and docstrings:
- def get(self, post_key): Display a post and its comments Generate a form to post comments :param post_key: Key of the post to display
- def post(self, post_key): Create a new com... | Implement the Python class `ViewPost` described below.
Class description:
Implement the ViewPost class.
Method signatures and docstrings:
- def get(self, post_key): Display a post and its comments Generate a form to post comments :param post_key: Key of the post to display
- def post(self, post_key): Create a new com... | 9a574d9abae7f3a599538956942c5439c8e0f69c | <|skeleton|>
class ViewPost:
def get(self, post_key):
"""Display a post and its comments Generate a form to post comments :param post_key: Key of the post to display"""
<|body_0|>
def post(self, post_key):
"""Create a new comment and redirect user to the post page If error, display for... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ViewPost:
def get(self, post_key):
"""Display a post and its comments Generate a form to post comments :param post_key: Key of the post to display"""
form = CommentForm()
is_liked = Like.is_liked_by_user(self.user, self.post)
self.render_viewpost(form, is_liked)
def post(s... | the_stack_v2_python_sparse | src/app/posts/view.py | fabriziou/UBlog | train | 0 | |
a9db689be705f11cb94b91e563e5c84b98f137e3 | [
"allowed_users = []\nfor user in self.targets:\n allows_emails = self.usersetting(user)\n if allows_emails:\n allowed_users.append(user)\nreturn EmailAddress.objects.filter(user__in=allowed_users)",
"html_message = render_to_string(self.context['template']['html'], self.context)\ntargets = self.targe... | <|body_start_0|>
allowed_users = []
for user in self.targets:
allows_emails = self.usersetting(user)
if allows_emails:
allowed_users.append(user)
return EmailAddress.objects.filter(user__in=allowed_users)
<|end_body_0|>
<|body_start_1|>
html_messa... | Notificationmethod for delivery via Email. | EmailNotification | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmailNotification:
"""Notificationmethod for delivery via Email."""
def get_targets(self):
"""Return a list of target email addresses, only for users which allow email notifications."""
<|body_0|>
def send_bulk(self):
"""Send the notifications out via email."""
... | stack_v2_sparse_classes_36k_train_008189 | 2,847 | permissive | [
{
"docstring": "Return a list of target email addresses, only for users which allow email notifications.",
"name": "get_targets",
"signature": "def get_targets(self)"
},
{
"docstring": "Send the notifications out via email.",
"name": "send_bulk",
"signature": "def send_bulk(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008646 | Implement the Python class `EmailNotification` described below.
Class description:
Notificationmethod for delivery via Email.
Method signatures and docstrings:
- def get_targets(self): Return a list of target email addresses, only for users which allow email notifications.
- def send_bulk(self): Send the notification... | Implement the Python class `EmailNotification` described below.
Class description:
Notificationmethod for delivery via Email.
Method signatures and docstrings:
- def get_targets(self): Return a list of target email addresses, only for users which allow email notifications.
- def send_bulk(self): Send the notification... | 5a08ef908dd5344b4433436a4679d122f7f99e41 | <|skeleton|>
class EmailNotification:
"""Notificationmethod for delivery via Email."""
def get_targets(self):
"""Return a list of target email addresses, only for users which allow email notifications."""
<|body_0|>
def send_bulk(self):
"""Send the notifications out via email."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EmailNotification:
"""Notificationmethod for delivery via Email."""
def get_targets(self):
"""Return a list of target email addresses, only for users which allow email notifications."""
allowed_users = []
for user in self.targets:
allows_emails = self.usersetting(user)... | the_stack_v2_python_sparse | InvenTree/plugin/builtin/integration/core_notifications.py | onurtatli/InvenTree | train | 0 |
3344af935d4a3683b121094c5159c21cbc1d51bb | [
"super().__init__()\nif json is None:\n json = {}\nself._manager: 'SideBarManager' = side_bar_manager\nself.align = json.get('align', alignment)\nself.minimum_access_level = json.get('minimumAccessLevel', minimum_access_level)\nself.maximum_access_level = json.get('maximumAccessLevel', maximum_access_level)\nsel... | <|body_start_0|>
super().__init__()
if json is None:
json = {}
self._manager: 'SideBarManager' = side_bar_manager
self.align = json.get('align', alignment)
self.minimum_access_level = json.get('minimumAccessLevel', minimum_access_level)
self.maximum_access_lev... | Side-bar card class. Every side-bar can have one or more cards and is maintained as an object of this class. :cvar allowed_attributes: allowed additional attributed provided as options alongside the specifically allowed ones. :cvar item_type: the item type of this class. Defaults to a BUTTON. | SideBarCard | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SideBarCard:
"""Side-bar card class. Every side-bar can have one or more cards and is maintained as an object of this class. :cvar allowed_attributes: allowed additional attributed provided as options alongside the specifically allowed ones. :cvar item_type: the item type of this class. Defaults ... | stack_v2_sparse_classes_36k_train_008190 | 6,308 | permissive | [
{
"docstring": "Create a side-bar card. :param side_bar_manager: Manager object to which the button is linked. :param json: the json response to construct the :class:`SideBarButton` from :param title: visible label of the button :param icon: FontAwesome icon of the button :param uri: Uniform Resource Identifier... | 2 | stack_v2_sparse_classes_30k_train_003067 | Implement the Python class `SideBarCard` described below.
Class description:
Side-bar card class. Every side-bar can have one or more cards and is maintained as an object of this class. :cvar allowed_attributes: allowed additional attributed provided as options alongside the specifically allowed ones. :cvar item_type:... | Implement the Python class `SideBarCard` described below.
Class description:
Side-bar card class. Every side-bar can have one or more cards and is maintained as an object of this class. :cvar allowed_attributes: allowed additional attributed provided as options alongside the specifically allowed ones. :cvar item_type:... | e8352e4d434bd5d0a5d76f7351f100d0b63f6fa8 | <|skeleton|>
class SideBarCard:
"""Side-bar card class. Every side-bar can have one or more cards and is maintained as an object of this class. :cvar allowed_attributes: allowed additional attributed provided as options alongside the specifically allowed ones. :cvar item_type: the item type of this class. Defaults ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SideBarCard:
"""Side-bar card class. Every side-bar can have one or more cards and is maintained as an object of this class. :cvar allowed_attributes: allowed additional attributed provided as options alongside the specifically allowed ones. :cvar item_type: the item type of this class. Defaults to a BUTTON."... | the_stack_v2_python_sparse | pykechain/models/sidebar/sidebar_card.py | KE-works/pykechain | train | 7 |
4cfc7dfb2a2762d1e56212844968a8b3e62782f8 | [
"ipAddress = '10.0.2.15'\nport = '5432'\ndbName = 'sample'\nuserName = 'inoue'\npassword = '****'\nself.connection = psycopg2.connect('host={0} port={1} dbname={2} user={3} password={4}'.format(ipAddress, port, dbName, userName, password))",
"cur = self.connection.cursor()\ncur.execute('select id,name from sample... | <|body_start_0|>
ipAddress = '10.0.2.15'
port = '5432'
dbName = 'sample'
userName = 'inoue'
password = '****'
self.connection = psycopg2.connect('host={0} port={1} dbname={2} user={3} password={4}'.format(ipAddress, port, dbName, userName, password))
<|end_body_0|>
<|bod... | ポスグレに接続してみる | ConnectPostgresql | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConnectPostgresql:
"""ポスグレに接続してみる"""
def __init__(self):
"""コンストラクタ DBに接続する"""
<|body_0|>
def Select(self):
"""selectする"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ipAddress = '10.0.2.15'
port = '5432'
dbName = 'sample'
... | stack_v2_sparse_classes_36k_train_008191 | 801 | no_license | [
{
"docstring": "コンストラクタ DBに接続する",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "selectする",
"name": "Select",
"signature": "def Select(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013524 | Implement the Python class `ConnectPostgresql` described below.
Class description:
ポスグレに接続してみる
Method signatures and docstrings:
- def __init__(self): コンストラクタ DBに接続する
- def Select(self): selectする | Implement the Python class `ConnectPostgresql` described below.
Class description:
ポスグレに接続してみる
Method signatures and docstrings:
- def __init__(self): コンストラクタ DBに接続する
- def Select(self): selectする
<|skeleton|>
class ConnectPostgresql:
"""ポスグレに接続してみる"""
def __init__(self):
"""コンストラクタ DBに接続する"""
... | 6028f92290de60eb2552825d850758099162397f | <|skeleton|>
class ConnectPostgresql:
"""ポスグレに接続してみる"""
def __init__(self):
"""コンストラクタ DBに接続する"""
<|body_0|>
def Select(self):
"""selectする"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConnectPostgresql:
"""ポスグレに接続してみる"""
def __init__(self):
"""コンストラクタ DBに接続する"""
ipAddress = '10.0.2.15'
port = '5432'
dbName = 'sample'
userName = 'inoue'
password = '****'
self.connection = psycopg2.connect('host={0} port={1} dbname={2} user={3} pas... | the_stack_v2_python_sparse | DB操作/connectdb.py | inoue0508/psample | train | 0 |
176f35bf9c74b4adefb9f0778b4d9954704e094c | [
"if self.required is False and value is None:\n return\nif isinstance(value, self.cls):\n return value\nelif isinstance(value, dict):\n f_instance = self.cls(**value)\n return f_instance\nelse:\n raise TypedClassValidationError('{name} is not instance of {cls}'.format(name=name, cls=self.cls))",
"i... | <|body_start_0|>
if self.required is False and value is None:
return
if isinstance(value, self.cls):
return value
elif isinstance(value, dict):
f_instance = self.cls(**value)
return f_instance
else:
raise TypedClassValidationErr... | Ref | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Ref:
def process(self, name, value):
""":type self: TypedClassBaseField :type name: str :type value: TypedClassAny :rtype: TypedClass|None"""
<|body_0|>
def simplify(self, value):
""":type self: TypedClassBaseField :type value: TypedClass|None :rtype: TypedClassAny""... | stack_v2_sparse_classes_36k_train_008192 | 1,133 | permissive | [
{
"docstring": ":type self: TypedClassBaseField :type name: str :type value: TypedClassAny :rtype: TypedClass|None",
"name": "process",
"signature": "def process(self, name, value)"
},
{
"docstring": ":type self: TypedClassBaseField :type value: TypedClass|None :rtype: TypedClassAny",
"name"... | 2 | stack_v2_sparse_classes_30k_train_000445 | Implement the Python class `Ref` described below.
Class description:
Implement the Ref class.
Method signatures and docstrings:
- def process(self, name, value): :type self: TypedClassBaseField :type name: str :type value: TypedClassAny :rtype: TypedClass|None
- def simplify(self, value): :type self: TypedClassBaseFi... | Implement the Python class `Ref` described below.
Class description:
Implement the Ref class.
Method signatures and docstrings:
- def process(self, name, value): :type self: TypedClassBaseField :type name: str :type value: TypedClassAny :rtype: TypedClass|None
- def simplify(self, value): :type self: TypedClassBaseFi... | c55b6a7506fc5ca21e7d20f03a5fbad1f5b5aa85 | <|skeleton|>
class Ref:
def process(self, name, value):
""":type self: TypedClassBaseField :type name: str :type value: TypedClassAny :rtype: TypedClass|None"""
<|body_0|>
def simplify(self, value):
""":type self: TypedClassBaseField :type value: TypedClass|None :rtype: TypedClassAny""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Ref:
def process(self, name, value):
""":type self: TypedClassBaseField :type name: str :type value: TypedClassAny :rtype: TypedClass|None"""
if self.required is False and value is None:
return
if isinstance(value, self.cls):
return value
elif isinstance... | the_stack_v2_python_sparse | typedclass/fields/special/ref.py | ostrovok-team/typedclass | train | 0 | |
bbe55b9abc10f45c57b564549597c03512dae025 | [
"if not root:\n return '[]'\nqueue = collections.deque()\nqueue.append(root)\nans = []\nwhile queue:\n node = queue.popleft()\n if node:\n ans.append(str(node.val))\n queue.append(node.left)\n queue.append(node.right)\n else:\n ans.append('null')\nreturn '[' + ','.join(ans) +... | <|body_start_0|>
if not root:
return '[]'
queue = collections.deque()
queue.append(root)
ans = []
while queue:
node = queue.popleft()
if node:
ans.append(str(node.val))
queue.append(node.left)
que... | 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_008193 | 7,095 | 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_005982 | 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:... | 58ad6836582da61bcdee649732fae5504b919e80 | <|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"""
if not root:
return '[]'
queue = collections.deque()
queue.append(root)
ans = []
while queue:
node = queue.popleft()
i... | the_stack_v2_python_sparse | binary_tree.py | lengyugo/leetcode | train | 0 | |
0a37a3dd9d3d263d6fa5fa7e5624585b8b140579 | [
"if nums is []:\n return None\nleft = 0\nright = len(nums) - 1\nwhile left < right - 1:\n mid = (left + right) / 2\n if nums[left] < nums[right]:\n return nums[left]\n if nums[left] < nums[mid]:\n left = mid\n else:\n right = mid\nreturn min(nums[left], nums[right])",
"if nums ... | <|body_start_0|>
if nums is []:
return None
left = 0
right = len(nums) - 1
while left < right - 1:
mid = (left + right) / 2
if nums[left] < nums[right]:
return nums[left]
if nums[left] < nums[mid]:
left = mid... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMin(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findMin_2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if nums is []:
return None
left... | stack_v2_sparse_classes_36k_train_008194 | 929 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findMin",
"signature": "def findMin(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findMin_2",
"signature": "def findMin_2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020238 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMin(self, nums): :type nums: List[int] :rtype: int
- def findMin_2(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMin(self, nums): :type nums: List[int] :rtype: int
- def findMin_2(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def findMin(self, num... | 4d24e9924a92d2f9517f82a07933b51a9ab32023 | <|skeleton|>
class Solution:
def findMin(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findMin_2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findMin(self, nums):
""":type nums: List[int] :rtype: int"""
if nums is []:
return None
left = 0
right = len(nums) - 1
while left < right - 1:
mid = (left + right) / 2
if nums[left] < nums[right]:
return ... | the_stack_v2_python_sparse | find-minimum-in-rotated-sorted-array.py | ceciet/leetcode | train | 0 | |
4667e18ebc89a24c0c724f80a3989f9fbc954ddb | [
"follower = request.user\nis_following = User.objects.filter(username=username).first()\n'Return HTTP 400 if the user is same as the following'\nif follower == is_following:\n return JsonResponse({'error': 'You cannot follow yourself'}, status=status.HTTP_400_BAD_REQUEST)\n'Return HTTP 404 if the following does ... | <|body_start_0|>
follower = request.user
is_following = User.objects.filter(username=username).first()
'Return HTTP 400 if the user is same as the following'
if follower == is_following:
return JsonResponse({'error': 'You cannot follow yourself'}, status=status.HTTP_400_BAD_R... | Follow and Unfollow the user with the username in the URL | CreateFollowing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateFollowing:
"""Follow and Unfollow the user with the username in the URL"""
def post(self, request, username):
"""This Class creates a new following record, the username in the URL is the user being followed The user that follows us gotten from the request object"""
<|bo... | stack_v2_sparse_classes_36k_train_008195 | 12,940 | no_license | [
{
"docstring": "This Class creates a new following record, the username in the URL is the user being followed The user that follows us gotten from the request object",
"name": "post",
"signature": "def post(self, request, username)"
},
{
"docstring": "This Class deletes a following record, the u... | 2 | stack_v2_sparse_classes_30k_train_013130 | Implement the Python class `CreateFollowing` described below.
Class description:
Follow and Unfollow the user with the username in the URL
Method signatures and docstrings:
- def post(self, request, username): This Class creates a new following record, the username in the URL is the user being followed The user that ... | Implement the Python class `CreateFollowing` described below.
Class description:
Follow and Unfollow the user with the username in the URL
Method signatures and docstrings:
- def post(self, request, username): This Class creates a new following record, the username in the URL is the user being followed The user that ... | bf30e4338a3bfe51f43d73b5ad876e27de6408da | <|skeleton|>
class CreateFollowing:
"""Follow and Unfollow the user with the username in the URL"""
def post(self, request, username):
"""This Class creates a new following record, the username in the URL is the user being followed The user that follows us gotten from the request object"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CreateFollowing:
"""Follow and Unfollow the user with the username in the URL"""
def post(self, request, username):
"""This Class creates a new following record, the username in the URL is the user being followed The user that follows us gotten from the request object"""
follower = reques... | the_stack_v2_python_sparse | Account/views.py | damey2011/AskolonyAPI | train | 0 |
bf6b8622ebd7cf8e4562142b9ad6ba13a6cabc7a | [
"if body_start is None and tail_start is None:\n raise ValueError('Both body start and tail start are None.')\nif tail_start is not None and tail_fn is None:\n raise ValueError(f'Tail start has value ({tail_start}) but tail_fn is None.')\nif body_start is None:\n body_start = tail_start if tail_start is no... | <|body_start_0|>
if body_start is None and tail_start is None:
raise ValueError('Both body start and tail start are None.')
if tail_start is not None and tail_fn is None:
raise ValueError(f'Tail start has value ({tail_start}) but tail_fn is None.')
if body_start is None:
... | Defines a curve over time as a linear ramp + constant body + curvy tail. The body is a span of constant learning rate, and can be the entire curve. The warm-up, if present, is based on the line connecting points (0, 0) and (body_start, body_value). The tail, if defined, is a function from time to learning rate that is ... | _BodyAndTail | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _BodyAndTail:
"""Defines a curve over time as a linear ramp + constant body + curvy tail. The body is a span of constant learning rate, and can be the entire curve. The warm-up, if present, is based on the line connecting points (0, 0) and (body_start, body_value). The tail, if defined, is a func... | stack_v2_sparse_classes_36k_train_008196 | 9,085 | permissive | [
{
"docstring": "Specifies a body-and-tail time curve. Args: body_value: Constant learning rate for the body of the curve (after warm-up and before tail). Also is the reference (maximum) value for calculating warm-up values and tail values. body_start: Training step number at which the body starts. If None, take... | 2 | null | Implement the Python class `_BodyAndTail` described below.
Class description:
Defines a curve over time as a linear ramp + constant body + curvy tail. The body is a span of constant learning rate, and can be the entire curve. The warm-up, if present, is based on the line connecting points (0, 0) and (body_start, body_... | Implement the Python class `_BodyAndTail` described below.
Class description:
Defines a curve over time as a linear ramp + constant body + curvy tail. The body is a span of constant learning rate, and can be the entire curve. The warm-up, if present, is based on the line connecting points (0, 0) and (body_start, body_... | 1bb3b89427f669f2f0ec84633952e21b68964a23 | <|skeleton|>
class _BodyAndTail:
"""Defines a curve over time as a linear ramp + constant body + curvy tail. The body is a span of constant learning rate, and can be the entire curve. The warm-up, if present, is based on the line connecting points (0, 0) and (body_start, body_value). The tail, if defined, is a func... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _BodyAndTail:
"""Defines a curve over time as a linear ramp + constant body + curvy tail. The body is a span of constant learning rate, and can be the entire curve. The warm-up, if present, is based on the line connecting points (0, 0) and (body_start, body_value). The tail, if defined, is a function from tim... | the_stack_v2_python_sparse | trax/supervised/lr_schedules.py | google/trax | train | 8,180 |
a9ff2f4b70ea6a1dd8f4aa0598757610d041c38c | [
"self.number_points = number_points\nself.x_values = [0]\nself.y_values = [0]",
"while len(self.x_values) < self.number_points:\n x_direction = choice([1, -1])\n x_distince = choice([0, 1, 2, 3, 4])\n x_step = x_direction * x_distince\n y_direction = choice([1, -1])\n y_distince = choice([0, 1, 2, ... | <|body_start_0|>
self.number_points = number_points
self.x_values = [0]
self.y_values = [0]
<|end_body_0|>
<|body_start_1|>
while len(self.x_values) < self.number_points:
x_direction = choice([1, -1])
x_distince = choice([0, 1, 2, 3, 4])
x_step = x_di... | 一个生成随机漫步数据的类 | RandomWalk | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomWalk:
"""一个生成随机漫步数据的类"""
def __init__(self, number_points=5000):
"""初始化随机漫步的属性"""
<|body_0|>
def fill_walk(self):
"""计算随机漫步包含的所有点"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.number_points = number_points
self.x_values = [0... | stack_v2_sparse_classes_36k_train_008197 | 1,021 | no_license | [
{
"docstring": "初始化随机漫步的属性",
"name": "__init__",
"signature": "def __init__(self, number_points=5000)"
},
{
"docstring": "计算随机漫步包含的所有点",
"name": "fill_walk",
"signature": "def fill_walk(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003223 | Implement the Python class `RandomWalk` described below.
Class description:
一个生成随机漫步数据的类
Method signatures and docstrings:
- def __init__(self, number_points=5000): 初始化随机漫步的属性
- def fill_walk(self): 计算随机漫步包含的所有点 | Implement the Python class `RandomWalk` described below.
Class description:
一个生成随机漫步数据的类
Method signatures and docstrings:
- def __init__(self, number_points=5000): 初始化随机漫步的属性
- def fill_walk(self): 计算随机漫步包含的所有点
<|skeleton|>
class RandomWalk:
"""一个生成随机漫步数据的类"""
def __init__(self, number_points=5000):
... | 30ad6ddfbf206fb7a0a76ae78ae5837cafcff842 | <|skeleton|>
class RandomWalk:
"""一个生成随机漫步数据的类"""
def __init__(self, number_points=5000):
"""初始化随机漫步的属性"""
<|body_0|>
def fill_walk(self):
"""计算随机漫步包含的所有点"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomWalk:
"""一个生成随机漫步数据的类"""
def __init__(self, number_points=5000):
"""初始化随机漫步的属性"""
self.number_points = number_points
self.x_values = [0]
self.y_values = [0]
def fill_walk(self):
"""计算随机漫步包含的所有点"""
while len(self.x_values) < self.number_points:
... | the_stack_v2_python_sparse | view_data/random_walk.py | 422518490/python | train | 0 |
e0b32925aee455ca49a8ba47f6d45a72e7d74ee0 | [
"super().__init__()\nself.self_attn_layer_norm = nn.LayerNorm(hid_dim)\nself.ff_layer_norm = nn.LayerNorm(hid_dim)\nself.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)\nself.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)\nself.dropout = nn.Dropout(dropout)",
... | <|body_start_0|>
super().__init__()
self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
self.ff_layer_norm = nn.LayerNorm(hid_dim)
self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)
self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, ... | TransformerEncoderLayer is made up of self-attn and feedforward network. Args: hid_dim: the hidden size of the encoder n_heads: the number of heads in the multi-head attention layers pf_dim: the dimension of the feedforward network model dropout: the dropout value device: the device on which the model is running | TransformerEncoderLayer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransformerEncoderLayer:
"""TransformerEncoderLayer is made up of self-attn and feedforward network. Args: hid_dim: the hidden size of the encoder n_heads: the number of heads in the multi-head attention layers pf_dim: the dimension of the feedforward network model dropout: the dropout value devi... | stack_v2_sparse_classes_36k_train_008198 | 10,223 | permissive | [
{
"docstring": "Initialize model with params.",
"name": "__init__",
"signature": "def __init__(self, hid_dim, n_heads, pf_dim, dropout)"
},
{
"docstring": "Run a forward pass of model over the data.",
"name": "forward",
"signature": "def forward(self, src, src_mask)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017363 | Implement the Python class `TransformerEncoderLayer` described below.
Class description:
TransformerEncoderLayer is made up of self-attn and feedforward network. Args: hid_dim: the hidden size of the encoder n_heads: the number of heads in the multi-head attention layers pf_dim: the dimension of the feedforward networ... | Implement the Python class `TransformerEncoderLayer` described below.
Class description:
TransformerEncoderLayer is made up of self-attn and feedforward network. Args: hid_dim: the hidden size of the encoder n_heads: the number of heads in the multi-head attention layers pf_dim: the dimension of the feedforward networ... | 9cdbf270487751a0ad6862b2fea2ccc0e23a0b67 | <|skeleton|>
class TransformerEncoderLayer:
"""TransformerEncoderLayer is made up of self-attn and feedforward network. Args: hid_dim: the hidden size of the encoder n_heads: the number of heads in the multi-head attention layers pf_dim: the dimension of the feedforward network model dropout: the dropout value devi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TransformerEncoderLayer:
"""TransformerEncoderLayer is made up of self-attn and feedforward network. Args: hid_dim: the hidden size of the encoder n_heads: the number of heads in the multi-head attention layers pf_dim: the dimension of the feedforward network model dropout: the dropout value device: the devic... | the_stack_v2_python_sparse | caspr/models/transformer.py | microsoft/CASPR | train | 29 |
da3bfc7b162b52722fedc81173c8bc55113e26cf | [
"if not self.request.user.is_external:\n raise Http404()\norganisation_slug = self.kwargs['organisation']\ncategory_slug = self.kwargs['category']\ncategory_qs = Category.objects.select_related().filter(organisation__slug=organisation_slug).filter(category_template__slug=category_slug)\ncategory = get_object_or_... | <|body_start_0|>
if not self.request.user.is_external:
raise Http404()
organisation_slug = self.kwargs['organisation']
category_slug = self.kwargs['category']
category_qs = Category.objects.select_related().filter(organisation__slug=organisation_slug).filter(category_template... | Allows a contractor to download a file_transmitted file. | FileTransmittedDownload | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileTransmittedDownload:
"""Allows a contractor to download a file_transmitted file."""
def get_object(self):
"""Extract the correct revision."""
<|body_0|>
def get(self, request, *args, **kwargs):
"""Serve the file."""
<|body_1|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_36k_train_008199 | 15,867 | permissive | [
{
"docstring": "Extract the correct revision.",
"name": "get_object",
"signature": "def get_object(self)"
},
{
"docstring": "Serve the file.",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019431 | Implement the Python class `FileTransmittedDownload` described below.
Class description:
Allows a contractor to download a file_transmitted file.
Method signatures and docstrings:
- def get_object(self): Extract the correct revision.
- def get(self, request, *args, **kwargs): Serve the file. | Implement the Python class `FileTransmittedDownload` described below.
Class description:
Allows a contractor to download a file_transmitted file.
Method signatures and docstrings:
- def get_object(self): Extract the correct revision.
- def get(self, request, *args, **kwargs): Serve the file.
<|skeleton|>
class FileT... | 60ff6f37778971ae356c5b2b20e0d174a8288bfe | <|skeleton|>
class FileTransmittedDownload:
"""Allows a contractor to download a file_transmitted file."""
def get_object(self):
"""Extract the correct revision."""
<|body_0|>
def get(self, request, *args, **kwargs):
"""Serve the file."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileTransmittedDownload:
"""Allows a contractor to download a file_transmitted file."""
def get_object(self):
"""Extract the correct revision."""
if not self.request.user.is_external:
raise Http404()
organisation_slug = self.kwargs['organisation']
category_slug... | the_stack_v2_python_sparse | src/transmittals/views.py | Talengi/phase | train | 8 |
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