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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7471a3b55ad48d7e5133a7db047e51fbf7d6a11e | [
"super().__init__(self.PARAMS, parameters)\nself.queries = parameters['queries']\nself.query_names = parameters['query_names']\nself.remove_types = parameters['remove_types']\nself.expression_parsers, self.query_names = get_expression_parsers(self.queries, query_names=parameters['query_names'])",
"if sidecar and ... | <|body_start_0|>
super().__init__(self.PARAMS, parameters)
self.queries = parameters['queries']
self.query_names = parameters['query_names']
self.remove_types = parameters['remove_types']
self.expression_parsers, self.query_names = get_expression_parsers(self.queries, query_names... | Create tabular file factors from tag queries. Required remodeling parameters: - **queries** (*list*): Queries to be applied successively as filters. - **query_names** (*list*): Column names for the query factors. - **remove_types** (*list*): Structural HED tags to be removed. - **expand_context** (*bool*): Expand the c... | FactorHedTagsOp | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FactorHedTagsOp:
"""Create tabular file factors from tag queries. Required remodeling parameters: - **queries** (*list*): Queries to be applied successively as filters. - **query_names** (*list*): Column names for the query factors. - **remove_types** (*list*): Structural HED tags to be removed. ... | stack_v2_sparse_classes_36k_train_007500 | 4,145 | permissive | [
{
"docstring": "Constructor for the factor HED tags operation. Parameters: parameters (dict): Actual values of the parameters for the operation. :raises KeyError: - If a required parameter is missing. - If an unexpected parameter is provided. :raises TypeError: - If a parameter has the wrong type. :raises Value... | 2 | stack_v2_sparse_classes_30k_train_010479 | Implement the Python class `FactorHedTagsOp` described below.
Class description:
Create tabular file factors from tag queries. Required remodeling parameters: - **queries** (*list*): Queries to be applied successively as filters. - **query_names** (*list*): Column names for the query factors. - **remove_types** (*list... | Implement the Python class `FactorHedTagsOp` described below.
Class description:
Create tabular file factors from tag queries. Required remodeling parameters: - **queries** (*list*): Queries to be applied successively as filters. - **query_names** (*list*): Column names for the query factors. - **remove_types** (*list... | b871cae44bdf0ee68c688562c3b0af50b93343f5 | <|skeleton|>
class FactorHedTagsOp:
"""Create tabular file factors from tag queries. Required remodeling parameters: - **queries** (*list*): Queries to be applied successively as filters. - **query_names** (*list*): Column names for the query factors. - **remove_types** (*list*): Structural HED tags to be removed. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FactorHedTagsOp:
"""Create tabular file factors from tag queries. Required remodeling parameters: - **queries** (*list*): Queries to be applied successively as filters. - **query_names** (*list*): Column names for the query factors. - **remove_types** (*list*): Structural HED tags to be removed. - **expand_co... | the_stack_v2_python_sparse | hed/tools/remodeling/operations/factor_hed_tags_op.py | hed-standard/hed-python | train | 5 |
39718b9f6590ac925cf2205047a8cf86fa0d7b2d | [
"out = []\nqueue = deque([root])\nwhile queue:\n node = queue.popleft()\n out.append(str(node.val) if node else '#')\n if node:\n queue.append(node.left)\n queue.append(node.right)\nreturn ' '.join(out).rstrip(' #')",
"if not data:\n return None\nout = data.split(' ')\nnodes_with_no = [T... | <|body_start_0|>
out = []
queue = deque([root])
while queue:
node = queue.popleft()
out.append(str(node.val) if node else '#')
if node:
queue.append(node.left)
queue.append(node.right)
return ' '.join(out).rstrip(' #')
<... | 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_007501 | 1,524 | 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_008990 | 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:... | 63b7eedc720c1ce14880b80744dcd5ef7107065c | <|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"""
out = []
queue = deque([root])
while queue:
node = queue.popleft()
out.append(str(node.val) if node else '#')
if node:
... | the_stack_v2_python_sparse | problems/serializeDeserializeTree.py | joddiy/leetcode | train | 1 | |
d2c9492d672a2eb3918aa2eb06efd0a26cf29d40 | [
"for i in range(len(list1) - 1):\n for j in range(len(list1) - 1 - i):\n if list1[j] > list1[j + 1]:\n list1[j], list1[j + 1] = (list1[j + 1], list1[j])\nreturn list1",
"for i in range(1, len(list1)):\n current = list1[i]\n pre_index = i - 1\n while pre_index >= 0 and list1[pre_index... | <|body_start_0|>
for i in range(len(list1) - 1):
for j in range(len(list1) - 1 - i):
if list1[j] > list1[j + 1]:
list1[j], list1[j + 1] = (list1[j + 1], list1[j])
return list1
<|end_body_0|>
<|body_start_1|>
for i in range(1, len(list1)):
... | 各种排序方法 | Sort | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Sort:
"""各种排序方法"""
def bubble_sort(list1):
"""冒泡排序"""
<|body_0|>
def insertion_sort(list1):
"""插入排序"""
<|body_1|>
def quick_sort(arr):
"""快速排序"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
for i in range(len(list1) - 1):
... | stack_v2_sparse_classes_36k_train_007502 | 1,922 | no_license | [
{
"docstring": "冒泡排序",
"name": "bubble_sort",
"signature": "def bubble_sort(list1)"
},
{
"docstring": "插入排序",
"name": "insertion_sort",
"signature": "def insertion_sort(list1)"
},
{
"docstring": "快速排序",
"name": "quick_sort",
"signature": "def quick_sort(arr)"
}
] | 3 | stack_v2_sparse_classes_30k_train_005010 | Implement the Python class `Sort` described below.
Class description:
各种排序方法
Method signatures and docstrings:
- def bubble_sort(list1): 冒泡排序
- def insertion_sort(list1): 插入排序
- def quick_sort(arr): 快速排序 | Implement the Python class `Sort` described below.
Class description:
各种排序方法
Method signatures and docstrings:
- def bubble_sort(list1): 冒泡排序
- def insertion_sort(list1): 插入排序
- def quick_sort(arr): 快速排序
<|skeleton|>
class Sort:
"""各种排序方法"""
def bubble_sort(list1):
"""冒泡排序"""
<|body_0|>
... | 5f843531d413202f4f4e48ed0c3d510db21f4396 | <|skeleton|>
class Sort:
"""各种排序方法"""
def bubble_sort(list1):
"""冒泡排序"""
<|body_0|>
def insertion_sort(list1):
"""插入排序"""
<|body_1|>
def quick_sort(arr):
"""快速排序"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Sort:
"""各种排序方法"""
def bubble_sort(list1):
"""冒泡排序"""
for i in range(len(list1) - 1):
for j in range(len(list1) - 1 - i):
if list1[j] > list1[j + 1]:
list1[j], list1[j + 1] = (list1[j + 1], list1[j])
return list1
def insertion_s... | the_stack_v2_python_sparse | pycharm/yz/common/Sort.py | yz9527-1/1YZ | train | 0 |
d204ec37394ca3d9c23e39ec01cb9c303a9927e1 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn Admin()",
"from .edge import Edge\nfrom .service_announcement import ServiceAnnouncement\nfrom .sharepoint import Sharepoint\nfrom .edge import Edge\nfrom .service_announcement import ServiceAnnouncement\nfrom .sharepoint import Sharep... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return Admin()
<|end_body_0|>
<|body_start_1|>
from .edge import Edge
from .service_announcement import ServiceAnnouncement
from .sharepoint import Sharepoint
from .edge import ... | Admin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Admin:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Admin:
"""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: Admin"""
... | stack_v2_sparse_classes_36k_train_007503 | 3,415 | 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: Admin",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(parse_n... | 3 | stack_v2_sparse_classes_30k_train_018430 | Implement the Python class `Admin` described below.
Class description:
Implement the Admin class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Admin: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The p... | Implement the Python class `Admin` described below.
Class description:
Implement the Admin class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Admin: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The p... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class Admin:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Admin:
"""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: Admin"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Admin:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Admin:
"""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: Admin"""
if not pars... | the_stack_v2_python_sparse | msgraph/generated/models/admin.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
8010f00620e7b8ae7b77539b05069e113e3fc079 | [
"modules = ('lxml', 'lxml.html', 'lxml.html.builder', 'datetime', 'dateutil.relativedelta', 'pytz', 'invenio.bibsched_tasklets.bst_fermilab_research_glance')\nmodule_import_err = []\nfor module in modules:\n try:\n __import__(module)\n except ImportError:\n module_import_err.append('failed to im... | <|body_start_0|>
modules = ('lxml', 'lxml.html', 'lxml.html.builder', 'datetime', 'dateutil.relativedelta', 'pytz', 'invenio.bibsched_tasklets.bst_fermilab_research_glance')
module_import_err = []
for module in modules:
try:
__import__(module)
except Impor... | Set of unit tests for fermilab_research_glance.py. | ResearchGlanceTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResearchGlanceTests:
"""Set of unit tests for fermilab_research_glance.py."""
def test_dependencies(self):
"""verify that required modules can be imported"""
<|body_0|>
def test_output_directory_exists(self):
"""verify that the destination directory for the repor... | stack_v2_sparse_classes_36k_train_007504 | 4,673 | no_license | [
{
"docstring": "verify that required modules can be imported",
"name": "test_dependencies",
"signature": "def test_dependencies(self)"
},
{
"docstring": "verify that the destination directory for the report exists",
"name": "test_output_directory_exists",
"signature": "def test_output_di... | 3 | null | Implement the Python class `ResearchGlanceTests` described below.
Class description:
Set of unit tests for fermilab_research_glance.py.
Method signatures and docstrings:
- def test_dependencies(self): verify that required modules can be imported
- def test_output_directory_exists(self): verify that the destination di... | Implement the Python class `ResearchGlanceTests` described below.
Class description:
Set of unit tests for fermilab_research_glance.py.
Method signatures and docstrings:
- def test_dependencies(self): verify that required modules can be imported
- def test_output_directory_exists(self): verify that the destination di... | 37c905be9a569a6c25ced045eb84545ddb7ac3a5 | <|skeleton|>
class ResearchGlanceTests:
"""Set of unit tests for fermilab_research_glance.py."""
def test_dependencies(self):
"""verify that required modules can be imported"""
<|body_0|>
def test_output_directory_exists(self):
"""verify that the destination directory for the repor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResearchGlanceTests:
"""Set of unit tests for fermilab_research_glance.py."""
def test_dependencies(self):
"""verify that required modules can be imported"""
modules = ('lxml', 'lxml.html', 'lxml.html.builder', 'datetime', 'dateutil.relativedelta', 'pytz', 'invenio.bibsched_tasklets.bst_f... | the_stack_v2_python_sparse | bibtasklets/bst_fermilab_research_glance_unit_test.py | inspirehep/inspire | train | 9 |
c5cf172bfa629e34727c98ea9a2fb7530988ebf7 | [
"self.mean = mean\nself.std = std\nself.is_scale = is_scale\nself.is_channel_first = is_channel_first",
"im = im.astype(np.float32, copy=False)\nif self.is_channel_first:\n mean = np.array(self.mean)[:, np.newaxis, np.newaxis]\n std = np.array(self.std)[:, np.newaxis, np.newaxis]\nelse:\n mean = np.array... | <|body_start_0|>
self.mean = mean
self.std = std
self.is_scale = is_scale
self.is_channel_first = is_channel_first
<|end_body_0|>
<|body_start_1|>
im = im.astype(np.float32, copy=False)
if self.is_channel_first:
mean = np.array(self.mean)[:, np.newaxis, np.ne... | NormalizeImage | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NormalizeImage:
def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1], is_scale=True, is_channel_first=True):
"""Args: mean (list): the pixel mean std (list): the pixel variance"""
<|body_0|>
def __call__(self, im):
"""Normalize the image. Operators: 1.(option... | stack_v2_sparse_classes_36k_train_007505 | 7,004 | permissive | [
{
"docstring": "Args: mean (list): the pixel mean std (list): the pixel variance",
"name": "__init__",
"signature": "def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1], is_scale=True, is_channel_first=True)"
},
{
"docstring": "Normalize the image. Operators: 1.(optional) Scale the imag... | 2 | stack_v2_sparse_classes_30k_train_017821 | Implement the Python class `NormalizeImage` described below.
Class description:
Implement the NormalizeImage class.
Method signatures and docstrings:
- def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1], is_scale=True, is_channel_first=True): Args: mean (list): the pixel mean std (list): the pixel variance
... | Implement the Python class `NormalizeImage` described below.
Class description:
Implement the NormalizeImage class.
Method signatures and docstrings:
- def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1], is_scale=True, is_channel_first=True): Args: mean (list): the pixel mean std (list): the pixel variance
... | b402610a6f0b382a978e82473b541ea1fc6cf09a | <|skeleton|>
class NormalizeImage:
def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1], is_scale=True, is_channel_first=True):
"""Args: mean (list): the pixel mean std (list): the pixel variance"""
<|body_0|>
def __call__(self, im):
"""Normalize the image. Operators: 1.(option... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NormalizeImage:
def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1], is_scale=True, is_channel_first=True):
"""Args: mean (list): the pixel mean std (list): the pixel variance"""
self.mean = mean
self.std = std
self.is_scale = is_scale
self.is_channel_first = i... | the_stack_v2_python_sparse | modules/image/object_detection/ssd_vgg16_512_coco2017/data_feed.py | PaddlePaddle/PaddleHub | train | 12,914 | |
a60ebebdb228325d569ab896d9bc7bca4f0d8500 | [
"longest = ''\ndp_matrix = [[True for _ in range(len(s))] for start in range(len(s))]\nfor end in range(0, len(s)):\n for start in range(0, end + 1):\n if start == end:\n if len(longest) == 0:\n longest = s[start]\n elif s[start] == s[end] and start + 1 <= end and dp_matri... | <|body_start_0|>
longest = ''
dp_matrix = [[True for _ in range(len(s))] for start in range(len(s))]
for end in range(0, len(s)):
for start in range(0, end + 1):
if start == end:
if len(longest) == 0:
longest = s[start]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestPalindrome(self, s: str) -> str:
"""naive dp; 8728ms 5%; 22.2MB, 5.23%; 遍历了所有可能的结果"""
<|body_0|>
def longestPalindrome2(self, s: str) -> str:
"""作者：skay2002 来源：力扣（LeetCode）"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
longest... | stack_v2_sparse_classes_36k_train_007506 | 1,909 | no_license | [
{
"docstring": "naive dp; 8728ms 5%; 22.2MB, 5.23%; 遍历了所有可能的结果",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s: str) -> str"
},
{
"docstring": "作者：skay2002 来源：力扣（LeetCode）",
"name": "longestPalindrome2",
"signature": "def longestPalindrome2(self, s: str) -> str... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s: str) -> str: naive dp; 8728ms 5%; 22.2MB, 5.23%; 遍历了所有可能的结果
- def longestPalindrome2(self, s: str) -> str: 作者：skay2002 来源：力扣（LeetCode） | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s: str) -> str: naive dp; 8728ms 5%; 22.2MB, 5.23%; 遍历了所有可能的结果
- def longestPalindrome2(self, s: str) -> str: 作者：skay2002 来源：力扣（LeetCode）
<|skeleton|... | b6712c793bbfe443953e7186b5dbd876c01cd9a0 | <|skeleton|>
class Solution:
def longestPalindrome(self, s: str) -> str:
"""naive dp; 8728ms 5%; 22.2MB, 5.23%; 遍历了所有可能的结果"""
<|body_0|>
def longestPalindrome2(self, s: str) -> str:
"""作者：skay2002 来源：力扣（LeetCode）"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestPalindrome(self, s: str) -> str:
"""naive dp; 8728ms 5%; 22.2MB, 5.23%; 遍历了所有可能的结果"""
longest = ''
dp_matrix = [[True for _ in range(len(s))] for start in range(len(s))]
for end in range(0, len(s)):
for start in range(0, end + 1):
... | the_stack_v2_python_sparse | 05_leetcode/5.最长回文子串.py | niceNASA/Python-Foundation-Suda | train | 0 | |
42bdc6d18d3394296c471f8f39f4cd62f052516a | [
"super(MultiHeadDenseLayer, self).__init__()\nself._output_units = output_units\nself._num_heads = num_heads\nself._use_bias = use_bias\nself._is_output_transform = is_output_transform\nself._activation = activation\nself._activation_fn = get_activation(activation)\nself._flatten_output_units = tf.nest.flatten(self... | <|body_start_0|>
super(MultiHeadDenseLayer, self).__init__()
self._output_units = output_units
self._num_heads = num_heads
self._use_bias = use_bias
self._is_output_transform = is_output_transform
self._activation = activation
self._activation_fn = get_activation(... | Auto splitting or combining heads for the linear transformation. | MultiHeadDenseLayer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadDenseLayer:
"""Auto splitting or combining heads for the linear transformation."""
def __init__(self, input_size, output_units, num_heads, activation=None, use_bias=True, is_output_transform=False):
"""Initializes MultiHeadDenseLayer. Args: input_size: The input dimension. o... | stack_v2_sparse_classes_36k_train_007507 | 15,012 | permissive | [
{
"docstring": "Initializes MultiHeadDenseLayer. Args: input_size: The input dimension. output_units: A int scalar or int list, indicating the transformed output units. It must be a int scalar when `is_output_transform` is True. num_heads: The head num. activation: A string or a callable function for activation... | 4 | null | Implement the Python class `MultiHeadDenseLayer` described below.
Class description:
Auto splitting or combining heads for the linear transformation.
Method signatures and docstrings:
- def __init__(self, input_size, output_units, num_heads, activation=None, use_bias=True, is_output_transform=False): Initializes Mult... | Implement the Python class `MultiHeadDenseLayer` described below.
Class description:
Auto splitting or combining heads for the linear transformation.
Method signatures and docstrings:
- def __init__(self, input_size, output_units, num_heads, activation=None, use_bias=True, is_output_transform=False): Initializes Mult... | 06613a99305f02312a0e64ee3c3c50e7b00dcf0e | <|skeleton|>
class MultiHeadDenseLayer:
"""Auto splitting or combining heads for the linear transformation."""
def __init__(self, input_size, output_units, num_heads, activation=None, use_bias=True, is_output_transform=False):
"""Initializes MultiHeadDenseLayer. Args: input_size: The input dimension. o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiHeadDenseLayer:
"""Auto splitting or combining heads for the linear transformation."""
def __init__(self, input_size, output_units, num_heads, activation=None, use_bias=True, is_output_transform=False):
"""Initializes MultiHeadDenseLayer. Args: input_size: The input dimension. output_units: ... | the_stack_v2_python_sparse | neurst/neurst_pt/layers/common_layers.py | ohlionel/Prune-Tune | train | 12 |
b42a9579e2e2f4b0c1857bfe3354a2d234819ecf | [
"self.search_list = {}\nif content is None:\n self.content = ''\n self.content_list\nelse:\n self.content = content\n self.content_list = [word.strip('\"\\',?!./\\\\') for word in content.split()]",
"if word not in self.search_list:\n self.search_list[word] = self.content_list.count(word)\nreturn s... | <|body_start_0|>
self.search_list = {}
if content is None:
self.content = ''
self.content_list
else:
self.content = content
self.content_list = [word.strip('"\',?!./\\') for word in content.split()]
<|end_body_0|>
<|body_start_1|>
if word ... | Book | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Book:
def __init__(self, content):
"""both the content as string and content_list as list, which is a list of all words, are stored"""
<|body_0|>
def find_frequency(self, word):
""">>> book = Book("The most mysterious season of Game of Thrones yet is also the best — ... | stack_v2_sparse_classes_36k_train_007508 | 1,247 | no_license | [
{
"docstring": "both the content as string and content_list as list, which is a list of all words, are stored",
"name": "__init__",
"signature": "def __init__(self, content)"
},
{
"docstring": ">>> book = Book(\"The most mysterious season of Game of Thrones yet is also the best — that’s accordin... | 2 | stack_v2_sparse_classes_30k_train_016006 | Implement the Python class `Book` described below.
Class description:
Implement the Book class.
Method signatures and docstrings:
- def __init__(self, content): both the content as string and content_list as list, which is a list of all words, are stored
- def find_frequency(self, word): >>> book = Book("The most mys... | Implement the Python class `Book` described below.
Class description:
Implement the Book class.
Method signatures and docstrings:
- def __init__(self, content): both the content as string and content_list as list, which is a list of all words, are stored
- def find_frequency(self, word): >>> book = Book("The most mys... | d28ea71a1a5aaa97b23e23bb04c84aaa5f590a78 | <|skeleton|>
class Book:
def __init__(self, content):
"""both the content as string and content_list as list, which is a list of all words, are stored"""
<|body_0|>
def find_frequency(self, word):
""">>> book = Book("The most mysterious season of Game of Thrones yet is also the best — ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Book:
def __init__(self, content):
"""both the content as string and content_list as list, which is a list of all words, are stored"""
self.search_list = {}
if content is None:
self.content = ''
self.content_list
else:
self.content = content
... | the_stack_v2_python_sparse | chapter16-moderate/16.2.py | yuanxu-li/careercup | train | 0 | |
d9764da633d7d0165f274e36e493ce62af080c72 | [
"super().__init__()\nself.embed_dim, self.n_heads, self.head_dim = (embed_dim, n_heads, head_dim)\nself.qk_layer_norms = qk_layer_norms\nself.context_layer_norm = nn.LayerNorm(self.embed_dim)\nself.latents_layer_norm = nn.LayerNorm(self.embed_dim)\nif self.qk_layer_norms:\n self.q_layer_norm = nn.LayerNorm(self.... | <|body_start_0|>
super().__init__()
self.embed_dim, self.n_heads, self.head_dim = (embed_dim, n_heads, head_dim)
self.qk_layer_norms = qk_layer_norms
self.context_layer_norm = nn.LayerNorm(self.embed_dim)
self.latents_layer_norm = nn.LayerNorm(self.embed_dim)
if self.qk_l... | IdeficsPerceiverAttention | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IdeficsPerceiverAttention:
def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
<|body_0|>
def forward(self, context: tor... | stack_v2_sparse_classes_36k_train_007509 | 9,432 | permissive | [
{
"docstring": "Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`",
"name": "__init__",
"signature": "def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None"
},
{
"docstring": "Runs Perceiver Self-Atte... | 2 | stack_v2_sparse_classes_30k_train_015690 | Implement the Python class `IdeficsPerceiverAttention` described below.
Class description:
Implement the IdeficsPerceiverAttention class.
Method signatures and docstrings:
- def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None: Perceiver Cross-Attention Module --> let long-for... | Implement the Python class `IdeficsPerceiverAttention` described below.
Class description:
Implement the IdeficsPerceiverAttention class.
Method signatures and docstrings:
- def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None: Perceiver Cross-Attention Module --> let long-for... | 4fa0aff21ee083d0197a898cdf17ff476fae2ac3 | <|skeleton|>
class IdeficsPerceiverAttention:
def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
<|body_0|>
def forward(self, context: tor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IdeficsPerceiverAttention:
def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.embed_dim, self.n_heads, sel... | the_stack_v2_python_sparse | src/transformers/models/idefics/perceiver.py | huggingface/transformers | train | 102,193 | |
3c44016df7badbd9f28932a29b14369687079c32 | [
"super(DCGAN_D, self).__init__()\nself.ngpu = ngpu\nself.use_sigmoid = use_sigmoid\nassert isize % 16 == 0, 'isize has to be a multiple of 16'\nmain = nn.Sequential()\nmain.add_module('initial_conv_{0}-{1}'.format(nc, ndf), nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))\nmain.add_module('initial_relu_{0}'.format(ndf), nn... | <|body_start_0|>
super(DCGAN_D, self).__init__()
self.ngpu = ngpu
self.use_sigmoid = use_sigmoid
assert isize % 16 == 0, 'isize has to be a multiple of 16'
main = nn.Sequential()
main.add_module('initial_conv_{0}-{1}'.format(nc, ndf), nn.Conv2d(nc, ndf, 4, 2, 1, bias=Fals... | DCGAN Discriminator. | DCGAN_D | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DCGAN_D:
"""DCGAN Discriminator."""
def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0, use_sigmoid=True, norm=nn.BatchNorm2d):
"""Constructor."""
<|body_0|>
def forward(self, input):
"""Forward method."""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_36k_train_007510 | 34,675 | permissive | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0, use_sigmoid=True, norm=nn.BatchNorm2d)"
},
{
"docstring": "Forward method.",
"name": "forward",
"signature": "def forward(self, input)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012550 | Implement the Python class `DCGAN_D` described below.
Class description:
DCGAN Discriminator.
Method signatures and docstrings:
- def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0, use_sigmoid=True, norm=nn.BatchNorm2d): Constructor.
- def forward(self, input): Forward method. | Implement the Python class `DCGAN_D` described below.
Class description:
DCGAN Discriminator.
Method signatures and docstrings:
- def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0, use_sigmoid=True, norm=nn.BatchNorm2d): Constructor.
- def forward(self, input): Forward method.
<|skeleton|>
class DCGAN_D:... | e1e4a8d9a2ab51c2108a4d167bc37fab101f0c2c | <|skeleton|>
class DCGAN_D:
"""DCGAN Discriminator."""
def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0, use_sigmoid=True, norm=nn.BatchNorm2d):
"""Constructor."""
<|body_0|>
def forward(self, input):
"""Forward method."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DCGAN_D:
"""DCGAN Discriminator."""
def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0, use_sigmoid=True, norm=nn.BatchNorm2d):
"""Constructor."""
super(DCGAN_D, self).__init__()
self.ngpu = ngpu
self.use_sigmoid = use_sigmoid
assert isize % 16 == 0, 'is... | the_stack_v2_python_sparse | diffrend/torch/GAN/twin_networks.py | sainatarajan/pix2shape | train | 0 |
1b4edbb96471d645944e4714ca19c7f2606b9a22 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Asset service definition. | AssetServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AssetServiceServicer:
"""Asset service definition."""
def ExportAssets(self, request, context):
"""Exports assets with time and resource types to a given Cloud Storage location. The output format is newline-delimited JSON. This API implements the [google.longrunning.Operation][google... | stack_v2_sparse_classes_36k_train_007511 | 3,532 | permissive | [
{
"docstring": "Exports assets with time and resource types to a given Cloud Storage location. The output format is newline-delimited JSON. This API implements the [google.longrunning.Operation][google.longrunning.Operation] API allowing you to keep track of the export.",
"name": "ExportAssets",
"signat... | 2 | stack_v2_sparse_classes_30k_train_007305 | Implement the Python class `AssetServiceServicer` described below.
Class description:
Asset service definition.
Method signatures and docstrings:
- def ExportAssets(self, request, context): Exports assets with time and resource types to a given Cloud Storage location. The output format is newline-delimited JSON. This... | Implement the Python class `AssetServiceServicer` described below.
Class description:
Asset service definition.
Method signatures and docstrings:
- def ExportAssets(self, request, context): Exports assets with time and resource types to a given Cloud Storage location. The output format is newline-delimited JSON. This... | d897d56bce03d1fda98b79afb08264e51d46c421 | <|skeleton|>
class AssetServiceServicer:
"""Asset service definition."""
def ExportAssets(self, request, context):
"""Exports assets with time and resource types to a given Cloud Storage location. The output format is newline-delimited JSON. This API implements the [google.longrunning.Operation][google... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AssetServiceServicer:
"""Asset service definition."""
def ExportAssets(self, request, context):
"""Exports assets with time and resource types to a given Cloud Storage location. The output format is newline-delimited JSON. This API implements the [google.longrunning.Operation][google.longrunning.... | the_stack_v2_python_sparse | asset/google/cloud/asset_v1beta1/proto/asset_service_pb2_grpc.py | tswast/google-cloud-python | train | 1 |
d99b5a3f0a61ae0e02d655921277d762d6d9fc53 | [
"limit = request.args.get('limit', 10, int)\npage = request.args.get('page', 1, int)\ninfo = request.args.get('info', '', str)\nskip = limit * (page - 1)\nweekpasswd_db = db_name_conf()['weekpasswd_db']\ntotal = mongo_cli[weekpasswd_db].find({'task_name': re.compile(info)}).count()\ndict_resp = mongo_cli[weekpasswd... | <|body_start_0|>
limit = request.args.get('limit', 10, int)
page = request.args.get('page', 1, int)
info = request.args.get('info', '', str)
skip = limit * (page - 1)
weekpasswd_db = db_name_conf()['weekpasswd_db']
total = mongo_cli[weekpasswd_db].find({'task_name': re.co... | AuthTesterDetectView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthTesterDetectView:
def get(self):
"""检出目标列表 --- tags: - 弱口令检测(auth_tester) definitions: - schema: id: dao.auth_tester_weekpasswd_info properties: _id: type: string description: _id date: type: string description: 扫描日期 username: type: string description: 账户 password: type: string descr... | stack_v2_sparse_classes_36k_train_007512 | 20,908 | no_license | [
{
"docstring": "检出目标列表 --- tags: - 弱口令检测(auth_tester) definitions: - schema: id: dao.auth_tester_weekpasswd_info properties: _id: type: string description: _id date: type: string description: 扫描日期 username: type: string description: 账户 password: type: string description: 密码 service: type: string description: 服务... | 2 | stack_v2_sparse_classes_30k_train_014719 | Implement the Python class `AuthTesterDetectView` described below.
Class description:
Implement the AuthTesterDetectView class.
Method signatures and docstrings:
- def get(self): 检出目标列表 --- tags: - 弱口令检测(auth_tester) definitions: - schema: id: dao.auth_tester_weekpasswd_info properties: _id: type: string description:... | Implement the Python class `AuthTesterDetectView` described below.
Class description:
Implement the AuthTesterDetectView class.
Method signatures and docstrings:
- def get(self): 检出目标列表 --- tags: - 弱口令检测(auth_tester) definitions: - schema: id: dao.auth_tester_weekpasswd_info properties: _id: type: string description:... | aa75f06ad25b1238176165a0dcf4685c59cd8284 | <|skeleton|>
class AuthTesterDetectView:
def get(self):
"""检出目标列表 --- tags: - 弱口令检测(auth_tester) definitions: - schema: id: dao.auth_tester_weekpasswd_info properties: _id: type: string description: _id date: type: string description: 扫描日期 username: type: string description: 账户 password: type: string descr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AuthTesterDetectView:
def get(self):
"""检出目标列表 --- tags: - 弱口令检测(auth_tester) definitions: - schema: id: dao.auth_tester_weekpasswd_info properties: _id: type: string description: _id date: type: string description: 扫描日期 username: type: string description: 账户 password: type: string description: 密码 ser... | the_stack_v2_python_sparse | aquaman/views/auth_tester.py | jstang9527/aquaman | train | 15 | |
07b50952865e5d5814311b953089b1ad29ea9ea0 | [
"with Database() as db:\n if id_lane is None and is_active is None:\n data = db.query(Table).all()\n elif id_lane is None:\n data = db.query(Table).filter(Table.is_active == is_active).all()\n else:\n data = db.query(Table).get(id_lane)\nreturn {'data': data}",
"if self.has_permissio... | <|body_start_0|>
with Database() as db:
if id_lane is None and is_active is None:
data = db.query(Table).all()
elif id_lane is None:
data = db.query(Table).filter(Table.is_active == is_active).all()
else:
data = db.query(Table).... | Lane | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Lane:
def get(self, id_lane=None, is_active=None):
"""Return all lane information :param id_lane: UUID :param is_active: Boolean"""
<|body_0|>
def create(self, body):
"""Create a new lane :param body: { name: JSON, id_city: UUID }"""
<|body_1|>
def modif... | stack_v2_sparse_classes_36k_train_007513 | 2,668 | no_license | [
{
"docstring": "Return all lane information :param id_lane: UUID :param is_active: Boolean",
"name": "get",
"signature": "def get(self, id_lane=None, is_active=None)"
},
{
"docstring": "Create a new lane :param body: { name: JSON, id_city: UUID }",
"name": "create",
"signature": "def cre... | 4 | stack_v2_sparse_classes_30k_train_001260 | Implement the Python class `Lane` described below.
Class description:
Implement the Lane class.
Method signatures and docstrings:
- def get(self, id_lane=None, is_active=None): Return all lane information :param id_lane: UUID :param is_active: Boolean
- def create(self, body): Create a new lane :param body: { name: J... | Implement the Python class `Lane` described below.
Class description:
Implement the Lane class.
Method signatures and docstrings:
- def get(self, id_lane=None, is_active=None): Return all lane information :param id_lane: UUID :param is_active: Boolean
- def create(self, body): Create a new lane :param body: { name: J... | 43bd57c466a5cd3b133ddc437cb4a6b9f007d267 | <|skeleton|>
class Lane:
def get(self, id_lane=None, is_active=None):
"""Return all lane information :param id_lane: UUID :param is_active: Boolean"""
<|body_0|>
def create(self, body):
"""Create a new lane :param body: { name: JSON, id_city: UUID }"""
<|body_1|>
def modif... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Lane:
def get(self, id_lane=None, is_active=None):
"""Return all lane information :param id_lane: UUID :param is_active: Boolean"""
with Database() as db:
if id_lane is None and is_active is None:
data = db.query(Table).all()
elif id_lane is None:
... | the_stack_v2_python_sparse | resturls/lane.py | CAUCA-9-1-1/survip-api | train | 1 | |
576b132b6214fca3950086f0e1cd3034fb945875 | [
"self._counters = counters\nself._name = name\nself._start = time.clock()",
"if self._counters != None:\n elapsed = (time.clock() - self._start) * 1000\n self._counters._set_timing(self._name, elapsed)"
] | <|body_start_0|>
self._counters = counters
self._name = name
self._start = time.clock()
<|end_body_0|>
<|body_start_1|>
if self._counters != None:
elapsed = (time.clock() - self._start) * 1000
self._counters._set_timing(self._name, elapsed)
<|end_body_1|>
| Implementation of ITiming interface that provides callback to end measuring execution time interface and update interval counter. | Timing | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Timing:
"""Implementation of ITiming interface that provides callback to end measuring execution time interface and update interval counter."""
def __init__(self, counters=None, name=None):
"""Creates instance of timing object that calculates elapsed time and stores it to specified p... | stack_v2_sparse_classes_36k_train_007514 | 1,356 | permissive | [
{
"docstring": "Creates instance of timing object that calculates elapsed time and stores it to specified performance counters component under specified name. Args: counters: a performance counters component to store calculated value. name: a name of the counter to record elapsed time interval.",
"name": "_... | 2 | stack_v2_sparse_classes_30k_train_014249 | Implement the Python class `Timing` described below.
Class description:
Implementation of ITiming interface that provides callback to end measuring execution time interface and update interval counter.
Method signatures and docstrings:
- def __init__(self, counters=None, name=None): Creates instance of timing object ... | Implement the Python class `Timing` described below.
Class description:
Implementation of ITiming interface that provides callback to end measuring execution time interface and update interval counter.
Method signatures and docstrings:
- def __init__(self, counters=None, name=None): Creates instance of timing object ... | 70eca1ffc44bfdc45c9c65b0ee347fa578368849 | <|skeleton|>
class Timing:
"""Implementation of ITiming interface that provides callback to end measuring execution time interface and update interval counter."""
def __init__(self, counters=None, name=None):
"""Creates instance of timing object that calculates elapsed time and stores it to specified p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Timing:
"""Implementation of ITiming interface that provides callback to end measuring execution time interface and update interval counter."""
def __init__(self, counters=None, name=None):
"""Creates instance of timing object that calculates elapsed time and stores it to specified performance co... | the_stack_v2_python_sparse | pip_services_runtime/counters/Timing.py | pip-services-archive/pip-services-runtime-python | train | 0 |
07aa91ae577356f7e02ed2642ea64e269421f89d | [
"model_summary_path = os.path.join(self.paths['exp_dir'], 'model_summary.csv')\nif self.model_summary is not None:\n self.model_summary.to_csv(model_summary_path)",
"loss_name = self.configs.LOSS.LOSS_NAME\nloss_data_frame = pandas.DataFrame(loss_meter.recorded_values, columns=[loss_name])\nmetrics_data = {}\n... | <|body_start_0|>
model_summary_path = os.path.join(self.paths['exp_dir'], 'model_summary.csv')
if self.model_summary is not None:
self.model_summary.to_csv(model_summary_path)
<|end_body_0|>
<|body_start_1|>
loss_name = self.configs.LOSS.LOSS_NAME
loss_data_frame = pandas.Da... | Agent for handling output files. | OutputHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OutputHandler:
"""Agent for handling output files."""
def save_model_summary(self):
"""Save model summary."""
<|body_0|>
def save_metrics(self, output_dir, sample_ids, loss_meter, metric_meters):
"""Write out files of metrics values."""
<|body_1|>
de... | stack_v2_sparse_classes_36k_train_007515 | 2,408 | no_license | [
{
"docstring": "Save model summary.",
"name": "save_model_summary",
"signature": "def save_model_summary(self)"
},
{
"docstring": "Write out files of metrics values.",
"name": "save_metrics",
"signature": "def save_metrics(self, output_dir, sample_ids, loss_meter, metric_meters)"
},
... | 3 | null | Implement the Python class `OutputHandler` described below.
Class description:
Agent for handling output files.
Method signatures and docstrings:
- def save_model_summary(self): Save model summary.
- def save_metrics(self, output_dir, sample_ids, loss_meter, metric_meters): Write out files of metrics values.
- def sa... | Implement the Python class `OutputHandler` described below.
Class description:
Agent for handling output files.
Method signatures and docstrings:
- def save_model_summary(self): Save model summary.
- def save_metrics(self, output_dir, sample_ids, loss_meter, metric_meters): Write out files of metrics values.
- def sa... | 2d35b2d75f700277d2b465ecc34c8c864c77b651 | <|skeleton|>
class OutputHandler:
"""Agent for handling output files."""
def save_model_summary(self):
"""Save model summary."""
<|body_0|>
def save_metrics(self, output_dir, sample_ids, loss_meter, metric_meters):
"""Write out files of metrics values."""
<|body_1|>
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OutputHandler:
"""Agent for handling output files."""
def save_model_summary(self):
"""Save model summary."""
model_summary_path = os.path.join(self.paths['exp_dir'], 'model_summary.csv')
if self.model_summary is not None:
self.model_summary.to_csv(model_summary_path)
... | the_stack_v2_python_sparse | agents/handlers/output_handler.py | templeblock/OctaveConv-LinearConv-UNET | train | 0 |
d899a9406e323751205c9405d8c20b1af8791ea9 | [
"self.email_addresses = email_addresses\nself.email_delivery_targets = email_delivery_targets\nself.raise_object_level_failure_alert = raise_object_level_failure_alert",
"if dictionary is None:\n return None\nemail_addresses = dictionary.get('emailAddresses')\nemail_delivery_targets = None\nif dictionary.get('... | <|body_start_0|>
self.email_addresses = email_addresses
self.email_delivery_targets = email_delivery_targets
self.raise_object_level_failure_alert = raise_object_level_failure_alert
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
email_addresses = ... | Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of EmailDeliveryTarget): Specifies additional email addresses where alert notificati... | AlertingConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlertingConfig:
"""Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of EmailDeliveryTarget): Specifies additio... | stack_v2_sparse_classes_36k_train_007516 | 2,719 | permissive | [
{
"docstring": "Constructor for the AlertingConfig class",
"name": "__init__",
"signature": "def __init__(self, email_addresses=None, email_delivery_targets=None, raise_object_level_failure_alert=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dict... | 2 | stack_v2_sparse_classes_30k_train_002091 | Implement the Python class `AlertingConfig` described below.
Class description:
Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of ... | Implement the Python class `AlertingConfig` described below.
Class description:
Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class AlertingConfig:
"""Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of EmailDeliveryTarget): Specifies additio... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AlertingConfig:
"""Implementation of the 'AlertingConfig' model. Specifies optional settings for alerting. Attributes: email_addresses (list of string): Exists to maintain backwards compatibility with versions before eff8198. email_delivery_targets (list of EmailDeliveryTarget): Specifies additional email add... | the_stack_v2_python_sparse | cohesity_management_sdk/models/alerting_config.py | cohesity/management-sdk-python | train | 24 |
6fa8ce9d1f166ef13328d0e5f538dff545cef425 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn ImportedWindowsAutopilotDeviceIdentityUpload()",
"from .entity import Entity\nfrom .imported_windows_autopilot_device_identity import ImportedWindowsAutopilotDeviceIdentity\nfrom .imported_windows_autopilot_device_identity_upload_statu... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return ImportedWindowsAutopilotDeviceIdentityUpload()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .imported_windows_autopilot_device_identity import ImportedWindowsAutopilot... | Import windows autopilot devices using upload. | ImportedWindowsAutopilotDeviceIdentityUpload | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImportedWindowsAutopilotDeviceIdentityUpload:
"""Import windows autopilot devices using upload."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ImportedWindowsAutopilotDeviceIdentityUpload:
"""Creates a new instance of the appropriate class based on di... | stack_v2_sparse_classes_36k_train_007517 | 3,621 | 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: ImportedWindowsAutopilotDeviceIdentityUpload",
"name": "create_from_discriminator_value",
"signature": "def ... | 3 | stack_v2_sparse_classes_30k_train_016642 | Implement the Python class `ImportedWindowsAutopilotDeviceIdentityUpload` described below.
Class description:
Import windows autopilot devices using upload.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ImportedWindowsAutopilotDeviceIdentityUpload: Cr... | Implement the Python class `ImportedWindowsAutopilotDeviceIdentityUpload` described below.
Class description:
Import windows autopilot devices using upload.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ImportedWindowsAutopilotDeviceIdentityUpload: Cr... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class ImportedWindowsAutopilotDeviceIdentityUpload:
"""Import windows autopilot devices using upload."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ImportedWindowsAutopilotDeviceIdentityUpload:
"""Creates a new instance of the appropriate class based on di... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImportedWindowsAutopilotDeviceIdentityUpload:
"""Import windows autopilot devices using upload."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ImportedWindowsAutopilotDeviceIdentityUpload:
"""Creates a new instance of the appropriate class based on discriminator v... | the_stack_v2_python_sparse | msgraph/generated/models/imported_windows_autopilot_device_identity_upload.py | microsoftgraph/msgraph-sdk-python | train | 135 |
4e737ca4e8538c819c0d4aca09c48ea1f543e585 | [
"if not bn_layers:\n _logger.info('High Bias folding is not supported for models without BatchNorm Layers')\n return\nfor cls_set_info in cls_set_info_list:\n for cls_pair_info in cls_set_info.cls_pair_info_list:\n if not cls_pair_info.layer1.use_bias or not cls_pair_info.layer2.use_bias or cls_pair... | <|body_start_0|>
if not bn_layers:
_logger.info('High Bias folding is not supported for models without BatchNorm Layers')
return
for cls_set_info in cls_set_info_list:
for cls_pair_info in cls_set_info.cls_pair_info_list:
if not cls_pair_info.layer1.us... | Code to apply the high-bias-fold technique to a model | HighBiasFold | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HighBiasFold:
"""Code to apply the high-bias-fold technique to a model"""
def bias_fold(cls_set_info_list: typing.List[ClsSetInfo], bn_layers: typing.Dict[tf.keras.layers.Conv2D, tf.keras.layers.BatchNormalization]):
"""Folds bias values greater than 3 * sigma to next layer's bias :p... | stack_v2_sparse_classes_36k_train_007518 | 23,575 | permissive | [
{
"docstring": "Folds bias values greater than 3 * sigma to next layer's bias :param cls_set_info_list: List of info elements for each cls set :param bn_layers: Key: Conv/Linear layer Value: Corresponding folded BN layer",
"name": "bias_fold",
"signature": "def bias_fold(cls_set_info_list: typing.List[C... | 4 | null | Implement the Python class `HighBiasFold` described below.
Class description:
Code to apply the high-bias-fold technique to a model
Method signatures and docstrings:
- def bias_fold(cls_set_info_list: typing.List[ClsSetInfo], bn_layers: typing.Dict[tf.keras.layers.Conv2D, tf.keras.layers.BatchNormalization]): Folds b... | Implement the Python class `HighBiasFold` described below.
Class description:
Code to apply the high-bias-fold technique to a model
Method signatures and docstrings:
- def bias_fold(cls_set_info_list: typing.List[ClsSetInfo], bn_layers: typing.Dict[tf.keras.layers.Conv2D, tf.keras.layers.BatchNormalization]): Folds b... | 5a406e657082b6a4f6e4bf48f0e46e085cb1e351 | <|skeleton|>
class HighBiasFold:
"""Code to apply the high-bias-fold technique to a model"""
def bias_fold(cls_set_info_list: typing.List[ClsSetInfo], bn_layers: typing.Dict[tf.keras.layers.Conv2D, tf.keras.layers.BatchNormalization]):
"""Folds bias values greater than 3 * sigma to next layer's bias :p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HighBiasFold:
"""Code to apply the high-bias-fold technique to a model"""
def bias_fold(cls_set_info_list: typing.List[ClsSetInfo], bn_layers: typing.Dict[tf.keras.layers.Conv2D, tf.keras.layers.BatchNormalization]):
"""Folds bias values greater than 3 * sigma to next layer's bias :param cls_set_... | the_stack_v2_python_sparse | TrainingExtensions/tensorflow/src/python/aimet_tensorflow/keras/cross_layer_equalization.py | quic/aimet | train | 1,676 |
1f264addd8dce1ffefa0c251d36e0791e1ef2ba9 | [
"self.acl = acl\nself.bucket_policy = bucket_policy\nself.efficient_mpu_enabled = efficient_mpu_enabled\nself.enable_obj_store_access = enable_obj_store_access\nself.init_cluster_id = init_cluster_id\nself.init_cluster_incarnation_id = init_cluster_incarnation_id\nself.lifecycle_config = lifecycle_config\nself.obje... | <|body_start_0|>
self.acl = acl
self.bucket_policy = bucket_policy
self.efficient_mpu_enabled = efficient_mpu_enabled
self.enable_obj_store_access = enable_obj_store_access
self.init_cluster_id = init_cluster_id
self.init_cluster_incarnation_id = init_cluster_incarnation_... | Implementation of the 'S3BucketConfigProto' model. TODO: type description here. Attributes: acl (ACLProto): ACL of the bucket. bucket_policy (BucketPolicy): Policy in effect for the bucket. efficient_mpu_enabled (bool): bool representing whether this mpu structure is based on S3 MPU 2.0 enable_obj_store_access (bool): ... | S3BucketConfigProto | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class S3BucketConfigProto:
"""Implementation of the 'S3BucketConfigProto' model. TODO: type description here. Attributes: acl (ACLProto): ACL of the bucket. bucket_policy (BucketPolicy): Policy in effect for the bucket. efficient_mpu_enabled (bool): bool representing whether this mpu structure is based... | stack_v2_sparse_classes_36k_train_007519 | 9,657 | permissive | [
{
"docstring": "Constructor for the S3BucketConfigProto class",
"name": "__init__",
"signature": "def __init__(self, acl=None, bucket_policy=None, efficient_mpu_enabled=None, enable_obj_store_access=None, init_cluster_id=None, init_cluster_incarnation_id=None, lifecycle_config=None, object_tags_added=No... | 2 | null | Implement the Python class `S3BucketConfigProto` described below.
Class description:
Implementation of the 'S3BucketConfigProto' model. TODO: type description here. Attributes: acl (ACLProto): ACL of the bucket. bucket_policy (BucketPolicy): Policy in effect for the bucket. efficient_mpu_enabled (bool): bool represent... | Implement the Python class `S3BucketConfigProto` described below.
Class description:
Implementation of the 'S3BucketConfigProto' model. TODO: type description here. Attributes: acl (ACLProto): ACL of the bucket. bucket_policy (BucketPolicy): Policy in effect for the bucket. efficient_mpu_enabled (bool): bool represent... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class S3BucketConfigProto:
"""Implementation of the 'S3BucketConfigProto' model. TODO: type description here. Attributes: acl (ACLProto): ACL of the bucket. bucket_policy (BucketPolicy): Policy in effect for the bucket. efficient_mpu_enabled (bool): bool representing whether this mpu structure is based... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class S3BucketConfigProto:
"""Implementation of the 'S3BucketConfigProto' model. TODO: type description here. Attributes: acl (ACLProto): ACL of the bucket. bucket_policy (BucketPolicy): Policy in effect for the bucket. efficient_mpu_enabled (bool): bool representing whether this mpu structure is based on S3 MPU 2.... | the_stack_v2_python_sparse | cohesity_management_sdk/models/s3_bucket_config_proto.py | cohesity/management-sdk-python | train | 24 |
3a8b2cf6d3f36cfe05234b8088f2db3fb8254e99 | [
"self._logger = logger\nself._no_run = False\nif not is_exe(exe_path):\n self._logger.error('No convert_format script available (exiting)')\n sys.exit(1)\nself._exe_path = exe_path\nself.informat = 'fastq'\nself.outformat = 'fasta'",
"self._outdirname = os.path.join(outdir)\nif not os.path.exists(self._outd... | <|body_start_0|>
self._logger = logger
self._no_run = False
if not is_exe(exe_path):
self._logger.error('No convert_format script available (exiting)')
sys.exit(1)
self._exe_path = exe_path
self.informat = 'fastq'
self.outformat = 'fasta'
<|end_bod... | Class for working with convert_format | Convert_Format | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Convert_Format:
"""Class for working with convert_format"""
def __init__(self, exe_path, logger):
"""Instantiate with location of executable"""
<|body_0|>
def run(self, infname, outdir, logger=None):
"""Run convert_format on the passed file"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_007520 | 3,597 | permissive | [
{
"docstring": "Instantiate with location of executable",
"name": "__init__",
"signature": "def __init__(self, exe_path, logger)"
},
{
"docstring": "Run convert_format on the passed file",
"name": "run",
"signature": "def run(self, infname, outdir, logger=None)"
},
{
"docstring":... | 3 | stack_v2_sparse_classes_30k_test_001067 | Implement the Python class `Convert_Format` described below.
Class description:
Class for working with convert_format
Method signatures and docstrings:
- def __init__(self, exe_path, logger): Instantiate with location of executable
- def run(self, infname, outdir, logger=None): Run convert_format on the passed file
-... | Implement the Python class `Convert_Format` described below.
Class description:
Class for working with convert_format
Method signatures and docstrings:
- def __init__(self, exe_path, logger): Instantiate with location of executable
- def run(self, infname, outdir, logger=None): Run convert_format on the passed file
-... | a3c64198aad3709a5c4d969f48ae0af11fdc25db | <|skeleton|>
class Convert_Format:
"""Class for working with convert_format"""
def __init__(self, exe_path, logger):
"""Instantiate with location of executable"""
<|body_0|>
def run(self, infname, outdir, logger=None):
"""Run convert_format on the passed file"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Convert_Format:
"""Class for working with convert_format"""
def __init__(self, exe_path, logger):
"""Instantiate with location of executable"""
self._logger = logger
self._no_run = False
if not is_exe(exe_path):
self._logger.error('No convert_format script avai... | the_stack_v2_python_sparse | metapy/pycits/seq_crumbs.py | peterthorpe5/public_scripts | train | 35 |
6eb2d0ebd4a5c5a609ec6372fc419bc927dc06fb | [
"size = len(nums)\ns = sum(nums)\nif s & 1 == 1:\n return False\ntarget = s // 2\ndp = [[False for _ in range(target + 1)] for _ in range(size)]\nfor i in range(target + 1):\n dp[0][i] = False if nums[0] != i else True\nfor i in range(1, size):\n for j in range(target + 1):\n if j >= nums[i]:\n ... | <|body_start_0|>
size = len(nums)
s = sum(nums)
if s & 1 == 1:
return False
target = s // 2
dp = [[False for _ in range(target + 1)] for _ in range(size)]
for i in range(target + 1):
dp[0][i] = False if nums[0] != i else True
for i in range... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canPartition(self, nums: list) -> bool:
"""提示: 方法一：二维动态规划 可以把这道题转换为 0-1 背包问题： 有一些物品，它们的重量存储在列表 nums 中， 而你刚好有两个包，怎么装能让这两个包装的物品重量相等？ 或者说，你只有一个包，怎么让这一个包刚好带走总重量一半的物品？ 0-1 背包问题也是最基础的背包问题，它的特点是：待挑选的物品有且仅有一个，可以选择也可以不选择。 下面我们定义状态，不妨就用问题的问法定义状态。 dp[i][j]：表示从数组的 [0, i] 这个子区间内挑选一些正整数，... | stack_v2_sparse_classes_36k_train_007521 | 3,963 | permissive | [
{
"docstring": "提示: 方法一：二维动态规划 可以把这道题转换为 0-1 背包问题： 有一些物品，它们的重量存储在列表 nums 中， 而你刚好有两个包，怎么装能让这两个包装的物品重量相等？ 或者说，你只有一个包，怎么让这一个包刚好带走总重量一半的物品？ 0-1 背包问题也是最基础的背包问题，它的特点是：待挑选的物品有且仅有一个，可以选择也可以不选择。 下面我们定义状态，不妨就用问题的问法定义状态。 dp[i][j]：表示从数组的 [0, i] 这个子区间内挑选一些正整数，每个数只能用一次，使得这些数的和等于 j。 新来一个数，例如是 nums[i]，这个数可能选择也可能不被选择： 如果不选择 num... | 3 | stack_v2_sparse_classes_30k_train_009137 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums: list) -> bool: 提示: 方法一：二维动态规划 可以把这道题转换为 0-1 背包问题： 有一些物品，它们的重量存储在列表 nums 中， 而你刚好有两个包，怎么装能让这两个包装的物品重量相等？ 或者说，你只有一个包，怎么让这一个包刚好带走总重量一半的物品？ 0-1 背包问题也是最基础的... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums: list) -> bool: 提示: 方法一：二维动态规划 可以把这道题转换为 0-1 背包问题： 有一些物品，它们的重量存储在列表 nums 中， 而你刚好有两个包，怎么装能让这两个包装的物品重量相等？ 或者说，你只有一个包，怎么让这一个包刚好带走总重量一半的物品？ 0-1 背包问题也是最基础的... | 889d8fa489f1f2719c5a0dafd3ae51df7b4bf978 | <|skeleton|>
class Solution:
def canPartition(self, nums: list) -> bool:
"""提示: 方法一：二维动态规划 可以把这道题转换为 0-1 背包问题： 有一些物品，它们的重量存储在列表 nums 中， 而你刚好有两个包，怎么装能让这两个包装的物品重量相等？ 或者说，你只有一个包，怎么让这一个包刚好带走总重量一半的物品？ 0-1 背包问题也是最基础的背包问题，它的特点是：待挑选的物品有且仅有一个，可以选择也可以不选择。 下面我们定义状态，不妨就用问题的问法定义状态。 dp[i][j]：表示从数组的 [0, i] 这个子区间内挑选一些正整数，... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def canPartition(self, nums: list) -> bool:
"""提示: 方法一：二维动态规划 可以把这道题转换为 0-1 背包问题： 有一些物品，它们的重量存储在列表 nums 中， 而你刚好有两个包，怎么装能让这两个包装的物品重量相等？ 或者说，你只有一个包，怎么让这一个包刚好带走总重量一半的物品？ 0-1 背包问题也是最基础的背包问题，它的特点是：待挑选的物品有且仅有一个，可以选择也可以不选择。 下面我们定义状态，不妨就用问题的问法定义状态。 dp[i][j]：表示从数组的 [0, i] 这个子区间内挑选一些正整数，每个数只能用一次，使得这些数... | the_stack_v2_python_sparse | LeetCode/416-分割等和子集/canPartition.py | jinbooooom/coding-for-algorithms | train | 14 | |
6c118095d8b9ba55ba115713ebdbfa6d922d7eb5 | [
"builder = plexus.builder\nlibs = tuple(self.filter(projects=plexus.projects))\nbuilder.add(plexus=plexus, assets=libs, target='libraries')\nbuilder.build(plexus=plexus, assets=libs)\nreturn",
"channel = journal.info('merlin.lib.info')\nfor lib in self.filter(projects=plexus.projects):\n channel.line(f'{lib.py... | <|body_start_0|>
builder = plexus.builder
libs = tuple(self.filter(projects=plexus.projects))
builder.add(plexus=plexus, assets=libs, target='libraries')
builder.build(plexus=plexus, assets=libs)
return
<|end_body_0|>
<|body_start_1|>
channel = journal.info('merlin.lib.i... | Access to the libraries of the current workspace | Libraries | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Libraries:
"""Access to the libraries of the current workspace"""
def build(self, plexus, **kwds):
"""Build the selected libraries"""
<|body_0|>
def info(self, plexus, **kwds):
"""Display the names of the selected libraries"""
<|body_1|>
def sources(... | stack_v2_sparse_classes_36k_train_007522 | 4,439 | permissive | [
{
"docstring": "Build the selected libraries",
"name": "build",
"signature": "def build(self, plexus, **kwds)"
},
{
"docstring": "Display the names of the selected libraries",
"name": "info",
"signature": "def info(self, plexus, **kwds)"
},
{
"docstring": "Display the sources of ... | 4 | null | Implement the Python class `Libraries` described below.
Class description:
Access to the libraries of the current workspace
Method signatures and docstrings:
- def build(self, plexus, **kwds): Build the selected libraries
- def info(self, plexus, **kwds): Display the names of the selected libraries
- def sources(self... | Implement the Python class `Libraries` described below.
Class description:
Access to the libraries of the current workspace
Method signatures and docstrings:
- def build(self, plexus, **kwds): Build the selected libraries
- def info(self, plexus, **kwds): Display the names of the selected libraries
- def sources(self... | d741c44ffb3e9e1f726bf492202ac8738bb4aa1c | <|skeleton|>
class Libraries:
"""Access to the libraries of the current workspace"""
def build(self, plexus, **kwds):
"""Build the selected libraries"""
<|body_0|>
def info(self, plexus, **kwds):
"""Display the names of the selected libraries"""
<|body_1|>
def sources(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Libraries:
"""Access to the libraries of the current workspace"""
def build(self, plexus, **kwds):
"""Build the selected libraries"""
builder = plexus.builder
libs = tuple(self.filter(projects=plexus.projects))
builder.add(plexus=plexus, assets=libs, target='libraries')
... | the_stack_v2_python_sparse | packages/merlin/cli/Libraries.py | pyre/pyre | train | 27 |
7e2c2b446199a82141470cc9a5b4c74c3b0e2ae2 | [
"keys = {key: value for key, value in cls.__dict__.items() if not isinstance(value, classmethod) and (not isinstance(value, staticmethod)) and (not callable(value)) and (not key.startswith('__'))}\nrequired = [v for k, v in keys.items() if not k.endswith('_')]\noptional = [v for k, v in keys.items() if k.endswith('... | <|body_start_0|>
keys = {key: value for key, value in cls.__dict__.items() if not isinstance(value, classmethod) and (not isinstance(value, staticmethod)) and (not callable(value)) and (not key.startswith('__'))}
required = [v for k, v in keys.items() if not k.endswith('_')]
optional = [v for k,... | Class to validate dictionary configurations. | ConfigKeys | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConfigKeys:
"""Class to validate dictionary configurations."""
def get_keys(cls) -> Tuple[List[str], List[str]]:
"""Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are lists of the required/optional keys for this class."""
... | stack_v2_sparse_classes_36k_train_007523 | 3,020 | permissive | [
{
"docstring": "Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are lists of the required/optional keys for this class.",
"name": "get_keys",
"signature": "def get_keys(cls) -> Tuple[List[str], List[str]]"
},
{
"docstring": "Checks wheth... | 2 | stack_v2_sparse_classes_30k_train_005597 | Implement the Python class `ConfigKeys` described below.
Class description:
Class to validate dictionary configurations.
Method signatures and docstrings:
- def get_keys(cls) -> Tuple[List[str], List[str]]: Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are ... | Implement the Python class `ConfigKeys` described below.
Class description:
Class to validate dictionary configurations.
Method signatures and docstrings:
- def get_keys(cls) -> Tuple[List[str], List[str]]: Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are ... | f1499e9c3fee00fd1d66de14cab66c4472c0085d | <|skeleton|>
class ConfigKeys:
"""Class to validate dictionary configurations."""
def get_keys(cls) -> Tuple[List[str], List[str]]:
"""Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are lists of the required/optional keys for this class."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConfigKeys:
"""Class to validate dictionary configurations."""
def get_keys(cls) -> Tuple[List[str], List[str]]:
"""Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are lists of the required/optional keys for this class."""
keys = ... | the_stack_v2_python_sparse | src/zenml/config/config_keys.py | stefannica/zenml | train | 0 |
0b28617aca1afad7a1da0bb406763eb3c1370f1c | [
"super(TrainConfiguration, self).__init__(**kwargs)\nself.cuda = False\nself.log_interval = 10\nself.optimizer_config = OptimizerConfiguration()\nself.save_config = None\nself.extra = {}\nself.set_necessary_configs(**kwargs)\nself.set_unnecessary_configs(**kwargs)",
"try:\n self.epochs = kwargs['epochs']\nexce... | <|body_start_0|>
super(TrainConfiguration, self).__init__(**kwargs)
self.cuda = False
self.log_interval = 10
self.optimizer_config = OptimizerConfiguration()
self.save_config = None
self.extra = {}
self.set_necessary_configs(**kwargs)
self.set_unnecessary_... | class stores the training configuration | TrainConfiguration | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrainConfiguration:
"""class stores the training configuration"""
def __init__(self, **kwargs):
"""initialize settings"""
<|body_0|>
def set_necessary_configs(self, **kwargs):
"""set train configs that necessarily provided by user"""
<|body_1|>
def s... | stack_v2_sparse_classes_36k_train_007524 | 3,307 | no_license | [
{
"docstring": "initialize settings",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "set train configs that necessarily provided by user",
"name": "set_necessary_configs",
"signature": "def set_necessary_configs(self, **kwargs)"
},
{
"docstring... | 3 | stack_v2_sparse_classes_30k_train_010164 | Implement the Python class `TrainConfiguration` described below.
Class description:
class stores the training configuration
Method signatures and docstrings:
- def __init__(self, **kwargs): initialize settings
- def set_necessary_configs(self, **kwargs): set train configs that necessarily provided by user
- def set_u... | Implement the Python class `TrainConfiguration` described below.
Class description:
class stores the training configuration
Method signatures and docstrings:
- def __init__(self, **kwargs): initialize settings
- def set_necessary_configs(self, **kwargs): set train configs that necessarily provided by user
- def set_u... | b0e8f66b3ade742445a41d3d5667032a931d94d2 | <|skeleton|>
class TrainConfiguration:
"""class stores the training configuration"""
def __init__(self, **kwargs):
"""initialize settings"""
<|body_0|>
def set_necessary_configs(self, **kwargs):
"""set train configs that necessarily provided by user"""
<|body_1|>
def s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrainConfiguration:
"""class stores the training configuration"""
def __init__(self, **kwargs):
"""initialize settings"""
super(TrainConfiguration, self).__init__(**kwargs)
self.cuda = False
self.log_interval = 10
self.optimizer_config = OptimizerConfiguration()
... | the_stack_v2_python_sparse | config/train_config.py | wz139704646/MBRL_on_VAEs | train | 1 |
5aa5e22ef4183a37e108d768ed49b2765474cfa1 | [
"attr_obj = Attribute()\nattr_obj.deployable_id = deployable_id\nattr_obj.set_key_value_pair(self.key, self.value)\nattr_obj.create(context)",
"attr_obj_list = Attribute.get_by_deployable_id(context, deployable_id)\nfor attr_obj in attr_obj_list:\n attr_obj.destroy(context)",
"attr_obj_list = Attribute.get_b... | <|body_start_0|>
attr_obj = Attribute()
attr_obj.deployable_id = deployable_id
attr_obj.set_key_value_pair(self.key, self.value)
attr_obj.create(context)
<|end_body_0|>
<|body_start_1|>
attr_obj_list = Attribute.get_by_deployable_id(context, deployable_id)
for attr_obj i... | DriverAttribute | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DriverAttribute:
def create(self, context, deployable_id):
"""Convert driver-side Attribute into Attribute Object so as to store in DB."""
<|body_0|>
def destroy(cls, context, deployable_id):
"""Delete driver-side attribute list from the DB."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_007525 | 2,630 | permissive | [
{
"docstring": "Convert driver-side Attribute into Attribute Object so as to store in DB.",
"name": "create",
"signature": "def create(self, context, deployable_id)"
},
{
"docstring": "Delete driver-side attribute list from the DB.",
"name": "destroy",
"signature": "def destroy(cls, cont... | 4 | stack_v2_sparse_classes_30k_train_001087 | Implement the Python class `DriverAttribute` described below.
Class description:
Implement the DriverAttribute class.
Method signatures and docstrings:
- def create(self, context, deployable_id): Convert driver-side Attribute into Attribute Object so as to store in DB.
- def destroy(cls, context, deployable_id): Dele... | Implement the Python class `DriverAttribute` described below.
Class description:
Implement the DriverAttribute class.
Method signatures and docstrings:
- def create(self, context, deployable_id): Convert driver-side Attribute into Attribute Object so as to store in DB.
- def destroy(cls, context, deployable_id): Dele... | ab8b8514242895b8adc2ec3dfbbb63a49f02c89e | <|skeleton|>
class DriverAttribute:
def create(self, context, deployable_id):
"""Convert driver-side Attribute into Attribute Object so as to store in DB."""
<|body_0|>
def destroy(cls, context, deployable_id):
"""Delete driver-side attribute list from the DB."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DriverAttribute:
def create(self, context, deployable_id):
"""Convert driver-side Attribute into Attribute Object so as to store in DB."""
attr_obj = Attribute()
attr_obj.deployable_id = deployable_id
attr_obj.set_key_value_pair(self.key, self.value)
attr_obj.create(con... | the_stack_v2_python_sparse | cyborg/objects/driver_objects/driver_attribute.py | openstack/cyborg | train | 41 | |
299f75773a38b14c028cf2bd3598a238a33693ca | [
"self.epsilon = epsilon\nself.learningrate = alpha\nself.discountrate = gamma\nself.discrete_os_size = size_table\nself.num_state = len(num_state)\nself.num_actions = num_actions\nself.action_space = action_space\nself.discrete = discrete",
"predict = self.Q[prev_state, prev_action]\ntarget = reward + self.gamma ... | <|body_start_0|>
self.epsilon = epsilon
self.learningrate = alpha
self.discountrate = gamma
self.discrete_os_size = size_table
self.num_state = len(num_state)
self.num_actions = num_actions
self.action_space = action_space
self.discrete = discrete
<|end_bo... | The Agent that uses SARSA update to improve it's behaviour | SarsaAgent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SarsaAgent:
"""The Agent that uses SARSA update to improve it's behaviour"""
def __init__(self, epsilon, alpha, gamma, size_table, num_state, num_actions, action_space, discrete=True):
"""Constructor Args: epsilon: The degree of exploration gamma: The discount factor num_state: The n... | stack_v2_sparse_classes_36k_train_007526 | 2,205 | no_license | [
{
"docstring": "Constructor Args: epsilon: The degree of exploration gamma: The discount factor num_state: The number of states num_actions: The number of actions action_space: To call the random action",
"name": "__init__",
"signature": "def __init__(self, epsilon, alpha, gamma, size_table, num_state, ... | 2 | stack_v2_sparse_classes_30k_train_010544 | Implement the Python class `SarsaAgent` described below.
Class description:
The Agent that uses SARSA update to improve it's behaviour
Method signatures and docstrings:
- def __init__(self, epsilon, alpha, gamma, size_table, num_state, num_actions, action_space, discrete=True): Constructor Args: epsilon: The degree o... | Implement the Python class `SarsaAgent` described below.
Class description:
The Agent that uses SARSA update to improve it's behaviour
Method signatures and docstrings:
- def __init__(self, epsilon, alpha, gamma, size_table, num_state, num_actions, action_space, discrete=True): Constructor Args: epsilon: The degree o... | 0e7f598d57fa294cfe4f6b4fadff6480068a0390 | <|skeleton|>
class SarsaAgent:
"""The Agent that uses SARSA update to improve it's behaviour"""
def __init__(self, epsilon, alpha, gamma, size_table, num_state, num_actions, action_space, discrete=True):
"""Constructor Args: epsilon: The degree of exploration gamma: The discount factor num_state: The n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SarsaAgent:
"""The Agent that uses SARSA update to improve it's behaviour"""
def __init__(self, epsilon, alpha, gamma, size_table, num_state, num_actions, action_space, discrete=True):
"""Constructor Args: epsilon: The degree of exploration gamma: The discount factor num_state: The number of stat... | the_stack_v2_python_sparse | TD_Tabular_Openai/Sarsa.py | RoboticsLabURJC/2018-phd-pedro-fernandez | train | 2 |
5dff5c57b6504327d3e559c14ea77aa7d1cf8deb | [
"user_input = None\nwhile user_input != 2:\n print('\\n\\n=== The Pokemon Tamagotchi Game ===\\n')\n print('Hello there! Welcome to the world of POKEMON!\\nMy name is EUCALYPTUS. People call me the \\nPOKEMON PROF!\\n')\n print('This world is inhabited by creatures called \\nPOKEMON! For some people, POKEM... | <|body_start_0|>
user_input = None
while user_input != 2:
print('\n\n=== The Pokemon Tamagotchi Game ===\n')
print('Hello there! Welcome to the world of POKEMON!\nMy name is EUCALYPTUS. People call me the \nPOKEMON PROF!\n')
print('This world is inhabited by creatures... | Display menu items and control the flow of logic for the menu itself. | GameUI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GameUI:
"""Display menu items and control the flow of logic for the menu itself."""
def display_start_menu(cls, game):
"""Display start menu (with no Pokemon hatched)."""
<|body_0|>
def display_pet_menu(cls, game):
"""Display list of possible pet interactions."""... | stack_v2_sparse_classes_36k_train_007527 | 9,307 | no_license | [
{
"docstring": "Display start menu (with no Pokemon hatched).",
"name": "display_start_menu",
"signature": "def display_start_menu(cls, game)"
},
{
"docstring": "Display list of possible pet interactions.",
"name": "display_pet_menu",
"signature": "def display_pet_menu(cls, game)"
},
... | 5 | stack_v2_sparse_classes_30k_test_000567 | Implement the Python class `GameUI` described below.
Class description:
Display menu items and control the flow of logic for the menu itself.
Method signatures and docstrings:
- def display_start_menu(cls, game): Display start menu (with no Pokemon hatched).
- def display_pet_menu(cls, game): Display list of possible... | Implement the Python class `GameUI` described below.
Class description:
Display menu items and control the flow of logic for the menu itself.
Method signatures and docstrings:
- def display_start_menu(cls, game): Display start menu (with no Pokemon hatched).
- def display_pet_menu(cls, game): Display list of possible... | b7695cc7cf0860aa9c8bf492b1bd06bd88b9af41 | <|skeleton|>
class GameUI:
"""Display menu items and control the flow of logic for the menu itself."""
def display_start_menu(cls, game):
"""Display start menu (with no Pokemon hatched)."""
<|body_0|>
def display_pet_menu(cls, game):
"""Display list of possible pet interactions."""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GameUI:
"""Display menu items and control the flow of logic for the menu itself."""
def display_start_menu(cls, game):
"""Display start menu (with no Pokemon hatched)."""
user_input = None
while user_input != 2:
print('\n\n=== The Pokemon Tamagotchi Game ===\n')
... | the_stack_v2_python_sparse | Lectures/Assignment2a/game.py | sakshambhardwaj523/Python-OOP-Projects | train | 0 |
b5a33f6450019fff4535086ce66fb17a23707776 | [
"context = context or {}\nids = isinstance(ids, (int, long)) and [ids] or ids\ncr_date = time.strftime('%Y-%m-%d')\nsp_brw = self.browse(cur, uid, ids[0], context=context)\nif not sp_brw.date_contract_expiry or (sp_brw.date_contract_expiry and cr_date <= sp_brw.date_contract_expiry) or context.get('force_expiry_pic... | <|body_start_0|>
context = context or {}
ids = isinstance(ids, (int, long)) and [ids] or ids
cr_date = time.strftime('%Y-%m-%d')
sp_brw = self.browse(cur, uid, ids[0], context=context)
if not sp_brw.date_contract_expiry or (sp_brw.date_contract_expiry and cr_date <= sp_brw.date_c... | StockPickingIn | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StockPickingIn:
def action_process(self, cur, uid, ids, context=None):
"""overwrite the method to add a verification of the contract due date before process the stock picking in."""
<|body_0|>
def copy(self, default=None):
"""Ovwerwrite the copy method to also copy t... | stack_v2_sparse_classes_36k_train_007528 | 5,912 | no_license | [
{
"docstring": "overwrite the method to add a verification of the contract due date before process the stock picking in.",
"name": "action_process",
"signature": "def action_process(self, cur, uid, ids, context=None)"
},
{
"docstring": "Ovwerwrite the copy method to also copy the date_contract_e... | 2 | null | Implement the Python class `StockPickingIn` described below.
Class description:
Implement the StockPickingIn class.
Method signatures and docstrings:
- def action_process(self, cur, uid, ids, context=None): overwrite the method to add a verification of the contract due date before process the stock picking in.
- def ... | Implement the Python class `StockPickingIn` described below.
Class description:
Implement the StockPickingIn class.
Method signatures and docstrings:
- def action_process(self, cur, uid, ids, context=None): overwrite the method to add a verification of the contract due date before process the stock picking in.
- def ... | 511dc410b4eba1f8ea939c6af02a5adea5122c92 | <|skeleton|>
class StockPickingIn:
def action_process(self, cur, uid, ids, context=None):
"""overwrite the method to add a verification of the contract due date before process the stock picking in."""
<|body_0|>
def copy(self, default=None):
"""Ovwerwrite the copy method to also copy t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StockPickingIn:
def action_process(self, cur, uid, ids, context=None):
"""overwrite the method to add a verification of the contract due date before process the stock picking in."""
context = context or {}
ids = isinstance(ids, (int, long)) and [ids] or ids
cr_date = time.strft... | the_stack_v2_python_sparse | stock_purchase_expiry/model/stock.py | yelizariev/addons-vauxoo | train | 3 | |
757d5ea0f100e99e02551a4e15d25a4f2e89afa9 | [
"tensor, mask = self.forward_embedding(input, positions, segments)\nif self.variant == 'xlm' or self.variant == 'bart':\n tensor = self.norm_embeddings(tensor)\ntensor = self.dropout(tensor)\ntensor *= mask.unsqueeze(-1).type_as(tensor)\ntensor = self.forward_layers(tensor, mask, **kwargs)\ntensor, weights = ten... | <|body_start_0|>
tensor, mask = self.forward_embedding(input, positions, segments)
if self.variant == 'xlm' or self.variant == 'bart':
tensor = self.norm_embeddings(tensor)
tensor = self.dropout(tensor)
tensor *= mask.unsqueeze(-1).type_as(tensor)
tensor = self.forwar... | Override TransformerEncoder to return the self-attn weights. | TransformerReturnWeightsEncoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransformerReturnWeightsEncoder:
"""Override TransformerEncoder to return the self-attn weights."""
def forward(self, input: torch.LongTensor, positions: Optional[torch.LongTensor]=None, segments: Optional[torch.LongTensor]=None, **kwargs) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]]... | stack_v2_sparse_classes_36k_train_007529 | 12,281 | permissive | [
{
"docstring": "Forward pass. Propagate kwargs",
"name": "forward",
"signature": "def forward(self, input: torch.LongTensor, positions: Optional[torch.LongTensor]=None, segments: Optional[torch.LongTensor]=None, **kwargs) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], Tuple[torch.Tensor, torch.Bo... | 3 | stack_v2_sparse_classes_30k_train_003399 | Implement the Python class `TransformerReturnWeightsEncoder` described below.
Class description:
Override TransformerEncoder to return the self-attn weights.
Method signatures and docstrings:
- def forward(self, input: torch.LongTensor, positions: Optional[torch.LongTensor]=None, segments: Optional[torch.LongTensor]=... | Implement the Python class `TransformerReturnWeightsEncoder` described below.
Class description:
Override TransformerEncoder to return the self-attn weights.
Method signatures and docstrings:
- def forward(self, input: torch.LongTensor, positions: Optional[torch.LongTensor]=None, segments: Optional[torch.LongTensor]=... | e1d899edfb92471552bae153f59ad30aa7fca468 | <|skeleton|>
class TransformerReturnWeightsEncoder:
"""Override TransformerEncoder to return the self-attn weights."""
def forward(self, input: torch.LongTensor, positions: Optional[torch.LongTensor]=None, segments: Optional[torch.LongTensor]=None, **kwargs) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]]... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TransformerReturnWeightsEncoder:
"""Override TransformerEncoder to return the self-attn weights."""
def forward(self, input: torch.LongTensor, positions: Optional[torch.LongTensor]=None, segments: Optional[torch.LongTensor]=None, **kwargs) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], Tuple[torch... | the_stack_v2_python_sparse | projects/light_whoami/agents/poly_return_weights.py | facebookresearch/ParlAI | train | 10,943 |
3acf05bcf141ed512f2b1033641a1c8336a22bbb | [
"self.num_points = num_points\nself.x_values = [0]\nself.y_values = [0]",
"direction = choice([1, -1])\ndistance = choice([0, 1, 2, 3, 4])\nstep = direction * distance\nreturn step",
"while len(self.x_values) < self.num_points:\n x_step = self.get_step()\n y_step = self.get_step()\n if x_step == 0 and ... | <|body_start_0|>
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
<|end_body_0|>
<|body_start_1|>
direction = choice([1, -1])
distance = choice([0, 1, 2, 3, 4])
step = direction * distance
return step
<|end_body_1|>
<|body_start_2|>
w... | Uma classe para gerar passeios aleatórios. | RandonWalk | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandonWalk:
"""Uma classe para gerar passeios aleatórios."""
def __init__(self, num_points=5000):
"""Inicia os atributos de um passeio."""
<|body_0|>
def get_step(self):
"""Decide a direção a ser seguida e a distância a ser percorrida."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_007530 | 1,317 | permissive | [
{
"docstring": "Inicia os atributos de um passeio.",
"name": "__init__",
"signature": "def __init__(self, num_points=5000)"
},
{
"docstring": "Decide a direção a ser seguida e a distância a ser percorrida.",
"name": "get_step",
"signature": "def get_step(self)"
},
{
"docstring": ... | 3 | null | Implement the Python class `RandonWalk` described below.
Class description:
Uma classe para gerar passeios aleatórios.
Method signatures and docstrings:
- def __init__(self, num_points=5000): Inicia os atributos de um passeio.
- def get_step(self): Decide a direção a ser seguida e a distância a ser percorrida.
- def ... | Implement the Python class `RandonWalk` described below.
Class description:
Uma classe para gerar passeios aleatórios.
Method signatures and docstrings:
- def __init__(self, num_points=5000): Inicia os atributos de um passeio.
- def get_step(self): Decide a direção a ser seguida e a distância a ser percorrida.
- def ... | de88ba326cdd9c17a456161cdb2f9ca69f7da65e | <|skeleton|>
class RandonWalk:
"""Uma classe para gerar passeios aleatórios."""
def __init__(self, num_points=5000):
"""Inicia os atributos de um passeio."""
<|body_0|>
def get_step(self):
"""Decide a direção a ser seguida e a distância a ser percorrida."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandonWalk:
"""Uma classe para gerar passeios aleatórios."""
def __init__(self, num_points=5000):
"""Inicia os atributos de um passeio."""
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
def get_step(self):
"""Decide a direção a ser seguid... | the_stack_v2_python_sparse | PYTHON/Python-VisualizacaoDeDados/Dados-Gráficos/Random Walk/random_walk.py | sourcery-ai-bot/Estudos | train | 0 |
44dc9f0a3f6effe38369f9184d746ea8b7c26f74 | [
"min_val = arr[qs]\nfor x in xrange(qs + 1, qe + 1):\n min_val = min(min_val, arr[x])\nreturn min_val",
"n = len(arr)\ntable = [[sys.maxint] * n for _ in xrange(n)]\nfor k in xrange(1, n + 1):\n for x in xrange(n - k):\n y = x + k - 1\n if x == y:\n table[x][y] = arr[x]\n els... | <|body_start_0|>
min_val = arr[qs]
for x in xrange(qs + 1, qe + 1):
min_val = min(min_val, arr[x])
return min_val
<|end_body_0|>
<|body_start_1|>
n = len(arr)
table = [[sys.maxint] * n for _ in xrange(n)]
for k in xrange(1, n + 1):
for x in xrange... | Given an array of integer numbers, find the minimum value from a specified range from qs to qe. In total, there are 5 approaches to RMQ question. Here 3 of them are given. The other two algorithms are Square Root Decomposition and Sparse Table Algorithm. | RangeMinimumQuery | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RangeMinimumQuery:
"""Given an array of integer numbers, find the minimum value from a specified range from qs to qe. In total, there are 5 approaches to RMQ question. Here 3 of them are given. The other two algorithms are Square Root Decomposition and Sparse Table Algorithm."""
def simple_s... | stack_v2_sparse_classes_36k_train_007531 | 4,188 | no_license | [
{
"docstring": "A simple solution is to iterate and compare each elements from qs to qe. Time complexity is O(n) in worst case, and space complexity is O(1).",
"name": "simple_solution",
"signature": "def simple_solution(cls, arr, qs, qe)"
},
{
"docstring": "The solution uses a table to store th... | 5 | stack_v2_sparse_classes_30k_train_012477 | Implement the Python class `RangeMinimumQuery` described below.
Class description:
Given an array of integer numbers, find the minimum value from a specified range from qs to qe. In total, there are 5 approaches to RMQ question. Here 3 of them are given. The other two algorithms are Square Root Decomposition and Spars... | Implement the Python class `RangeMinimumQuery` described below.
Class description:
Given an array of integer numbers, find the minimum value from a specified range from qs to qe. In total, there are 5 approaches to RMQ question. Here 3 of them are given. The other two algorithms are Square Root Decomposition and Spars... | cbe6a7e7f05eccb4f9c5fce8651c0d87e5168516 | <|skeleton|>
class RangeMinimumQuery:
"""Given an array of integer numbers, find the minimum value from a specified range from qs to qe. In total, there are 5 approaches to RMQ question. Here 3 of them are given. The other two algorithms are Square Root Decomposition and Sparse Table Algorithm."""
def simple_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RangeMinimumQuery:
"""Given an array of integer numbers, find the minimum value from a specified range from qs to qe. In total, there are 5 approaches to RMQ question. Here 3 of them are given. The other two algorithms are Square Root Decomposition and Sparse Table Algorithm."""
def simple_solution(cls, ... | the_stack_v2_python_sparse | src/array/RangeMinimumQuery.py | apepkuss/Cracking-Leetcode-in-Python | train | 2 |
58a8b03777c5771f9c0479a0f92d5892c8b1e392 | [
"super(SingleTaskStudent, self).__init__()\nif kwargs['language_model_type'] == 'bilstm':\n self.languageModel = BiLSTM(h_dim=kwargs['h_dim_l'], o_dim=kwargs['o_dim_l'], d_prob=kwargs['d_prob_l'], with_self_att=kwargs['with_self_att'], d_dim=kwargs['d_dim_l'], r_dim=kwargs['r_dim_l'], num_layers=kwargs['num_laye... | <|body_start_0|>
super(SingleTaskStudent, self).__init__()
if kwargs['language_model_type'] == 'bilstm':
self.languageModel = BiLSTM(h_dim=kwargs['h_dim_l'], o_dim=kwargs['o_dim_l'], d_prob=kwargs['d_prob_l'], with_self_att=kwargs['with_self_att'], d_dim=kwargs['d_dim_l'], r_dim=kwargs['r_di... | Student takes langauge as input, develops a representation of the language and input stimulus, concatenates these representations together, and produces logits for the probability that the given stimulus belongs to the class described by the language. | SingleTaskStudent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SingleTaskStudent:
"""Student takes langauge as input, develops a representation of the language and input stimulus, concatenates these representations together, and produces logits for the probability that the given stimulus belongs to the class described by the language."""
def __init__(se... | stack_v2_sparse_classes_36k_train_007532 | 3,199 | no_license | [
{
"docstring": "@param **kwargs: parameters associated with initializing the language model and the stimulus model.",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "x1: language (describing concept) x2: stimulus (from test set) x1_lengths: true lengths of text... | 2 | stack_v2_sparse_classes_30k_train_004268 | Implement the Python class `SingleTaskStudent` described below.
Class description:
Student takes langauge as input, develops a representation of the language and input stimulus, concatenates these representations together, and produces logits for the probability that the given stimulus belongs to the class described b... | Implement the Python class `SingleTaskStudent` described below.
Class description:
Student takes langauge as input, develops a representation of the language and input stimulus, concatenates these representations together, and produces logits for the probability that the given stimulus belongs to the class described b... | 2dca3ba909078739b49468ea8b772f346710d60b | <|skeleton|>
class SingleTaskStudent:
"""Student takes langauge as input, develops a representation of the language and input stimulus, concatenates these representations together, and produces logits for the probability that the given stimulus belongs to the class described by the language."""
def __init__(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SingleTaskStudent:
"""Student takes langauge as input, develops a representation of the language and input stimulus, concatenates these representations together, and produces logits for the probability that the given stimulus belongs to the class described by the language."""
def __init__(self, **kwargs)... | the_stack_v2_python_sparse | models/student/lfl/single_task_student.py | cocolab-projects/concept-captioning | train | 0 |
60f20bb4d7b7964ceca30bd694f06e3681d15528 | [
"neighbors = {}\nencoder = _IntegerEncoder()\nn_entries = len(table)\nfor n, (sequence, value) in enumerate(table.items()):\n if n % 1000000 == 0 or (n < 1000000 and n % 100000 == 0):\n logging.info('loading ScaM results %r/%r', n, n_entries)\n neighbor_sequences = [neighbor.docid for neighbor in value... | <|body_start_0|>
neighbors = {}
encoder = _IntegerEncoder()
n_entries = len(table)
for n, (sequence, value) in enumerate(table.items()):
if n % 1000000 == 0 or (n < 1000000 and n % 100000 == 0):
logging.info('loading ScaM results %r/%r', n, n_entries)
... | Matcher that uses pre-computed lookup tables generated by ScaM. | ScaMMatcher | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScaMMatcher:
"""Matcher that uses pre-computed lookup tables generated by ScaM."""
def __init__(self, table, dtype='u4'):
"""Initialize as ScaMMatcher. Args: table: Mapping[str, result_pb2.NearestNeighbor] mapping each sequence to all of its (approximate) neighbors within some fixed ... | stack_v2_sparse_classes_36k_train_007533 | 19,209 | permissive | [
{
"docstring": "Initialize as ScaMMatcher. Args: table: Mapping[str, result_pb2.NearestNeighbor] mapping each sequence to all of its (approximate) neighbors within some fixed edit distance. dtype: optional object convertable to numpy.dtype to use for storing positive integer IDs. Raises: ValueError: if dtype wa... | 3 | stack_v2_sparse_classes_30k_train_010167 | Implement the Python class `ScaMMatcher` described below.
Class description:
Matcher that uses pre-computed lookup tables generated by ScaM.
Method signatures and docstrings:
- def __init__(self, table, dtype='u4'): Initialize as ScaMMatcher. Args: table: Mapping[str, result_pb2.NearestNeighbor] mapping each sequence... | Implement the Python class `ScaMMatcher` described below.
Class description:
Matcher that uses pre-computed lookup tables generated by ScaM.
Method signatures and docstrings:
- def __init__(self, table, dtype='u4'): Initialize as ScaMMatcher. Args: table: Mapping[str, result_pb2.NearestNeighbor] mapping each sequence... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class ScaMMatcher:
"""Matcher that uses pre-computed lookup tables generated by ScaM."""
def __init__(self, table, dtype='u4'):
"""Initialize as ScaMMatcher. Args: table: Mapping[str, result_pb2.NearestNeighbor] mapping each sequence to all of its (approximate) neighbors within some fixed ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScaMMatcher:
"""Matcher that uses pre-computed lookup tables generated by ScaM."""
def __init__(self, table, dtype='u4'):
"""Initialize as ScaMMatcher. Args: table: Mapping[str, result_pb2.NearestNeighbor] mapping each sequence to all of its (approximate) neighbors within some fixed edit distance... | the_stack_v2_python_sparse | aptamers_mlpd/preprocess/clustering.py | Jimmy-INL/google-research | train | 1 |
fe8540e1f9cd1760caa9b891945d32cb652b2039 | [
"given = [[9, 8, 7, 6, 5, 4, 3, 2, 1, 0], [4, 1, 2, 1, 2, 4, 1, 2, 1, 4, 1], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [random.randrange(0, 10) for _ in range(10)], [random.randrange(0, 10) for _ in range(1000)], [random.randrange(0, 1000) for _ in range(1000)], [random.randrange(0, 100) for _ in range(10 ** 5)], [random.ran... | <|body_start_0|>
given = [[9, 8, 7, 6, 5, 4, 3, 2, 1, 0], [4, 1, 2, 1, 2, 4, 1, 2, 1, 4, 1], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [random.randrange(0, 10) for _ in range(10)], [random.randrange(0, 10) for _ in range(1000)], [random.randrange(0, 1000) for _ in range(1000)], [random.randrange(0, 100) for _ in range(10... | Test the quick sort function | TestQuickSort | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestQuickSort:
"""Test the quick sort function"""
def test_quick_sort_median(self):
"""Test the quick sort function for various cases. Should match the results of Python's sort func."""
<|body_0|>
def test_quick_sort_simple(self):
"""Test the quick sort function ... | stack_v2_sparse_classes_36k_train_007534 | 3,778 | no_license | [
{
"docstring": "Test the quick sort function for various cases. Should match the results of Python's sort func.",
"name": "test_quick_sort_median",
"signature": "def test_quick_sort_median(self)"
},
{
"docstring": "Test the quick sort function for various cases. This test case uses the 'simple' ... | 2 | stack_v2_sparse_classes_30k_train_003656 | Implement the Python class `TestQuickSort` described below.
Class description:
Test the quick sort function
Method signatures and docstrings:
- def test_quick_sort_median(self): Test the quick sort function for various cases. Should match the results of Python's sort func.
- def test_quick_sort_simple(self): Test the... | Implement the Python class `TestQuickSort` described below.
Class description:
Test the quick sort function
Method signatures and docstrings:
- def test_quick_sort_median(self): Test the quick sort function for various cases. Should match the results of Python's sort func.
- def test_quick_sort_simple(self): Test the... | 0e8b528207faa44977f5b9d446d45d13c4fb430d | <|skeleton|>
class TestQuickSort:
"""Test the quick sort function"""
def test_quick_sort_median(self):
"""Test the quick sort function for various cases. Should match the results of Python's sort func."""
<|body_0|>
def test_quick_sort_simple(self):
"""Test the quick sort function ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestQuickSort:
"""Test the quick sort function"""
def test_quick_sort_median(self):
"""Test the quick sort function for various cases. Should match the results of Python's sort func."""
given = [[9, 8, 7, 6, 5, 4, 3, 2, 1, 0], [4, 1, 2, 1, 2, 4, 1, 2, 1, 4, 1], [0, 1, 2, 3, 4, 5, 6, 7, 8,... | the_stack_v2_python_sparse | __test__/quick_sort_test.py | marcus-grant/python-cs | train | 0 |
da9ae6207c659f85d42aa5bf7811fa3e74720908 | [
"self.host = host\nself.port = port\nself.user = user\nself.password = password\nself.database = database",
"try:\n connection = connector.connect(host=self.host, port=self.port, user=self.user, password=self.password, database=self.database, use_pure=True)\n cursor = connection.cursor()\n cursor.execute... | <|body_start_0|>
self.host = host
self.port = port
self.user = user
self.password = password
self.database = database
<|end_body_0|>
<|body_start_1|>
try:
connection = connector.connect(host=self.host, port=self.port, user=self.user, password=self.password, d... | MySqlHelper | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MySqlHelper:
def __init__(self, host, port, user, password, database):
"""[summary]: Constructor Args: host ([type]): [description] port ([type]): [description] user ([type]): [description] password ([type]): [description] database ([type]): [description]"""
<|body_0|>
def f... | stack_v2_sparse_classes_36k_train_007535 | 4,113 | permissive | [
{
"docstring": "[summary]: Constructor Args: host ([type]): [description] port ([type]): [description] user ([type]): [description] password ([type]): [description] database ([type]): [description]",
"name": "__init__",
"signature": "def __init__(self, host, port, user, password, database)"
},
{
... | 5 | stack_v2_sparse_classes_30k_train_015797 | Implement the Python class `MySqlHelper` described below.
Class description:
Implement the MySqlHelper class.
Method signatures and docstrings:
- def __init__(self, host, port, user, password, database): [summary]: Constructor Args: host ([type]): [description] port ([type]): [description] user ([type]): [description... | Implement the Python class `MySqlHelper` described below.
Class description:
Implement the MySqlHelper class.
Method signatures and docstrings:
- def __init__(self, host, port, user, password, database): [summary]: Constructor Args: host ([type]): [description] port ([type]): [description] user ([type]): [description... | 8332485421b04120924d4640d221f40cacb78741 | <|skeleton|>
class MySqlHelper:
def __init__(self, host, port, user, password, database):
"""[summary]: Constructor Args: host ([type]): [description] port ([type]): [description] user ([type]): [description] password ([type]): [description] database ([type]): [description]"""
<|body_0|>
def f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MySqlHelper:
def __init__(self, host, port, user, password, database):
"""[summary]: Constructor Args: host ([type]): [description] port ([type]): [description] user ([type]): [description] password ([type]): [description] database ([type]): [description]"""
self.host = host
self.port ... | the_stack_v2_python_sparse | src/utils/mysql_helper.py | Supreeth-Shetty/neuro-data | train | 0 | |
d6f49ba349e6f5ebe4536c845bad0af64518df0a | [
"if directory == None:\n directory = Settings.Settings().data_directory + '\\\\GlobalWarming'\nif not directory.endswith('\\\\'):\n directory += '\\\\'\nself.directory = directory\nraw_lines = self.__loadLines__('CodeTemplates.txt')\nlines = []\nself.codes_per_document = []\nfor l in raw_lines:\n ltrim = l... | <|body_start_0|>
if directory == None:
directory = Settings.Settings().data_directory + '\\GlobalWarming'
if not directory.endswith('\\'):
directory += '\\'
self.directory = directory
raw_lines = self.__loadLines__('CodeTemplates.txt')
lines = []
s... | Creates an object with .codes and .templates properties | GwCodeTemplates | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GwCodeTemplates:
"""Creates an object with .codes and .templates properties"""
def __init__(self, directory=None):
"""Constructor"""
<|body_0|>
def __loadLines__(self, fName):
"""Loads lines from a file and returns as a list file => []"""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k_train_007536 | 1,896 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, directory=None)"
},
{
"docstring": "Loads lines from a file and returns as a list file => []",
"name": "__loadLines__",
"signature": "def __loadLines__(self, fName)"
}
] | 2 | null | Implement the Python class `GwCodeTemplates` described below.
Class description:
Creates an object with .codes and .templates properties
Method signatures and docstrings:
- def __init__(self, directory=None): Constructor
- def __loadLines__(self, fName): Loads lines from a file and returns as a list file => [] | Implement the Python class `GwCodeTemplates` described below.
Class description:
Creates an object with .codes and .templates properties
Method signatures and docstrings:
- def __init__(self, directory=None): Constructor
- def __loadLines__(self, fName): Loads lines from a file and returns as a list file => []
<|ske... | 2bc2914ce93fcef6dbd26f8097eec20b7d0e476d | <|skeleton|>
class GwCodeTemplates:
"""Creates an object with .codes and .templates properties"""
def __init__(self, directory=None):
"""Constructor"""
<|body_0|>
def __loadLines__(self, fName):
"""Loads lines from a file and returns as a list file => []"""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GwCodeTemplates:
"""Creates an object with .codes and .templates properties"""
def __init__(self, directory=None):
"""Constructor"""
if directory == None:
directory = Settings.Settings().data_directory + '\\GlobalWarming'
if not directory.endswith('\\'):
di... | the_stack_v2_python_sparse | Data/GlobalWarming/GwCodeTemplates.py | simonhughes22/PythonNlpResearch | train | 17 |
6d7a2eb11cf2f14d3092f0d9a0d0d4afd66740c3 | [
"super().__init__()\npadding = int((kSize - 1) / 2)\nself.conv = nn.Conv2d(nIn, nOut, (kSize, kSize), stride=stride, padding=(padding, padding), bias=False)\nself.bn = nn.BatchNorm2d(nOut, eps=0.001)\nself.act = nn.PReLU(nOut)",
"output = self.conv(input)\noutput = self.bn(output)\noutput = self.act(output)\nretu... | <|body_start_0|>
super().__init__()
padding = int((kSize - 1) / 2)
self.conv = nn.Conv2d(nIn, nOut, (kSize, kSize), stride=stride, padding=(padding, padding), bias=False)
self.bn = nn.BatchNorm2d(nOut, eps=0.001)
self.act = nn.PReLU(nOut)
<|end_body_0|>
<|body_start_1|>
... | This class defines the convolution layer with batch normalization and PReLU activation | CBR | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CBR:
"""This class defines the convolution layer with batch normalization and PReLU activation"""
def __init__(self, nIn, nOut, kSize, stride=1):
""":param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param stride: stride rate for do... | stack_v2_sparse_classes_36k_train_007537 | 15,567 | permissive | [
{
"docstring": ":param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param stride: stride rate for down-sampling. Default is 1",
"name": "__init__",
"signature": "def __init__(self, nIn, nOut, kSize, stride=1)"
},
{
"docstring": ":param input: in... | 2 | null | Implement the Python class `CBR` described below.
Class description:
This class defines the convolution layer with batch normalization and PReLU activation
Method signatures and docstrings:
- def __init__(self, nIn, nOut, kSize, stride=1): :param nIn: number of input channels :param nOut: number of output channels :p... | Implement the Python class `CBR` described below.
Class description:
This class defines the convolution layer with batch normalization and PReLU activation
Method signatures and docstrings:
- def __init__(self, nIn, nOut, kSize, stride=1): :param nIn: number of input channels :param nOut: number of output channels :p... | f2993d3ce73a2f7ddba05da3891defb08547d504 | <|skeleton|>
class CBR:
"""This class defines the convolution layer with batch normalization and PReLU activation"""
def __init__(self, nIn, nOut, kSize, stride=1):
""":param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param stride: stride rate for do... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CBR:
"""This class defines the convolution layer with batch normalization and PReLU activation"""
def __init__(self, nIn, nOut, kSize, stride=1):
""":param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param stride: stride rate for down-sampling. ... | the_stack_v2_python_sparse | pytorch/pytorchcv/models/others/oth_espnet.py | osmr/imgclsmob | train | 3,017 |
4f062843b3ea012798eb29633c3484d5a82d1d8e | [
"self.d = {'0': '0', '1': '1', '6': '9', '8': '8', '9': '6'}\n\ndef dfs(half, path, n):\n if len(path) == half:\n pathStr = ''.join(path)\n if half * 2 == n:\n toAppend = pathStr + ''.join([self.d[x] for x in pathStr[::-1]])\n toAppendInt = int(toAppend)\n if self.l... | <|body_start_0|>
self.d = {'0': '0', '1': '1', '6': '9', '8': '8', '9': '6'}
def dfs(half, path, n):
if len(path) == half:
pathStr = ''.join(path)
if half * 2 == n:
toAppend = pathStr + ''.join([self.d[x] for x in pathStr[::-1]])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findStrobogrammatic(self, n):
""":type n: int :rtype: List[str]"""
<|body_0|>
def strobogrammaticInRange(self, low, high):
""":type low: str :type high: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.d = {'0': '0'... | stack_v2_sparse_classes_36k_train_007538 | 1,292 | no_license | [
{
"docstring": ":type n: int :rtype: List[str]",
"name": "findStrobogrammatic",
"signature": "def findStrobogrammatic(self, n)"
},
{
"docstring": ":type low: str :type high: str :rtype: int",
"name": "strobogrammaticInRange",
"signature": "def strobogrammaticInRange(self, low, high)"
}... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findStrobogrammatic(self, n): :type n: int :rtype: List[str]
- def strobogrammaticInRange(self, low, high): :type low: str :type high: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findStrobogrammatic(self, n): :type n: int :rtype: List[str]
- def strobogrammaticInRange(self, low, high): :type low: str :type high: str :rtype: int
<|skeleton|>
class Sol... | 929dde1723fb2f54870c8a9badc80fc23e8400d3 | <|skeleton|>
class Solution:
def findStrobogrammatic(self, n):
""":type n: int :rtype: List[str]"""
<|body_0|>
def strobogrammaticInRange(self, low, high):
""":type low: str :type high: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findStrobogrammatic(self, n):
""":type n: int :rtype: List[str]"""
self.d = {'0': '0', '1': '1', '6': '9', '8': '8', '9': '6'}
def dfs(half, path, n):
if len(path) == half:
pathStr = ''.join(path)
if half * 2 == n:
... | the_stack_v2_python_sparse | _algorithms_challenges/leetcode/lc-all-solutions/248.strobogrammatic-number-iii/strobogrammatic-number-iii.py | syurskyi/Algorithms_and_Data_Structure | train | 4 | |
8dc399961b337d8324ae2ef537fed33ca75f82cd | [
"super(WikiParser, self).__init__(base_url)\nself.inclusions = [doc_id] if doc_id else []\nself.registerInternalLinkHook('Include', self._hook_include)\nself.registerInternalLinkHook('I', self._hook_include)\nself.registerInternalLinkHook('Template', self._hook_template)\nself.registerInternalLinkHook('T', self._ho... | <|body_start_0|>
super(WikiParser, self).__init__(base_url)
self.inclusions = [doc_id] if doc_id else []
self.registerInternalLinkHook('Include', self._hook_include)
self.registerInternalLinkHook('I', self._hook_include)
self.registerInternalLinkHook('Template', self._hook_templa... | An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my! | WikiParser | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WikiParser:
"""An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!"""
def __init__(self, base_url=None, doc_id=None):
"""doc_id -- If you want to be nice, pass the ID of the Document you are rendering. This will make rec... | stack_v2_sparse_classes_36k_train_007539 | 19,586 | permissive | [
{
"docstring": "doc_id -- If you want to be nice, pass the ID of the Document you are rendering. This will make recursive inclusions fail immediately rather than after the first round of recursion.",
"name": "__init__",
"signature": "def __init__(self, base_url=None, doc_id=None)"
},
{
"docstrin... | 4 | stack_v2_sparse_classes_30k_train_015292 | Implement the Python class `WikiParser` described below.
Class description:
An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!
Method signatures and docstrings:
- def __init__(self, base_url=None, doc_id=None): doc_id -- If you want to be nice, pass the... | Implement the Python class `WikiParser` described below.
Class description:
An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!
Method signatures and docstrings:
- def __init__(self, base_url=None, doc_id=None): doc_id -- If you want to be nice, pass the... | 67ec527bfc32c715bf9f29d5e01362c4903aebd2 | <|skeleton|>
class WikiParser:
"""An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!"""
def __init__(self, base_url=None, doc_id=None):
"""doc_id -- If you want to be nice, pass the ID of the Document you are rendering. This will make rec... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WikiParser:
"""An extension of the parser from the forums adding more crazy features {for} tags, inclusions, and templates--oh my!"""
def __init__(self, base_url=None, doc_id=None):
"""doc_id -- If you want to be nice, pass the ID of the Document you are rendering. This will make recursive inclus... | the_stack_v2_python_sparse | kitsune/wiki/parser.py | mozilla/kitsune | train | 1,218 |
77c0634663d0a59ecd54f2326c60e5f7402067ec | [
"LOGGER.debug('Updating %r: %r', record.key, record.dict())\nfor source in self:\n await source.update(record)",
"for source in self:\n async for record in source.records():\n for other_source in self.data[1:]:\n record.merge(await other_source.record(record.key))\n if validation is... | <|body_start_0|>
LOGGER.debug('Updating %r: %r', record.key, record.dict())
for source in self:
await source.update(record)
<|end_body_0|>
<|body_start_1|>
for source in self:
async for record in source.records():
for other_source in self.data[1:]:
... | SourcesContext | [
"MIT",
"LicenseRef-scancode-generic-export-compliance"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SourcesContext:
async def update(self, record: Record):
"""Updates a record for a source"""
<|body_0|>
async def records(self, validation: Optional[Callable[[Record], bool]]=None) -> AsyncIterator[Record]:
"""Retrieves records from all sources"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_007540 | 8,476 | permissive | [
{
"docstring": "Updates a record for a source",
"name": "update",
"signature": "async def update(self, record: Record)"
},
{
"docstring": "Retrieves records from all sources",
"name": "records",
"signature": "async def records(self, validation: Optional[Callable[[Record], bool]]=None) ->... | 4 | null | Implement the Python class `SourcesContext` described below.
Class description:
Implement the SourcesContext class.
Method signatures and docstrings:
- async def update(self, record: Record): Updates a record for a source
- async def records(self, validation: Optional[Callable[[Record], bool]]=None) -> AsyncIterator[... | Implement the Python class `SourcesContext` described below.
Class description:
Implement the SourcesContext class.
Method signatures and docstrings:
- async def update(self, record: Record): Updates a record for a source
- async def records(self, validation: Optional[Callable[[Record], bool]]=None) -> AsyncIterator[... | 7d381bf67a72fe1ecb1012393d5726085564cb0e | <|skeleton|>
class SourcesContext:
async def update(self, record: Record):
"""Updates a record for a source"""
<|body_0|>
async def records(self, validation: Optional[Callable[[Record], bool]]=None) -> AsyncIterator[Record]:
"""Retrieves records from all sources"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SourcesContext:
async def update(self, record: Record):
"""Updates a record for a source"""
LOGGER.debug('Updating %r: %r', record.key, record.dict())
for source in self:
await source.update(record)
async def records(self, validation: Optional[Callable[[Record], bool]]... | the_stack_v2_python_sparse | dffml/source/source.py | intel/dffml | train | 237 | |
7628fce98bfabf0f71ab59c7dc6ef468947e416f | [
"if not len(matrix) or not len(matrix[0]):\n return False\nm, n = (len(matrix), len(matrix[0]))\nr, c = (0, n - 1)\nwhile r < m and c >= 0:\n if matrix[r][c] == target:\n return True\n elif matrix[r][c] > target:\n c -= 1\n else:\n r += 1\nreturn False",
"if not len(matrix) or not... | <|body_start_0|>
if not len(matrix) or not len(matrix[0]):
return False
m, n = (len(matrix), len(matrix[0]))
r, c = (0, n - 1)
while r < m and c >= 0:
if matrix[r][c] == target:
return True
elif matrix[r][c] > target:
c ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool"""
<|body_0|>
def searchMatrix_v1(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool"""
<|body_1|>
def s... | stack_v2_sparse_classes_36k_train_007541 | 6,291 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :type target: int :rtype: bool",
"name": "searchMatrix",
"signature": "def searchMatrix(self, matrix, target)"
},
{
"docstring": ":type matrix: List[List[int]] :type target: int :rtype: bool",
"name": "searchMatrix_v1",
"signature": "def sear... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchMatrix(self, matrix, target): :type matrix: List[List[int]] :type target: int :rtype: bool
- def searchMatrix_v1(self, matrix, target): :type matrix: List[List[int]] :t... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchMatrix(self, matrix, target): :type matrix: List[List[int]] :type target: int :rtype: bool
- def searchMatrix_v1(self, matrix, target): :type matrix: List[List[int]] :t... | b5e09f24e8e96454dc99e20281e853fb9fcc85ed | <|skeleton|>
class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool"""
<|body_0|>
def searchMatrix_v1(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool"""
<|body_1|>
def s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool"""
if not len(matrix) or not len(matrix[0]):
return False
m, n = (len(matrix), len(matrix[0]))
r, c = (0, n - 1)
while r < m and c >= 0:
... | the_stack_v2_python_sparse | python/240_Search_a_2D_Matrix_II.py | Moby5/myleetcode | train | 2 | |
357afb0935bf369f97134a252697ba05bb117e23 | [
"if isinstance(value, dict):\n value = json.dumps(value)\nself.map[key] = value",
"try:\n for key_, value_ in keys.items():\n self.map[key_] = str(value_)\n print(key_ + ':' + str(value_))\nexcept BaseException as msg:\n print(msg)\n raise msg",
"try:\n del self.map[key]\n return... | <|body_start_0|>
if isinstance(value, dict):
value = json.dumps(value)
self.map[key] = value
<|end_body_0|>
<|body_start_1|>
try:
for key_, value_ in keys.items():
self.map[key_] = str(value_)
print(key_ + ':' + str(value_))
except... | 拼装成字典构造全局变量 借鉴map 包含变量的增删改查 各参数说明 luaDriver：公共drvier，初始化时创建的driver | GlobalVars | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GlobalVars:
"""拼装成字典构造全局变量 借鉴map 包含变量的增删改查 各参数说明 luaDriver：公共drvier，初始化时创建的driver"""
def set_map(self, key, value):
"""设置单一变量值 :param key:变量名 :param value:变量值 :return:"""
<|body_0|>
def set(self, **keys):
"""设置多个变量 key-value :param keys: :return:"""
<|bod... | stack_v2_sparse_classes_36k_train_007542 | 1,778 | no_license | [
{
"docstring": "设置单一变量值 :param key:变量名 :param value:变量值 :return:",
"name": "set_map",
"signature": "def set_map(self, key, value)"
},
{
"docstring": "设置多个变量 key-value :param keys: :return:",
"name": "set",
"signature": "def set(self, **keys)"
},
{
"docstring": "删除key对应值 :param ke... | 4 | stack_v2_sparse_classes_30k_test_000346 | Implement the Python class `GlobalVars` described below.
Class description:
拼装成字典构造全局变量 借鉴map 包含变量的增删改查 各参数说明 luaDriver：公共drvier，初始化时创建的driver
Method signatures and docstrings:
- def set_map(self, key, value): 设置单一变量值 :param key:变量名 :param value:变量值 :return:
- def set(self, **keys): 设置多个变量 key-value :param keys: :ret... | Implement the Python class `GlobalVars` described below.
Class description:
拼装成字典构造全局变量 借鉴map 包含变量的增删改查 各参数说明 luaDriver：公共drvier，初始化时创建的driver
Method signatures and docstrings:
- def set_map(self, key, value): 设置单一变量值 :param key:变量名 :param value:变量值 :return:
- def set(self, **keys): 设置多个变量 key-value :param keys: :ret... | edc19480c3e94cbcbf004aa9d20099ec6d1b9304 | <|skeleton|>
class GlobalVars:
"""拼装成字典构造全局变量 借鉴map 包含变量的增删改查 各参数说明 luaDriver：公共drvier，初始化时创建的driver"""
def set_map(self, key, value):
"""设置单一变量值 :param key:变量名 :param value:变量值 :return:"""
<|body_0|>
def set(self, **keys):
"""设置多个变量 key-value :param keys: :return:"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GlobalVars:
"""拼装成字典构造全局变量 借鉴map 包含变量的增删改查 各参数说明 luaDriver：公共drvier，初始化时创建的driver"""
def set_map(self, key, value):
"""设置单一变量值 :param key:变量名 :param value:变量值 :return:"""
if isinstance(value, dict):
value = json.dumps(value)
self.map[key] = value
def set(self, **k... | the_stack_v2_python_sparse | 性能项目/德州-普通场/lua4.0/ali/src/com/globalVars.py | YiFeng0755/testcase | train | 0 |
5ff5c10aaa745d32b60a640bd36f75a2f6afed6a | [
"if params.getboolean('Multiprocessing', 'measures'):\n logger.debug('Measuring the average fingerprint size using multiprocessing...')\n self._execute_using_multiprocessing()\nelse:\n logger.debug('Measuring the average fingerprint on a single process...')\n self._result = _compute_attribute_avg_size(s... | <|body_start_0|>
if params.getboolean('Multiprocessing', 'measures'):
logger.debug('Measuring the average fingerprint size using multiprocessing...')
self._execute_using_multiprocessing()
else:
logger.debug('Measuring the average fingerprint on a single process...')
... | Measure the average fingerprint size of the attributes of a dataset. | AverageFingerprintSize | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AverageFingerprintSize:
"""Measure the average fingerprint size of the attributes of a dataset."""
def execute(self):
"""Measure the average fingerprint size of the attributes."""
<|body_0|>
def _execute_using_multiprocessing(self):
"""Measure the average fingerp... | stack_v2_sparse_classes_36k_train_007543 | 5,221 | permissive | [
{
"docstring": "Measure the average fingerprint size of the attributes.",
"name": "execute",
"signature": "def execute(self)"
},
{
"docstring": "Measure the average fingerprint size using multiprocessing.",
"name": "_execute_using_multiprocessing",
"signature": "def _execute_using_multip... | 3 | stack_v2_sparse_classes_30k_train_011724 | Implement the Python class `AverageFingerprintSize` described below.
Class description:
Measure the average fingerprint size of the attributes of a dataset.
Method signatures and docstrings:
- def execute(self): Measure the average fingerprint size of the attributes.
- def _execute_using_multiprocessing(self): Measur... | Implement the Python class `AverageFingerprintSize` described below.
Class description:
Measure the average fingerprint size of the attributes of a dataset.
Method signatures and docstrings:
- def execute(self): Measure the average fingerprint size of the attributes.
- def _execute_using_multiprocessing(self): Measur... | b687a356acc813d45dbaf5b5eb0f360df181904a | <|skeleton|>
class AverageFingerprintSize:
"""Measure the average fingerprint size of the attributes of a dataset."""
def execute(self):
"""Measure the average fingerprint size of the attributes."""
<|body_0|>
def _execute_using_multiprocessing(self):
"""Measure the average fingerp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AverageFingerprintSize:
"""Measure the average fingerprint size of the attributes of a dataset."""
def execute(self):
"""Measure the average fingerprint size of the attributes."""
if params.getboolean('Multiprocessing', 'measures'):
logger.debug('Measuring the average fingerpr... | the_stack_v2_python_sparse | brfast/measures/usability_cost/memory.py | trinhvanvuong/BrFAST | train | 0 |
1f2011aa16b4793522f6d81e1fd09a5007f0b3c4 | [
"if not root:\n return ''\nqueue = deque()\nqueue.append(root)\nresult = ''\nwhile queue:\n node = queue.popleft()\n if node:\n result += str(node.val) + ','\n queue.append(node.left)\n queue.append(node.right)\n else:\n result += '#,'\nresult = result[:-1]\nreturn result",
... | <|body_start_0|>
if not root:
return ''
queue = deque()
queue.append(root)
result = ''
while queue:
node = queue.popleft()
if node:
result += str(node.val) + ','
queue.append(node.left)
queue.appe... | 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_007544 | 2,640 | 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_001410 | 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:... | b62862b90886f85c33271b881ac1365871731dcc | <|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 = deque()
queue.append(root)
result = ''
while queue:
node = queue.popleft()
if node:
... | the_stack_v2_python_sparse | serialize_tree.py | ashutosh-narkar/LeetCode | train | 0 | |
2710effbac552ef12ecfc8ad7d45104b25f771a6 | [
"self.drizzle_params = drizzle_params.copy()\nself.mult_drizzle_par = mult_drizzle_par.copy()\nself.cont_info = cont_info\nself.opt_extr = opt_extr\nself.back = back\nif drztmp_dir != None:\n self.drztmp_dir = drztmp_dir\nelse:\n self.drztmp_dir = axeutils.getDRZTMP()\nif drizzle_dir != None:\n self.drizzl... | <|body_start_0|>
self.drizzle_params = drizzle_params.copy()
self.mult_drizzle_par = mult_drizzle_par.copy()
self.cont_info = cont_info
self.opt_extr = opt_extr
self.back = back
if drztmp_dir != None:
self.drztmp_dir = drztmp_dir
else:
self... | List class for all objects to be drizzled | MulDrzObjList | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MulDrzObjList:
"""List class for all objects to be drizzled"""
def __init__(self, drizzle_params, mult_drizzle_par, cont_info=None, opt_extr=False, back=False, drztmp_dir=None, drizzle_dir=None):
"""Initializes the class"""
<|body_0|>
def _objlist_to_drzobjects(self, obj... | stack_v2_sparse_classes_36k_train_007545 | 10,632 | permissive | [
{
"docstring": "Initializes the class",
"name": "__init__",
"signature": "def __init__(self, drizzle_params, mult_drizzle_par, cont_info=None, opt_extr=False, back=False, drztmp_dir=None, drizzle_dir=None)"
},
{
"docstring": "Converts the object list into drizzle objects",
"name": "_objlist_... | 4 | stack_v2_sparse_classes_30k_train_012702 | Implement the Python class `MulDrzObjList` described below.
Class description:
List class for all objects to be drizzled
Method signatures and docstrings:
- def __init__(self, drizzle_params, mult_drizzle_par, cont_info=None, opt_extr=False, back=False, drztmp_dir=None, drizzle_dir=None): Initializes the class
- def ... | Implement the Python class `MulDrzObjList` described below.
Class description:
List class for all objects to be drizzled
Method signatures and docstrings:
- def __init__(self, drizzle_params, mult_drizzle_par, cont_info=None, opt_extr=False, back=False, drztmp_dir=None, drizzle_dir=None): Initializes the class
- def ... | 043c173fd5497c18c2b1bfe8bcff65180bca3996 | <|skeleton|>
class MulDrzObjList:
"""List class for all objects to be drizzled"""
def __init__(self, drizzle_params, mult_drizzle_par, cont_info=None, opt_extr=False, back=False, drztmp_dir=None, drizzle_dir=None):
"""Initializes the class"""
<|body_0|>
def _objlist_to_drzobjects(self, obj... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MulDrzObjList:
"""List class for all objects to be drizzled"""
def __init__(self, drizzle_params, mult_drizzle_par, cont_info=None, opt_extr=False, back=False, drztmp_dir=None, drizzle_dir=None):
"""Initializes the class"""
self.drizzle_params = drizzle_params.copy()
self.mult_dri... | the_stack_v2_python_sparse | stsdas/pkg/analysis/slitless/axe/axesrc/mdrzobjects.py | spacetelescope/stsdas_stripped | train | 1 |
dad5f17927bee8733466622e63b95b0cda7948f5 | [
"self.continuous_schedule = continuous_schedule\nself.daily_schedule = daily_schedule\nself.monthly_schedule = monthly_schedule\nself.name = name\nself.num_days_to_keep = num_days_to_keep\nself.num_retries = num_retries\nself.one_off_schedule = one_off_schedule\nself.periodicity = periodicity\nself.retry_delay_mins... | <|body_start_0|>
self.continuous_schedule = continuous_schedule
self.daily_schedule = daily_schedule
self.monthly_schedule = monthly_schedule
self.name = name
self.num_days_to_keep = num_days_to_keep
self.num_retries = num_retries
self.one_off_schedule = one_off_s... | Implementation of the 'BackupPolicyProto' model. If a backup does not get a chance to when it's due (either due to the system being busy or a conflict with another instance of the same job), the backup will still be run when the conflicts go away. But, if there are multiple instances of the same job that are due to be ... | BackupPolicyProto | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BackupPolicyProto:
"""Implementation of the 'BackupPolicyProto' model. If a backup does not get a chance to when it's due (either due to the system being busy or a conflict with another instance of the same job), the backup will still be run when the conflicts go away. But, if there are multiple ... | stack_v2_sparse_classes_36k_train_007546 | 7,222 | permissive | [
{
"docstring": "Constructor for the BackupPolicyProto class",
"name": "__init__",
"signature": "def __init__(self, continuous_schedule=None, daily_schedule=None, monthly_schedule=None, name=None, num_days_to_keep=None, num_retries=None, one_off_schedule=None, periodicity=None, retry_delay_mins=None, sch... | 2 | null | Implement the Python class `BackupPolicyProto` described below.
Class description:
Implementation of the 'BackupPolicyProto' model. If a backup does not get a chance to when it's due (either due to the system being busy or a conflict with another instance of the same job), the backup will still be run when the conflic... | Implement the Python class `BackupPolicyProto` described below.
Class description:
Implementation of the 'BackupPolicyProto' model. If a backup does not get a chance to when it's due (either due to the system being busy or a conflict with another instance of the same job), the backup will still be run when the conflic... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class BackupPolicyProto:
"""Implementation of the 'BackupPolicyProto' model. If a backup does not get a chance to when it's due (either due to the system being busy or a conflict with another instance of the same job), the backup will still be run when the conflicts go away. But, if there are multiple ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BackupPolicyProto:
"""Implementation of the 'BackupPolicyProto' model. If a backup does not get a chance to when it's due (either due to the system being busy or a conflict with another instance of the same job), the backup will still be run when the conflicts go away. But, if there are multiple instances of ... | the_stack_v2_python_sparse | cohesity_management_sdk/models/backup_policy_proto.py | cohesity/management-sdk-python | train | 24 |
32a311abc2bd003adb0748b6219b1878599289b4 | [
"if issubclass(type(val), Vector):\n xyz = (val.x, val.y, val.z)\n return ' '.join((str(x) for x in xyz))\nelif not isinstance(val, str) and isinstance(val, Iterable):\n return ' '.join((str(x) for x in val))\nelse:\n return str(val)",
"assert self.__class__.TAG is not None or root_el is not None, 'Xm... | <|body_start_0|>
if issubclass(type(val), Vector):
xyz = (val.x, val.y, val.z)
return ' '.join((str(x) for x in xyz))
elif not isinstance(val, str) and isinstance(val, Iterable):
return ' '.join((str(x) for x in val))
else:
return str(val)
<|end_bo... | Base class for all urdf xml objects. | XmlObject | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XmlObject:
"""Base class for all urdf xml objects."""
def _get_val(self, val: Any) -> str:
"""Convert a value to a string that is included into the xml."""
<|body_0|>
def create_xml(self, root_el: cET.Element=None) -> cET.Element:
"""Convert this instance into a ... | stack_v2_sparse_classes_36k_train_007547 | 5,284 | permissive | [
{
"docstring": "Convert a value to a string that is included into the xml.",
"name": "_get_val",
"signature": "def _get_val(self, val: Any) -> str"
},
{
"docstring": "Convert this instance into a xml element. * If the class of this instance does not define a tag, new xml elements from this insta... | 3 | null | Implement the Python class `XmlObject` described below.
Class description:
Base class for all urdf xml objects.
Method signatures and docstrings:
- def _get_val(self, val: Any) -> str: Convert a value to a string that is included into the xml.
- def create_xml(self, root_el: cET.Element=None) -> cET.Element: Convert ... | Implement the Python class `XmlObject` described below.
Class description:
Base class for all urdf xml objects.
Method signatures and docstrings:
- def _get_val(self, val: Any) -> str: Convert a value to a string that is included into the xml.
- def create_xml(self, root_el: cET.Element=None) -> cET.Element: Convert ... | 8a9438b5a24c288721ae0302889fe55e26046310 | <|skeleton|>
class XmlObject:
"""Base class for all urdf xml objects."""
def _get_val(self, val: Any) -> str:
"""Convert a value to a string that is included into the xml."""
<|body_0|>
def create_xml(self, root_el: cET.Element=None) -> cET.Element:
"""Convert this instance into a ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XmlObject:
"""Base class for all urdf xml objects."""
def _get_val(self, val: Any) -> str:
"""Convert a value to a string that is included into the xml."""
if issubclass(type(val), Vector):
xyz = (val.x, val.y, val.z)
return ' '.join((str(x) for x in xyz))
... | the_stack_v2_python_sparse | simulation/utils/urdf/core.py | KITcar-Team/kitcar-gazebo-simulation | train | 19 |
f04a87ecd97e496d1c08f7773c7828cd31a7b949 | [
"super().__init__()\nself.conv1 = nn.Conv3d(1, 96, kernel_size=(7, 7, 7), stride=2, padding=(3, 3, 3))\nself.bn1 = nn.BatchNorm3d(96)\nself.relu = nn.ReLU()\nself.maxpool1 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=(1, 1, 1))\nself.transition = TransitionBlock(32)\nself.dense1 = DenseBlock(96, 128, 32,... | <|body_start_0|>
super().__init__()
self.conv1 = nn.Conv3d(1, 96, kernel_size=(7, 7, 7), stride=2, padding=(3, 3, 3))
self.bn1 = nn.BatchNorm3d(96)
self.relu = nn.ReLU()
self.maxpool1 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=(1, 1, 1))
self.transition = Tra... | DenseUNet3d | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DenseUNet3d:
def __init__(self):
"""Create the layers for the model"""
<|body_0|>
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass through the model :param x: image tensor :return: output of the forward pass"""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_007548 | 2,341 | no_license | [
{
"docstring": "Create the layers for the model",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Forward pass through the model :param x: image tensor :return: output of the forward pass",
"name": "forward",
"signature": "def forward(self, x: torch.Tensor) -> to... | 2 | stack_v2_sparse_classes_30k_train_007278 | Implement the Python class `DenseUNet3d` described below.
Class description:
Implement the DenseUNet3d class.
Method signatures and docstrings:
- def __init__(self): Create the layers for the model
- def forward(self, x: torch.Tensor) -> torch.Tensor: Forward pass through the model :param x: image tensor :return: out... | Implement the Python class `DenseUNet3d` described below.
Class description:
Implement the DenseUNet3d class.
Method signatures and docstrings:
- def __init__(self): Create the layers for the model
- def forward(self, x: torch.Tensor) -> torch.Tensor: Forward pass through the model :param x: image tensor :return: out... | d3dd6310588770174e11752808b7fe4b71842220 | <|skeleton|>
class DenseUNet3d:
def __init__(self):
"""Create the layers for the model"""
<|body_0|>
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass through the model :param x: image tensor :return: output of the forward pass"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DenseUNet3d:
def __init__(self):
"""Create the layers for the model"""
super().__init__()
self.conv1 = nn.Conv3d(1, 96, kernel_size=(7, 7, 7), stride=2, padding=(3, 3, 3))
self.bn1 = nn.BatchNorm3d(96)
self.relu = nn.ReLU()
self.maxpool1 = nn.MaxPool3d(kernel_si... | the_stack_v2_python_sparse | dense_unet_3d/model/DenseUNet3d.py | NguyenJus/pytorch-dense-unet-3d | train | 8 | |
23a2efecaaee93fba349b83370d7d234e29a4885 | [
"super(PFDict, self).__init__(inp)\nself.keyType = keyType\nself.valueType = valueType",
"if type(key) == self.getClassFromType(self.keyType):\n super(PFDict, self).__setitem__(key, item)\nelse:\n raise Exception('Valid key should be a {0}'.format(self.getClassFromType(self.keyType)))",
"pin = findPinClas... | <|body_start_0|>
super(PFDict, self).__init__(inp)
self.keyType = keyType
self.valueType = valueType
<|end_body_0|>
<|body_start_1|>
if type(key) == self.getClassFromType(self.keyType):
super(PFDict, self).__setitem__(key, item)
else:
raise Exception('Val... | This subclass of python's :class:`dict` implements a key typed dictionary. Only defined data types can be used as keys, and only hashable ones as determined by >>> isinstance(dataType, collections.abc.Hashable) To make a class Hashable some methods should be implemented: Example: :: class C: def __init__(self, x): self... | PFDict | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PFDict:
"""This subclass of python's :class:`dict` implements a key typed dictionary. Only defined data types can be used as keys, and only hashable ones as determined by >>> isinstance(dataType, collections.abc.Hashable) To make a class Hashable some methods should be implemented: Example: :: cl... | stack_v2_sparse_classes_36k_train_007549 | 29,109 | permissive | [
{
"docstring": ":param keyType: Key dataType :param valueType: value dataType, defaults to None :type valueType: optional :param inp: Construct from another dict, defaults to {} :type inp: dict, optional",
"name": "__init__",
"signature": "def __init__(self, keyType, valueType='AnyPin', inp={})"
},
... | 3 | null | Implement the Python class `PFDict` described below.
Class description:
This subclass of python's :class:`dict` implements a key typed dictionary. Only defined data types can be used as keys, and only hashable ones as determined by >>> isinstance(dataType, collections.abc.Hashable) To make a class Hashable some method... | Implement the Python class `PFDict` described below.
Class description:
This subclass of python's :class:`dict` implements a key typed dictionary. Only defined data types can be used as keys, and only hashable ones as determined by >>> isinstance(dataType, collections.abc.Hashable) To make a class Hashable some method... | 6a4445254b0024b39e1b9ce8d938748219d57fd5 | <|skeleton|>
class PFDict:
"""This subclass of python's :class:`dict` implements a key typed dictionary. Only defined data types can be used as keys, and only hashable ones as determined by >>> isinstance(dataType, collections.abc.Hashable) To make a class Hashable some methods should be implemented: Example: :: cl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PFDict:
"""This subclass of python's :class:`dict` implements a key typed dictionary. Only defined data types can be used as keys, and only hashable ones as determined by >>> isinstance(dataType, collections.abc.Hashable) To make a class Hashable some methods should be implemented: Example: :: class C: def __... | the_stack_v2_python_sparse | PyFlow/Core/Common.py | dlario/PyFlow | train | 2 |
8a738aab595e384bb893fd2e95dd87db4caf6114 | [
"self.rate_limit_s = max(rate_limit_s, 0)\nself.period_s = 1.0 / self.rate_limit_s if self.rate_limit_s > 0 else 0\nself.last_event = 0",
"elapsed_s = time.time() - self.last_event\nsleep_amount = max(self.period_s - elapsed_s, 0)\ntime.sleep(sleep_amount)\nself.last_event = time.time()"
] | <|body_start_0|>
self.rate_limit_s = max(rate_limit_s, 0)
self.period_s = 1.0 / self.rate_limit_s if self.rate_limit_s > 0 else 0
self.last_event = 0
<|end_body_0|>
<|body_start_1|>
elapsed_s = time.time() - self.last_event
sleep_amount = max(self.period_s - elapsed_s, 0)
... | Basic rate limiter that sleeps long enough to ensure the rate limit is not exceeded. Not thread safe. | ConstantRateLimiter | [
"BSD-3-Clause",
"MIT",
"BSD-3-Clause-Modification",
"Unlicense",
"Apache-2.0",
"LGPL-3.0-only",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause",
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConstantRateLimiter:
"""Basic rate limiter that sleeps long enough to ensure the rate limit is not exceeded. Not thread safe."""
def __init__(self, rate_limit_s):
""":param rate_limit_s: rate limit in seconds"""
<|body_0|>
def sleep(self):
"""Sleeps long enough t... | stack_v2_sparse_classes_36k_train_007550 | 13,340 | permissive | [
{
"docstring": ":param rate_limit_s: rate limit in seconds",
"name": "__init__",
"signature": "def __init__(self, rate_limit_s)"
},
{
"docstring": "Sleeps long enough to enforce the rate limit",
"name": "sleep",
"signature": "def sleep(self)"
}
] | 2 | null | Implement the Python class `ConstantRateLimiter` described below.
Class description:
Basic rate limiter that sleeps long enough to ensure the rate limit is not exceeded. Not thread safe.
Method signatures and docstrings:
- def __init__(self, rate_limit_s): :param rate_limit_s: rate limit in seconds
- def sleep(self):... | Implement the Python class `ConstantRateLimiter` described below.
Class description:
Basic rate limiter that sleeps long enough to ensure the rate limit is not exceeded. Not thread safe.
Method signatures and docstrings:
- def __init__(self, rate_limit_s): :param rate_limit_s: rate limit in seconds
- def sleep(self):... | 406072e4294edff5b46b513f0cdf7c2c00fac9d2 | <|skeleton|>
class ConstantRateLimiter:
"""Basic rate limiter that sleeps long enough to ensure the rate limit is not exceeded. Not thread safe."""
def __init__(self, rate_limit_s):
""":param rate_limit_s: rate limit in seconds"""
<|body_0|>
def sleep(self):
"""Sleeps long enough t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConstantRateLimiter:
"""Basic rate limiter that sleeps long enough to ensure the rate limit is not exceeded. Not thread safe."""
def __init__(self, rate_limit_s):
""":param rate_limit_s: rate limit in seconds"""
self.rate_limit_s = max(rate_limit_s, 0)
self.period_s = 1.0 / self.r... | the_stack_v2_python_sparse | datadog_checks_base/datadog_checks/base/utils/db/utils.py | DataDog/integrations-core | train | 852 |
edabaa1e51463808e7b04d6f8a8b900cd165f0b1 | [
"self.event_dispatcher = event_dispatcher or EventDispatcher\nself.logger = _logging.adapt_logger(logger or _logging.NoOpLogger())\nself.notification_center = notification_center or _notification_center.NotificationCenter(self.logger)\nif not validator.is_notification_center_valid(self.notification_center):\n se... | <|body_start_0|>
self.event_dispatcher = event_dispatcher or EventDispatcher
self.logger = _logging.adapt_logger(logger or _logging.NoOpLogger())
self.notification_center = notification_center or _notification_center.NotificationCenter(self.logger)
if not validator.is_notification_center... | ForwardingEventProcessor serves as the default EventProcessor. The ForwardingEventProcessor sends the LogEvent to EventDispatcher as soon as it is received. | ForwardingEventProcessor | [
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ForwardingEventProcessor:
"""ForwardingEventProcessor serves as the default EventProcessor. The ForwardingEventProcessor sends the LogEvent to EventDispatcher as soon as it is received."""
def __init__(self, event_dispatcher: type[EventDispatcher] | CustomEventDispatcher, logger: Optional[_l... | stack_v2_sparse_classes_36k_train_007551 | 15,516 | permissive | [
{
"docstring": "ForwardingEventProcessor init method to configure event dispatching. Args: event_dispatcher: Provides a dispatch_event method which if given a URL and params sends a request to it. logger: Optional component which provides a log method to log messages. By default nothing would be logged. notific... | 2 | stack_v2_sparse_classes_30k_val_000447 | Implement the Python class `ForwardingEventProcessor` described below.
Class description:
ForwardingEventProcessor serves as the default EventProcessor. The ForwardingEventProcessor sends the LogEvent to EventDispatcher as soon as it is received.
Method signatures and docstrings:
- def __init__(self, event_dispatcher... | Implement the Python class `ForwardingEventProcessor` described below.
Class description:
ForwardingEventProcessor serves as the default EventProcessor. The ForwardingEventProcessor sends the LogEvent to EventDispatcher as soon as it is received.
Method signatures and docstrings:
- def __init__(self, event_dispatcher... | bf000e737f391270f9adec97606646ce4761ecd8 | <|skeleton|>
class ForwardingEventProcessor:
"""ForwardingEventProcessor serves as the default EventProcessor. The ForwardingEventProcessor sends the LogEvent to EventDispatcher as soon as it is received."""
def __init__(self, event_dispatcher: type[EventDispatcher] | CustomEventDispatcher, logger: Optional[_l... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ForwardingEventProcessor:
"""ForwardingEventProcessor serves as the default EventProcessor. The ForwardingEventProcessor sends the LogEvent to EventDispatcher as soon as it is received."""
def __init__(self, event_dispatcher: type[EventDispatcher] | CustomEventDispatcher, logger: Optional[_logging.Logger... | the_stack_v2_python_sparse | optimizely/event/event_processor.py | optimizely/python-sdk | train | 34 |
26d21503faa6a87c9423c36d17bbc0f0ef4a0a46 | [
"if not array:\n return 0\nres = []\nmiddle_sum = 0\nfor x in array:\n if middle_sum <= 0:\n middle_sum = x\n else:\n middle_sum += x\n if not res or middle_sum > res[-1]:\n res.append(middle_sum)\n else:\n res.append(res[-1])\nprint(res)\nreturn max(res)",
"if not array... | <|body_start_0|>
if not array:
return 0
res = []
middle_sum = 0
for x in array:
if middle_sum <= 0:
middle_sum = x
else:
middle_sum += x
if not res or middle_sum > res[-1]:
res.append(middle_s... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def max_sum_array(self, array):
"""动态规划"""
<|body_0|>
def max_sum_array_1(self, array):
"""动态规划"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not array:
return 0
res = []
middle_sum = 0
for x in arr... | stack_v2_sparse_classes_36k_train_007552 | 1,493 | no_license | [
{
"docstring": "动态规划",
"name": "max_sum_array",
"signature": "def max_sum_array(self, array)"
},
{
"docstring": "动态规划",
"name": "max_sum_array_1",
"signature": "def max_sum_array_1(self, array)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def max_sum_array(self, array): 动态规划
- def max_sum_array_1(self, array): 动态规划 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def max_sum_array(self, array): 动态规划
- def max_sum_array_1(self, array): 动态规划
<|skeleton|>
class Solution:
def max_sum_array(self, array):
"""动态规划"""
<|body... | 3b8b36bcf8a983de4d8ce29734a85b6bfbe59fbc | <|skeleton|>
class Solution:
def max_sum_array(self, array):
"""动态规划"""
<|body_0|>
def max_sum_array_1(self, array):
"""动态规划"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def max_sum_array(self, array):
"""动态规划"""
if not array:
return 0
res = []
middle_sum = 0
for x in array:
if middle_sum <= 0:
middle_sum = x
else:
middle_sum += x
if not res or mid... | the_stack_v2_python_sparse | TargetOffer/42、连续子数组的最大和.py | a625687551/Leetcode | train | 0 | |
956c867ee0d618937ac8725481f50878fb179bbe | [
"req_data, ret_data = init_views(request)\nfaq_db = req_data.get('faq_db', '')\nfaq_collection = req_data.get('faq_collection', '')\nif faq_db and faq_collection:\n db = MongoDB(faq_db)\n data = db.search_all({}, faq_collection)\n ret_data['data'] = list(map(lambda x: {'_id': str(x['_id']), 'question': x['... | <|body_start_0|>
req_data, ret_data = init_views(request)
faq_db = req_data.get('faq_db', '')
faq_collection = req_data.get('faq_collection', '')
if faq_db and faq_collection:
db = MongoDB(faq_db)
data = db.search_all({}, faq_collection)
ret_data['data... | 知识库操作 | FaqViewSet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FaqViewSet:
"""知识库操作"""
def describe_qas(self, request):
"""查询QA"""
<|body_0|>
def create_qa(self, request):
"""创建QA"""
<|body_1|>
def update_qa(self, request):
"""变更QA"""
<|body_2|>
def delete_qa(self, request):
"""删除QA"... | stack_v2_sparse_classes_36k_train_007553 | 5,178 | permissive | [
{
"docstring": "查询QA",
"name": "describe_qas",
"signature": "def describe_qas(self, request)"
},
{
"docstring": "创建QA",
"name": "create_qa",
"signature": "def create_qa(self, request)"
},
{
"docstring": "变更QA",
"name": "update_qa",
"signature": "def update_qa(self, reques... | 4 | null | Implement the Python class `FaqViewSet` described below.
Class description:
知识库操作
Method signatures and docstrings:
- def describe_qas(self, request): 查询QA
- def create_qa(self, request): 创建QA
- def update_qa(self, request): 变更QA
- def delete_qa(self, request): 删除QA | Implement the Python class `FaqViewSet` described below.
Class description:
知识库操作
Method signatures and docstrings:
- def describe_qas(self, request): 查询QA
- def create_qa(self, request): 创建QA
- def update_qa(self, request): 变更QA
- def delete_qa(self, request): 删除QA
<|skeleton|>
class FaqViewSet:
"""知识库操作"""
... | da37fb2197142eae32158cdb5c2b658100133fff | <|skeleton|>
class FaqViewSet:
"""知识库操作"""
def describe_qas(self, request):
"""查询QA"""
<|body_0|>
def create_qa(self, request):
"""创建QA"""
<|body_1|>
def update_qa(self, request):
"""变更QA"""
<|body_2|>
def delete_qa(self, request):
"""删除QA"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FaqViewSet:
"""知识库操作"""
def describe_qas(self, request):
"""查询QA"""
req_data, ret_data = init_views(request)
faq_db = req_data.get('faq_db', '')
faq_collection = req_data.get('faq_collection', '')
if faq_db and faq_collection:
db = MongoDB(faq_db)
... | the_stack_v2_python_sparse | module_faq/views.py | cz-qq/bk-chatbot | train | 0 |
1a87ae6e617cb66cb05c4f62ac70995a8b0b91eb | [
"super().__init__()\nself.input_conv = Conv1d(in_channels, hidden_channels, 1)\nself.encoder = WaveNet(in_channels=-1, out_channels=-1, kernel_size=kernel_size, layers=layers, stacks=stacks, base_dilation=base_dilation, residual_channels=hidden_channels, aux_channels=-1, gate_channels=hidden_channels * 2, skip_chan... | <|body_start_0|>
super().__init__()
self.input_conv = Conv1d(in_channels, hidden_channels, 1)
self.encoder = WaveNet(in_channels=-1, out_channels=-1, kernel_size=kernel_size, layers=layers, stacks=stacks, base_dilation=base_dilation, residual_channels=hidden_channels, aux_channels=-1, gate_chann... | Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`: https://arxiv.org/abs/2006.04558 | PosteriorEncoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PosteriorEncoder:
"""Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speec... | stack_v2_sparse_classes_36k_train_007554 | 4,037 | permissive | [
{
"docstring": "Initilialize PosteriorEncoder module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. hidden_channels (int): Number of hidden channels. kernel_size (int): Kernel size in WaveNet. layers (int): Number of layers of WaveNet. stacks (int): Number of ... | 2 | stack_v2_sparse_classes_30k_train_017291 | Implement the Python class `PosteriorEncoder` described below.
Class description:
Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversaria... | Implement the Python class `PosteriorEncoder` described below.
Class description:
Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversaria... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class PosteriorEncoder:
"""Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speec... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PosteriorEncoder:
"""Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`: https://a... | the_stack_v2_python_sparse | espnet2/gan_tts/vits/posterior_encoder.py | espnet/espnet | train | 7,242 |
761d059bc51ee29c9b235411e3002479972c7202 | [
"adm = ProjectAdministration()\nproject_list = adm.get_all_projects()\nreturn project_list",
"adm = ProjectAdministration()\nproj = Project.from_dict(api.payload)\nif proj is not None:\n proj = adm.create_project(proj.get_name(), proj.get_id(), proj.get_external_partners(), proj.get_capacity(), proj.get_weekly... | <|body_start_0|>
adm = ProjectAdministration()
project_list = adm.get_all_projects()
return project_list
<|end_body_0|>
<|body_start_1|>
adm = ProjectAdministration()
proj = Project.from_dict(api.payload)
if proj is not None:
proj = adm.create_project(proj.ge... | ProjectListOperations | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProjectListOperations:
def get(self):
"""Auslesen aller Project-Objekte"""
<|body_0|>
def post(self):
"""Anlegen eines neuen Project-Objekts"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
adm = ProjectAdministration()
project_list = adm.get... | stack_v2_sparse_classes_36k_train_007555 | 44,493 | no_license | [
{
"docstring": "Auslesen aller Project-Objekte",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Anlegen eines neuen Project-Objekts",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016735 | Implement the Python class `ProjectListOperations` described below.
Class description:
Implement the ProjectListOperations class.
Method signatures and docstrings:
- def get(self): Auslesen aller Project-Objekte
- def post(self): Anlegen eines neuen Project-Objekts | Implement the Python class `ProjectListOperations` described below.
Class description:
Implement the ProjectListOperations class.
Method signatures and docstrings:
- def get(self): Auslesen aller Project-Objekte
- def post(self): Anlegen eines neuen Project-Objekts
<|skeleton|>
class ProjectListOperations:
def ... | 4b2826225525ae855e15e1174f5cf90466097021 | <|skeleton|>
class ProjectListOperations:
def get(self):
"""Auslesen aller Project-Objekte"""
<|body_0|>
def post(self):
"""Anlegen eines neuen Project-Objekts"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProjectListOperations:
def get(self):
"""Auslesen aller Project-Objekte"""
adm = ProjectAdministration()
project_list = adm.get_all_projects()
return project_list
def post(self):
"""Anlegen eines neuen Project-Objekts"""
adm = ProjectAdministration()
... | the_stack_v2_python_sparse | src/main.py | KieserChristian/SW_Praktikum_Gruppe1 | train | 0 | |
e2409730572d54a14b577d16af1a291b0906a650 | [
"if not self.is_property_available('ServerRelativeUrl'):\n raise ValueError\nresponse = File.open_binary(self.context, self.properties['ServerRelativeUrl'])\nreturn response.content",
"if not self.is_property_available('ServerRelativeUrl'):\n raise ValueError\nresponse = File.save_binary(self.context, self.... | <|body_start_0|>
if not self.is_property_available('ServerRelativeUrl'):
raise ValueError
response = File.open_binary(self.context, self.properties['ServerRelativeUrl'])
return response.content
<|end_body_0|>
<|body_start_1|>
if not self.is_property_available('ServerRelative... | AbstractFile | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AbstractFile:
def read(self):
"""Immediately read content of file"""
<|body_0|>
def write(self, content):
"""Immediately writes content of file"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not self.is_property_available('ServerRelativeUrl'):
... | stack_v2_sparse_classes_36k_train_007556 | 35,548 | permissive | [
{
"docstring": "Immediately read content of file",
"name": "read",
"signature": "def read(self)"
},
{
"docstring": "Immediately writes content of file",
"name": "write",
"signature": "def write(self, content)"
}
] | 2 | null | Implement the Python class `AbstractFile` described below.
Class description:
Implement the AbstractFile class.
Method signatures and docstrings:
- def read(self): Immediately read content of file
- def write(self, content): Immediately writes content of file | Implement the Python class `AbstractFile` described below.
Class description:
Implement the AbstractFile class.
Method signatures and docstrings:
- def read(self): Immediately read content of file
- def write(self, content): Immediately writes content of file
<|skeleton|>
class AbstractFile:
def read(self):
... | cbd245d1af8d69e013c469cfc2a9851f51c91417 | <|skeleton|>
class AbstractFile:
def read(self):
"""Immediately read content of file"""
<|body_0|>
def write(self, content):
"""Immediately writes content of file"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AbstractFile:
def read(self):
"""Immediately read content of file"""
if not self.is_property_available('ServerRelativeUrl'):
raise ValueError
response = File.open_binary(self.context, self.properties['ServerRelativeUrl'])
return response.content
def write(self,... | the_stack_v2_python_sparse | office365/sharepoint/files/file.py | vgrem/Office365-REST-Python-Client | train | 1,006 | |
3c30d4554b1013af19b7b35b68268411715019ec | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn EntitlementManagementSettings()",
"from .access_package_external_user_lifecycle_action import AccessPackageExternalUserLifecycleAction\nfrom .entity import Entity\nfrom .access_package_external_user_lifecycle_action import AccessPackag... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return EntitlementManagementSettings()
<|end_body_0|>
<|body_start_1|>
from .access_package_external_user_lifecycle_action import AccessPackageExternalUserLifecycleAction
from .entity import En... | EntitlementManagementSettings | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntitlementManagementSettings:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EntitlementManagementSettings:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator val... | stack_v2_sparse_classes_36k_train_007557 | 3,282 | 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: EntitlementManagementSettings",
"name": "create_from_discriminator_value",
"signature": "def create_from_dis... | 3 | stack_v2_sparse_classes_30k_train_004089 | Implement the Python class `EntitlementManagementSettings` described below.
Class description:
Implement the EntitlementManagementSettings class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EntitlementManagementSettings: Creates a new instance of th... | Implement the Python class `EntitlementManagementSettings` described below.
Class description:
Implement the EntitlementManagementSettings class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EntitlementManagementSettings: Creates a new instance of th... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class EntitlementManagementSettings:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EntitlementManagementSettings:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator val... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EntitlementManagementSettings:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EntitlementManagementSettings:
"""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_stack_v2_python_sparse | msgraph/generated/models/entitlement_management_settings.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
fa301fa0fb17209ed56fab2450a499942fd6eb41 | [
"self.control = QtGui.QDateEdit()\nif hasattr(self.factory, 'qt_date_format'):\n self.control.setDisplayFormat(self.factory.qt_date_format)\nif not self.factory.allow_future:\n self.control.setMaximumDate(QtCore.QDate.currentDate())\nif getattr(self.factory, 'maximum_date_name', None):\n obj, extended_name... | <|body_start_0|>
self.control = QtGui.QDateEdit()
if hasattr(self.factory, 'qt_date_format'):
self.control.setDisplayFormat(self.factory.qt_date_format)
if not self.factory.allow_future:
self.control.setMaximumDate(QtCore.QDate.currentDate())
if getattr(self.facto... | Simple Traits UI date editor that wraps QDateEdit. | SimpleEditor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleEditor:
"""Simple Traits UI date editor that wraps QDateEdit."""
def init(self, parent):
"""Finishes initializing the editor by creating the underlying toolkit widget."""
<|body_0|>
def update_editor(self):
"""Updates the editor when the object trait change... | stack_v2_sparse_classes_36k_train_007558 | 6,787 | no_license | [
{
"docstring": "Finishes initializing the editor by creating the underlying toolkit widget.",
"name": "init",
"signature": "def init(self, parent)"
},
{
"docstring": "Updates the editor when the object trait changes externally to the editor.",
"name": "update_editor",
"signature": "def u... | 3 | null | Implement the Python class `SimpleEditor` described below.
Class description:
Simple Traits UI date editor that wraps QDateEdit.
Method signatures and docstrings:
- def init(self, parent): Finishes initializing the editor by creating the underlying toolkit widget.
- def update_editor(self): Updates the editor when th... | Implement the Python class `SimpleEditor` described below.
Class description:
Simple Traits UI date editor that wraps QDateEdit.
Method signatures and docstrings:
- def init(self, parent): Finishes initializing the editor by creating the underlying toolkit widget.
- def update_editor(self): Updates the editor when th... | b5059e7f121e4abb6888893f91f95dd79aed9ca4 | <|skeleton|>
class SimpleEditor:
"""Simple Traits UI date editor that wraps QDateEdit."""
def init(self, parent):
"""Finishes initializing the editor by creating the underlying toolkit widget."""
<|body_0|>
def update_editor(self):
"""Updates the editor when the object trait change... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleEditor:
"""Simple Traits UI date editor that wraps QDateEdit."""
def init(self, parent):
"""Finishes initializing the editor by creating the underlying toolkit widget."""
self.control = QtGui.QDateEdit()
if hasattr(self.factory, 'qt_date_format'):
self.control.se... | the_stack_v2_python_sparse | venv/Lib/site-packages/traitsui/qt4/date_editor.py | GenomePhD/Bio1-HIV | train | 0 |
0f9244d00e04275cfad90becf80c7928b50a756e | [
"super().__init__(db_file_name, read_only)\nself.std = std\nself.mo = mo",
"val_types = ['r', 'l']\nif sim_type not in val_types:\n raise ValueError('sim_type {0} is not valid has to be one of {1}'.format(sim_type, val_types))\nval_states = ['naive', 'trained', 'ideal', 'bfevolve', 'partevolve']\nif network_st... | <|body_start_0|>
super().__init__(db_file_name, read_only)
self.std = std
self.mo = mo
<|end_body_0|>
<|body_start_1|>
val_types = ['r', 'l']
if sim_type not in val_types:
raise ValueError('sim_type {0} is not valid has to be one of {1}'.format(sim_type, val_types))
... | Hdf5 backed store of simulation data | SimulationStore | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimulationStore:
"""Hdf5 backed store of simulation data"""
def __init__(self, db_file_name, std: GradientStandards, mo: MoTypes, read_only=False):
"""Creates a new simulation store :param db_file_name: The backend database filename :param std: Gradient standards for input normalizat... | stack_v2_sparse_classes_36k_train_007559 | 10,990 | permissive | [
{
"docstring": "Creates a new simulation store :param db_file_name: The backend database filename :param std: Gradient standards for input normalization :param mo: Definition of model organism to use :param read_only: If true, no modifications will be made to the database",
"name": "__init__",
"signatur... | 5 | stack_v2_sparse_classes_30k_train_007767 | Implement the Python class `SimulationStore` described below.
Class description:
Hdf5 backed store of simulation data
Method signatures and docstrings:
- def __init__(self, db_file_name, std: GradientStandards, mo: MoTypes, read_only=False): Creates a new simulation store :param db_file_name: The backend database fil... | Implement the Python class `SimulationStore` described below.
Class description:
Hdf5 backed store of simulation data
Method signatures and docstrings:
- def __init__(self, db_file_name, std: GradientStandards, mo: MoTypes, read_only=False): Creates a new simulation store :param db_file_name: The backend database fil... | 679b48768ad74dccd58f8c2f434ad60036fc5cb7 | <|skeleton|>
class SimulationStore:
"""Hdf5 backed store of simulation data"""
def __init__(self, db_file_name, std: GradientStandards, mo: MoTypes, read_only=False):
"""Creates a new simulation store :param db_file_name: The backend database filename :param std: Gradient standards for input normalizat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimulationStore:
"""Hdf5 backed store of simulation data"""
def __init__(self, db_file_name, std: GradientStandards, mo: MoTypes, read_only=False):
"""Creates a new simulation store :param db_file_name: The backend database filename :param std: Gradient standards for input normalization :param mo... | the_stack_v2_python_sparse | data_stores.py | treestreamymw/GradientPrediction | train | 0 |
edba1402e44e984f36352b564538403c60656216 | [
"from collections import Counter\ncounter = Counter(nums)\ni = 0\nfor color in range(3):\n for _ in range(counter[color]):\n nums[i] = color\n i += 1",
"\"\"\"https://leetcode.com/problems/sort-colors/discuss/26481/Python-O(n)-1-pass-in-place-solution-with-explanation\"\"\"\nn = len(nums)\nl, r =... | <|body_start_0|>
from collections import Counter
counter = Counter(nums)
i = 0
for color in range(3):
for _ in range(counter[color]):
nums[i] = color
i += 1
<|end_body_0|>
<|body_start_1|>
"""https://leetcode.com/problems/sort-colors/d... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sortColors(self, nums: List[int]) -> None:
"""Counting Sort, Time: O(n), Space: O(n)"""
<|body_0|>
def sortColors(self, nums: List[int]) -> None:
"""Two Pointer, Time: O(n), Space: O(1)"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
f... | stack_v2_sparse_classes_36k_train_007560 | 1,275 | no_license | [
{
"docstring": "Counting Sort, Time: O(n), Space: O(n)",
"name": "sortColors",
"signature": "def sortColors(self, nums: List[int]) -> None"
},
{
"docstring": "Two Pointer, Time: O(n), Space: O(1)",
"name": "sortColors",
"signature": "def sortColors(self, nums: List[int]) -> None"
}
] | 2 | stack_v2_sparse_classes_30k_train_019417 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortColors(self, nums: List[int]) -> None: Counting Sort, Time: O(n), Space: O(n)
- def sortColors(self, nums: List[int]) -> None: Two Pointer, Time: O(n), Space: O(1) | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortColors(self, nums: List[int]) -> None: Counting Sort, Time: O(n), Space: O(n)
- def sortColors(self, nums: List[int]) -> None: Two Pointer, Time: O(n), Space: O(1)
<|ske... | 72136e3487d239f5b37e2d6393e034262a6bf599 | <|skeleton|>
class Solution:
def sortColors(self, nums: List[int]) -> None:
"""Counting Sort, Time: O(n), Space: O(n)"""
<|body_0|>
def sortColors(self, nums: List[int]) -> None:
"""Two Pointer, Time: O(n), Space: O(1)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def sortColors(self, nums: List[int]) -> None:
"""Counting Sort, Time: O(n), Space: O(n)"""
from collections import Counter
counter = Counter(nums)
i = 0
for color in range(3):
for _ in range(counter[color]):
nums[i] = color
... | the_stack_v2_python_sparse | python/75-Sort Colors.py | cwza/leetcode | train | 0 | |
b841e6e88e31852c0c612edb34d5ce72a9efbb62 | [
"if not exactly_one(table, sql):\n raise ETLInputError('Only one of table, sql needed')\nsuper(ExtractRdsStep, self).__init__(**kwargs)\nif table:\n sql = 'SELECT * FROM %s;' % table\nelif sql:\n table = SelectStatement(sql).dependencies[0]\nelse:\n raise ETLInputError('Provide a sql statement or a tabl... | <|body_start_0|>
if not exactly_one(table, sql):
raise ETLInputError('Only one of table, sql needed')
super(ExtractRdsStep, self).__init__(**kwargs)
if table:
sql = 'SELECT * FROM %s;' % table
elif sql:
table = SelectStatement(sql).dependencies[0]
... | Extract Redshift Step class that helps get data out of redshift | ExtractRdsStep | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExtractRdsStep:
"""Extract Redshift Step class that helps get data out of redshift"""
def __init__(self, table=None, sql=None, host_name=None, database=None, output_path=None, splits=1, **kwargs):
"""Constructor for the ExtractRdsStep class Args: schema(str): schema from which table ... | stack_v2_sparse_classes_36k_train_007561 | 4,587 | permissive | [
{
"docstring": "Constructor for the ExtractRdsStep class Args: schema(str): schema from which table should be extracted table(path): table name for extract insert_mode(str): insert mode for redshift copy activity database(MysqlNode): database to excute the query splits(int): Number of files to split the output ... | 2 | null | Implement the Python class `ExtractRdsStep` described below.
Class description:
Extract Redshift Step class that helps get data out of redshift
Method signatures and docstrings:
- def __init__(self, table=None, sql=None, host_name=None, database=None, output_path=None, splits=1, **kwargs): Constructor for the Extract... | Implement the Python class `ExtractRdsStep` described below.
Class description:
Extract Redshift Step class that helps get data out of redshift
Method signatures and docstrings:
- def __init__(self, table=None, sql=None, host_name=None, database=None, output_path=None, splits=1, **kwargs): Constructor for the Extract... | 797cb719e6c2abeda0751ada3339c72bfb19c8f2 | <|skeleton|>
class ExtractRdsStep:
"""Extract Redshift Step class that helps get data out of redshift"""
def __init__(self, table=None, sql=None, host_name=None, database=None, output_path=None, splits=1, **kwargs):
"""Constructor for the ExtractRdsStep class Args: schema(str): schema from which table ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExtractRdsStep:
"""Extract Redshift Step class that helps get data out of redshift"""
def __init__(self, table=None, sql=None, host_name=None, database=None, output_path=None, splits=1, **kwargs):
"""Constructor for the ExtractRdsStep class Args: schema(str): schema from which table should be ext... | the_stack_v2_python_sparse | dataduct/steps/extract_rds.py | EverFi/dataduct | train | 3 |
10b90d3a609ebc9e366b286bc7946afadb8b97c0 | [
"if member_id < 1 or not items:\n return False\nfor item in items:\n Cart.query.filter_by(product_id=item['id'], member_id=member_id).delete()\ndb.session.commit()\nreturn True",
"if member_id < 1 or product_id < 1 or number < 1:\n return False\ncart_info = Cart.query.filter_by(product_id=product_id, mem... | <|body_start_0|>
if member_id < 1 or not items:
return False
for item in items:
Cart.query.filter_by(product_id=item['id'], member_id=member_id).delete()
db.session.commit()
return True
<|end_body_0|>
<|body_start_1|>
if member_id < 1 or product_id < 1 or... | CartService | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CartService:
def deleteItem(member_id=0, items=None):
""":param member_id: :param items: :return: 会员是否存在"""
<|body_0|>
def setItems(member_id=0, product_id=0, number=0):
""":param member_id: :param product_id: :param number: :return:向购物车添加/更新数量成功与否"""
<|body_... | stack_v2_sparse_classes_36k_train_007562 | 2,346 | no_license | [
{
"docstring": ":param member_id: :param items: :return: 会员是否存在",
"name": "deleteItem",
"signature": "def deleteItem(member_id=0, items=None)"
},
{
"docstring": ":param member_id: :param product_id: :param number: :return:向购物车添加/更新数量成功与否",
"name": "setItems",
"signature": "def setItems(m... | 3 | stack_v2_sparse_classes_30k_train_012843 | Implement the Python class `CartService` described below.
Class description:
Implement the CartService class.
Method signatures and docstrings:
- def deleteItem(member_id=0, items=None): :param member_id: :param items: :return: 会员是否存在
- def setItems(member_id=0, product_id=0, number=0): :param member_id: :param produ... | Implement the Python class `CartService` described below.
Class description:
Implement the CartService class.
Method signatures and docstrings:
- def deleteItem(member_id=0, items=None): :param member_id: :param items: :return: 会员是否存在
- def setItems(member_id=0, product_id=0, number=0): :param member_id: :param produ... | 16e7110474fa24f1c05e16d13b0bca55e57c58e4 | <|skeleton|>
class CartService:
def deleteItem(member_id=0, items=None):
""":param member_id: :param items: :return: 会员是否存在"""
<|body_0|>
def setItems(member_id=0, product_id=0, number=0):
""":param member_id: :param product_id: :param number: :return:向购物车添加/更新数量成功与否"""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CartService:
def deleteItem(member_id=0, items=None):
""":param member_id: :param items: :return: 会员是否存在"""
if member_id < 1 or not items:
return False
for item in items:
Cart.query.filter_by(product_id=item['id'], member_id=member_id).delete()
db.sessio... | the_stack_v2_python_sparse | backend/ciwei/common/libs/mall/CartService.py | 100101001/HedgehogHunt | train | 1 | |
8fbed2c779b46af5fb0ffd088004c2f3150d1ed7 | [
"super(PerformanceKMeans, self).setUp()\nschema = [('Vec1', float), ('Vec2', float), ('Vec3', float), ('Vec4', float), ('Vec5', float), ('term', str)]\nds = self.get_file(self.id(), performance_file=True)\nself.frame_train = self.context.frame.import_csv(ds, schema=schema)",
"with profiler.Timer('profile.' + self... | <|body_start_0|>
super(PerformanceKMeans, self).setUp()
schema = [('Vec1', float), ('Vec2', float), ('Vec3', float), ('Vec4', float), ('Vec5', float), ('term', str)]
ds = self.get_file(self.id(), performance_file=True)
self.frame_train = self.context.frame.import_csv(ds, schema=schema)
<... | PerformanceKMeans | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PerformanceKMeans:
def setUp(self):
"""Import the files to test against."""
<|body_0|>
def test_kmeans_5by5(self):
"""Train a 5-feature, 5-class KMeans model"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(PerformanceKMeans, self).setUp()
... | stack_v2_sparse_classes_36k_train_007563 | 1,888 | permissive | [
{
"docstring": "Import the files to test against.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Train a 5-feature, 5-class KMeans model",
"name": "test_kmeans_5by5",
"signature": "def test_kmeans_5by5(self)"
}
] | 2 | null | Implement the Python class `PerformanceKMeans` described below.
Class description:
Implement the PerformanceKMeans class.
Method signatures and docstrings:
- def setUp(self): Import the files to test against.
- def test_kmeans_5by5(self): Train a 5-feature, 5-class KMeans model | Implement the Python class `PerformanceKMeans` described below.
Class description:
Implement the PerformanceKMeans class.
Method signatures and docstrings:
- def setUp(self): Import the files to test against.
- def test_kmeans_5by5(self): Train a 5-feature, 5-class KMeans model
<|skeleton|>
class PerformanceKMeans:
... | 5548fc925b5c278263cbdebbd9e8c7593320c2f4 | <|skeleton|>
class PerformanceKMeans:
def setUp(self):
"""Import the files to test against."""
<|body_0|>
def test_kmeans_5by5(self):
"""Train a 5-feature, 5-class KMeans model"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PerformanceKMeans:
def setUp(self):
"""Import the files to test against."""
super(PerformanceKMeans, self).setUp()
schema = [('Vec1', float), ('Vec2', float), ('Vec3', float), ('Vec4', float), ('Vec5', float), ('term', str)]
ds = self.get_file(self.id(), performance_file=True)
... | the_stack_v2_python_sparse | regression-tests/sparktkregtests/testcases/performance/kmeans_perf_test.py | trustedanalytics/spark-tk | train | 35 | |
2c06afe79c4e3c68f8cb87f444efc1edbaf2739f | [
"self.project_name = self.project.title\nself.asso_name = self.project.asso.title\nself.update_project_levels()\nsuper().save(*args, **kwargs)",
"self.project.achievement += self.amount\nself.project.donation_count += 1\nself.project.achievement_percent = round(self.project.achievement / self.project.target, 4)\n... | <|body_start_0|>
self.project_name = self.project.title
self.asso_name = self.project.asso.title
self.update_project_levels()
super().save(*args, **kwargs)
<|end_body_0|>
<|body_start_1|>
self.project.achievement += self.amount
self.project.donation_count += 1
se... | table that accounts for every donations done during orders | Donation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Donation:
"""table that accounts for every donations done during orders"""
def save(self, *args, **kwargs):
"""to keep a track of asso and project even if the will be deleted"""
<|body_0|>
def update_project_levels(self):
"""update project levels"""
<|bod... | stack_v2_sparse_classes_36k_train_007564 | 2,885 | no_license | [
{
"docstring": "to keep a track of asso and project even if the will be deleted",
"name": "save",
"signature": "def save(self, *args, **kwargs)"
},
{
"docstring": "update project levels",
"name": "update_project_levels",
"signature": "def update_project_levels(self)"
}
] | 2 | null | Implement the Python class `Donation` described below.
Class description:
table that accounts for every donations done during orders
Method signatures and docstrings:
- def save(self, *args, **kwargs): to keep a track of asso and project even if the will be deleted
- def update_project_levels(self): update project le... | Implement the Python class `Donation` described below.
Class description:
table that accounts for every donations done during orders
Method signatures and docstrings:
- def save(self, *args, **kwargs): to keep a track of asso and project even if the will be deleted
- def update_project_levels(self): update project le... | 616d68ecd102f3c39a0a0714dd5048812875ceb6 | <|skeleton|>
class Donation:
"""table that accounts for every donations done during orders"""
def save(self, *args, **kwargs):
"""to keep a track of asso and project even if the will be deleted"""
<|body_0|>
def update_project_levels(self):
"""update project levels"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Donation:
"""table that accounts for every donations done during orders"""
def save(self, *args, **kwargs):
"""to keep a track of asso and project even if the will be deleted"""
self.project_name = self.project.title
self.asso_name = self.project.asso.title
self.update_pro... | the_stack_v2_python_sparse | apps_fork/order/models.py | vitrolom/ecommerce | train | 0 |
1044890ca8f16c8a24d44d4082f23e837b435235 | [
"self.orgnr_field = orgnr_field\nself.kode_type_field = kode_type_field\nself.kode_tekst_field = kode_tekst_field\nself.navn_field = navn_field\nself.postnr_field = postnr_field\nself.poststed_field = poststed_field\nself.eierandel_field = eierandel_field\nself.additional_properties = additional_properties",
"if ... | <|body_start_0|>
self.orgnr_field = orgnr_field
self.kode_type_field = kode_type_field
self.kode_tekst_field = kode_tekst_field
self.navn_field = navn_field
self.postnr_field = postnr_field
self.poststed_field = poststed_field
self.eierandel_field = eierandel_fiel... | Implementation of the 'Datterselskap' model. TODO: type model description here. Attributes: orgnr_field (long|int): TODO: type description here. kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TODO: type description here. postnr_field (... | Datterselskap | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Datterselskap:
"""Implementation of the 'Datterselskap' model. TODO: type model description here. Attributes: orgnr_field (long|int): TODO: type description here. kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TO... | stack_v2_sparse_classes_36k_train_007565 | 3,522 | permissive | [
{
"docstring": "Constructor for the Datterselskap class",
"name": "__init__",
"signature": "def __init__(self, orgnr_field=None, kode_type_field=None, kode_tekst_field=None, navn_field=None, postnr_field=None, poststed_field=None, eierandel_field=None, additional_properties={})"
},
{
"docstring"... | 2 | null | Implement the Python class `Datterselskap` described below.
Class description:
Implementation of the 'Datterselskap' model. TODO: type model description here. Attributes: orgnr_field (long|int): TODO: type description here. kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type de... | Implement the Python class `Datterselskap` described below.
Class description:
Implementation of the 'Datterselskap' model. TODO: type model description here. Attributes: orgnr_field (long|int): TODO: type description here. kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type de... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class Datterselskap:
"""Implementation of the 'Datterselskap' model. TODO: type model description here. Attributes: orgnr_field (long|int): TODO: type description here. kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TO... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Datterselskap:
"""Implementation of the 'Datterselskap' model. TODO: type model description here. Attributes: orgnr_field (long|int): TODO: type description here. kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TODO: type desc... | the_stack_v2_python_sparse | idfy_rest_client/models/datterselskap.py | dealflowteam/Idfy | train | 0 |
9068143ea16fbe4a7ace7f46d559e1d650ff4774 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn KubernetesServiceEvidence()",
"from .alert_evidence import AlertEvidence\nfrom .dictionary import Dictionary\nfrom .ip_evidence import IpEvidence\nfrom .kubernetes_namespace_evidence import KubernetesNamespaceEvidence\nfrom .kubernetes... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return KubernetesServiceEvidence()
<|end_body_0|>
<|body_start_1|>
from .alert_evidence import AlertEvidence
from .dictionary import Dictionary
from .ip_evidence import IpEvidence
... | KubernetesServiceEvidence | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KubernetesServiceEvidence:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> KubernetesServiceEvidence:
"""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 c... | stack_v2_sparse_classes_36k_train_007566 | 4,761 | 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: KubernetesServiceEvidence",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrim... | 3 | stack_v2_sparse_classes_30k_train_007379 | Implement the Python class `KubernetesServiceEvidence` described below.
Class description:
Implement the KubernetesServiceEvidence class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> KubernetesServiceEvidence: Creates a new instance of the appropriat... | Implement the Python class `KubernetesServiceEvidence` described below.
Class description:
Implement the KubernetesServiceEvidence class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> KubernetesServiceEvidence: Creates a new instance of the appropriat... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class KubernetesServiceEvidence:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> KubernetesServiceEvidence:
"""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 c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KubernetesServiceEvidence:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> KubernetesServiceEvidence:
"""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 obje... | the_stack_v2_python_sparse | msgraph/generated/models/security/kubernetes_service_evidence.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
46188758fbda68dd02400a4c68ab39701895dd56 | [
"self.client = sclient.Client(wsdl_file)\nif is_ssl:\n trans = HttpAuthUsingCert('', '')\n self.client.set_options(transport=trans)\nif endpoint is not None:\n self.client.options.location = endpoint\nif is_ssl and username:\n passman = urllib2.HTTPPasswordMgrWithDefaultRealm()\n passman.add_password... | <|body_start_0|>
self.client = sclient.Client(wsdl_file)
if is_ssl:
trans = HttpAuthUsingCert('', '')
self.client.set_options(transport=trans)
if endpoint is not None:
self.client.options.location = endpoint
if is_ssl and username:
passman ... | This class repsent wraper for SOAP client | SoapInterface | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SoapInterface:
"""This class repsent wraper for SOAP client"""
def __init__(self, wsdl_file, endpoint=None, is_ssl=False, username=None, password=None):
"""Constructor method @wsdl_file - URL representing WSDL file (local or global URL) @endpoint - SOAP endpoint URL @is_ssl - if True... | stack_v2_sparse_classes_36k_train_007567 | 5,178 | no_license | [
{
"docstring": "Constructor method @wsdl_file - URL representing WSDL file (local or global URL) @endpoint - SOAP endpoint URL @is_ssl - if True then initiate ssl connection @username - username for HTTP authentication (if None then no HTTP authentication) @password - password for HTTP authentication",
"nam... | 3 | stack_v2_sparse_classes_30k_train_019480 | Implement the Python class `SoapInterface` described below.
Class description:
This class repsent wraper for SOAP client
Method signatures and docstrings:
- def __init__(self, wsdl_file, endpoint=None, is_ssl=False, username=None, password=None): Constructor method @wsdl_file - URL representing WSDL file (local or gl... | Implement the Python class `SoapInterface` described below.
Class description:
This class repsent wraper for SOAP client
Method signatures and docstrings:
- def __init__(self, wsdl_file, endpoint=None, is_ssl=False, username=None, password=None): Constructor method @wsdl_file - URL representing WSDL file (local or gl... | e6bc6eb747e39dcacf5f3fd0738d82f16ed0f76d | <|skeleton|>
class SoapInterface:
"""This class repsent wraper for SOAP client"""
def __init__(self, wsdl_file, endpoint=None, is_ssl=False, username=None, password=None):
"""Constructor method @wsdl_file - URL representing WSDL file (local or global URL) @endpoint - SOAP endpoint URL @is_ssl - if True... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SoapInterface:
"""This class repsent wraper for SOAP client"""
def __init__(self, wsdl_file, endpoint=None, is_ssl=False, username=None, password=None):
"""Constructor method @wsdl_file - URL representing WSDL file (local or global URL) @endpoint - SOAP endpoint URL @is_ssl - if True then initiat... | the_stack_v2_python_sparse | FablikFramework/FablikClient/bin/soapClient.py | fabregas/old_projects | train | 0 |
aaab82149b0f00287a29b9afb8cda85599dcd5df | [
"mock_input = MockInputApi()\nmock_input.files = [MockFile('path/One.java', ['new Notification.Builder()']), MockFile('path/Two.java', ['new NotificationCompat.Builder()'])]\nerrors = PRESUBMIT._CheckNotificationConstructors(mock_input, MockOutputApi())\nself.assertEqual(1, len(errors))\nself.assertEqual(2, len(err... | <|body_start_0|>
mock_input = MockInputApi()
mock_input.files = [MockFile('path/One.java', ['new Notification.Builder()']), MockFile('path/Two.java', ['new NotificationCompat.Builder()'])]
errors = PRESUBMIT._CheckNotificationConstructors(mock_input, MockOutputApi())
self.assertEqual(1, ... | Test the _CheckNotificationConstructors presubmit check. | CheckNotificationConstructors | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CheckNotificationConstructors:
"""Test the _CheckNotificationConstructors presubmit check."""
def testTruePositives(self):
"""Examples of when Notification.Builder use is correctly flagged."""
<|body_0|>
def testFalsePositives(self):
"""Examples of when Notificat... | stack_v2_sparse_classes_36k_train_007568 | 4,016 | permissive | [
{
"docstring": "Examples of when Notification.Builder use is correctly flagged.",
"name": "testTruePositives",
"signature": "def testTruePositives(self)"
},
{
"docstring": "Examples of when Notification.Builder should not be flagged.",
"name": "testFalsePositives",
"signature": "def test... | 2 | null | Implement the Python class `CheckNotificationConstructors` described below.
Class description:
Test the _CheckNotificationConstructors presubmit check.
Method signatures and docstrings:
- def testTruePositives(self): Examples of when Notification.Builder use is correctly flagged.
- def testFalsePositives(self): Examp... | Implement the Python class `CheckNotificationConstructors` described below.
Class description:
Test the _CheckNotificationConstructors presubmit check.
Method signatures and docstrings:
- def testTruePositives(self): Examples of when Notification.Builder use is correctly flagged.
- def testFalsePositives(self): Examp... | d92465f71fb8e4345e27bd889532339204b26f1e | <|skeleton|>
class CheckNotificationConstructors:
"""Test the _CheckNotificationConstructors presubmit check."""
def testTruePositives(self):
"""Examples of when Notification.Builder use is correctly flagged."""
<|body_0|>
def testFalsePositives(self):
"""Examples of when Notificat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CheckNotificationConstructors:
"""Test the _CheckNotificationConstructors presubmit check."""
def testTruePositives(self):
"""Examples of when Notification.Builder use is correctly flagged."""
mock_input = MockInputApi()
mock_input.files = [MockFile('path/One.java', ['new Notifica... | the_stack_v2_python_sparse | chromium/chrome/android/java/src/PRESUBMIT_test.py | Csineneo/Vivaldi | train | 5 |
1a55c6789e5d75c60c38e232bfa5b6b43938252d | [
"if len(s) < 2:\n return s\nss = s + '#' + s[::-1]\nlength, M = (0, len(ss))\ni, lps = (1, [0] * M)\nwhile i < M:\n if ss[i] == ss[length]:\n length += 1\n lps[i] = length\n i += 1\n elif length == 0:\n lps[i] = 0\n i += 1\n else:\n length = lps[length - 1]\nret... | <|body_start_0|>
if len(s) < 2:
return s
ss = s + '#' + s[::-1]
length, M = (0, len(ss))
i, lps = (1, [0] * M)
while i < M:
if ss[i] == ss[length]:
length += 1
lps[i] = length
i += 1
elif length =... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def shortestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def shortestPalindrome2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(s) < 2:
return s
ss = s + '... | stack_v2_sparse_classes_36k_train_007569 | 2,505 | no_license | [
{
"docstring": ":type s: str :rtype: str",
"name": "shortestPalindrome",
"signature": "def shortestPalindrome(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name": "shortestPalindrome2",
"signature": "def shortestPalindrome2(self, s)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003429 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestPalindrome(self, s): :type s: str :rtype: str
- def shortestPalindrome2(self, s): :type s: str :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestPalindrome(self, s): :type s: str :rtype: str
- def shortestPalindrome2(self, s): :type s: str :rtype: str
<|skeleton|>
class Solution:
def shortestPalindrome(s... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def shortestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def shortestPalindrome2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def shortestPalindrome(self, s):
""":type s: str :rtype: str"""
if len(s) < 2:
return s
ss = s + '#' + s[::-1]
length, M = (0, len(ss))
i, lps = (1, [0] * M)
while i < M:
if ss[i] == ss[length]:
length += 1
... | the_stack_v2_python_sparse | code214ShortestPalindrome.py | cybelewang/leetcode-python | train | 0 | |
db2bdd7c70f8e62d120d5a90e91f0fc749c33cac | [
"if name == 'ALLOW_CHANGE':\n raise AttributeError(\"attribute name 'ALLOW_CHANGE' has been occupied, please use another name\")\nif getattr(self, 'ALLOW_CHANGE', None):\n self.__dict__[name] = value\nelse:\n raise AttributeReadOnlyError(self, name)",
"try:\n self.__dict__['ALLOW_CHANGE'] = True\n ... | <|body_start_0|>
if name == 'ALLOW_CHANGE':
raise AttributeError("attribute name 'ALLOW_CHANGE' has been occupied, please use another name")
if getattr(self, 'ALLOW_CHANGE', None):
self.__dict__[name] = value
else:
raise AttributeReadOnlyError(self, name)
<|en... | a base class whose attributes are read-only, unless use the context _context_allow_change to change attributes | ReadOnlySpace | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReadOnlySpace:
"""a base class whose attributes are read-only, unless use the context _context_allow_change to change attributes"""
def __setattr__(self, name, value):
"""attributes could not be change, unless in context _context_allow_change"""
<|body_0|>
def _context_a... | stack_v2_sparse_classes_36k_train_007570 | 1,524 | permissive | [
{
"docstring": "attributes could not be change, unless in context _context_allow_change",
"name": "__setattr__",
"signature": "def __setattr__(self, name, value)"
},
{
"docstring": "the context in which attributes could be change for example: 1.wrong way: would raise AttributeReadOnlyError self.... | 2 | stack_v2_sparse_classes_30k_val_000078 | Implement the Python class `ReadOnlySpace` described below.
Class description:
a base class whose attributes are read-only, unless use the context _context_allow_change to change attributes
Method signatures and docstrings:
- def __setattr__(self, name, value): attributes could not be change, unless in context _conte... | Implement the Python class `ReadOnlySpace` described below.
Class description:
a base class whose attributes are read-only, unless use the context _context_allow_change to change attributes
Method signatures and docstrings:
- def __setattr__(self, name, value): attributes could not be change, unless in context _conte... | f4abc48fff907a0785372b941afcd67e62eec825 | <|skeleton|>
class ReadOnlySpace:
"""a base class whose attributes are read-only, unless use the context _context_allow_change to change attributes"""
def __setattr__(self, name, value):
"""attributes could not be change, unless in context _context_allow_change"""
<|body_0|>
def _context_a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReadOnlySpace:
"""a base class whose attributes are read-only, unless use the context _context_allow_change to change attributes"""
def __setattr__(self, name, value):
"""attributes could not be change, unless in context _context_allow_change"""
if name == 'ALLOW_CHANGE':
rais... | the_stack_v2_python_sparse | api/BackendAPI/ReadOnlySpace.py | AutoCoinDCF/NEW_API | train | 0 |
bad26b4500eb89e3da48ab3d363241ef355f823e | [
"self.ckpt_folder, self.summary_folder, self.save_path = prepare_folder(filename, sub_folder=sub_folder)\nself.load_ckpt = load_ckpt\nself.do_trace = do_trace\nself.do_save = do_save\nself.debug = debug_mode\nself.debug_step = debug_step\nself.log_device = log_device\nself.query_step = query_step\nself.imbalanced_u... | <|body_start_0|>
self.ckpt_folder, self.summary_folder, self.save_path = prepare_folder(filename, sub_folder=sub_folder)
self.load_ckpt = load_ckpt
self.do_trace = do_trace
self.do_save = do_save
self.debug = debug_mode
self.debug_step = debug_step
self.log_device... | Agent | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Agent:
def __init__(self, filename, sub_folder, load_ckpt=False, do_trace=False, do_save=True, debug_mode=False, debug_step=800, query_step=500, log_device=False, imbalanced_update=None, print_loss=True):
"""Agent is a wrapper for the MySession class, used for training and evaluating com... | stack_v2_sparse_classes_36k_train_007571 | 4,163 | permissive | [
{
"docstring": "Agent is a wrapper for the MySession class, used for training and evaluating complex model :param filename: :param sub_folder: :param load_ckpt: :param do_trace: :param do_save: :param debug_mode: :param log_device: :param query_step: :param imbalanced_update:",
"name": "__init__",
"sign... | 2 | stack_v2_sparse_classes_30k_train_000060 | Implement the Python class `Agent` described below.
Class description:
Implement the Agent class.
Method signatures and docstrings:
- def __init__(self, filename, sub_folder, load_ckpt=False, do_trace=False, do_save=True, debug_mode=False, debug_step=800, query_step=500, log_device=False, imbalanced_update=None, prin... | Implement the Python class `Agent` described below.
Class description:
Implement the Agent class.
Method signatures and docstrings:
- def __init__(self, filename, sub_folder, load_ckpt=False, do_trace=False, do_save=True, debug_mode=False, debug_step=800, query_step=500, log_device=False, imbalanced_update=None, prin... | 7522093498b658026344541ddd5c248095763fb6 | <|skeleton|>
class Agent:
def __init__(self, filename, sub_folder, load_ckpt=False, do_trace=False, do_save=True, debug_mode=False, debug_step=800, query_step=500, log_device=False, imbalanced_update=None, print_loss=True):
"""Agent is a wrapper for the MySession class, used for training and evaluating com... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Agent:
def __init__(self, filename, sub_folder, load_ckpt=False, do_trace=False, do_save=True, debug_mode=False, debug_step=800, query_step=500, log_device=False, imbalanced_update=None, print_loss=True):
"""Agent is a wrapper for the MySession class, used for training and evaluating complex model :pa... | the_stack_v2_python_sparse | GeneralTools/graph_funcs/agent.py | frhrdr/MMD-GAN | train | 0 | |
cbd0ef0ecb4c751f9bcaad01ca19239cb985318c | [
"id_sede = request.data['primary_key']\nsede = Sede.objects.get(id=id_sede)\nif len(sede.grupos.filter(activo=True)) > 0:\n return Response({'mensaje': 'La sede contiene grupos activos'}, status=status.HTTP_406_NOT_ACCEPTABLE)\nelse:\n sede.activa = False\n sede.save()\n return Response({'mensaje': 'Cam... | <|body_start_0|>
id_sede = request.data['primary_key']
sede = Sede.objects.get(id=id_sede)
if len(sede.grupos.filter(activo=True)) > 0:
return Response({'mensaje': 'La sede contiene grupos activos'}, status=status.HTTP_406_NOT_ACCEPTABLE)
else:
sede.activa = False... | SedeViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SedeViewSet:
def desactivar_sede(self, request, pk=None):
"""Metodo que cambia la disponibilidad de la sede"""
<|body_0|>
def desactivar_participante(self, request, pk=None):
"""Metodo que cambia la disponibilidad del participante"""
<|body_1|>
def actua... | stack_v2_sparse_classes_36k_train_007572 | 13,765 | no_license | [
{
"docstring": "Metodo que cambia la disponibilidad de la sede",
"name": "desactivar_sede",
"signature": "def desactivar_sede(self, request, pk=None)"
},
{
"docstring": "Metodo que cambia la disponibilidad del participante",
"name": "desactivar_participante",
"signature": "def desactivar... | 3 | null | Implement the Python class `SedeViewSet` described below.
Class description:
Implement the SedeViewSet class.
Method signatures and docstrings:
- def desactivar_sede(self, request, pk=None): Metodo que cambia la disponibilidad de la sede
- def desactivar_participante(self, request, pk=None): Metodo que cambia la disp... | Implement the Python class `SedeViewSet` described below.
Class description:
Implement the SedeViewSet class.
Method signatures and docstrings:
- def desactivar_sede(self, request, pk=None): Metodo que cambia la disponibilidad de la sede
- def desactivar_participante(self, request, pk=None): Metodo que cambia la disp... | 0e37786d7173abe820fd10b094ffcc2db9593a9c | <|skeleton|>
class SedeViewSet:
def desactivar_sede(self, request, pk=None):
"""Metodo que cambia la disponibilidad de la sede"""
<|body_0|>
def desactivar_participante(self, request, pk=None):
"""Metodo que cambia la disponibilidad del participante"""
<|body_1|>
def actua... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SedeViewSet:
def desactivar_sede(self, request, pk=None):
"""Metodo que cambia la disponibilidad de la sede"""
id_sede = request.data['primary_key']
sede = Sede.objects.get(id=id_sede)
if len(sede.grupos.filter(activo=True)) > 0:
return Response({'mensaje': 'La sede... | the_stack_v2_python_sparse | src/apps/cyd/api_views.py | jinchuika/app-suni | train | 7 | |
7913db807fb37bb459bd9f1c8a69f1613fbe57b5 | [
"self.check_parameters(params)\ncos = np.cos(params[0] / 2)\nsin = np.sin(params[0] / 2)\nreturn UnitaryMatrix([[cos, -sin], [sin, cos]])",
"self.check_parameters(params)\ndcos = -np.sin(params[0] / 2) / 2\ndsin = np.cos(params[0] / 2) / 2\nreturn np.array([[[dcos, -dsin], [dsin, dcos]]], dtype=np.complex128)",
... | <|body_start_0|>
self.check_parameters(params)
cos = np.cos(params[0] / 2)
sin = np.sin(params[0] / 2)
return UnitaryMatrix([[cos, -sin], [sin, cos]])
<|end_body_0|>
<|body_start_1|>
self.check_parameters(params)
dcos = -np.sin(params[0] / 2) / 2
dsin = np.cos(pa... | A gate representing an arbitrary rotation around the Y axis. It is given by the following parameterized unitary: .. math:: \\begin{pmatrix} \\cos{\\frac{\\theta}{2}} & -\\sin{\\frac{\\theta}{2}} \\\\ \\sin{\\frac{\\theta}{2}} & \\cos{\\frac{\\theta}{2}} \\\\ \\end{pmatrix} | RYGate | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RYGate:
"""A gate representing an arbitrary rotation around the Y axis. It is given by the following parameterized unitary: .. math:: \\begin{pmatrix} \\cos{\\frac{\\theta}{2}} & -\\sin{\\frac{\\theta}{2}} \\\\ \\sin{\\frac{\\theta}{2}} & \\cos{\\frac{\\theta}{2}} \\\\ \\end{pmatrix}"""
def ... | stack_v2_sparse_classes_36k_train_007573 | 2,402 | permissive | [
{
"docstring": "Return the unitary for this gate, see :class:`Unitary` for more.",
"name": "get_unitary",
"signature": "def get_unitary(self, params: RealVector=[]) -> UnitaryMatrix"
},
{
"docstring": "Return the gradient for this gate. See :class:`DifferentiableUnitary` for more info.",
"na... | 3 | stack_v2_sparse_classes_30k_train_019302 | Implement the Python class `RYGate` described below.
Class description:
A gate representing an arbitrary rotation around the Y axis. It is given by the following parameterized unitary: .. math:: \\begin{pmatrix} \\cos{\\frac{\\theta}{2}} & -\\sin{\\frac{\\theta}{2}} \\\\ \\sin{\\frac{\\theta}{2}} & \\cos{\\frac{\\thet... | Implement the Python class `RYGate` described below.
Class description:
A gate representing an arbitrary rotation around the Y axis. It is given by the following parameterized unitary: .. math:: \\begin{pmatrix} \\cos{\\frac{\\theta}{2}} & -\\sin{\\frac{\\theta}{2}} \\\\ \\sin{\\frac{\\theta}{2}} & \\cos{\\frac{\\thet... | c89112d15072e8ffffb68cf1757b184e2aeb3dc8 | <|skeleton|>
class RYGate:
"""A gate representing an arbitrary rotation around the Y axis. It is given by the following parameterized unitary: .. math:: \\begin{pmatrix} \\cos{\\frac{\\theta}{2}} & -\\sin{\\frac{\\theta}{2}} \\\\ \\sin{\\frac{\\theta}{2}} & \\cos{\\frac{\\theta}{2}} \\\\ \\end{pmatrix}"""
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RYGate:
"""A gate representing an arbitrary rotation around the Y axis. It is given by the following parameterized unitary: .. math:: \\begin{pmatrix} \\cos{\\frac{\\theta}{2}} & -\\sin{\\frac{\\theta}{2}} \\\\ \\sin{\\frac{\\theta}{2}} & \\cos{\\frac{\\theta}{2}} \\\\ \\end{pmatrix}"""
def get_unitary(s... | the_stack_v2_python_sparse | bqskit/ir/gates/parameterized/ry.py | BQSKit/bqskit | train | 54 |
00b12a3aa7477cd4baf4dbf41de1b90a0f1e59e0 | [
"setNums = set(nums)\nres = []\nfor i in range(1, len(nums) + 1):\n if i not in setNums:\n res.append(i)\nreturn res",
"res = []\nfor i in range(len(nums)):\n m = abs(nums[i]) - 1\n nums[m] = -nums[m] if nums[m] > 0 else nums[m]\nfor i in range(len(nums)):\n if nums[i] > 0:\n res.append(... | <|body_start_0|>
setNums = set(nums)
res = []
for i in range(1, len(nums) + 1):
if i not in setNums:
res.append(i)
return res
<|end_body_0|>
<|body_start_1|>
res = []
for i in range(len(nums)):
m = abs(nums[i]) - 1
nums... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findDisappearedNumbers2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def findDisappearedNumbers(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
setNums = ... | stack_v2_sparse_classes_36k_train_007574 | 1,383 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "findDisappearedNumbers2",
"signature": "def findDisappearedNumbers2(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "findDisappearedNumbers",
"signature": "def findDisappearedNumbers(self... | 2 | stack_v2_sparse_classes_30k_train_009290 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDisappearedNumbers2(self, nums): :type nums: List[int] :rtype: List[int]
- def findDisappearedNumbers(self, nums): :type nums: List[int] :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDisappearedNumbers2(self, nums): :type nums: List[int] :rtype: List[int]
- def findDisappearedNumbers(self, nums): :type nums: List[int] :rtype: List[int]
<|skeleton|>
c... | 0fdc1d60cfb3f4c26698a493da4986bfc873e02a | <|skeleton|>
class Solution:
def findDisappearedNumbers2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def findDisappearedNumbers(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findDisappearedNumbers2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
setNums = set(nums)
res = []
for i in range(1, len(nums) + 1):
if i not in setNums:
res.append(i)
return res
def findDisappearedNumbers(self... | the_stack_v2_python_sparse | 448_FindAllNumbersDisappearedInAnArray/448_FindAllNumbersDisappearedInAnArray.py | ranson/leetcode | train | 0 | |
42e73af0a8a0595994a59e3400f84348ec0959e1 | [
"try:\n encounter: models.Encounter = models.Encounter.create_from_json(data=request.data, patient_profile=request.user.patient_profile)\nexcept custom_exceptions.DataForNewEncounterNotProvidedException as e:\n return response.Response(data=e.get_response_format(), status=status.HTTP_400_BAD_REQUEST)\nseriali... | <|body_start_0|>
try:
encounter: models.Encounter = models.Encounter.create_from_json(data=request.data, patient_profile=request.user.patient_profile)
except custom_exceptions.DataForNewEncounterNotProvidedException as e:
return response.Response(data=e.get_response_format(), sta... | Endpoints for Encounter objects. | EncountersEndpoint | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncountersEndpoint:
"""Endpoints for Encounter objects."""
def post(self, request: Request) -> response.Response:
"""Adds a new encounter for the user."""
<|body_0|>
def put(self, request: Request) -> response.Response:
"""Updates an existing encounter."""
... | stack_v2_sparse_classes_36k_train_007575 | 14,860 | no_license | [
{
"docstring": "Adds a new encounter for the user.",
"name": "post",
"signature": "def post(self, request: Request) -> response.Response"
},
{
"docstring": "Updates an existing encounter.",
"name": "put",
"signature": "def put(self, request: Request) -> response.Response"
},
{
"d... | 3 | stack_v2_sparse_classes_30k_val_000181 | Implement the Python class `EncountersEndpoint` described below.
Class description:
Endpoints for Encounter objects.
Method signatures and docstrings:
- def post(self, request: Request) -> response.Response: Adds a new encounter for the user.
- def put(self, request: Request) -> response.Response: Updates an existing... | Implement the Python class `EncountersEndpoint` described below.
Class description:
Endpoints for Encounter objects.
Method signatures and docstrings:
- def post(self, request: Request) -> response.Response: Adds a new encounter for the user.
- def put(self, request: Request) -> response.Response: Updates an existing... | b6d757895132b9b3c8c6682c11efadf993d5905b | <|skeleton|>
class EncountersEndpoint:
"""Endpoints for Encounter objects."""
def post(self, request: Request) -> response.Response:
"""Adds a new encounter for the user."""
<|body_0|>
def put(self, request: Request) -> response.Response:
"""Updates an existing encounter."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncountersEndpoint:
"""Endpoints for Encounter objects."""
def post(self, request: Request) -> response.Response:
"""Adds a new encounter for the user."""
try:
encounter: models.Encounter = models.Encounter.create_from_json(data=request.data, patient_profile=request.user.patie... | the_stack_v2_python_sparse | main/model_api.py | kalolad1/cosmos | train | 0 |
47ab5ac4fc8f1707236e4b7c785c21d539943c9c | [
"self.instance = kwargs.pop('instance', None)\ninitial = kwargs.setdefault('initial', {})\ninitial['name'] = self.instance.name\ninitial['description'] = self.instance.description\ninitial['status'] = self.instance.status\ninitial['cc_version'] = self.instance.cc_version\ninitial['idprefix'] = self.instance.case.id... | <|body_start_0|>
self.instance = kwargs.pop('instance', None)
initial = kwargs.setdefault('initial', {})
initial['name'] = self.instance.name
initial['description'] = self.instance.description
initial['status'] = self.instance.status
initial['cc_version'] = self.instance.... | Form for editing a case version. | EditCaseVersionForm | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EditCaseVersionForm:
"""Form for editing a case version."""
def __init__(self, *args, **kwargs):
"""Initialize EditCaseVersionForm, pulling instance from kwargs."""
<|body_0|>
def save(self, user=None):
"""Save the edited caseversion."""
<|body_1|>
<|end... | stack_v2_sparse_classes_36k_train_007576 | 16,711 | permissive | [
{
"docstring": "Initialize EditCaseVersionForm, pulling instance from kwargs.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Save the edited caseversion.",
"name": "save",
"signature": "def save(self, user=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003417 | Implement the Python class `EditCaseVersionForm` described below.
Class description:
Form for editing a case version.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize EditCaseVersionForm, pulling instance from kwargs.
- def save(self, user=None): Save the edited caseversion. | Implement the Python class `EditCaseVersionForm` described below.
Class description:
Form for editing a case version.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize EditCaseVersionForm, pulling instance from kwargs.
- def save(self, user=None): Save the edited caseversion.
<|skel... | ee54db2fe8ffbf2216d359b7a093b51f2574878e | <|skeleton|>
class EditCaseVersionForm:
"""Form for editing a case version."""
def __init__(self, *args, **kwargs):
"""Initialize EditCaseVersionForm, pulling instance from kwargs."""
<|body_0|>
def save(self, user=None):
"""Save the edited caseversion."""
<|body_1|>
<|end... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EditCaseVersionForm:
"""Form for editing a case version."""
def __init__(self, *args, **kwargs):
"""Initialize EditCaseVersionForm, pulling instance from kwargs."""
self.instance = kwargs.pop('instance', None)
initial = kwargs.setdefault('initial', {})
initial['name'] = se... | the_stack_v2_python_sparse | moztrap/view/manage/cases/forms.py | isakib/moztrap | train | 1 |
45677cbd1a4371b89bfcf8d0c7b4f4c36348713a | [
"to_xml = format == 'xml'\nmimetype = self.JSON_FORMAT if not to_xml else self.XML_FORMAT\nres_dict = {'result': {'code': status_code, 'error': message}}\ncontent = convert_format(res_dict, to_xml)\nresponse = Response(content, mimetype=mimetype)\nresponse.status_code = status_code\nreturn response",
"status_code... | <|body_start_0|>
to_xml = format == 'xml'
mimetype = self.JSON_FORMAT if not to_xml else self.XML_FORMAT
res_dict = {'result': {'code': status_code, 'error': message}}
content = convert_format(res_dict, to_xml)
response = Response(content, mimetype=mimetype)
response.stat... | ErrorResponse | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ErrorResponse:
def response(self, status_code, message, format='json'):
"""make error response body :param status_code: http status code :param message: error message :param format: json or xml :returns flask.Response"""
<|body_0|>
def unauthorized(cls, message=None, format=... | stack_v2_sparse_classes_36k_train_007577 | 1,659 | no_license | [
{
"docstring": "make error response body :param status_code: http status code :param message: error message :param format: json or xml :returns flask.Response",
"name": "response",
"signature": "def response(self, status_code, message, format='json')"
},
{
"docstring": "Unauthorized response",
... | 4 | stack_v2_sparse_classes_30k_train_007991 | Implement the Python class `ErrorResponse` described below.
Class description:
Implement the ErrorResponse class.
Method signatures and docstrings:
- def response(self, status_code, message, format='json'): make error response body :param status_code: http status code :param message: error message :param format: json... | Implement the Python class `ErrorResponse` described below.
Class description:
Implement the ErrorResponse class.
Method signatures and docstrings:
- def response(self, status_code, message, format='json'): make error response body :param status_code: http status code :param message: error message :param format: json... | b37cbd55e2b45c47c689cda8eefebebb29ee2c25 | <|skeleton|>
class ErrorResponse:
def response(self, status_code, message, format='json'):
"""make error response body :param status_code: http status code :param message: error message :param format: json or xml :returns flask.Response"""
<|body_0|>
def unauthorized(cls, message=None, format=... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ErrorResponse:
def response(self, status_code, message, format='json'):
"""make error response body :param status_code: http status code :param message: error message :param format: json or xml :returns flask.Response"""
to_xml = format == 'xml'
mimetype = self.JSON_FORMAT if not to_xm... | the_stack_v2_python_sparse | zip_address/error_response.py | shaper60/ZipToAddress | train | 0 | |
95f84fd12c584fdd90fb54d9f5b193c0c61bb680 | [
"questions = Question.objects.filter(created_by=request.user)\nworld_id = request.query_params.get('world_id')\nif world_id:\n world = CustomWorld.objects.get(id=world_id)\n if world.created_by != request.user:\n raise PermissionDenied(detail='You do not have access to this Custom World')\n section ... | <|body_start_0|>
questions = Question.objects.filter(created_by=request.user)
world_id = request.query_params.get('world_id')
if world_id:
world = CustomWorld.objects.get(id=world_id)
if world.created_by != request.user:
raise PermissionDenied(detail='You ... | API for creating custom questions Requests handled: GET, POST | CustomQuestionView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomQuestionView:
"""API for creating custom questions Requests handled: GET, POST"""
def get(self, request):
"""GET request handler. Accepts world_id as a query param for getting questions in the specified World id. :return: Custom questions and answers the user has created :raise... | stack_v2_sparse_classes_36k_train_007578 | 30,034 | no_license | [
{
"docstring": "GET request handler. Accepts world_id as a query param for getting questions in the specified World id. :return: Custom questions and answers the user has created :raises: PermissionDenied if world_id specified and User is not the creator of the World.",
"name": "get",
"signature": "def ... | 2 | stack_v2_sparse_classes_30k_test_001089 | Implement the Python class `CustomQuestionView` described below.
Class description:
API for creating custom questions Requests handled: GET, POST
Method signatures and docstrings:
- def get(self, request): GET request handler. Accepts world_id as a query param for getting questions in the specified World id. :return:... | Implement the Python class `CustomQuestionView` described below.
Class description:
API for creating custom questions Requests handled: GET, POST
Method signatures and docstrings:
- def get(self, request): GET request handler. Accepts world_id as a query param for getting questions in the specified World id. :return:... | ea0e59de38505beba3b490a3b107f884b35986fd | <|skeleton|>
class CustomQuestionView:
"""API for creating custom questions Requests handled: GET, POST"""
def get(self, request):
"""GET request handler. Accepts world_id as a query param for getting questions in the specified World id. :return: Custom questions and answers the user has created :raise... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomQuestionView:
"""API for creating custom questions Requests handled: GET, POST"""
def get(self, request):
"""GET request handler. Accepts world_id as a query param for getting questions in the specified World id. :return: Custom questions and answers the user has created :raises: Permission... | the_stack_v2_python_sparse | main/views.py | weixingp/slay-the-software-backend | train | 0 |
435f48322403ca8e571f3bccfe8cc3a0a1677b7e | [
"super().__init__()\ncheck_boundaries(boundaries)\nself.filling = filling\nself.mode = mode\nself.boundaries = boundaries",
"self.randomize(None)\nself.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])\nlength = signal.shape[1]\nshift_idx = round(self.magnitude * length)\nsig = convert_d... | <|body_start_0|>
super().__init__()
check_boundaries(boundaries)
self.filling = filling
self.mode = mode
self.boundaries = boundaries
<|end_body_0|>
<|body_start_1|>
self.randomize(None)
self.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries... | Apply a random shift on a signal | SignalRandShift | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignalRandShift:
"""Apply a random shift on a signal"""
def __init__(self, mode: str | None='wrap', filling: float | None=0.0, boundaries: Sequence[float]=(-1.0, 1.0)) -> None:
"""Args: mode: define how the extension of the input array is done beyond its boundaries, see for more deta... | stack_v2_sparse_classes_36k_train_007579 | 16,322 | permissive | [
{
"docstring": "Args: mode: define how the extension of the input array is done beyond its boundaries, see for more details : https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.shift.html. filling: value to fill past edges of input if mode is ‘constant’. Default is 0.0. see for mode details : ht... | 2 | stack_v2_sparse_classes_30k_train_013452 | Implement the Python class `SignalRandShift` described below.
Class description:
Apply a random shift on a signal
Method signatures and docstrings:
- def __init__(self, mode: str | None='wrap', filling: float | None=0.0, boundaries: Sequence[float]=(-1.0, 1.0)) -> None: Args: mode: define how the extension of the inp... | Implement the Python class `SignalRandShift` described below.
Class description:
Apply a random shift on a signal
Method signatures and docstrings:
- def __init__(self, mode: str | None='wrap', filling: float | None=0.0, boundaries: Sequence[float]=(-1.0, 1.0)) -> None: Args: mode: define how the extension of the inp... | e48c3e2c741fa3fc705c4425d17ac4a5afac6c47 | <|skeleton|>
class SignalRandShift:
"""Apply a random shift on a signal"""
def __init__(self, mode: str | None='wrap', filling: float | None=0.0, boundaries: Sequence[float]=(-1.0, 1.0)) -> None:
"""Args: mode: define how the extension of the input array is done beyond its boundaries, see for more deta... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SignalRandShift:
"""Apply a random shift on a signal"""
def __init__(self, mode: str | None='wrap', filling: float | None=0.0, boundaries: Sequence[float]=(-1.0, 1.0)) -> None:
"""Args: mode: define how the extension of the input array is done beyond its boundaries, see for more details : https:/... | the_stack_v2_python_sparse | monai/transforms/signal/array.py | Project-MONAI/MONAI | train | 4,805 |
a2027fc35fac278eedc0d6b16539ecf32c5ecaa2 | [
"super(FeatureNN, self).__init__()\nself._num_units = num_units\nself._dropout = dropout\nself._trainable = trainable\nself._tf_name_scope = name_scope\nself._feature_num = feature_num\nself._shallow = shallow\nself._activation = activation",
"self.hidden_layers = [ActivationLayer(self._num_units, trainable=self.... | <|body_start_0|>
super(FeatureNN, self).__init__()
self._num_units = num_units
self._dropout = dropout
self._trainable = trainable
self._tf_name_scope = name_scope
self._feature_num = feature_num
self._shallow = shallow
self._activation = activation
<|end_... | Neural Network model for each individual feature. Attributes: hidden_layers: A list containing hidden layers. The first layer is an `ActivationLayer` containing `num_units` neurons with specified `activation`. If `shallow` is False, then it additionally contains 2 tf.keras.layers.Dense ReLU layers with 64, 32 hidden un... | FeatureNN | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeatureNN:
"""Neural Network model for each individual feature. Attributes: hidden_layers: A list containing hidden layers. The first layer is an `ActivationLayer` containing `num_units` neurons with specified `activation`. If `shallow` is False, then it additionally contains 2 tf.keras.layers.De... | stack_v2_sparse_classes_36k_train_007580 | 10,796 | permissive | [
{
"docstring": "Initializes FeatureNN hyperparameters. Args: num_units: Number of hidden units in first hidden layer. dropout: Coefficient for dropout regularization. trainable: Whether the FeatureNN parameters are trainable or not. shallow: If True, then a shallow network with a single hidden layer is created,... | 3 | stack_v2_sparse_classes_30k_train_011110 | Implement the Python class `FeatureNN` described below.
Class description:
Neural Network model for each individual feature. Attributes: hidden_layers: A list containing hidden layers. The first layer is an `ActivationLayer` containing `num_units` neurons with specified `activation`. If `shallow` is False, then it add... | Implement the Python class `FeatureNN` described below.
Class description:
Neural Network model for each individual feature. Attributes: hidden_layers: A list containing hidden layers. The first layer is an `ActivationLayer` containing `num_units` neurons with specified `activation`. If `shallow` is False, then it add... | 727ec399ad17b4dd1f71ce69a26fc3b0371d9fa7 | <|skeleton|>
class FeatureNN:
"""Neural Network model for each individual feature. Attributes: hidden_layers: A list containing hidden layers. The first layer is an `ActivationLayer` containing `num_units` neurons with specified `activation`. If `shallow` is False, then it additionally contains 2 tf.keras.layers.De... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeatureNN:
"""Neural Network model for each individual feature. Attributes: hidden_layers: A list containing hidden layers. The first layer is an `ActivationLayer` containing `num_units` neurons with specified `activation`. If `shallow` is False, then it additionally contains 2 tf.keras.layers.Dense ReLU laye... | the_stack_v2_python_sparse | neural_additive_models/models.py | Ayoob7/google-research | train | 2 |
942e663d7dbb3c6e3cca7b1e16143f767e7f8059 | [
"if not feature_vectors:\n return np.zeros((0, 0), dtype=int)\nfirst_vector = feature_vectors[0]\nmatrix_shape = (0, first_vector.shape[1])\nfeatures_matrix = np.zeros(matrix_shape, dtype=first_vector.dtype)\nfor lfv in feature_vectors:\n if lfv.shape[1] != first_vector.shape[1]:\n raise IndexError('Gi... | <|body_start_0|>
if not feature_vectors:
return np.zeros((0, 0), dtype=int)
first_vector = feature_vectors[0]
matrix_shape = (0, first_vector.shape[1])
features_matrix = np.zeros(matrix_shape, dtype=first_vector.dtype)
for lfv in feature_vectors:
if lfv.sh... | FeatureExtractor | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeatureExtractor:
def get_feature_matrix(feature_vectors: List[np.ndarray]) -> np.ndarray:
"""Given a list of feature vectors returns a matrix with each feature vector on one row with the row ordering matching the original ordering of the vector in the list. :param feature_vectors: The f... | stack_v2_sparse_classes_36k_train_007581 | 7,852 | permissive | [
{
"docstring": "Given a list of feature vectors returns a matrix with each feature vector on one row with the row ordering matching the original ordering of the vector in the list. :param feature_vectors: The feature vectors. :return: The matrix of feature vectors.",
"name": "get_feature_matrix",
"signa... | 4 | stack_v2_sparse_classes_30k_test_001103 | Implement the Python class `FeatureExtractor` described below.
Class description:
Implement the FeatureExtractor class.
Method signatures and docstrings:
- def get_feature_matrix(feature_vectors: List[np.ndarray]) -> np.ndarray: Given a list of feature vectors returns a matrix with each feature vector on one row with... | Implement the Python class `FeatureExtractor` described below.
Class description:
Implement the FeatureExtractor class.
Method signatures and docstrings:
- def get_feature_matrix(feature_vectors: List[np.ndarray]) -> np.ndarray: Given a list of feature vectors returns a matrix with each feature vector on one row with... | abadbb1feca1fc970c1180641aaa00a268bb5692 | <|skeleton|>
class FeatureExtractor:
def get_feature_matrix(feature_vectors: List[np.ndarray]) -> np.ndarray:
"""Given a list of feature vectors returns a matrix with each feature vector on one row with the row ordering matching the original ordering of the vector in the list. :param feature_vectors: The f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeatureExtractor:
def get_feature_matrix(feature_vectors: List[np.ndarray]) -> np.ndarray:
"""Given a list of feature vectors returns a matrix with each feature vector on one row with the row ordering matching the original ordering of the vector in the list. :param feature_vectors: The feature vectors... | the_stack_v2_python_sparse | feature_extraction.py | apoorvkhurasia/texpredict | train | 0 | |
acb83f03c8d760d3f16be11135a047cbfcfd08b3 | [
"super().__init__(**kwargs)\nself.id = str(uuid.uuid1())\nself.coupon = coupon\nself.bond_yield = bond_yield\nfor name, value in kwargs.items():\n self.__setattr__(name, value)",
"output = ''\noutput += self.stock_name + self.get_spaces(len(header[0]) - len(self.stock_name))\noutput += str(self.num_shares) + s... | <|body_start_0|>
super().__init__(**kwargs)
self.id = str(uuid.uuid1())
self.coupon = coupon
self.bond_yield = bond_yield
for name, value in kwargs.items():
self.__setattr__(name, value)
<|end_body_0|>
<|body_start_1|>
output = ''
output += self.stock... | A class for managing bonds. | Bond | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bond:
"""A class for managing bonds."""
def __init__(self, coupon=None, bond_yield=None, **kwargs):
"""Sets the initial variables. These are used for printing content and storing initial data."""
<|body_0|>
def print_output_row(self, header):
"""Prints individual... | stack_v2_sparse_classes_36k_train_007582 | 2,075 | no_license | [
{
"docstring": "Sets the initial variables. These are used for printing content and storing initial data.",
"name": "__init__",
"signature": "def __init__(self, coupon=None, bond_yield=None, **kwargs)"
},
{
"docstring": "Prints individual rows of bond data.",
"name": "print_output_row",
... | 2 | stack_v2_sparse_classes_30k_train_010903 | Implement the Python class `Bond` described below.
Class description:
A class for managing bonds.
Method signatures and docstrings:
- def __init__(self, coupon=None, bond_yield=None, **kwargs): Sets the initial variables. These are used for printing content and storing initial data.
- def print_output_row(self, heade... | Implement the Python class `Bond` described below.
Class description:
A class for managing bonds.
Method signatures and docstrings:
- def __init__(self, coupon=None, bond_yield=None, **kwargs): Sets the initial variables. These are used for printing content and storing initial data.
- def print_output_row(self, heade... | c6cf1d5367f2f5f8ee9cd63a8f7dd9a09fb07d6f | <|skeleton|>
class Bond:
"""A class for managing bonds."""
def __init__(self, coupon=None, bond_yield=None, **kwargs):
"""Sets the initial variables. These are used for printing content and storing initial data."""
<|body_0|>
def print_output_row(self, header):
"""Prints individual... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Bond:
"""A class for managing bonds."""
def __init__(self, coupon=None, bond_yield=None, **kwargs):
"""Sets the initial variables. These are used for printing content and storing initial data."""
super().__init__(**kwargs)
self.id = str(uuid.uuid1())
self.coupon = coupon
... | the_stack_v2_python_sparse | five/carterk_assignment5_bond.py | kylecarter/ict-4370-python-programming | train | 1 |
52199d5344bb74983cb53ee0493b9ae79490b3d4 | [
"username = request.user.get_username()\nserializer = ViewSerializer(username=username, repo_base=repo_base, request=request)\nviews = serializer.list_views(repo_name)\nreturn Response(views, status=status.HTTP_200_OK)",
"username = request.user.get_username()\nserializer = ViewSerializer(username=username, repo_... | <|body_start_0|>
username = request.user.get_username()
serializer = ViewSerializer(username=username, repo_base=repo_base, request=request)
views = serializer.list_views(repo_name)
return Response(views, status=status.HTTP_200_OK)
<|end_body_0|>
<|body_start_1|>
username = requ... | Views | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Views:
def get(self, request, repo_base, repo_name, format=None):
"""Views in a repo"""
<|body_0|>
def post(self, request, repo_base, repo_name, format=None):
"""Create a view in a repo --- omit_serializer: true parameters: - name: view_name in: body type: string des... | stack_v2_sparse_classes_36k_train_007583 | 31,465 | permissive | [
{
"docstring": "Views in a repo",
"name": "get",
"signature": "def get(self, request, repo_base, repo_name, format=None)"
},
{
"docstring": "Create a view in a repo --- omit_serializer: true parameters: - name: view_name in: body type: string description: name of the the view to be created requi... | 2 | stack_v2_sparse_classes_30k_train_000415 | Implement the Python class `Views` described below.
Class description:
Implement the Views class.
Method signatures and docstrings:
- def get(self, request, repo_base, repo_name, format=None): Views in a repo
- def post(self, request, repo_base, repo_name, format=None): Create a view in a repo --- omit_serializer: tr... | Implement the Python class `Views` described below.
Class description:
Implement the Views class.
Method signatures and docstrings:
- def get(self, request, repo_base, repo_name, format=None): Views in a repo
- def post(self, request, repo_base, repo_name, format=None): Create a view in a repo --- omit_serializer: tr... | f066b472c2b66cc3b868bbe433aed2d4557aea32 | <|skeleton|>
class Views:
def get(self, request, repo_base, repo_name, format=None):
"""Views in a repo"""
<|body_0|>
def post(self, request, repo_base, repo_name, format=None):
"""Create a view in a repo --- omit_serializer: true parameters: - name: view_name in: body type: string des... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Views:
def get(self, request, repo_base, repo_name, format=None):
"""Views in a repo"""
username = request.user.get_username()
serializer = ViewSerializer(username=username, repo_base=repo_base, request=request)
views = serializer.list_views(repo_name)
return Response(v... | the_stack_v2_python_sparse | src/api/views.py | datahuborg/datahub | train | 199 | |
f05047ef220c21327da84322fb94a13c32dd9f6b | [
"super().__init__()\nself.cache_prior = cache_prior\nself._cache = {}\nself.t_conv1 = nn.Conv1d(adim, adim, kernel_size=3, padding=1)\nself.t_conv2 = nn.Conv1d(adim, adim, kernel_size=1, padding=0)\nself.f_conv1 = nn.Conv1d(odim, adim, kernel_size=3, padding=1)\nself.f_conv2 = nn.Conv1d(adim, adim, kernel_size=3, p... | <|body_start_0|>
super().__init__()
self.cache_prior = cache_prior
self._cache = {}
self.t_conv1 = nn.Conv1d(adim, adim, kernel_size=3, padding=1)
self.t_conv2 = nn.Conv1d(adim, adim, kernel_size=1, padding=0)
self.f_conv1 = nn.Conv1d(odim, adim, kernel_size=3, padding=1)... | Alignment Learning Framework proposed for parallel TTS models in: https://arxiv.org/abs/2108.10447 | AlignmentModule | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlignmentModule:
"""Alignment Learning Framework proposed for parallel TTS models in: https://arxiv.org/abs/2108.10447"""
def __init__(self, adim, odim, cache_prior=True):
"""Initialize AlignmentModule. Args: adim (int): Dimension of attention. odim (int): Dimension of feats. cache_p... | stack_v2_sparse_classes_36k_train_007584 | 7,515 | permissive | [
{
"docstring": "Initialize AlignmentModule. Args: adim (int): Dimension of attention. odim (int): Dimension of feats. cache_prior (bool): Whether to cache beta-binomial prior.",
"name": "__init__",
"signature": "def __init__(self, adim, odim, cache_prior=True)"
},
{
"docstring": "Calculate align... | 3 | null | Implement the Python class `AlignmentModule` described below.
Class description:
Alignment Learning Framework proposed for parallel TTS models in: https://arxiv.org/abs/2108.10447
Method signatures and docstrings:
- def __init__(self, adim, odim, cache_prior=True): Initialize AlignmentModule. Args: adim (int): Dimens... | Implement the Python class `AlignmentModule` described below.
Class description:
Alignment Learning Framework proposed for parallel TTS models in: https://arxiv.org/abs/2108.10447
Method signatures and docstrings:
- def __init__(self, adim, odim, cache_prior=True): Initialize AlignmentModule. Args: adim (int): Dimens... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class AlignmentModule:
"""Alignment Learning Framework proposed for parallel TTS models in: https://arxiv.org/abs/2108.10447"""
def __init__(self, adim, odim, cache_prior=True):
"""Initialize AlignmentModule. Args: adim (int): Dimension of attention. odim (int): Dimension of feats. cache_p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AlignmentModule:
"""Alignment Learning Framework proposed for parallel TTS models in: https://arxiv.org/abs/2108.10447"""
def __init__(self, adim, odim, cache_prior=True):
"""Initialize AlignmentModule. Args: adim (int): Dimension of attention. odim (int): Dimension of feats. cache_prior (bool): ... | the_stack_v2_python_sparse | espnet2/gan_tts/jets/alignments.py | espnet/espnet | train | 7,242 |
30d5c2e0091a6f2423f959a21bb21b035af51d12 | [
"super(VenueModel, self).__init__()\nself.config = config\nself.vocab = vocab\nself.dim = 1",
"num_ments = entity['count']\nif entity['v']:\n return len(entity['v']) / float(num_ments)\nelse:\n return 0.0",
"fv = []\nfv.append(self.num_vs_over_num_ments(entity))\nreturn fv"
] | <|body_start_0|>
super(VenueModel, self).__init__()
self.config = config
self.vocab = vocab
self.dim = 1
<|end_body_0|>
<|body_start_1|>
num_ments = entity['count']
if entity['v']:
return len(entity['v']) / float(num_ments)
else:
return 0.... | VenueModel | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VenueModel:
def __init__(self, config, vocab):
"""Init."""
<|body_0|>
def num_vs_over_num_ments(self, entity):
"""(max year - min year) / # menmtions"""
<|body_1|>
def emb(self, entity):
""":param routee: :param dest: :return: Some kind of vector... | stack_v2_sparse_classes_36k_train_007585 | 1,373 | permissive | [
{
"docstring": "Init.",
"name": "__init__",
"signature": "def __init__(self, config, vocab)"
},
{
"docstring": "(max year - min year) / # menmtions",
"name": "num_vs_over_num_ments",
"signature": "def num_vs_over_num_ments(self, entity)"
},
{
"docstring": ":param routee: :param d... | 3 | null | Implement the Python class `VenueModel` described below.
Class description:
Implement the VenueModel class.
Method signatures and docstrings:
- def __init__(self, config, vocab): Init.
- def num_vs_over_num_ments(self, entity): (max year - min year) / # menmtions
- def emb(self, entity): :param routee: :param dest: :... | Implement the Python class `VenueModel` described below.
Class description:
Implement the VenueModel class.
Method signatures and docstrings:
- def __init__(self, config, vocab): Init.
- def num_vs_over_num_ments(self, entity): (max year - min year) / # menmtions
- def emb(self, entity): :param routee: :param dest: :... | 542659170897ad05f7612639cb918886859ae9d6 | <|skeleton|>
class VenueModel:
def __init__(self, config, vocab):
"""Init."""
<|body_0|>
def num_vs_over_num_ments(self, entity):
"""(max year - min year) / # menmtions"""
<|body_1|>
def emb(self, entity):
""":param routee: :param dest: :return: Some kind of vector... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VenueModel:
def __init__(self, config, vocab):
"""Init."""
super(VenueModel, self).__init__()
self.config = config
self.vocab = vocab
self.dim = 1
def num_vs_over_num_ments(self, entity):
"""(max year - min year) / # menmtions"""
num_ments = entity[... | the_stack_v2_python_sparse | src/python/coref/models/entity/VenueModel.py | nmonath/coref_tools | train | 0 | |
ff6289c34e50b80b05a4e2b1b9f700998e05b40b | [
"distribution = Counter(nums).keys()\nraw = set(range(1, len(nums) + 1))\nres = list(raw.difference(set(distribution)))\nreturn res",
"for i in xrange(len(nums)):\n index = abs(nums[i]) - 1\n nums[index] = -abs(nums[index])\nreturn [i + 1 for i in range(len(nums)) if nums[i] > 0]"
] | <|body_start_0|>
distribution = Counter(nums).keys()
raw = set(range(1, len(nums) + 1))
res = list(raw.difference(set(distribution)))
return res
<|end_body_0|>
<|body_start_1|>
for i in xrange(len(nums)):
index = abs(nums[i]) - 1
nums[index] = -abs(nums[i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findDisappearedNumbers(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def findDisappearedNumbers2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
distributi... | stack_v2_sparse_classes_36k_train_007586 | 875 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "findDisappearedNumbers",
"signature": "def findDisappearedNumbers(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "findDisappearedNumbers2",
"signature": "def findDisappearedNumbers2(self... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDisappearedNumbers(self, nums): :type nums: List[int] :rtype: List[int]
- def findDisappearedNumbers2(self, nums): :type nums: List[int] :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDisappearedNumbers(self, nums): :type nums: List[int] :rtype: List[int]
- def findDisappearedNumbers2(self, nums): :type nums: List[int] :rtype: List[int]
<|skeleton|>
c... | 0fc4c7af59246e3064db41989a45d9db413a624b | <|skeleton|>
class Solution:
def findDisappearedNumbers(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def findDisappearedNumbers2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findDisappearedNumbers(self, nums):
""":type nums: List[int] :rtype: List[int]"""
distribution = Counter(nums).keys()
raw = set(range(1, len(nums) + 1))
res = list(raw.difference(set(distribution)))
return res
def findDisappearedNumbers2(self, nums):
... | the_stack_v2_python_sparse | 448. Find All Numbers Disappeared in an Array/disappeared.py | Macielyoung/LeetCode | train | 1 | |
f11a0afe24f19099e5b33366d1722047b217899e | [
"super(DecodingAlgorithm, self).__init__(train_state_spec=decoder.state_spec, name=name)\nself._decoder = decoder\nself._loss = loss\nself._loss_weight = loss_weight",
"input, target = inputs\npred, state = self._decoder(input, state=state)\nassert pred.shape == target.shape\nloss = self._loss(pred, target)\nasse... | <|body_start_0|>
super(DecodingAlgorithm, self).__init__(train_state_spec=decoder.state_spec, name=name)
self._decoder = decoder
self._loss = loss
self._loss_weight = loss_weight
<|end_body_0|>
<|body_start_1|>
input, target = inputs
pred, state = self._decoder(input, st... | Generic decoding algorithm. | DecodingAlgorithm | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecodingAlgorithm:
"""Generic decoding algorithm."""
def __init__(self, decoder: Network, loss=torch.nn.MSELoss(reduction='none'), loss_weight=1.0, name='DecodingAlgorithm'):
"""Args: decoder (Network): network for decoding target from input. loss (Callable): loss function with signa... | stack_v2_sparse_classes_36k_train_007587 | 2,571 | permissive | [
{
"docstring": "Args: decoder (Network): network for decoding target from input. loss (Callable): loss function with signature ``loss(y_pred, y_true)``. Note that it should not reduce to a scalar. It should at least keep the batch dimension in the returned loss. loss_weight (float): weight for the loss.",
"... | 2 | stack_v2_sparse_classes_30k_train_001649 | Implement the Python class `DecodingAlgorithm` described below.
Class description:
Generic decoding algorithm.
Method signatures and docstrings:
- def __init__(self, decoder: Network, loss=torch.nn.MSELoss(reduction='none'), loss_weight=1.0, name='DecodingAlgorithm'): Args: decoder (Network): network for decoding tar... | Implement the Python class `DecodingAlgorithm` described below.
Class description:
Generic decoding algorithm.
Method signatures and docstrings:
- def __init__(self, decoder: Network, loss=torch.nn.MSELoss(reduction='none'), loss_weight=1.0, name='DecodingAlgorithm'): Args: decoder (Network): network for decoding tar... | b00ff2fa5e660de31020338ba340263183fbeaa4 | <|skeleton|>
class DecodingAlgorithm:
"""Generic decoding algorithm."""
def __init__(self, decoder: Network, loss=torch.nn.MSELoss(reduction='none'), loss_weight=1.0, name='DecodingAlgorithm'):
"""Args: decoder (Network): network for decoding target from input. loss (Callable): loss function with signa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DecodingAlgorithm:
"""Generic decoding algorithm."""
def __init__(self, decoder: Network, loss=torch.nn.MSELoss(reduction='none'), loss_weight=1.0, name='DecodingAlgorithm'):
"""Args: decoder (Network): network for decoding target from input. loss (Callable): loss function with signature ``loss(y... | the_stack_v2_python_sparse | alf/algorithms/decoding_algorithm.py | HorizonRobotics/alf | train | 288 |
d01e533c15be3ffa5d7717e6909ec649a258309c | [
"self.__ops = ops\nself.__nops = len(ops)\nfor iop in range(self.__nops):\n if not isinstance(self.__ops[iop], operator):\n raise Exception('Elements of ops list must be of type operator')\nif self.__nops != len(dims):\n raise Exception('Number of dimensions (%d) must equal number of operators (%d)' % ... | <|body_start_0|>
self.__ops = ops
self.__nops = len(ops)
for iop in range(self.__nops):
if not isinstance(self.__ops[iop], operator):
raise Exception('Elements of ops list must be of type operator')
if self.__nops != len(dims):
raise Exception('Num... | A diagonal operator | diagop | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class diagop:
"""A diagonal operator"""
def __init__(self, ops, dims, epss=None):
"""diagop constructor Parameters ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of the inputs and outputs of the arrays For example dims = [{... | stack_v2_sparse_classes_36k_train_007588 | 13,837 | no_license | [
{
"docstring": "diagop constructor Parameters ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of the inputs and outputs of the arrays For example dims = [{'nrows': 10, 'ncols': 10},...] epss - a list of scalar values to be applied to the output o... | 4 | stack_v2_sparse_classes_30k_train_011893 | Implement the Python class `diagop` described below.
Class description:
A diagonal operator
Method signatures and docstrings:
- def __init__(self, ops, dims, epss=None): diagop constructor Parameters ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of t... | Implement the Python class `diagop` described below.
Class description:
A diagonal operator
Method signatures and docstrings:
- def __init__(self, ops, dims, epss=None): diagop constructor Parameters ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of t... | 32a303eddd13385d8778b8bb3b4fbbfbe78bea51 | <|skeleton|>
class diagop:
"""A diagonal operator"""
def __init__(self, ops, dims, epss=None):
"""diagop constructor Parameters ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of the inputs and outputs of the arrays For example dims = [{... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class diagop:
"""A diagonal operator"""
def __init__(self, ops, dims, epss=None):
"""diagop constructor Parameters ops - a list of operators used to form the row operator dims - a list of dictionaries that contain the dimensions of the inputs and outputs of the arrays For example dims = [{'nrows': 10, ... | the_stack_v2_python_sparse | opt/linopt/combops.py | ke0m/scaas | train | 2 |
40ace0706c50fd0f067d7c3a9cc4e692ddc0ac5f | [
"self.Whf = np.random.randn(i + h, h)\nself.Whb = np.random.randn(i + h, h)\nself.Wy = np.random.randn(2 * h, o)\nself.bhf = np.zeros((1, h))\nself.bhb = np.zeros((1, h))\nself.by = np.zeros((1, o))",
"stacked = np.hstack((h_prev, x_t))\nh_next = np.tanh(stacked @ self.Whf + self.bhf)\nreturn h_next"
] | <|body_start_0|>
self.Whf = np.random.randn(i + h, h)
self.Whb = np.random.randn(i + h, h)
self.Wy = np.random.randn(2 * h, o)
self.bhf = np.zeros((1, h))
self.bhb = np.zeros((1, h))
self.by = np.zeros((1, o))
<|end_body_0|>
<|body_start_1|>
stacked = np.hstack((... | BidirectionalCell class | BidirectionalCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BidirectionalCell:
"""BidirectionalCell class"""
def __init__(self, i, h, o):
"""Initializer Arguments: i {int} -- Is dimensionality of the data h {int} -- Is dimensionality of hidden state o {int} -- Is dimensionality of the outputs"""
<|body_0|>
def forward(self, h_pre... | stack_v2_sparse_classes_36k_train_007589 | 1,119 | no_license | [
{
"docstring": "Initializer Arguments: i {int} -- Is dimensionality of the data h {int} -- Is dimensionality of hidden state o {int} -- Is dimensionality of the outputs",
"name": "__init__",
"signature": "def __init__(self, i, h, o)"
},
{
"docstring": "Performs forward propagation for one time s... | 2 | null | Implement the Python class `BidirectionalCell` described below.
Class description:
BidirectionalCell class
Method signatures and docstrings:
- def __init__(self, i, h, o): Initializer Arguments: i {int} -- Is dimensionality of the data h {int} -- Is dimensionality of hidden state o {int} -- Is dimensionality of the o... | Implement the Python class `BidirectionalCell` described below.
Class description:
BidirectionalCell class
Method signatures and docstrings:
- def __init__(self, i, h, o): Initializer Arguments: i {int} -- Is dimensionality of the data h {int} -- Is dimensionality of hidden state o {int} -- Is dimensionality of the o... | 2ddae38cc25d914488451b8c30e1234f1fa55ebe | <|skeleton|>
class BidirectionalCell:
"""BidirectionalCell class"""
def __init__(self, i, h, o):
"""Initializer Arguments: i {int} -- Is dimensionality of the data h {int} -- Is dimensionality of hidden state o {int} -- Is dimensionality of the outputs"""
<|body_0|>
def forward(self, h_pre... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BidirectionalCell:
"""BidirectionalCell class"""
def __init__(self, i, h, o):
"""Initializer Arguments: i {int} -- Is dimensionality of the data h {int} -- Is dimensionality of hidden state o {int} -- Is dimensionality of the outputs"""
self.Whf = np.random.randn(i + h, h)
self.Wh... | the_stack_v2_python_sparse | supervised_learning/0x0D-RNNs/5-bi_forward.py | KoeusIss/holbertonschool-machine_learning | train | 0 |
b7117c6d58b6c2e48f6abe080cd7e4d15144f522 | [
"starttime = request.query_params.get('value1')\nendtime = request.query_params.get('value2')\nprint(starttime, endtime)\nif starttime == '0':\n myBugResult = Bugs.objects.filter(delete_flag=0).order_by('team')\n serializer = Bugsserializer(myBugResult, many=True)\n return Response({'status': True, 'messag... | <|body_start_0|>
starttime = request.query_params.get('value1')
endtime = request.query_params.get('value2')
print(starttime, endtime)
if starttime == '0':
myBugResult = Bugs.objects.filter(delete_flag=0).order_by('team')
serializer = Bugsserializer(myBugResult, m... | bug汇总 | Bug | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bug:
"""bug汇总"""
def get(self, request):
"""获取bug列表"""
<|body_0|>
def post(self, request):
"""新增bug"""
<|body_1|>
def put(self, request):
"""修改bug"""
<|body_2|>
def delete(self, request):
"""删除bug"""
<|body_3|>
<... | stack_v2_sparse_classes_36k_train_007590 | 3,316 | no_license | [
{
"docstring": "获取bug列表",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "新增bug",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "修改bug",
"name": "put",
"signature": "def put(self, request)"
},
{
"docstring": "删除bu... | 4 | stack_v2_sparse_classes_30k_train_016386 | Implement the Python class `Bug` described below.
Class description:
bug汇总
Method signatures and docstrings:
- def get(self, request): 获取bug列表
- def post(self, request): 新增bug
- def put(self, request): 修改bug
- def delete(self, request): 删除bug | Implement the Python class `Bug` described below.
Class description:
bug汇总
Method signatures and docstrings:
- def get(self, request): 获取bug列表
- def post(self, request): 新增bug
- def put(self, request): 修改bug
- def delete(self, request): 删除bug
<|skeleton|>
class Bug:
"""bug汇总"""
def get(self, request):
... | 9ccebcc6820af3f950c28fc2a4dee4f41a3157f1 | <|skeleton|>
class Bug:
"""bug汇总"""
def get(self, request):
"""获取bug列表"""
<|body_0|>
def post(self, request):
"""新增bug"""
<|body_1|>
def put(self, request):
"""修改bug"""
<|body_2|>
def delete(self, request):
"""删除bug"""
<|body_3|>
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Bug:
"""bug汇总"""
def get(self, request):
"""获取bug列表"""
starttime = request.query_params.get('value1')
endtime = request.query_params.get('value2')
print(starttime, endtime)
if starttime == '0':
myBugResult = Bugs.objects.filter(delete_flag=0).order_by('... | the_stack_v2_python_sparse | moon/task/views_bug.py | xiaominwanglast/python | train | 0 |
78f36744db74815d55abded3d2df0faf98fa8cd6 | [
"edges_conclude_nodes = np.array([])\nfor node in I_list:\n edges_conclude_nodes = np.where(np.array(df_hyper_matrix.loc[node]) == 1)[0]\nnodes_in_edges = np.array([])\nfor edge in edges_conclude_nodes:\n nodes = np.where(np.array(df_hyper_matrix[edge]) == 1)[0]\n nodes_in_edges = np.append(nodes_in_edges,... | <|body_start_0|>
edges_conclude_nodes = np.array([])
for node in I_list:
edges_conclude_nodes = np.where(np.array(df_hyper_matrix.loc[node]) == 1)[0]
nodes_in_edges = np.array([])
for edge in edges_conclude_nodes:
nodes = np.where(np.array(df_hyper_matrix[edge]) =... | 策略过程封装类：用于不同策略寻找可能感染的节点过程及筛选过程 | ProcessFuncs | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProcessFuncs:
"""策略过程封装类：用于不同策略寻找可能感染的节点过程及筛选过程"""
def findAdjNode_RP(self, I_list, df_hyper_matrix):
"""找到邻居节点集合 :param I_list: 感染节点集 :param df_hyper_matrix: 超图的点边矩阵 :return: 不重复的邻居节点集 np.unique(nodes_in_edges)"""
<|body_0|>
def findAdjNode_CP(self, I_list, df_hyper_mat... | stack_v2_sparse_classes_36k_train_007591 | 2,714 | no_license | [
{
"docstring": "找到邻居节点集合 :param I_list: 感染节点集 :param df_hyper_matrix: 超图的点边矩阵 :return: 不重复的邻居节点集 np.unique(nodes_in_edges)",
"name": "findAdjNode_RP",
"signature": "def findAdjNode_RP(self, I_list, df_hyper_matrix)"
},
{
"docstring": "找到邻居节点集合 :param I_list: 感染节点集 :param df_hyper_matrix: 超图的点边矩阵... | 4 | stack_v2_sparse_classes_30k_train_009696 | Implement the Python class `ProcessFuncs` described below.
Class description:
策略过程封装类：用于不同策略寻找可能感染的节点过程及筛选过程
Method signatures and docstrings:
- def findAdjNode_RP(self, I_list, df_hyper_matrix): 找到邻居节点集合 :param I_list: 感染节点集 :param df_hyper_matrix: 超图的点边矩阵 :return: 不重复的邻居节点集 np.unique(nodes_in_edges)
- def findAdjNo... | Implement the Python class `ProcessFuncs` described below.
Class description:
策略过程封装类：用于不同策略寻找可能感染的节点过程及筛选过程
Method signatures and docstrings:
- def findAdjNode_RP(self, I_list, df_hyper_matrix): 找到邻居节点集合 :param I_list: 感染节点集 :param df_hyper_matrix: 超图的点边矩阵 :return: 不重复的邻居节点集 np.unique(nodes_in_edges)
- def findAdjNo... | 42026f77b758168d59bc1d11ae643a5cadc7ce0d | <|skeleton|>
class ProcessFuncs:
"""策略过程封装类：用于不同策略寻找可能感染的节点过程及筛选过程"""
def findAdjNode_RP(self, I_list, df_hyper_matrix):
"""找到邻居节点集合 :param I_list: 感染节点集 :param df_hyper_matrix: 超图的点边矩阵 :return: 不重复的邻居节点集 np.unique(nodes_in_edges)"""
<|body_0|>
def findAdjNode_CP(self, I_list, df_hyper_mat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProcessFuncs:
"""策略过程封装类：用于不同策略寻找可能感染的节点过程及筛选过程"""
def findAdjNode_RP(self, I_list, df_hyper_matrix):
"""找到邻居节点集合 :param I_list: 感染节点集 :param df_hyper_matrix: 超图的点边矩阵 :return: 不重复的邻居节点集 np.unique(nodes_in_edges)"""
edges_conclude_nodes = np.array([])
for node in I_list:
... | the_stack_v2_python_sparse | practice/contagions/Hypergraph SI SIS SIR/packages/process_functions.py | chqlee/Hypergraphs | train | 0 |
06e850714657e9824d7d193c6e7476800c351a07 | [
"if not root:\n return 0\ntr_l = root.left\ntr_r = root.right\nmin_depth = 1\nif not tr_l and (not tr_r):\n return min_depth\nelif tr_l and (not tr_r):\n min_depth += self.minDepth(tr_l)\nelif not tr_l and tr_r:\n min_depth += self.minDepth(tr_r)\nelse:\n min_depth += min(self.minDepth(tr_l), self.mi... | <|body_start_0|>
if not root:
return 0
tr_l = root.left
tr_r = root.right
min_depth = 1
if not tr_l and (not tr_r):
return min_depth
elif tr_l and (not tr_r):
min_depth += self.minDepth(tr_l)
elif not tr_l and tr_r:
... | Solution | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minDepth(self, root: TreeNode) -> int:
"""DFS"""
<|body_0|>
def minDepth2(self, root):
"""BFS"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
return 0
tr_l = root.left
tr_r = root.right
... | stack_v2_sparse_classes_36k_train_007592 | 1,714 | permissive | [
{
"docstring": "DFS",
"name": "minDepth",
"signature": "def minDepth(self, root: TreeNode) -> int"
},
{
"docstring": "BFS",
"name": "minDepth2",
"signature": "def minDepth2(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000548 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDepth(self, root: TreeNode) -> int: DFS
- def minDepth2(self, root): BFS | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDepth(self, root: TreeNode) -> int: DFS
- def minDepth2(self, root): BFS
<|skeleton|>
class Solution:
def minDepth(self, root: TreeNode) -> int:
"""DFS"""
... | 49a0b03c55d8a702785888d473ef96539265ce9c | <|skeleton|>
class Solution:
def minDepth(self, root: TreeNode) -> int:
"""DFS"""
<|body_0|>
def minDepth2(self, root):
"""BFS"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minDepth(self, root: TreeNode) -> int:
"""DFS"""
if not root:
return 0
tr_l = root.left
tr_r = root.right
min_depth = 1
if not tr_l and (not tr_r):
return min_depth
elif tr_l and (not tr_r):
min_depth += ... | the_stack_v2_python_sparse | leetcode/0111_minimum_depth_of_binary_tree.py | chaosWsF/Python-Practice | train | 1 | |
8e1b2eb3033057d6a72f7428d58b3e1f888b430c | [
"super().__init__(port)\nself._prefix = None\nself._postfix = None\nself._replace_msg = None\nself._delay = 0\nself.lock = RLock()",
"with self.lock:\n self._prefix = pre\n self._postfix = post\n self._replace_msg = msg\n self._delay = dly",
"with self.lock:\n if self._replace_msg is not None:\n ... | <|body_start_0|>
super().__init__(port)
self._prefix = None
self._postfix = None
self._replace_msg = None
self._delay = 0
self.lock = RLock()
<|end_body_0|>
<|body_start_1|>
with self.lock:
self._prefix = pre
self._postfix = post
... | Responding COM-Client with configurable response. | ConfigurableEcho | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConfigurableEcho:
"""Responding COM-Client with configurable response."""
def __init__(self, port: str):
"""Create a COM-client with configrable echo. :param port: String of the port name to connect to"""
<|body_0|>
def reconfigure(self, pre: Optional[str]=None, post: Op... | stack_v2_sparse_classes_36k_train_007593 | 4,322 | permissive | [
{
"docstring": "Create a COM-client with configrable echo. :param port: String of the port name to connect to",
"name": "__init__",
"signature": "def __init__(self, port: str)"
},
{
"docstring": "Adjust the calculation of the server response. :param pre: String to be inserted before each actual ... | 3 | null | Implement the Python class `ConfigurableEcho` described below.
Class description:
Responding COM-Client with configurable response.
Method signatures and docstrings:
- def __init__(self, port: str): Create a COM-client with configrable echo. :param port: String of the port name to connect to
- def reconfigure(self, p... | Implement the Python class `ConfigurableEcho` described below.
Class description:
Responding COM-Client with configurable response.
Method signatures and docstrings:
- def __init__(self, port: str): Create a COM-client with configrable echo. :param port: String of the port name to connect to
- def reconfigure(self, p... | 4c35f7dc08f976c05d0b7f27902236132f19c024 | <|skeleton|>
class ConfigurableEcho:
"""Responding COM-Client with configurable response."""
def __init__(self, port: str):
"""Create a COM-client with configrable echo. :param port: String of the port name to connect to"""
<|body_0|>
def reconfigure(self, pre: Optional[str]=None, post: Op... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConfigurableEcho:
"""Responding COM-Client with configurable response."""
def __init__(self, port: str):
"""Create a COM-client with configrable echo. :param port: String of the port name to connect to"""
super().__init__(port)
self._prefix = None
self._postfix = None
... | the_stack_v2_python_sparse | src/clients/SerialEcho.py | pat-bert/gcode | train | 0 |
ffe9dea314dd6227979c7d435704bab832fd39c6 | [
"pages = self.for_slot(slot, barcamp=barcamp)\nif len(indexes) != pages.count():\n raise PageError('length of indexes (%s) does not match amount of pages (%s)' % (len(indexes), pages.count()))\npages = list(pages)\nfor page in pages:\n if page.index not in indexes:\n raise PageError('page with index %s... | <|body_start_0|>
pages = self.for_slot(slot, barcamp=barcamp)
if len(indexes) != pages.count():
raise PageError('length of indexes (%s) does not match amount of pages (%s)' % (len(indexes), pages.count()))
pages = list(pages)
for page in pages:
if page.index not i... | Pages | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Pages:
def reorder_slot(self, slot, indexes, barcamp=None):
"""reorders a slot. You give it the slot id in ``slot`` and the new sequence order in indexes in form of a list. Passing in [2,3,1] will reorder the existing pages in this order"""
<|body_0|>
def add_to_slot(self, s... | stack_v2_sparse_classes_36k_train_007594 | 4,171 | permissive | [
{
"docstring": "reorders a slot. You give it the slot id in ``slot`` and the new sequence order in indexes in form of a list. Passing in [2,3,1] will reorder the existing pages in this order",
"name": "reorder_slot",
"signature": "def reorder_slot(self, slot, indexes, barcamp=None)"
},
{
"docstr... | 5 | stack_v2_sparse_classes_30k_train_019986 | Implement the Python class `Pages` described below.
Class description:
Implement the Pages class.
Method signatures and docstrings:
- def reorder_slot(self, slot, indexes, barcamp=None): reorders a slot. You give it the slot id in ``slot`` and the new sequence order in indexes in form of a list. Passing in [2,3,1] wi... | Implement the Python class `Pages` described below.
Class description:
Implement the Pages class.
Method signatures and docstrings:
- def reorder_slot(self, slot, indexes, barcamp=None): reorders a slot. You give it the slot id in ``slot`` and the new sequence order in indexes in form of a list. Passing in [2,3,1] wi... | 9b45664e46c451b2cbe00bb55583b043e769083d | <|skeleton|>
class Pages:
def reorder_slot(self, slot, indexes, barcamp=None):
"""reorders a slot. You give it the slot id in ``slot`` and the new sequence order in indexes in form of a list. Passing in [2,3,1] will reorder the existing pages in this order"""
<|body_0|>
def add_to_slot(self, s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Pages:
def reorder_slot(self, slot, indexes, barcamp=None):
"""reorders a slot. You give it the slot id in ``slot`` and the new sequence order in indexes in form of a list. Passing in [2,3,1] will reorder the existing pages in this order"""
pages = self.for_slot(slot, barcamp=barcamp)
... | the_stack_v2_python_sparse | camper/db/pages.py | comlounge/camper | train | 14 | |
800e556eac6165aa814f0ed3e300451e5aeae838 | [
"nastran_filename1 = os.path.join(NASTRAN_PATH, 'solid_bending', 'solid_bending.bdf')\nskin_filename = os.path.join(NASTRAN_PATH, 'solid_bending', 'solid_bending_skin.bdf')\nlog = get_logger(level='warning')\nwrite_skin_solid_faces(nastran_filename1, skin_filename, write_solids=True, write_shells=True, size=8, is_d... | <|body_start_0|>
nastran_filename1 = os.path.join(NASTRAN_PATH, 'solid_bending', 'solid_bending.bdf')
skin_filename = os.path.join(NASTRAN_PATH, 'solid_bending', 'solid_bending_skin.bdf')
log = get_logger(level='warning')
write_skin_solid_faces(nastran_filename1, skin_filename, write_sol... | defines UGRID tests | TestUgridGui | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestUgridGui:
"""defines UGRID tests"""
def test_ugrid_gui_01(self):
"""tests solid_bending.bdf"""
<|body_0|>
def test_ugrid_gui_02(self):
"""tests plate_with_circular_hole"""
<|body_1|>
def test_ugrid2d_gui(self):
"""simple UGRID2D model"""
... | stack_v2_sparse_classes_36k_train_007595 | 4,389 | no_license | [
{
"docstring": "tests solid_bending.bdf",
"name": "test_ugrid_gui_01",
"signature": "def test_ugrid_gui_01(self)"
},
{
"docstring": "tests plate_with_circular_hole",
"name": "test_ugrid_gui_02",
"signature": "def test_ugrid_gui_02(self)"
},
{
"docstring": "simple UGRID2D model",
... | 4 | stack_v2_sparse_classes_30k_train_020358 | Implement the Python class `TestUgridGui` described below.
Class description:
defines UGRID tests
Method signatures and docstrings:
- def test_ugrid_gui_01(self): tests solid_bending.bdf
- def test_ugrid_gui_02(self): tests plate_with_circular_hole
- def test_ugrid2d_gui(self): simple UGRID2D model
- def test_ugrid3d... | Implement the Python class `TestUgridGui` described below.
Class description:
defines UGRID tests
Method signatures and docstrings:
- def test_ugrid_gui_01(self): tests solid_bending.bdf
- def test_ugrid_gui_02(self): tests plate_with_circular_hole
- def test_ugrid2d_gui(self): simple UGRID2D model
- def test_ugrid3d... | d9ffdb4ac845b13bcf6aea96ff5d37cc026c5267 | <|skeleton|>
class TestUgridGui:
"""defines UGRID tests"""
def test_ugrid_gui_01(self):
"""tests solid_bending.bdf"""
<|body_0|>
def test_ugrid_gui_02(self):
"""tests plate_with_circular_hole"""
<|body_1|>
def test_ugrid2d_gui(self):
"""simple UGRID2D model"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestUgridGui:
"""defines UGRID tests"""
def test_ugrid_gui_01(self):
"""tests solid_bending.bdf"""
nastran_filename1 = os.path.join(NASTRAN_PATH, 'solid_bending', 'solid_bending.bdf')
skin_filename = os.path.join(NASTRAN_PATH, 'solid_bending', 'solid_bending_skin.bdf')
log... | the_stack_v2_python_sparse | pyNastran/converters/aflr/ugrid/test_ugrid_gui.py | ratalex/pyNastran | train | 0 |
88f6a00540fdc3710155857954fd928e5e11e26e | [
"first_max = second_max = third_max = -float('inf')\nfor val in nums:\n if val in (first_max, second_max, third_max):\n continue\n elif val > first_max:\n third_max, second_max, first_max = (second_max, first_max, val)\n elif val > second_max:\n third_max, second_max = (second_max, val... | <|body_start_0|>
first_max = second_max = third_max = -float('inf')
for val in nums:
if val in (first_max, second_max, third_max):
continue
elif val > first_max:
third_max, second_max, first_max = (second_max, first_max, val)
elif val >... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def thirdMax_first_solution(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def third_max_second_solution(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
first_max = second_... | stack_v2_sparse_classes_36k_train_007596 | 1,342 | permissive | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "thirdMax_first_solution",
"signature": "def thirdMax_first_solution(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "third_max_second_solution",
"signature": "def third_max_second_solution(self, nums... | 2 | stack_v2_sparse_classes_30k_train_004436 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def thirdMax_first_solution(self, nums): :type nums: List[int] :rtype: int
- def third_max_second_solution(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 thirdMax_first_solution(self, nums): :type nums: List[int] :rtype: int
- def third_max_second_solution(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solu... | 1e99e0852b8329bf699eb149e7dfe312f82144bc | <|skeleton|>
class Solution:
def thirdMax_first_solution(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def third_max_second_solution(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 thirdMax_first_solution(self, nums):
""":type nums: List[int] :rtype: int"""
first_max = second_max = third_max = -float('inf')
for val in nums:
if val in (first_max, second_max, third_max):
continue
elif val > first_max:
... | the_stack_v2_python_sparse | easy/array/third_max/third_max.py | deepshig/leetcode-solutions | train | 0 | |
0f6c9292a28035d62f014f019b553edc200c0911 | [
"default_optimizer_fn, optimizer_class = opt.get_optimizer_fn(opt.default_optimization_hparams()['optimizer'])\ndefault_optimizer = default_optimizer_fn(1.0)\nself.assertTrue(optimizer_class, tf.train.Optimizer)\nself.assertIsInstance(default_optimizer, tf.train.AdamOptimizer)\nhparams = {'type': 'MomentumOptimizer... | <|body_start_0|>
default_optimizer_fn, optimizer_class = opt.get_optimizer_fn(opt.default_optimization_hparams()['optimizer'])
default_optimizer = default_optimizer_fn(1.0)
self.assertTrue(optimizer_class, tf.train.Optimizer)
self.assertIsInstance(default_optimizer, tf.train.AdamOptimize... | Tests optimization. | OptimizationTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OptimizationTest:
"""Tests optimization."""
def test_get_optimizer(self):
"""Tests get_optimizer."""
<|body_0|>
def test_get_learning_rate_decay_fn(self):
"""Tests get_learning_rate_decay_fn."""
<|body_1|>
def test_get_gradient_clip_fn(self):
... | stack_v2_sparse_classes_36k_train_007597 | 5,510 | permissive | [
{
"docstring": "Tests get_optimizer.",
"name": "test_get_optimizer",
"signature": "def test_get_optimizer(self)"
},
{
"docstring": "Tests get_learning_rate_decay_fn.",
"name": "test_get_learning_rate_decay_fn",
"signature": "def test_get_learning_rate_decay_fn(self)"
},
{
"docstr... | 4 | stack_v2_sparse_classes_30k_train_015487 | Implement the Python class `OptimizationTest` described below.
Class description:
Tests optimization.
Method signatures and docstrings:
- def test_get_optimizer(self): Tests get_optimizer.
- def test_get_learning_rate_decay_fn(self): Tests get_learning_rate_decay_fn.
- def test_get_gradient_clip_fn(self): Tests get_g... | Implement the Python class `OptimizationTest` described below.
Class description:
Tests optimization.
Method signatures and docstrings:
- def test_get_optimizer(self): Tests get_optimizer.
- def test_get_learning_rate_decay_fn(self): Tests get_learning_rate_decay_fn.
- def test_get_gradient_clip_fn(self): Tests get_g... | 0704b3d4c93915b9a6f96b725e49ae20bf5d1e90 | <|skeleton|>
class OptimizationTest:
"""Tests optimization."""
def test_get_optimizer(self):
"""Tests get_optimizer."""
<|body_0|>
def test_get_learning_rate_decay_fn(self):
"""Tests get_learning_rate_decay_fn."""
<|body_1|>
def test_get_gradient_clip_fn(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OptimizationTest:
"""Tests optimization."""
def test_get_optimizer(self):
"""Tests get_optimizer."""
default_optimizer_fn, optimizer_class = opt.get_optimizer_fn(opt.default_optimization_hparams()['optimizer'])
default_optimizer = default_optimizer_fn(1.0)
self.assertTrue(... | the_stack_v2_python_sparse | texar/tf/core/optimization_test.py | arita37/texar | train | 2 |
09e3f9402887eb02ea24f2612bcb67cdf655287f | [
"if method_name != 'knn' and method_name != 'lof' and (method_name != 'ocsvm'):\n sys.exit(\"There is no ad method named '{0}'. Please check the variable of method_name.\".format(method_name))\nself.method_name = method_name\nself.rate_of_outliers = rate_of_outliers\nself.gamma = gamma\nself.nu = nu\nself.n_neig... | <|body_start_0|>
if method_name != 'knn' and method_name != 'lof' and (method_name != 'ocsvm'):
sys.exit("There is no ad method named '{0}'. Please check the variable of method_name.".format(method_name))
self.method_name = method_name
self.rate_of_outliers = rate_of_outliers
... | ApplicabilityDomain | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApplicabilityDomain:
def __init__(self, method_name='ocsvm', rate_of_outliers=0.01, gamma='auto', nu=0.5, n_neighbors=10, metric='minkowski', p=2):
"""Applicability Domain (AD) Parameters ---------- method_name: str, default 'ocsvm' The name of method to set AD. 'knn', 'lof', or 'ocsvm' ... | stack_v2_sparse_classes_36k_train_007598 | 5,370 | permissive | [
{
"docstring": "Applicability Domain (AD) Parameters ---------- method_name: str, default 'ocsvm' The name of method to set AD. 'knn', 'lof', or 'ocsvm' rate_of_outliers: float, default 0.01 Rate of outlier samples. This is used to set threshold gamma : (only for 'ocsvm') float, default ’auto’ Kernel coefficien... | 3 | stack_v2_sparse_classes_30k_train_007238 | Implement the Python class `ApplicabilityDomain` described below.
Class description:
Implement the ApplicabilityDomain class.
Method signatures and docstrings:
- def __init__(self, method_name='ocsvm', rate_of_outliers=0.01, gamma='auto', nu=0.5, n_neighbors=10, metric='minkowski', p=2): Applicability Domain (AD) Par... | Implement the Python class `ApplicabilityDomain` described below.
Class description:
Implement the ApplicabilityDomain class.
Method signatures and docstrings:
- def __init__(self, method_name='ocsvm', rate_of_outliers=0.01, gamma='auto', nu=0.5, n_neighbors=10, metric='minkowski', p=2): Applicability Domain (AD) Par... | ed966e79ab21f726c0f870258e486bde37166ffd | <|skeleton|>
class ApplicabilityDomain:
def __init__(self, method_name='ocsvm', rate_of_outliers=0.01, gamma='auto', nu=0.5, n_neighbors=10, metric='minkowski', p=2):
"""Applicability Domain (AD) Parameters ---------- method_name: str, default 'ocsvm' The name of method to set AD. 'knn', 'lof', or 'ocsvm' ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApplicabilityDomain:
def __init__(self, method_name='ocsvm', rate_of_outliers=0.01, gamma='auto', nu=0.5, n_neighbors=10, metric='minkowski', p=2):
"""Applicability Domain (AD) Parameters ---------- method_name: str, default 'ocsvm' The name of method to set AD. 'knn', 'lof', or 'ocsvm' rate_of_outlie... | the_stack_v2_python_sparse | dcekit/validation/applicability_domain.py | hkaneko1985/dcekit | train | 44 | |
c793207626c423bbf2cc159ffc8d8a5e88c08c86 | [
"output = []\nfor rate in rates:\n _rate, created = Rate.objects.get_or_create(base_currency=base_currency, currency=rate.get('currency'), value_date=rate.get('date'), user=None, key=None)\n _rate.value = rate.get('value')\n _rate.save()\n output.append(_rate)\nreturn output",
"service_name = rate_ser... | <|body_start_0|>
output = []
for rate in rates:
_rate, created = Rate.objects.get_or_create(base_currency=base_currency, currency=rate.get('currency'), value_date=rate.get('date'), user=None, key=None)
_rate.value = rate.get('value')
_rate.save()
output.ap... | Manager for Rate model | RateManager | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RateManager:
"""Manager for Rate model"""
def __sync_rates__(rates: [], base_currency: str):
"""Sync rates to the database :param rates: array of dict of rates from service :param base_currency: base currency to fetch"""
<|body_0|>
def fetch_rates(self, base_currency: st... | stack_v2_sparse_classes_36k_train_007599 | 16,208 | permissive | [
{
"docstring": "Sync rates to the database :param rates: array of dict of rates from service :param base_currency: base currency to fetch",
"name": "__sync_rates__",
"signature": "def __sync_rates__(rates: [], base_currency: str)"
},
{
"docstring": "Get rates from a service for a base currency a... | 5 | stack_v2_sparse_classes_30k_train_015416 | Implement the Python class `RateManager` described below.
Class description:
Manager for Rate model
Method signatures and docstrings:
- def __sync_rates__(rates: [], base_currency: str): Sync rates to the database :param rates: array of dict of rates from service :param base_currency: base currency to fetch
- def fet... | Implement the Python class `RateManager` described below.
Class description:
Manager for Rate model
Method signatures and docstrings:
- def __sync_rates__(rates: [], base_currency: str): Sync rates to the database :param rates: array of dict of rates from service :param base_currency: base currency to fetch
- def fet... | 23cc075377d47ac631634cd71fd0e7d6b0a57bad | <|skeleton|>
class RateManager:
"""Manager for Rate model"""
def __sync_rates__(rates: [], base_currency: str):
"""Sync rates to the database :param rates: array of dict of rates from service :param base_currency: base currency to fetch"""
<|body_0|>
def fetch_rates(self, base_currency: st... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RateManager:
"""Manager for Rate model"""
def __sync_rates__(rates: [], base_currency: str):
"""Sync rates to the database :param rates: array of dict of rates from service :param base_currency: base currency to fetch"""
output = []
for rate in rates:
_rate, created = ... | the_stack_v2_python_sparse | src/geocurrency/rates/models.py | fmeurou/geocurrency | train | 5 |
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