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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
99405d29c65e4bc9b51461d58353efbec1e3489a | [
"super().__init__('watchd-cfg:watchdog')\nif is_not_none(threshold_memory):\n self.append(threshold_memory)\nif is_not_none(disk_limit):\n self.append(disk_limit)",
"overload_notification_ = SubElement(self, 'watchd-cfg:overload-notification')\nif is_not_none(operation):\n overload_notification_.set('xc:... | <|body_start_0|>
super().__init__('watchd-cfg:watchdog')
if is_not_none(threshold_memory):
self.append(threshold_memory)
if is_not_none(disk_limit):
self.append(disk_limit)
<|end_body_0|>
<|body_start_1|>
overload_notification_ = SubElement(self, 'watchd-cfg:over... | WatchdogCfg | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WatchdogCfg:
def __init__(self, threshold_memory: ThresholdMemory=None, disk_limit: DiskLimit=None):
"""module: Cisco-IOS-XR-watchd-cfg. Watchdog configuration commands."""
<|body_0|>
def overload_notification(self, operation: OperationEnum.Operation=None):
"""Disabl... | stack_v2_sparse_classes_36k_train_000900 | 10,023 | no_license | [
{
"docstring": "module: Cisco-IOS-XR-watchd-cfg. Watchdog configuration commands.",
"name": "__init__",
"signature": "def __init__(self, threshold_memory: ThresholdMemory=None, disk_limit: DiskLimit=None)"
},
{
"docstring": "Disable critical event notification.",
"name": "overload_notificati... | 5 | stack_v2_sparse_classes_30k_train_015478 | Implement the Python class `WatchdogCfg` described below.
Class description:
Implement the WatchdogCfg class.
Method signatures and docstrings:
- def __init__(self, threshold_memory: ThresholdMemory=None, disk_limit: DiskLimit=None): module: Cisco-IOS-XR-watchd-cfg. Watchdog configuration commands.
- def overload_not... | Implement the Python class `WatchdogCfg` described below.
Class description:
Implement the WatchdogCfg class.
Method signatures and docstrings:
- def __init__(self, threshold_memory: ThresholdMemory=None, disk_limit: DiskLimit=None): module: Cisco-IOS-XR-watchd-cfg. Watchdog configuration commands.
- def overload_not... | 9b02bbed95de373df4c31f6c157afe43263bde0a | <|skeleton|>
class WatchdogCfg:
def __init__(self, threshold_memory: ThresholdMemory=None, disk_limit: DiskLimit=None):
"""module: Cisco-IOS-XR-watchd-cfg. Watchdog configuration commands."""
<|body_0|>
def overload_notification(self, operation: OperationEnum.Operation=None):
"""Disabl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WatchdogCfg:
def __init__(self, threshold_memory: ThresholdMemory=None, disk_limit: DiskLimit=None):
"""module: Cisco-IOS-XR-watchd-cfg. Watchdog configuration commands."""
super().__init__('watchd-cfg:watchdog')
if is_not_none(threshold_memory):
self.append(threshold_memor... | the_stack_v2_python_sparse | citrino/cisco/xr/resources/watchdog_cfg.py | johneandredejesus/Citrino | train | 1 | |
bec1bea47957fa8706724c3fc3d6578a2f692a87 | [
"self.arch_id = None\nself.netlist_id = None\nself.name = None\nself.instance = None\nself.ports = {'inputs': [], 'outputs': [], 'clocks': []}\nself.blocks = {}",
"assert root.tag == 'block', root.tag\nnetlist = PackedNetlist()\nnetlist.name = root.attrib['name']\nnetlist.instance = root.attrib['instance']\nnetli... | <|body_start_0|>
self.arch_id = None
self.netlist_id = None
self.name = None
self.instance = None
self.ports = {'inputs': [], 'outputs': [], 'clocks': []}
self.blocks = {}
<|end_body_0|>
<|body_start_1|>
assert root.tag == 'block', root.tag
netlist = Pack... | A VPR Packed netlist representation. The packed netlist is organized as one huge block representing the whole FPGA with all placeable blocks (CLBs) as its children. Here we store the top-level block implicitly so all blocks mentioned in a PackedNetlist instance refer to individual placeable CLBs. | PackedNetlist | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PackedNetlist:
"""A VPR Packed netlist representation. The packed netlist is organized as one huge block representing the whole FPGA with all placeable blocks (CLBs) as its children. Here we store the top-level block implicitly so all blocks mentioned in a PackedNetlist instance refer to individu... | stack_v2_sparse_classes_36k_train_000901 | 21,179 | permissive | [
{
"docstring": "Basic constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Reads the packed netlist from the given element tree",
"name": "from_etree",
"signature": "def from_etree(root)"
},
{
"docstring": "Builds an element tree (XML) that repre... | 3 | stack_v2_sparse_classes_30k_train_021085 | Implement the Python class `PackedNetlist` described below.
Class description:
A VPR Packed netlist representation. The packed netlist is organized as one huge block representing the whole FPGA with all placeable blocks (CLBs) as its children. Here we store the top-level block implicitly so all blocks mentioned in a P... | Implement the Python class `PackedNetlist` described below.
Class description:
A VPR Packed netlist representation. The packed netlist is organized as one huge block representing the whole FPGA with all placeable blocks (CLBs) as its children. Here we store the top-level block implicitly so all blocks mentioned in a P... | 835a40534f9efd70770d74f56f25fef6cfc6ebc6 | <|skeleton|>
class PackedNetlist:
"""A VPR Packed netlist representation. The packed netlist is organized as one huge block representing the whole FPGA with all placeable blocks (CLBs) as its children. Here we store the top-level block implicitly so all blocks mentioned in a PackedNetlist instance refer to individu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PackedNetlist:
"""A VPR Packed netlist representation. The packed netlist is organized as one huge block representing the whole FPGA with all placeable blocks (CLBs) as its children. Here we store the top-level block implicitly so all blocks mentioned in a PackedNetlist instance refer to individual placeable ... | the_stack_v2_python_sparse | f4pga/utils/quicklogic/repacker/packed_netlist.py | f4pga/f4pga | train | 19 |
6d1166eb71c49e2afa4e4cb9d2374abc2276cc61 | [
"super().fit(dataset)\ndataset = dataset.to_numpy()\ndata = dataset.data\nself.means = np.nanmean(data, axis=0)\nself.stds = np.nanstd(data, axis=0)\nself.stds[(self.stds == 0) | np.isnan(self.stds)] = 1\nreturn self",
"super().transform(dataset)\ndataset = dataset.to_numpy()\ndata = dataset.data\ndata = (data - ... | <|body_start_0|>
super().fit(dataset)
dataset = dataset.to_numpy()
data = dataset.data
self.means = np.nanmean(data, axis=0)
self.stds = np.nanstd(data, axis=0)
self.stds[(self.stds == 0) | np.isnan(self.stds)] = 1
return self
<|end_body_0|>
<|body_start_1|>
... | Classic StandardScaler. | StandardScaler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StandardScaler:
"""Classic StandardScaler."""
def fit(self, dataset: NumpyTransformable):
"""Estimate means and stds. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self."""
<|body_0|>
def transform(self, dataset: NumpyTransformable) -> NumpyDat... | stack_v2_sparse_classes_36k_train_000902 | 9,262 | permissive | [
{
"docstring": "Estimate means and stds. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self.",
"name": "fit",
"signature": "def fit(self, dataset: NumpyTransformable)"
},
{
"docstring": "Scale test data. Args: dataset: Pandas or Numpy dataset of numeric features. Retur... | 2 | null | Implement the Python class `StandardScaler` described below.
Class description:
Classic StandardScaler.
Method signatures and docstrings:
- def fit(self, dataset: NumpyTransformable): Estimate means and stds. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self.
- def transform(self, dataset:... | Implement the Python class `StandardScaler` described below.
Class description:
Classic StandardScaler.
Method signatures and docstrings:
- def fit(self, dataset: NumpyTransformable): Estimate means and stds. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self.
- def transform(self, dataset:... | a4c3bfb4f1239d05c5d5d36a386c507c6f561324 | <|skeleton|>
class StandardScaler:
"""Classic StandardScaler."""
def fit(self, dataset: NumpyTransformable):
"""Estimate means and stds. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self."""
<|body_0|>
def transform(self, dataset: NumpyTransformable) -> NumpyDat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StandardScaler:
"""Classic StandardScaler."""
def fit(self, dataset: NumpyTransformable):
"""Estimate means and stds. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self."""
super().fit(dataset)
dataset = dataset.to_numpy()
data = dataset.data
... | the_stack_v2_python_sparse | lightautoml/transformers/numeric.py | sberbank-ai-lab/LightAutoML | train | 851 |
b086ff106deb5c7a92d5a32ff39423214fabfa86 | [
"a0 = -4.1236\na1 = 13.788\na2 = -26.068\na3 = 26.272\na4 = -14.663\na5 = 4.4954\na6 = -0.6905\na7 = 0.0397\nlog_t = math.log10(temperature)\nf_exp = a0 + a1 * log_t + a2 * log_t ** 2.0 + a3 * log_t ** 3.0 + a4 * log_t ** 4.0 + a5 * log_t ** 5.0 + a6 * log_t ** 6.0 + a7 * log_t ** 7\ng10_thermal_conductivity = 10.0... | <|body_start_0|>
a0 = -4.1236
a1 = 13.788
a2 = -26.068
a3 = 26.272
a4 = -14.663
a5 = 4.4954
a6 = -0.6905
a7 = 0.0397
log_t = math.log10(temperature)
f_exp = a0 + a1 * log_t + a2 * log_t ** 2.0 + a3 * log_t ** 3.0 + a4 * log_t ** 4.0 + a5 * ... | G10NISTMaterialProperties | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class G10NISTMaterialProperties:
def thermal_conductivity(temperature):
"""Calculates thermal conductivity of G10 according to NIST standards :param temperature: temperature as float :return: thermal conductivity as float"""
<|body_0|>
def volumetric_heat_capacity(temperature):
... | stack_v2_sparse_classes_36k_train_000903 | 1,623 | no_license | [
{
"docstring": "Calculates thermal conductivity of G10 according to NIST standards :param temperature: temperature as float :return: thermal conductivity as float",
"name": "thermal_conductivity",
"signature": "def thermal_conductivity(temperature)"
},
{
"docstring": "Calculates volumetric heat ... | 2 | stack_v2_sparse_classes_30k_train_008423 | Implement the Python class `G10NISTMaterialProperties` described below.
Class description:
Implement the G10NISTMaterialProperties class.
Method signatures and docstrings:
- def thermal_conductivity(temperature): Calculates thermal conductivity of G10 according to NIST standards :param temperature: temperature as flo... | Implement the Python class `G10NISTMaterialProperties` described below.
Class description:
Implement the G10NISTMaterialProperties class.
Method signatures and docstrings:
- def thermal_conductivity(temperature): Calculates thermal conductivity of G10 according to NIST standards :param temperature: temperature as flo... | 3872a62c01b6d0f7dca97042bbd26b95a2ecc952 | <|skeleton|>
class G10NISTMaterialProperties:
def thermal_conductivity(temperature):
"""Calculates thermal conductivity of G10 according to NIST standards :param temperature: temperature as float :return: thermal conductivity as float"""
<|body_0|>
def volumetric_heat_capacity(temperature):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class G10NISTMaterialProperties:
def thermal_conductivity(temperature):
"""Calculates thermal conductivity of G10 according to NIST standards :param temperature: temperature as float :return: thermal conductivity as float"""
a0 = -4.1236
a1 = 13.788
a2 = -26.068
a3 = 26.272
... | the_stack_v2_python_sparse | source/materials/g10_nist_material_properties.py | MichalWilczek/steam-ansys-modelling | train | 1 | |
4a9cf49521b1d5efb99f675eb5a7431c06f342fc | [
"super().__init__()\nself.dropout = nn.Dropout(p=dropout)\nself.layers = numlayers\nself.hsz = hiddensize\nself.esz = embeddingsize\nself.lt = nn.Embedding(num_features, embeddingsize, padding_idx=padding_idx, sparse=sparse)\nself.rnn = rnn_class(embeddingsize, hiddensize, numlayers, dropout=dropout if numlayers > ... | <|body_start_0|>
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.layers = numlayers
self.hsz = hiddensize
self.esz = embeddingsize
self.lt = nn.Embedding(num_features, embeddingsize, padding_idx=padding_idx, sparse=sparse)
self.rnn = rnn_class(embeddi... | Recurrent decoder module. Can be used as a standalone language model or paired with an encoder. | RNNDecoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNDecoder:
"""Recurrent decoder module. Can be used as a standalone language model or paired with an encoder."""
def __init__(self, num_features, embeddingsize, hiddensize, padding_idx=0, rnn_class='lstm', numlayers=2, dropout=0.1, bidir_input=False, attn_type='none', attn_time='pre', attn_... | stack_v2_sparse_classes_36k_train_000904 | 24,820 | permissive | [
{
"docstring": "Initialize recurrent decoder.",
"name": "__init__",
"signature": "def __init__(self, num_features, embeddingsize, hiddensize, padding_idx=0, rnn_class='lstm', numlayers=2, dropout=0.1, bidir_input=False, attn_type='none', attn_time='pre', attn_length=-1, sparse=False)"
},
{
"docs... | 2 | null | Implement the Python class `RNNDecoder` described below.
Class description:
Recurrent decoder module. Can be used as a standalone language model or paired with an encoder.
Method signatures and docstrings:
- def __init__(self, num_features, embeddingsize, hiddensize, padding_idx=0, rnn_class='lstm', numlayers=2, drop... | Implement the Python class `RNNDecoder` described below.
Class description:
Recurrent decoder module. Can be used as a standalone language model or paired with an encoder.
Method signatures and docstrings:
- def __init__(self, num_features, embeddingsize, hiddensize, padding_idx=0, rnn_class='lstm', numlayers=2, drop... | e1d899edfb92471552bae153f59ad30aa7fca468 | <|skeleton|>
class RNNDecoder:
"""Recurrent decoder module. Can be used as a standalone language model or paired with an encoder."""
def __init__(self, num_features, embeddingsize, hiddensize, padding_idx=0, rnn_class='lstm', numlayers=2, dropout=0.1, bidir_input=False, attn_type='none', attn_time='pre', attn_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RNNDecoder:
"""Recurrent decoder module. Can be used as a standalone language model or paired with an encoder."""
def __init__(self, num_features, embeddingsize, hiddensize, padding_idx=0, rnn_class='lstm', numlayers=2, dropout=0.1, bidir_input=False, attn_type='none', attn_time='pre', attn_length=-1, sp... | the_stack_v2_python_sparse | parlai/agents/seq2seq/modules.py | facebookresearch/ParlAI | train | 10,943 |
9ad1f5f42c6118578b47c7c484af87f4c645de9d | [
"super(colorFrame, self).__init__(parent)\nself.kind = number\nself.hexadecimal = QLabel('', self)\nself.color = QColor(red, green, blue)\nself.update_color(self.color)\nvertical_layout = QVBoxLayout()\nvertical_layout.addWidget(self.hexadecimal)\nvertical_layout.setAlignment(QtCore.Qt.AlignCenter)\nself.setFrameSt... | <|body_start_0|>
super(colorFrame, self).__init__(parent)
self.kind = number
self.hexadecimal = QLabel('', self)
self.color = QColor(red, green, blue)
self.update_color(self.color)
vertical_layout = QVBoxLayout()
vertical_layout.addWidget(self.hexadecimal)
... | colorFrame | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class colorFrame:
def __init__(self, parent, number, red, green, blue):
"""Create a customizable QFrame that reacts to a click by opening a QColorDialog :param parent: Widget parent of this frame :param number: 1 if it's Color Light, 2 if it's Shadow Color :param red: Red value to set on a new... | stack_v2_sparse_classes_36k_train_000905 | 21,305 | no_license | [
{
"docstring": "Create a customizable QFrame that reacts to a click by opening a QColorDialog :param parent: Widget parent of this frame :param number: 1 if it's Color Light, 2 if it's Shadow Color :param red: Red value to set on a new QColor :param green: Green value to set on a new QColor :param blue: Blue va... | 2 | null | Implement the Python class `colorFrame` described below.
Class description:
Implement the colorFrame class.
Method signatures and docstrings:
- def __init__(self, parent, number, red, green, blue): Create a customizable QFrame that reacts to a click by opening a QColorDialog :param parent: Widget parent of this frame... | Implement the Python class `colorFrame` described below.
Class description:
Implement the colorFrame class.
Method signatures and docstrings:
- def __init__(self, parent, number, red, green, blue): Create a customizable QFrame that reacts to a click by opening a QColorDialog :param parent: Widget parent of this frame... | 45301c31814e87a6e5a28d857e9b2ef6421b5c16 | <|skeleton|>
class colorFrame:
def __init__(self, parent, number, red, green, blue):
"""Create a customizable QFrame that reacts to a click by opening a QColorDialog :param parent: Widget parent of this frame :param number: 1 if it's Color Light, 2 if it's Shadow Color :param red: Red value to set on a new... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class colorFrame:
def __init__(self, parent, number, red, green, blue):
"""Create a customizable QFrame that reacts to a click by opening a QColorDialog :param parent: Widget parent of this frame :param number: 1 if it's Color Light, 2 if it's Shadow Color :param red: Red value to set on a new QColor :param... | the_stack_v2_python_sparse | recipies/python/LightInterface_recreation.py | igor-si/shared | train | 1 | |
cd12156a6e7f73c286c45431e767df6dd0b40b10 | [
"self.url = url\nself.proxy = Http(cache=self.cache, timeout=self.timeout)\nself.proxy.disable_ssl_certificate_validation = not validate_ssl\nif isinstance(credential, UsernamePassword):\n self.proxy.add_credentials(credential.username, credential.password)",
"if url is None:\n url = self.url\nstatus, respo... | <|body_start_0|>
self.url = url
self.proxy = Http(cache=self.cache, timeout=self.timeout)
self.proxy.disable_ssl_certificate_validation = not validate_ssl
if isinstance(credential, UsernamePassword):
self.proxy.add_credentials(credential.username, credential.password)
<|end_b... | @summary: implements REST driver to fetch using http GET @cvar timeout: timeout of connection @type timeout: float @cvar cache: a cache directory @type cache: str @ivar url: a default document locator to be reused @type url: str @ivar proxy: an interface to the http server @type proxy: httplib2.Http | RESTDriver | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RESTDriver:
"""@summary: implements REST driver to fetch using http GET @cvar timeout: timeout of connection @type timeout: float @cvar cache: a cache directory @type cache: str @ivar url: a default document locator to be reused @type url: str @ivar proxy: an interface to the http server @type pr... | stack_v2_sparse_classes_36k_train_000906 | 2,251 | no_license | [
{
"docstring": "@summary: initializes a proxy to the http service and saves a default document locator @param url: the default document locator @type url: str @param credential: an authentication secret @type credential: L{Credential} or None @param validate_ssl: whether to apply strick certificate validation, ... | 2 | stack_v2_sparse_classes_30k_train_002101 | Implement the Python class `RESTDriver` described below.
Class description:
@summary: implements REST driver to fetch using http GET @cvar timeout: timeout of connection @type timeout: float @cvar cache: a cache directory @type cache: str @ivar url: a default document locator to be reused @type url: str @ivar proxy: a... | Implement the Python class `RESTDriver` described below.
Class description:
@summary: implements REST driver to fetch using http GET @cvar timeout: timeout of connection @type timeout: float @cvar cache: a cache directory @type cache: str @ivar url: a default document locator to be reused @type url: str @ivar proxy: a... | 0932550a42ba1ca634225ccb3a4748336e69e022 | <|skeleton|>
class RESTDriver:
"""@summary: implements REST driver to fetch using http GET @cvar timeout: timeout of connection @type timeout: float @cvar cache: a cache directory @type cache: str @ivar url: a default document locator to be reused @type url: str @ivar proxy: an interface to the http server @type pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RESTDriver:
"""@summary: implements REST driver to fetch using http GET @cvar timeout: timeout of connection @type timeout: float @cvar cache: a cache directory @type cache: str @ivar url: a default document locator to be reused @type url: str @ivar proxy: an interface to the http server @type proxy: httplib2... | the_stack_v2_python_sparse | Monitoring/MonitoringService/Driver/REST.py | vitorsfarias/novi-public | train | 0 |
d99a8113e20a32a9462a09a070a92e09a11dec56 | [
"self.tfidf_features = {}\nself.category = None\nself.referers = []\nself.named_entities = defaultdict(int)",
"representation = 'Category: ' + self.category + '\\n'\nrepresentation += 'Named Entities:\\n'\nfor ent in self.named_entities:\n if self.named_entities[ent] > 0:\n representation += '\\t' + ent... | <|body_start_0|>
self.tfidf_features = {}
self.category = None
self.referers = []
self.named_entities = defaultdict(int)
<|end_body_0|>
<|body_start_1|>
representation = 'Category: ' + self.category + '\n'
representation += 'Named Entities:\n'
for ent in self.nam... | Object that contains the feature representation of a given clue. | FeatureRepresentation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeatureRepresentation:
"""Object that contains the feature representation of a given clue."""
def __init__(self):
"""Constructor"""
<|body_0|>
def __str__(self):
"""Returns a string representation of this object"""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_000907 | 10,222 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Returns a string representation of this object",
"name": "__str__",
"signature": "def __str__(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009737 | Implement the Python class `FeatureRepresentation` described below.
Class description:
Object that contains the feature representation of a given clue.
Method signatures and docstrings:
- def __init__(self): Constructor
- def __str__(self): Returns a string representation of this object | Implement the Python class `FeatureRepresentation` described below.
Class description:
Object that contains the feature representation of a given clue.
Method signatures and docstrings:
- def __init__(self): Constructor
- def __str__(self): Returns a string representation of this object
<|skeleton|>
class FeatureRep... | 8399c88ab0fdc7736dddcf5239eea655d613c61d | <|skeleton|>
class FeatureRepresentation:
"""Object that contains the feature representation of a given clue."""
def __init__(self):
"""Constructor"""
<|body_0|>
def __str__(self):
"""Returns a string representation of this object"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeatureRepresentation:
"""Object that contains the feature representation of a given clue."""
def __init__(self):
"""Constructor"""
self.tfidf_features = {}
self.category = None
self.referers = []
self.named_entities = defaultdict(int)
def __str__(self):
... | the_stack_v2_python_sparse | featurespace.py | timdestan/quiz-bowl-entity-resolution | train | 1 |
b1fe2b73b7bf45f7fd84998c05214da302e328e9 | [
"_url_path = '/InsuranceCentre/PortalLandingPage'\n_query_builder = Configuration.get_base_uri()\n_query_builder += _url_path\n_query_parameters = {'id': id}\n_query_builder = APIHelper.append_url_with_query_parameters(_query_builder, _query_parameters, Configuration.array_serialization)\n_query_url = APIHelper.cle... | <|body_start_0|>
_url_path = '/InsuranceCentre/PortalLandingPage'
_query_builder = Configuration.get_base_uri()
_query_builder += _url_path
_query_parameters = {'id': id}
_query_builder = APIHelper.append_url_with_query_parameters(_query_builder, _query_parameters, Configuration.... | A Controller to access Endpoints in the easybimehlanding API. | MainController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MainController:
"""A Controller to access Endpoints in the easybimehlanding API."""
def get_portal_landing_page(self, id, x_api_key):
"""Does a GET request to /InsuranceCentre/PortalLandingPage. در یافت اطلاعات لندینگ مراکز بیمه Args: id (string): دامنه یا زیر دامنه ی مرکز بیمه x_api... | stack_v2_sparse_classes_36k_train_000908 | 4,330 | permissive | [
{
"docstring": "Does a GET request to /InsuranceCentre/PortalLandingPage. در یافت اطلاعات لندینگ مراکز بیمه Args: id (string): دامنه یا زیر دامنه ی مرکز بیمه x_api_key (string): کلید اختصاصی ارتباط با سرور Returns: BaseModelPortalLandingPage: Response from the API. Raises: APIException: When an error occurs whi... | 2 | stack_v2_sparse_classes_30k_train_010995 | Implement the Python class `MainController` described below.
Class description:
A Controller to access Endpoints in the easybimehlanding API.
Method signatures and docstrings:
- def get_portal_landing_page(self, id, x_api_key): Does a GET request to /InsuranceCentre/PortalLandingPage. در یافت اطلاعات لندینگ مراکز بیم... | Implement the Python class `MainController` described below.
Class description:
A Controller to access Endpoints in the easybimehlanding API.
Method signatures and docstrings:
- def get_portal_landing_page(self, id, x_api_key): Does a GET request to /InsuranceCentre/PortalLandingPage. در یافت اطلاعات لندینگ مراکز بیم... | b574a76a8805b306a423229b572c36dae0159def | <|skeleton|>
class MainController:
"""A Controller to access Endpoints in the easybimehlanding API."""
def get_portal_landing_page(self, id, x_api_key):
"""Does a GET request to /InsuranceCentre/PortalLandingPage. در یافت اطلاعات لندینگ مراکز بیمه Args: id (string): دامنه یا زیر دامنه ی مرکز بیمه x_api... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MainController:
"""A Controller to access Endpoints in the easybimehlanding API."""
def get_portal_landing_page(self, id, x_api_key):
"""Does a GET request to /InsuranceCentre/PortalLandingPage. در یافت اطلاعات لندینگ مراکز بیمه Args: id (string): دامنه یا زیر دامنه ی مرکز بیمه x_api_key (string)... | the_stack_v2_python_sparse | easybimehlanding/controllers/main_controller.py | kmelodi/EasyBimehLanding_Python | train | 0 |
e4f7163b275f899bd445f54d239ca163b369b31e | [
"self.ua_number = int(ua_number)\nif self.ua_number <= 0:\n raise ValueError(\"'ua_number' should be a postive int.\")\nif savepath is None:\n savepath = os.path.join(os.getcwd(), 'UAList.txt')\nplatform = sys.platform\nif platform == 'linux' or platform == 'linux2':\n self.ua_url = UA_address['UA_Linux']\... | <|body_start_0|>
self.ua_number = int(ua_number)
if self.ua_number <= 0:
raise ValueError("'ua_number' should be a postive int.")
if savepath is None:
savepath = os.path.join(os.getcwd(), 'UAList.txt')
platform = sys.platform
if platform == 'linux' or plat... | BuildUAPool | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BuildUAPool:
def __init__(self, ua_number=10, savepath=None):
"""Get ua from predefined url, and saved into file. Parameters ---------- ua_number: the number of getting ua, default is 10. savepath: file path that saves ua_list into, default is default is None, means saving into 'UAList.t... | stack_v2_sparse_classes_36k_train_000909 | 4,011 | no_license | [
{
"docstring": "Get ua from predefined url, and saved into file. Parameters ---------- ua_number: the number of getting ua, default is 10. savepath: file path that saves ua_list into, default is default is None, means saving into 'UAList.txt' under current working dir.",
"name": "__init__",
"signature":... | 5 | stack_v2_sparse_classes_30k_train_005012 | Implement the Python class `BuildUAPool` described below.
Class description:
Implement the BuildUAPool class.
Method signatures and docstrings:
- def __init__(self, ua_number=10, savepath=None): Get ua from predefined url, and saved into file. Parameters ---------- ua_number: the number of getting ua, default is 10. ... | Implement the Python class `BuildUAPool` described below.
Class description:
Implement the BuildUAPool class.
Method signatures and docstrings:
- def __init__(self, ua_number=10, savepath=None): Get ua from predefined url, and saved into file. Parameters ---------- ua_number: the number of getting ua, default is 10. ... | 127a7a47c8afea812a3fa89dddc693ed22e58c23 | <|skeleton|>
class BuildUAPool:
def __init__(self, ua_number=10, savepath=None):
"""Get ua from predefined url, and saved into file. Parameters ---------- ua_number: the number of getting ua, default is 10. savepath: file path that saves ua_list into, default is default is None, means saving into 'UAList.t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BuildUAPool:
def __init__(self, ua_number=10, savepath=None):
"""Get ua from predefined url, and saved into file. Parameters ---------- ua_number: the number of getting ua, default is 10. savepath: file path that saves ua_list into, default is default is None, means saving into 'UAList.txt' under curr... | the_stack_v2_python_sparse | NetworkTools/requestUA.py | Stuming/Harbor | train | 0 | |
7960f4a31f2291c23eafedbfcdf76f44e58dbe79 | [
"try:\n return [x.to_dict() for x in SctParser.from_bytes(data=self.EXT.children[2].contents)]\nexcept Exception:\n self.LOG.debug(f'Failed to parse SCT data from {self.EXT}', exc_info=True)\n return []",
"def handle(item):\n \"\"\"Pass.\"\"\"\n operator = item.pop('log_operator')\n ret = {f'ope... | <|body_start_0|>
try:
return [x.to_dict() for x in SctParser.from_bytes(data=self.EXT.children[2].contents)]
except Exception:
self.LOG.debug(f'Failed to parse SCT data from {self.EXT}', exc_info=True)
return []
<|end_body_0|>
<|body_start_1|>
def handle(item... | Pass. | SignedCertificateTimestampList | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignedCertificateTimestampList:
"""Pass."""
def value(self) -> List[dict]:
"""Pass."""
<|body_0|>
def value_for_str(self) -> Any:
"""Pass."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
return [x.to_dict() for x in SctParser.fr... | stack_v2_sparse_classes_36k_train_000910 | 13,328 | permissive | [
{
"docstring": "Pass.",
"name": "value",
"signature": "def value(self) -> List[dict]"
},
{
"docstring": "Pass.",
"name": "value_for_str",
"signature": "def value_for_str(self) -> Any"
}
] | 2 | null | Implement the Python class `SignedCertificateTimestampList` described below.
Class description:
Pass.
Method signatures and docstrings:
- def value(self) -> List[dict]: Pass.
- def value_for_str(self) -> Any: Pass. | Implement the Python class `SignedCertificateTimestampList` described below.
Class description:
Pass.
Method signatures and docstrings:
- def value(self) -> List[dict]: Pass.
- def value_for_str(self) -> Any: Pass.
<|skeleton|>
class SignedCertificateTimestampList:
"""Pass."""
def value(self) -> List[dict]:... | be49566e590834df1b46494c8588651fa029b8c5 | <|skeleton|>
class SignedCertificateTimestampList:
"""Pass."""
def value(self) -> List[dict]:
"""Pass."""
<|body_0|>
def value_for_str(self) -> Any:
"""Pass."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SignedCertificateTimestampList:
"""Pass."""
def value(self) -> List[dict]:
"""Pass."""
try:
return [x.to_dict() for x in SctParser.from_bytes(data=self.EXT.children[2].contents)]
except Exception:
self.LOG.debug(f'Failed to parse SCT data from {self.EXT}', ... | the_stack_v2_python_sparse | axonius_api_client/projects/cert_human/ssl_extensions.py | Axonius/axonius_api_client | train | 17 |
fbbfb6de8fbd4df96b45ee534353a30aff34d5da | [
"super().__init__(api, coordinator, description)\nself._previous_uptime: str | None = None\nself._last_boot: str | None = None",
"attr = getattr(self._api.information, self.entity_description.key)\nif attr is None:\n return None\nif self.entity_description.key == 'uptime':\n if self._previous_uptime is None... | <|body_start_0|>
super().__init__(api, coordinator, description)
self._previous_uptime: str | None = None
self._last_boot: str | None = None
<|end_body_0|>
<|body_start_1|>
attr = getattr(self._api.information, self.entity_description.key)
if attr is None:
return Non... | Representation a Synology information sensor. | SynoDSMInfoSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SynoDSMInfoSensor:
"""Representation a Synology information sensor."""
def __init__(self, api: SynoApi, coordinator: DataUpdateCoordinator[dict[str, dict[str, Any]]], description: SynologyDSMSensorEntityDescription) -> None:
"""Initialize the Synology SynoDSMInfoSensor entity."""
... | stack_v2_sparse_classes_36k_train_000911 | 5,756 | permissive | [
{
"docstring": "Initialize the Synology SynoDSMInfoSensor entity.",
"name": "__init__",
"signature": "def __init__(self, api: SynoApi, coordinator: DataUpdateCoordinator[dict[str, dict[str, Any]]], description: SynologyDSMSensorEntityDescription) -> None"
},
{
"docstring": "Return the state.",
... | 2 | null | Implement the Python class `SynoDSMInfoSensor` described below.
Class description:
Representation a Synology information sensor.
Method signatures and docstrings:
- def __init__(self, api: SynoApi, coordinator: DataUpdateCoordinator[dict[str, dict[str, Any]]], description: SynologyDSMSensorEntityDescription) -> None:... | Implement the Python class `SynoDSMInfoSensor` described below.
Class description:
Representation a Synology information sensor.
Method signatures and docstrings:
- def __init__(self, api: SynoApi, coordinator: DataUpdateCoordinator[dict[str, dict[str, Any]]], description: SynologyDSMSensorEntityDescription) -> None:... | 8de7966104911bca6f855a1755a6d71a07afb9de | <|skeleton|>
class SynoDSMInfoSensor:
"""Representation a Synology information sensor."""
def __init__(self, api: SynoApi, coordinator: DataUpdateCoordinator[dict[str, dict[str, Any]]], description: SynologyDSMSensorEntityDescription) -> None:
"""Initialize the Synology SynoDSMInfoSensor entity."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SynoDSMInfoSensor:
"""Representation a Synology information sensor."""
def __init__(self, api: SynoApi, coordinator: DataUpdateCoordinator[dict[str, dict[str, Any]]], description: SynologyDSMSensorEntityDescription) -> None:
"""Initialize the Synology SynoDSMInfoSensor entity."""
super().... | the_stack_v2_python_sparse | homeassistant/components/synology_dsm/sensor.py | AlexxIT/home-assistant | train | 9 |
846705e24ccbbbf4a3fa54e5e36c41dbfb4dfd9a | [
"res = super(ResConfigSettings, self).get_values()\nres.update(generate_payslip=self.env['ir.config_parameter'].sudo().get_param('generate_payslip'), generate_day=int(self.env['ir.config_parameter'].sudo().get_param('generate_day') or 1), option=self.env['ir.config_parameter'].sudo().get_param('option') or 'first')... | <|body_start_0|>
res = super(ResConfigSettings, self).get_values()
res.update(generate_payslip=self.env['ir.config_parameter'].sudo().get_param('generate_payslip'), generate_day=int(self.env['ir.config_parameter'].sudo().get_param('generate_day') or 1), option=self.env['ir.config_parameter'].sudo().get_... | ResConfigSettings | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResConfigSettings:
def get_values(self):
"""get values from the fields"""
<|body_0|>
def set_values(self):
"""Set values in the fields"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = super(ResConfigSettings, self).get_values()
res.upda... | stack_v2_sparse_classes_36k_train_000912 | 1,536 | no_license | [
{
"docstring": "get values from the fields",
"name": "get_values",
"signature": "def get_values(self)"
},
{
"docstring": "Set values in the fields",
"name": "set_values",
"signature": "def set_values(self)"
}
] | 2 | null | Implement the Python class `ResConfigSettings` described below.
Class description:
Implement the ResConfigSettings class.
Method signatures and docstrings:
- def get_values(self): get values from the fields
- def set_values(self): Set values in the fields | Implement the Python class `ResConfigSettings` described below.
Class description:
Implement the ResConfigSettings class.
Method signatures and docstrings:
- def get_values(self): get values from the fields
- def set_values(self): Set values in the fields
<|skeleton|>
class ResConfigSettings:
def get_values(sel... | 4b1bcb8f17aad44fe9c80a8180eb0128e6bb2c14 | <|skeleton|>
class ResConfigSettings:
def get_values(self):
"""get values from the fields"""
<|body_0|>
def set_values(self):
"""Set values in the fields"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResConfigSettings:
def get_values(self):
"""get values from the fields"""
res = super(ResConfigSettings, self).get_values()
res.update(generate_payslip=self.env['ir.config_parameter'].sudo().get_param('generate_payslip'), generate_day=int(self.env['ir.config_parameter'].sudo().get_para... | the_stack_v2_python_sparse | automatic_payroll/models/res_config_settings.py | CybroOdoo/CybroAddons | train | 209 | |
9e2fb424f2d1622e94310ea94e12a4bfbc2cb94e | [
"if root == None:\n return 0\nelse:\n return self.pathSumVer2(root, sum, False)",
"num = 0\nif root.val == sum:\n num += 1\nif ifStart == False:\n if root.left:\n num += self.pathSumVer2(root.left, sum, False)\n num += self.pathSumVer2(root.left, sum - root.val, True)\n if root.right:... | <|body_start_0|>
if root == None:
return 0
else:
return self.pathSumVer2(root, sum, False)
<|end_body_0|>
<|body_start_1|>
num = 0
if root.val == sum:
num += 1
if ifStart == False:
if root.left:
num += self.pathSumV... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def pathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: int"""
<|body_0|>
def pathSumVer2(self, root, sum, ifStart):
"""ifStart=True说明之前已经选了某个点了，就必须选到底了"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root == None:
... | stack_v2_sparse_classes_36k_train_000913 | 1,361 | no_license | [
{
"docstring": ":type root: TreeNode :type sum: int :rtype: int",
"name": "pathSum",
"signature": "def pathSum(self, root, sum)"
},
{
"docstring": "ifStart=True说明之前已经选了某个点了，就必须选到底了",
"name": "pathSumVer2",
"signature": "def pathSumVer2(self, root, sum, ifStart)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def pathSum(self, root, sum): :type root: TreeNode :type sum: int :rtype: int
- def pathSumVer2(self, root, sum, ifStart): ifStart=True说明之前已经选了某个点了，就必须选到底了 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def pathSum(self, root, sum): :type root: TreeNode :type sum: int :rtype: int
- def pathSumVer2(self, root, sum, ifStart): ifStart=True说明之前已经选了某个点了，就必须选到底了
<|skeleton|>
class So... | f1a3930c571a6d062208ee1c1aadfe93a5684c40 | <|skeleton|>
class Solution:
def pathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: int"""
<|body_0|>
def pathSumVer2(self, root, sum, ifStart):
"""ifStart=True说明之前已经选了某个点了，就必须选到底了"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def pathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: int"""
if root == None:
return 0
else:
return self.pathSumVer2(root, sum, False)
def pathSumVer2(self, root, sum, ifStart):
"""ifStart=True说明之前已经选了某个点了，就必须选到底了"""... | the_stack_v2_python_sparse | solution/problem 76.py | Fay321/leetcode-exercise | train | 0 | |
045900bf89057e4fae9a7a2120d5c3e602de28d0 | [
"super().__init__()\nself.left = left\nself.right = right\nself.op = op",
"right = self.right.make_il(il_code, symbol_table, c)\nlvalue = self.left.lvalue(il_code, symbol_table, c)\nif not lvalue or not lvalue.modable():\n err = f\"expression on left of '{str(self.op)}' is not assignable\"\n raise CompilerE... | <|body_start_0|>
super().__init__()
self.left = left
self.right = right
self.op = op
<|end_body_0|>
<|body_start_1|>
right = self.right.make_il(il_code, symbol_table, c)
lvalue = self.left.lvalue(il_code, symbol_table, c)
if not lvalue or not lvalue.modable():
... | Expression that is += or -=. | _CompoundPlusMinus | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _CompoundPlusMinus:
"""Expression that is += or -=."""
def __init__(self, left, right, op):
"""Initialize node."""
<|body_0|>
def make_il(self, il_code, symbol_table, c):
"""Make code for this node."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_000914 | 45,651 | permissive | [
{
"docstring": "Initialize node.",
"name": "__init__",
"signature": "def __init__(self, left, right, op)"
},
{
"docstring": "Make code for this node.",
"name": "make_il",
"signature": "def make_il(self, il_code, symbol_table, c)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007931 | Implement the Python class `_CompoundPlusMinus` described below.
Class description:
Expression that is += or -=.
Method signatures and docstrings:
- def __init__(self, left, right, op): Initialize node.
- def make_il(self, il_code, symbol_table, c): Make code for this node. | Implement the Python class `_CompoundPlusMinus` described below.
Class description:
Expression that is += or -=.
Method signatures and docstrings:
- def __init__(self, left, right, op): Initialize node.
- def make_il(self, il_code, symbol_table, c): Make code for this node.
<|skeleton|>
class _CompoundPlusMinus:
... | 6232136be38a29e8c18beae3d23e49ecfb7906fd | <|skeleton|>
class _CompoundPlusMinus:
"""Expression that is += or -=."""
def __init__(self, left, right, op):
"""Initialize node."""
<|body_0|>
def make_il(self, il_code, symbol_table, c):
"""Make code for this node."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _CompoundPlusMinus:
"""Expression that is += or -=."""
def __init__(self, left, right, op):
"""Initialize node."""
super().__init__()
self.left = left
self.right = right
self.op = op
def make_il(self, il_code, symbol_table, c):
"""Make code for this no... | the_stack_v2_python_sparse | shivyc/tree/expr_nodes.py | ShivamSarodia/ShivyC | train | 1,072 |
94876b1dde609ad32965fee9597bd933fb084861 | [
"def doit(node):\n if node:\n vals.append(str(node.val))\n doit(node.left)\n doit(node.right)\n else:\n vals.append('#')\nvals = []\ndoit(root)\nreturn ' '.join(vals)",
"def doit():\n val = next(vals)\n if val == '#':\n return None\n node = TreeNode(int(val))\n ... | <|body_start_0|>
def doit(node):
if node:
vals.append(str(node.val))
doit(node.left)
doit(node.right)
else:
vals.append('#')
vals = []
doit(root)
return ' '.join(vals)
<|end_body_0|>
<|body_start_1|>... | Codec2 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec2:
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_000915 | 4,578 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec2` described below.
Class description:
Implement the Codec2 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 :rtyp... | Implement the Python class `Codec2` described below.
Class description:
Implement the Codec2 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 :rtyp... | e2fecd266bfced6208694b19a2d81182b13dacd6 | <|skeleton|>
class Codec2:
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 Codec2:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def doit(node):
if node:
vals.append(str(node.val))
doit(node.left)
doit(node.right)
else:
vals.a... | the_stack_v2_python_sparse | Codec.py | HuipengXu/leetcode | train | 0 | |
892cbc07a1524f47caaf9eddeb1e1485bb79c915 | [
"data = form.cleaned_data\nself.success_url = reverse('course_result', kwargs={'course': int(data['course'].id)})\nreturn super().form_valid(form)",
"context = super().get_context_data(**kwargs)\ncontext['title_text'] = 'Choose Course Result To Display'\ncontext['detail_text'] = 'Please select the <strong>Course\... | <|body_start_0|>
data = form.cleaned_data
self.success_url = reverse('course_result', kwargs={'course': int(data['course'].id)})
return super().form_valid(form)
<|end_body_0|>
<|body_start_1|>
context = super().get_context_data(**kwargs)
context['title_text'] = 'Choose Course Re... | View for choosing which course result to display. Check that the user's account is still active. Redirects to course_result view on form valid. | ShowCourseResultView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShowCourseResultView:
"""View for choosing which course result to display. Check that the user's account is still active. Redirects to course_result view on form valid."""
def form_valid(self, form):
"""Compute the success URL and call super.form_valid()"""
<|body_0|>
de... | stack_v2_sparse_classes_36k_train_000916 | 29,759 | no_license | [
{
"docstring": "Compute the success URL and call super.form_valid()",
"name": "form_valid",
"signature": "def form_valid(self, form)"
},
{
"docstring": "Return the data used in the templates rendering.",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
}
... | 2 | stack_v2_sparse_classes_30k_train_005386 | Implement the Python class `ShowCourseResultView` described below.
Class description:
View for choosing which course result to display. Check that the user's account is still active. Redirects to course_result view on form valid.
Method signatures and docstrings:
- def form_valid(self, form): Compute the success URL ... | Implement the Python class `ShowCourseResultView` described below.
Class description:
View for choosing which course result to display. Check that the user's account is still active. Redirects to course_result view on form valid.
Method signatures and docstrings:
- def form_valid(self, form): Compute the success URL ... | 06bc577d01d3dbf6c425e03dcb903977a38e377c | <|skeleton|>
class ShowCourseResultView:
"""View for choosing which course result to display. Check that the user's account is still active. Redirects to course_result view on form valid."""
def form_valid(self, form):
"""Compute the success URL and call super.form_valid()"""
<|body_0|>
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ShowCourseResultView:
"""View for choosing which course result to display. Check that the user's account is still active. Redirects to course_result view on form valid."""
def form_valid(self, form):
"""Compute the success URL and call super.form_valid()"""
data = form.cleaned_data
... | the_stack_v2_python_sparse | cbt/views.py | Festusali/CBTest | train | 6 |
7e413261c060b0c54fab878b41f699400c5c6609 | [
"super().__init__(methodname)\nself.ntrain, self.ntest = (50, 100)\nself.nclass = 5\nself.ndim = 10\nself.train_data = np.random.rand(self.ntrain, self.ndim)\nself.test_data = np.random.rand(self.ntest, self.ndim)\nself.train_labels = np.random.randint(self.nclass, size=self.ntrain)\nself.test_labels = np.random.ra... | <|body_start_0|>
super().__init__(methodname)
self.ntrain, self.ntest = (50, 100)
self.nclass = 5
self.ndim = 10
self.train_data = np.random.rand(self.ntrain, self.ndim)
self.test_data = np.random.rand(self.ntest, self.ndim)
self.train_labels = np.random.randint(s... | UtilsTest | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UtilsTest:
def __init__(self, methodname):
"""Initialize the test class."""
<|body_0|>
def test_calculate_losses(self):
"""Test calculating the loss."""
<|body_1|>
def test_run_attack_helper(self):
"""Test the attack."""
<|body_2|>
d... | stack_v2_sparse_classes_36k_train_000917 | 6,871 | permissive | [
{
"docstring": "Initialize the test class.",
"name": "__init__",
"signature": "def __init__(self, methodname)"
},
{
"docstring": "Test calculating the loss.",
"name": "test_calculate_losses",
"signature": "def test_calculate_losses(self)"
},
{
"docstring": "Test the attack.",
... | 6 | null | Implement the Python class `UtilsTest` described below.
Class description:
Implement the UtilsTest class.
Method signatures and docstrings:
- def __init__(self, methodname): Initialize the test class.
- def test_calculate_losses(self): Test calculating the loss.
- def test_run_attack_helper(self): Test the attack.
- ... | Implement the Python class `UtilsTest` described below.
Class description:
Implement the UtilsTest class.
Method signatures and docstrings:
- def __init__(self, methodname): Initialize the test class.
- def test_calculate_losses(self): Test calculating the loss.
- def test_run_attack_helper(self): Test the attack.
- ... | c92610e37aa340932ed2d963813e0890035a22bc | <|skeleton|>
class UtilsTest:
def __init__(self, methodname):
"""Initialize the test class."""
<|body_0|>
def test_calculate_losses(self):
"""Test calculating the loss."""
<|body_1|>
def test_run_attack_helper(self):
"""Test the attack."""
<|body_2|>
d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UtilsTest:
def __init__(self, methodname):
"""Initialize the test class."""
super().__init__(methodname)
self.ntrain, self.ntest = (50, 100)
self.nclass = 5
self.ndim = 10
self.train_data = np.random.rand(self.ntrain, self.ndim)
self.test_data = np.rando... | the_stack_v2_python_sparse | tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/tf_estimator_evaluation_test.py | tensorflow/privacy | train | 1,881 | |
2639e78b4a554c3d0793b581bc816f465ff0c57b | [
"self.file = file\nself.id = id\nself.type = type\nself.state = state\nself.title = title\nself.purpose = purpose\nself.groups = groups\nself.modes = modes\nself.classname = classname\nself.module = module\nself.input = input\nself.output = output\nself.reference = reference\nself.traceability = traceability",
"s... | <|body_start_0|>
self.file = file
self.id = id
self.type = type
self.state = state
self.title = title
self.purpose = purpose
self.groups = groups
self.modes = modes
self.classname = classname
self.module = module
self.input = input
... | Holder class for the contents of a testcase descriptor. | XMLDescriptorContainer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XMLDescriptorContainer:
"""Holder class for the contents of a testcase descriptor."""
def __init__(self, file, id, type, state, title, purpose, groups, modes, classname, module, input, output, reference, traceability):
"""Create an instance of the XMLDescriptorContainer class. @param... | stack_v2_sparse_classes_36k_train_000918 | 12,973 | no_license | [
{
"docstring": "Create an instance of the XMLDescriptorContainer class. @param id: The testcase identifier @param type: The type of the testcase (automated or manual) @param state: The state of the testcase (runable, deprecated or skipped) @param title: The title of the testcase @param purpose: The purpose of t... | 2 | stack_v2_sparse_classes_30k_train_003657 | Implement the Python class `XMLDescriptorContainer` described below.
Class description:
Holder class for the contents of a testcase descriptor.
Method signatures and docstrings:
- def __init__(self, file, id, type, state, title, purpose, groups, modes, classname, module, input, output, reference, traceability): Creat... | Implement the Python class `XMLDescriptorContainer` described below.
Class description:
Holder class for the contents of a testcase descriptor.
Method signatures and docstrings:
- def __init__(self, file, id, type, state, title, purpose, groups, modes, classname, module, input, output, reference, traceability): Creat... | 3f93cbedbb806b6c53de89358025f93c740ebdc3 | <|skeleton|>
class XMLDescriptorContainer:
"""Holder class for the contents of a testcase descriptor."""
def __init__(self, file, id, type, state, title, purpose, groups, modes, classname, module, input, output, reference, traceability):
"""Create an instance of the XMLDescriptorContainer class. @param... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XMLDescriptorContainer:
"""Holder class for the contents of a testcase descriptor."""
def __init__(self, file, id, type, state, title, purpose, groups, modes, classname, module, input, output, reference, traceability):
"""Create an instance of the XMLDescriptorContainer class. @param id: The test... | the_stack_v2_python_sparse | pysys/xml/descriptor.py | moraygrieve/pysys | train | 0 |
3bd58e0409fc500e81a85348c1413c3dbbf89680 | [
"@lru_cache(None)\ndef lcs(i, j):\n if i == -1 or j == -1:\n return 0\n m = 0\n if word1[i] == word2[j]:\n m = max([m, lcs(i - 1, j - 1) + 1])\n else:\n m = max([lcs(i - 1, j), lcs(i, j - 1)])\n return m\nlcs_length = lcs(len(word1) - 1, len(word2) - 1)\nreturn len(word1) + len(w... | <|body_start_0|>
@lru_cache(None)
def lcs(i, j):
if i == -1 or j == -1:
return 0
m = 0
if word1[i] == word2[j]:
m = max([m, lcs(i - 1, j - 1) + 1])
else:
m = max([lcs(i - 1, j), lcs(i, j - 1)])
re... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minDistance(self, word1: str, word2: str) -> int:
"""06/13/2020 10:44"""
<|body_0|>
def minDistance(self, word1: str, word2: str) -> int:
"""Top-down DP (recursion) Time complexity: O(n^2) Space complexity: O(n^2)"""
<|body_1|>
def minDista... | stack_v2_sparse_classes_36k_train_000919 | 3,799 | no_license | [
{
"docstring": "06/13/2020 10:44",
"name": "minDistance",
"signature": "def minDistance(self, word1: str, word2: str) -> int"
},
{
"docstring": "Top-down DP (recursion) Time complexity: O(n^2) Space complexity: O(n^2)",
"name": "minDistance",
"signature": "def minDistance(self, word1: st... | 5 | stack_v2_sparse_classes_30k_test_000703 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDistance(self, word1: str, word2: str) -> int: 06/13/2020 10:44
- def minDistance(self, word1: str, word2: str) -> int: Top-down DP (recursion) Time complexity: O(n^2) Spa... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDistance(self, word1: str, word2: str) -> int: 06/13/2020 10:44
- def minDistance(self, word1: str, word2: str) -> int: Top-down DP (recursion) Time complexity: O(n^2) Spa... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def minDistance(self, word1: str, word2: str) -> int:
"""06/13/2020 10:44"""
<|body_0|>
def minDistance(self, word1: str, word2: str) -> int:
"""Top-down DP (recursion) Time complexity: O(n^2) Space complexity: O(n^2)"""
<|body_1|>
def minDista... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minDistance(self, word1: str, word2: str) -> int:
"""06/13/2020 10:44"""
@lru_cache(None)
def lcs(i, j):
if i == -1 or j == -1:
return 0
m = 0
if word1[i] == word2[j]:
m = max([m, lcs(i - 1, j - 1) + 1])
... | the_stack_v2_python_sparse | leetcode/solved/583_Delete_Operation_for_Two_Strings/solution.py | sungminoh/algorithms | train | 0 | |
c176bab87b2b24dd18ff0fa9072fb09fe04f5514 | [
"self._data = {}\nself._lock = threading.Lock()\nself._max_size = max_size\nself._lru = None\nself._mru = None",
"with self._lock:\n node = self._data.get(key)\n to_return = node.value if node else None\n if node is None:\n node = SimpleLruCache._Node(key, value, None, None)\n node = self._bubb... | <|body_start_0|>
self._data = {}
self._lock = threading.Lock()
self._max_size = max_size
self._lru = None
self._mru = None
<|end_body_0|>
<|body_start_1|>
with self._lock:
node = self._data.get(key)
to_return = node.value if node else None
... | Key/Value local memory cache. with expiration & LRU eviction. LRU double-linked-list format: { 'key1'--------------------------------------------------------------- 'key2'------------------------------------ | 'key3'------------ | | } | | | V V V || MRU || -previous-> || X || ... -previous-> || LRU || -previous-> None ... | SimpleLruCache | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleLruCache:
"""Key/Value local memory cache. with expiration & LRU eviction. LRU double-linked-list format: { 'key1'--------------------------------------------------------------- 'key2'------------------------------------ | 'key3'------------ | | } | | | V V V || MRU || -previous-> || X || .... | stack_v2_sparse_classes_36k_train_000920 | 3,926 | permissive | [
{
"docstring": "Class constructor.",
"name": "__init__",
"signature": "def __init__(self, max_size=DEFAULT_MAX_SIZE)"
},
{
"docstring": "Set an item in the cache and return the previous value. :param key: object key :type args: object :param value: object value :type kwargs: object :return: prev... | 6 | stack_v2_sparse_classes_30k_train_016061 | Implement the Python class `SimpleLruCache` described below.
Class description:
Key/Value local memory cache. with expiration & LRU eviction. LRU double-linked-list format: { 'key1'--------------------------------------------------------------- 'key2'------------------------------------ | 'key3'------------ | | } | | ... | Implement the Python class `SimpleLruCache` described below.
Class description:
Key/Value local memory cache. with expiration & LRU eviction. LRU double-linked-list format: { 'key1'--------------------------------------------------------------- 'key2'------------------------------------ | 'key3'------------ | | } | | ... | 523d2395d39d189772b1db1c944db0cf4ca5769a | <|skeleton|>
class SimpleLruCache:
"""Key/Value local memory cache. with expiration & LRU eviction. LRU double-linked-list format: { 'key1'--------------------------------------------------------------- 'key2'------------------------------------ | 'key3'------------ | | } | | | V V V || MRU || -previous-> || X || .... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleLruCache:
"""Key/Value local memory cache. with expiration & LRU eviction. LRU double-linked-list format: { 'key1'--------------------------------------------------------------- 'key2'------------------------------------ | 'key3'------------ | | } | | | V V V || MRU || -previous-> || X || ... -previous-... | the_stack_v2_python_sparse | splitio/engine/cache/lru.py | splitio/python-client | train | 17 |
a5fdf182d391ef1b00713fabb23dc8abbc048892 | [
"resp = get_model_list_method(*get_method_args, **get_method_kwargs)\nif not resp.ok:\n raise DatasetGeneratorError('Request for list of {0} during data-driven-test setup failed with an HTTP {1} ERROR'.format(model_type_name, resp.status_code))\nif resp.entity is None:\n raise DatasetGeneratorError('Unable to... | <|body_start_0|>
resp = get_model_list_method(*get_method_args, **get_method_kwargs)
if not resp.ok:
raise DatasetGeneratorError('Request for list of {0} during data-driven-test setup failed with an HTTP {1} ERROR'.format(model_type_name, resp.status_code))
if resp.entity is None:
... | Collection of dataset generators and helper methods for developing data driven tests | ModelBasedDatasetToolkit | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelBasedDatasetToolkit:
"""Collection of dataset generators and helper methods for developing data driven tests"""
def _get_model_list(cls, get_model_list_method, model_type_name, *get_method_args, **get_method_kwargs):
"""Gets list of all models in the environment."""
<|bo... | stack_v2_sparse_classes_36k_train_000921 | 3,872 | permissive | [
{
"docstring": "Gets list of all models in the environment.",
"name": "_get_model_list",
"signature": "def _get_model_list(cls, get_model_list_method, model_type_name, *get_method_args, **get_method_kwargs)"
},
{
"docstring": "Filters should be dictionaries with model attributes as keys and list... | 3 | stack_v2_sparse_classes_30k_train_018834 | Implement the Python class `ModelBasedDatasetToolkit` described below.
Class description:
Collection of dataset generators and helper methods for developing data driven tests
Method signatures and docstrings:
- def _get_model_list(cls, get_model_list_method, model_type_name, *get_method_args, **get_method_kwargs): Ge... | Implement the Python class `ModelBasedDatasetToolkit` described below.
Class description:
Collection of dataset generators and helper methods for developing data driven tests
Method signatures and docstrings:
- def _get_model_list(cls, get_model_list_method, model_type_name, *get_method_args, **get_method_kwargs): Ge... | 7d49cf6bfd7e1a6e5b739e7de52f2e18e5ccf924 | <|skeleton|>
class ModelBasedDatasetToolkit:
"""Collection of dataset generators and helper methods for developing data driven tests"""
def _get_model_list(cls, get_model_list_method, model_type_name, *get_method_args, **get_method_kwargs):
"""Gets list of all models in the environment."""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModelBasedDatasetToolkit:
"""Collection of dataset generators and helper methods for developing data driven tests"""
def _get_model_list(cls, get_model_list_method, model_type_name, *get_method_args, **get_method_kwargs):
"""Gets list of all models in the environment."""
resp = get_model_... | the_stack_v2_python_sparse | cloudcafe/common/datasets.py | kurhula/cloudcafe | train | 0 |
828d9763d6e0abdf026eaae73155494c70397ddc | [
"super(self.__class__, self).__init__(parent)\nself.setupUi(self)\nself.treeWidget.setColumnWidth(0, 400)\nrootItem = self.treeWidget.invisibleRootItem()\ndbItem = self.createItem(rootItem, 'database')\nself.createChildrenItems(dbItem, permissionsDict)\nself.treeWidget.sortByColumn(0, QtCore.Qt.AscendingOrder)\nsel... | <|body_start_0|>
super(self.__class__, self).__init__(parent)
self.setupUi(self)
self.treeWidget.setColumnWidth(0, 400)
rootItem = self.treeWidget.invisibleRootItem()
dbItem = self.createItem(rootItem, 'database')
self.createChildrenItems(dbItem, permissionsDict)
... | PermissionProperties | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PermissionProperties:
def __init__(self, permissionsDict, parent=None):
"""Constructor"""
<|body_0|>
def createItem(self, parent, text):
"""Creates a tree widget item parent: parent item text: item text"""
<|body_1|>
def createChildrenItems(self, parent,... | stack_v2_sparse_classes_36k_train_000922 | 3,693 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, permissionsDict, parent=None)"
},
{
"docstring": "Creates a tree widget item parent: parent item text: item text",
"name": "createItem",
"signature": "def createItem(self, parent, text)"
},
{
"docs... | 4 | null | Implement the Python class `PermissionProperties` described below.
Class description:
Implement the PermissionProperties class.
Method signatures and docstrings:
- def __init__(self, permissionsDict, parent=None): Constructor
- def createItem(self, parent, text): Creates a tree widget item parent: parent item text: i... | Implement the Python class `PermissionProperties` described below.
Class description:
Implement the PermissionProperties class.
Method signatures and docstrings:
- def __init__(self, permissionsDict, parent=None): Constructor
- def createItem(self, parent, text): Creates a tree widget item parent: parent item text: i... | edff378f356db3c0577ce34e618c5ae493d296ba | <|skeleton|>
class PermissionProperties:
def __init__(self, permissionsDict, parent=None):
"""Constructor"""
<|body_0|>
def createItem(self, parent, text):
"""Creates a tree widget item parent: parent item text: item text"""
<|body_1|>
def createChildrenItems(self, parent,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PermissionProperties:
def __init__(self, permissionsDict, parent=None):
"""Constructor"""
super(self.__class__, self).__init__(parent)
self.setupUi(self)
self.treeWidget.setColumnWidth(0, 400)
rootItem = self.treeWidget.invisibleRootItem()
dbItem = self.createIt... | the_stack_v2_python_sparse | UserTools/permission_properties.py | euriconicacio/DsgTools | train | 0 | |
b1d87e445e67c953078d22c2f059a49f67b39851 | [
"\"\"\":field\n The name of the visual material associated with this cloth material.\n \"\"\"\nself.visual_material: str = visual_material\n':field\\n The texture scale of the visual material.\\n '\nself.texture_scale: Dict[str, float] = texture_scale\n':field\\n The smoothness va... | <|body_start_0|>
""":field
The name of the visual material associated with this cloth material.
"""
self.visual_material: str = visual_material
':field\n The texture scale of the visual material.\n '
self.texture_scale: Dict[str, float] = tex... | An Obi cloth material. For more information, [read this](http://obi.virtualmethodstudio.com/tutorials/clothsetup.html). | ClothMaterial | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClothMaterial:
"""An Obi cloth material. For more information, [read this](http://obi.virtualmethodstudio.com/tutorials/clothsetup.html)."""
def __init__(self, visual_material: str, texture_scale: Dict[str, float], visual_smoothness: float=0, stretching_scale: float=1.0, stretch_compliance: ... | stack_v2_sparse_classes_36k_train_000923 | 4,062 | permissive | [
{
"docstring": ":param visual_material: The name of the visual material associated with this cloth material. :param texture_scale: The texture scale of the visual material. :param visual_smoothness: The smoothness value of the visual material. :param stretching_scale: The scale factor for the rest length of eac... | 2 | stack_v2_sparse_classes_30k_train_000282 | Implement the Python class `ClothMaterial` described below.
Class description:
An Obi cloth material. For more information, [read this](http://obi.virtualmethodstudio.com/tutorials/clothsetup.html).
Method signatures and docstrings:
- def __init__(self, visual_material: str, texture_scale: Dict[str, float], visual_sm... | Implement the Python class `ClothMaterial` described below.
Class description:
An Obi cloth material. For more information, [read this](http://obi.virtualmethodstudio.com/tutorials/clothsetup.html).
Method signatures and docstrings:
- def __init__(self, visual_material: str, texture_scale: Dict[str, float], visual_sm... | 9df96fba455b327bb360d8dd5886d8754046c690 | <|skeleton|>
class ClothMaterial:
"""An Obi cloth material. For more information, [read this](http://obi.virtualmethodstudio.com/tutorials/clothsetup.html)."""
def __init__(self, visual_material: str, texture_scale: Dict[str, float], visual_smoothness: float=0, stretching_scale: float=1.0, stretch_compliance: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClothMaterial:
"""An Obi cloth material. For more information, [read this](http://obi.virtualmethodstudio.com/tutorials/clothsetup.html)."""
def __init__(self, visual_material: str, texture_scale: Dict[str, float], visual_smoothness: float=0, stretching_scale: float=1.0, stretch_compliance: float=0, max_... | the_stack_v2_python_sparse | Python/tdw/obi_data/cloth/cloth_material.py | threedworld-mit/tdw | train | 427 |
d2a6df517d92fb32c42df662b7c251d550d5176b | [
"_LOGGER.debug('KiraRemote device init started for: %s', name)\nself._attr_name = name\nself._kira = kira",
"for single_command in command:\n code_tuple = (single_command, kwargs.get(remote.ATTR_DEVICE))\n _LOGGER.info('Sending Command: %s to %s', *code_tuple)\n self._kira.sendCode(code_tuple)"
] | <|body_start_0|>
_LOGGER.debug('KiraRemote device init started for: %s', name)
self._attr_name = name
self._kira = kira
<|end_body_0|>
<|body_start_1|>
for single_command in command:
code_tuple = (single_command, kwargs.get(remote.ATTR_DEVICE))
_LOGGER.info('Send... | Remote representation used to send commands to a Kira device. | KiraRemote | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KiraRemote:
"""Remote representation used to send commands to a Kira device."""
def __init__(self, name, kira):
"""Initialize KiraRemote class."""
<|body_0|>
def send_command(self, command: Iterable[str], **kwargs: Any) -> None:
"""Send a command to one device.""... | stack_v2_sparse_classes_36k_train_000924 | 1,615 | permissive | [
{
"docstring": "Initialize KiraRemote class.",
"name": "__init__",
"signature": "def __init__(self, name, kira)"
},
{
"docstring": "Send a command to one device.",
"name": "send_command",
"signature": "def send_command(self, command: Iterable[str], **kwargs: Any) -> None"
}
] | 2 | null | Implement the Python class `KiraRemote` described below.
Class description:
Remote representation used to send commands to a Kira device.
Method signatures and docstrings:
- def __init__(self, name, kira): Initialize KiraRemote class.
- def send_command(self, command: Iterable[str], **kwargs: Any) -> None: Send a com... | Implement the Python class `KiraRemote` described below.
Class description:
Remote representation used to send commands to a Kira device.
Method signatures and docstrings:
- def __init__(self, name, kira): Initialize KiraRemote class.
- def send_command(self, command: Iterable[str], **kwargs: Any) -> None: Send a com... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class KiraRemote:
"""Remote representation used to send commands to a Kira device."""
def __init__(self, name, kira):
"""Initialize KiraRemote class."""
<|body_0|>
def send_command(self, command: Iterable[str], **kwargs: Any) -> None:
"""Send a command to one device.""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KiraRemote:
"""Remote representation used to send commands to a Kira device."""
def __init__(self, name, kira):
"""Initialize KiraRemote class."""
_LOGGER.debug('KiraRemote device init started for: %s', name)
self._attr_name = name
self._kira = kira
def send_command(s... | the_stack_v2_python_sparse | homeassistant/components/kira/remote.py | home-assistant/core | train | 35,501 |
aa535cf73827caf07d6571a183251aab0b26f916 | [
"Log().info('======开始考前资料核查======')\nsleep(2)\nself.find_element(*self.Editer).click()\nsleep(2)\nself.switch_to_frame(self.find_element(*self.iframe1))\nself.implicity_wait(5)\nLog().info('======审核附件======')\nself.find_element(*self.AdoptOne).click()\nsleep(2)\nself.find_element(*self.AdoptTwo).click()\nsleep(2)\n... | <|body_start_0|>
Log().info('======开始考前资料核查======')
sleep(2)
self.find_element(*self.Editer).click()
sleep(2)
self.switch_to_frame(self.find_element(*self.iframe1))
self.implicity_wait(5)
Log().info('======审核附件======')
self.find_element(*self.AdoptOne).cli... | 考前资料核查页面 | AdultCheck | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdultCheck:
"""考前资料核查页面"""
def type_audit_data(self):
"""考前资料核查通过"""
<|body_0|>
def type_audit_pass(self):
"""审核通过断言"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
Log().info('======开始考前资料核查======')
sleep(2)
self.find_element(*s... | stack_v2_sparse_classes_36k_train_000925 | 1,886 | no_license | [
{
"docstring": "考前资料核查通过",
"name": "type_audit_data",
"signature": "def type_audit_data(self)"
},
{
"docstring": "审核通过断言",
"name": "type_audit_pass",
"signature": "def type_audit_pass(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014036 | Implement the Python class `AdultCheck` described below.
Class description:
考前资料核查页面
Method signatures and docstrings:
- def type_audit_data(self): 考前资料核查通过
- def type_audit_pass(self): 审核通过断言 | Implement the Python class `AdultCheck` described below.
Class description:
考前资料核查页面
Method signatures and docstrings:
- def type_audit_data(self): 考前资料核查通过
- def type_audit_pass(self): 审核通过断言
<|skeleton|>
class AdultCheck:
"""考前资料核查页面"""
def type_audit_data(self):
"""考前资料核查通过"""
<|body_0|>
... | 08d7fab053f6797016d827396fc7b48a3eba9b6e | <|skeleton|>
class AdultCheck:
"""考前资料核查页面"""
def type_audit_data(self):
"""考前资料核查通过"""
<|body_0|>
def type_audit_pass(self):
"""审核通过断言"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdultCheck:
"""考前资料核查页面"""
def type_audit_data(self):
"""考前资料核查通过"""
Log().info('======开始考前资料核查======')
sleep(2)
self.find_element(*self.Editer).click()
sleep(2)
self.switch_to_frame(self.find_element(*self.iframe1))
self.implicity_wait(5)
L... | the_stack_v2_python_sparse | YZ_AutoTest_Project/Website/test_case/page_object/AdultCheckFilePage.py | MikeDarkCloud/YZ | train | 0 |
7188f4eb39c5c7021e91919d10da159d2f547c47 | [
"kw = super(EventTaskView, self).get_form_kwargs()\nkw.update({'organization': self.request.user.organization})\nreturn kw",
"context = super(EventTaskView, self).get_context_data(**kwargs)\nevent = get_object_or_404(Event, id=self.kwargs['pk'])\ntasks = event.task_set.all()\ncount = tasks.count()\nidentified_ct ... | <|body_start_0|>
kw = super(EventTaskView, self).get_form_kwargs()
kw.update({'organization': self.request.user.organization})
return kw
<|end_body_0|>
<|body_start_1|>
context = super(EventTaskView, self).get_context_data(**kwargs)
event = get_object_or_404(Event, id=self.kwarg... | Create event task. | EventTaskView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventTaskView:
"""Create event task."""
def get_form_kwargs(self):
"""Pass organization to form."""
<|body_0|>
def get_context_data(self, **kwargs):
"""Return tasks belonging to the story."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
kw = sup... | stack_v2_sparse_classes_36k_train_000926 | 11,257 | permissive | [
{
"docstring": "Pass organization to form.",
"name": "get_form_kwargs",
"signature": "def get_form_kwargs(self)"
},
{
"docstring": "Return tasks belonging to the story.",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010602 | Implement the Python class `EventTaskView` described below.
Class description:
Create event task.
Method signatures and docstrings:
- def get_form_kwargs(self): Pass organization to form.
- def get_context_data(self, **kwargs): Return tasks belonging to the story. | Implement the Python class `EventTaskView` described below.
Class description:
Create event task.
Method signatures and docstrings:
- def get_form_kwargs(self): Pass organization to form.
- def get_context_data(self, **kwargs): Return tasks belonging to the story.
<|skeleton|>
class EventTaskView:
"""Create even... | dc6bc79d450f7e2bdf59cfbcd306d05a736e4db9 | <|skeleton|>
class EventTaskView:
"""Create event task."""
def get_form_kwargs(self):
"""Pass organization to form."""
<|body_0|>
def get_context_data(self, **kwargs):
"""Return tasks belonging to the story."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventTaskView:
"""Create event task."""
def get_form_kwargs(self):
"""Pass organization to form."""
kw = super(EventTaskView, self).get_form_kwargs()
kw.update({'organization': self.request.user.organization})
return kw
def get_context_data(self, **kwargs):
""... | the_stack_v2_python_sparse | project/editorial/views/tasks.py | ProjectFacet/facet | train | 25 |
c1156d2c7b020d8cdd232c935b449d22ab617acd | [
"domain = self._client.create(domain_name)\nif check:\n self.check_domain_presence(domain)\nreturn domain",
"self._client.update(domain, enabled=False)\nself._client.delete(domain.id)\nif check:\n self.check_domain_presence(domain, present=False)",
"def predicate():\n try:\n self._client.get(dom... | <|body_start_0|>
domain = self._client.create(domain_name)
if check:
self.check_domain_presence(domain)
return domain
<|end_body_0|>
<|body_start_1|>
self._client.update(domain, enabled=False)
self._client.delete(domain.id)
if check:
self.check_do... | Domain steps. | DomainSteps | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DomainSteps:
"""Domain steps."""
def create_domain(self, domain_name, check=True):
"""Step to create domain."""
<|body_0|>
def delete_domain(self, domain, check=True):
"""Step to delete domain."""
<|body_1|>
def check_domain_presence(self, domain, pr... | stack_v2_sparse_classes_36k_train_000927 | 1,679 | no_license | [
{
"docstring": "Step to create domain.",
"name": "create_domain",
"signature": "def create_domain(self, domain_name, check=True)"
},
{
"docstring": "Step to delete domain.",
"name": "delete_domain",
"signature": "def delete_domain(self, domain, check=True)"
},
{
"docstring": "Ver... | 3 | stack_v2_sparse_classes_30k_train_015559 | Implement the Python class `DomainSteps` described below.
Class description:
Domain steps.
Method signatures and docstrings:
- def create_domain(self, domain_name, check=True): Step to create domain.
- def delete_domain(self, domain, check=True): Step to delete domain.
- def check_domain_presence(self, domain, presen... | Implement the Python class `DomainSteps` described below.
Class description:
Domain steps.
Method signatures and docstrings:
- def create_domain(self, domain_name, check=True): Step to create domain.
- def delete_domain(self, domain, check=True): Step to delete domain.
- def check_domain_presence(self, domain, presen... | 380849c3f24d3322caa49f895d0d89cc6c998fa8 | <|skeleton|>
class DomainSteps:
"""Domain steps."""
def create_domain(self, domain_name, check=True):
"""Step to create domain."""
<|body_0|>
def delete_domain(self, domain, check=True):
"""Step to delete domain."""
<|body_1|>
def check_domain_presence(self, domain, pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DomainSteps:
"""Domain steps."""
def create_domain(self, domain_name, check=True):
"""Step to create domain."""
domain = self._client.create(domain_name)
if check:
self.check_domain_presence(domain)
return domain
def delete_domain(self, domain, check=True)... | the_stack_v2_python_sparse | stepler/keystone/steps/domains.py | gdyuldin/stepler | train | 0 |
e1c99043eed4bc38ecd1e976655d50a3978eb1f8 | [
"if not field_type:\n self.field_type = 'BYTE'\n self.field_name = ''\nelse:\n self.field_type = field_type\n self.field_name = field_name\nself.array_size = array_size\nself.selector_value = selector\nself.conditional_value = conditional_value\nself.run_time_size = run_time_size",
"if self.array_size... | <|body_start_0|>
if not field_type:
self.field_type = 'BYTE'
self.field_name = ''
else:
self.field_type = field_type
self.field_name = field_name
self.array_size = array_size
self.selector_value = selector
self.conditional_value = c... | Represents a field in TPM structure or union. This object is used in several not fully overlapping cases, not all attributes apply to all use cases. The 'array_size' and 'run_time_size' attributes below are related to the following code example: struct { int size; byte array[MAX_SIZE] } object. In this structure the ac... | Field | [
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-tcg-spec-license-v1"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Field:
"""Represents a field in TPM structure or union. This object is used in several not fully overlapping cases, not all attributes apply to all use cases. The 'array_size' and 'run_time_size' attributes below are related to the following code example: struct { int size; byte array[MAX_SIZE] }... | stack_v2_sparse_classes_36k_train_000928 | 48,677 | permissive | [
{
"docstring": "Initializes a Field instance. Args: field_type: Initial value for the field type attribute. field_name: Initial value for the field name attribute. selector: Initial value for the selector attribute. array_size: Initial value for the array_size attribute. conditional_value: Initial value of the ... | 3 | null | Implement the Python class `Field` described below.
Class description:
Represents a field in TPM structure or union. This object is used in several not fully overlapping cases, not all attributes apply to all use cases. The 'array_size' and 'run_time_size' attributes below are related to the following code example: st... | Implement the Python class `Field` described below.
Class description:
Represents a field in TPM structure or union. This object is used in several not fully overlapping cases, not all attributes apply to all use cases. The 'array_size' and 'run_time_size' attributes below are related to the following code example: st... | e2745b756317aac3c7a27a4c10bdfe0921a82a1c | <|skeleton|>
class Field:
"""Represents a field in TPM structure or union. This object is used in several not fully overlapping cases, not all attributes apply to all use cases. The 'array_size' and 'run_time_size' attributes below are related to the following code example: struct { int size; byte array[MAX_SIZE] }... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Field:
"""Represents a field in TPM structure or union. This object is used in several not fully overlapping cases, not all attributes apply to all use cases. The 'array_size' and 'run_time_size' attributes below are related to the following code example: struct { int size; byte array[MAX_SIZE] } object. In t... | the_stack_v2_python_sparse | app/src/main/java/com/syd/source/aosp/external/tpm2/generator/structure_generator.py | lz-purple/Source | train | 4 |
d1c49b88d4243adf071d4c9c93321227ac8afc30 | [
"encoder_output, extra = encoder_state\nextra_output, extra_mask, *_ = extra\nreturn super().forward(input, encoder_output, incr_state, extra_output=extra_output, extra_mask=extra_mask, **kwargs)",
"new_incr_state = {}\nif getattr(self.layers, 'is_model_parallel', False):\n tensor, new_incr_state = self._apply... | <|body_start_0|>
encoder_output, extra = encoder_state
extra_output, extra_mask, *_ = extra
return super().forward(input, encoder_output, incr_state, extra_output=extra_output, extra_mask=extra_mask, **kwargs)
<|end_body_0|>
<|body_start_1|>
new_incr_state = {}
if getattr(self.l... | TransformerExpandedDecoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransformerExpandedDecoder:
def forward(self, input: torch.Tensor, encoder_state, incr_state: Optional[DecoderIncrState]=None, **kwargs) -> Tuple[torch.Tensor, DecoderIncrState]:
"""Override TD.Forward to include extra encoder outputs."""
<|body_0|>
def forward_layers(self, ... | stack_v2_sparse_classes_36k_train_000929 | 43,118 | permissive | [
{
"docstring": "Override TD.Forward to include extra encoder outputs.",
"name": "forward",
"signature": "def forward(self, input: torch.Tensor, encoder_state, incr_state: Optional[DecoderIncrState]=None, **kwargs) -> Tuple[torch.Tensor, DecoderIncrState]"
},
{
"docstring": "Override to pass more... | 3 | null | Implement the Python class `TransformerExpandedDecoder` described below.
Class description:
Implement the TransformerExpandedDecoder class.
Method signatures and docstrings:
- def forward(self, input: torch.Tensor, encoder_state, incr_state: Optional[DecoderIncrState]=None, **kwargs) -> Tuple[torch.Tensor, DecoderInc... | Implement the Python class `TransformerExpandedDecoder` described below.
Class description:
Implement the TransformerExpandedDecoder class.
Method signatures and docstrings:
- def forward(self, input: torch.Tensor, encoder_state, incr_state: Optional[DecoderIncrState]=None, **kwargs) -> Tuple[torch.Tensor, DecoderInc... | e1d899edfb92471552bae153f59ad30aa7fca468 | <|skeleton|>
class TransformerExpandedDecoder:
def forward(self, input: torch.Tensor, encoder_state, incr_state: Optional[DecoderIncrState]=None, **kwargs) -> Tuple[torch.Tensor, DecoderIncrState]:
"""Override TD.Forward to include extra encoder outputs."""
<|body_0|>
def forward_layers(self, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TransformerExpandedDecoder:
def forward(self, input: torch.Tensor, encoder_state, incr_state: Optional[DecoderIncrState]=None, **kwargs) -> Tuple[torch.Tensor, DecoderIncrState]:
"""Override TD.Forward to include extra encoder outputs."""
encoder_output, extra = encoder_state
extra_out... | the_stack_v2_python_sparse | projects/light_whoami/agents/expanded_attention.py | facebookresearch/ParlAI | train | 10,943 | |
43a73ef0257f028502cbd0db1109cb91a9c7a96b | [
"node = Node(value)\nif not self.root:\n self.root = node\n return\n\ndef walk(root, new_node):\n if not root:\n return\n if new_node.value < root.value:\n if not root.left:\n root.left = new_node\n else:\n walk(root.left, new_node)\n elif not root.right:\n ... | <|body_start_0|>
node = Node(value)
if not self.root:
self.root = node
return
def walk(root, new_node):
if not root:
return
if new_node.value < root.value:
if not root.left:
root.left = new_node
... | BinarySearchTree | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinarySearchTree:
def add(self, value):
"""This will add a new element to the tree, based on a tradtional binary search tree condtional. If value is smaller than the root it will be added to the left, else add to the right."""
<|body_0|>
def contains(self, value):
""... | stack_v2_sparse_classes_36k_train_000930 | 3,622 | permissive | [
{
"docstring": "This will add a new element to the tree, based on a tradtional binary search tree condtional. If value is smaller than the root it will be added to the left, else add to the right.",
"name": "add",
"signature": "def add(self, value)"
},
{
"docstring": "This searches the tree in o... | 2 | stack_v2_sparse_classes_30k_test_000939 | Implement the Python class `BinarySearchTree` described below.
Class description:
Implement the BinarySearchTree class.
Method signatures and docstrings:
- def add(self, value): This will add a new element to the tree, based on a tradtional binary search tree condtional. If value is smaller than the root it will be a... | Implement the Python class `BinarySearchTree` described below.
Class description:
Implement the BinarySearchTree class.
Method signatures and docstrings:
- def add(self, value): This will add a new element to the tree, based on a tradtional binary search tree condtional. If value is smaller than the root it will be a... | b11b5ef50f52e3d505474fe5fffe4357933da251 | <|skeleton|>
class BinarySearchTree:
def add(self, value):
"""This will add a new element to the tree, based on a tradtional binary search tree condtional. If value is smaller than the root it will be added to the left, else add to the right."""
<|body_0|>
def contains(self, value):
""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BinarySearchTree:
def add(self, value):
"""This will add a new element to the tree, based on a tradtional binary search tree condtional. If value is smaller than the root it will be added to the left, else add to the right."""
node = Node(value)
if not self.root:
self.root ... | the_stack_v2_python_sparse | dsa/data_structures/tree/tree.py | 401-python-joseph-zabaleta/401-python-data-structures-and-algorithms | train | 0 | |
14c4bc9375274d83f0d9cdd9799505ba24934674 | [
"assert isinstance(return_tensor, bool)\nassert isinstance(channel_first, bool)\nself.return_tensor = return_tensor\nself.channel_first = channel_first",
"assert isinstance(frames, (np.ndarray, torch.Tensor)), 'Array must be a numpy.ndarray or torch.Tensor instance.'\nassert len(frames) > 0, 'Array must contain a... | <|body_start_0|>
assert isinstance(return_tensor, bool)
assert isinstance(channel_first, bool)
self.return_tensor = return_tensor
self.channel_first = channel_first
<|end_body_0|>
<|body_start_1|>
assert isinstance(frames, (np.ndarray, torch.Tensor)), 'Array must be a numpy.ndar... | Normalizes the color dimension, on a collection of images, to have zero mean and unit standard deviation. | BatchNormalizeRGB | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BatchNormalizeRGB:
"""Normalizes the color dimension, on a collection of images, to have zero mean and unit standard deviation."""
def __init__(self, return_tensor=True, channel_first=True):
"""Instantiates a new NormalizeRGB object. Parameters ---------- return_tensor : {True, False... | stack_v2_sparse_classes_36k_train_000931 | 14,169 | no_license | [
{
"docstring": "Instantiates a new NormalizeRGB object. Parameters ---------- return_tensor : {True, False}, bool, optional If True then the output is returned as a torch.Tensor instance, by default True. Otherwise the output is returned as a numpy.ndarray instance. channel_first : {True, False}, bool, optional... | 2 | stack_v2_sparse_classes_30k_train_013765 | Implement the Python class `BatchNormalizeRGB` described below.
Class description:
Normalizes the color dimension, on a collection of images, to have zero mean and unit standard deviation.
Method signatures and docstrings:
- def __init__(self, return_tensor=True, channel_first=True): Instantiates a new NormalizeRGB o... | Implement the Python class `BatchNormalizeRGB` described below.
Class description:
Normalizes the color dimension, on a collection of images, to have zero mean and unit standard deviation.
Method signatures and docstrings:
- def __init__(self, return_tensor=True, channel_first=True): Instantiates a new NormalizeRGB o... | a7c30481822ecb945e3ff6ad184d104361a40ed1 | <|skeleton|>
class BatchNormalizeRGB:
"""Normalizes the color dimension, on a collection of images, to have zero mean and unit standard deviation."""
def __init__(self, return_tensor=True, channel_first=True):
"""Instantiates a new NormalizeRGB object. Parameters ---------- return_tensor : {True, False... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BatchNormalizeRGB:
"""Normalizes the color dimension, on a collection of images, to have zero mean and unit standard deviation."""
def __init__(self, return_tensor=True, channel_first=True):
"""Instantiates a new NormalizeRGB object. Parameters ---------- return_tensor : {True, False}, bool, opti... | the_stack_v2_python_sparse | cheapfake/contrib/transforms.py | hu-simon/cheapfake | train | 0 |
3b391a9388ddcb7cf3f8a579b9c93da096767dd6 | [
"try:\n super(UserManagementVmmSide, self).__init__(guest_obj, args)\n self.logger.info('function: UserManagementVmmSide::__init__')\n self.window_id = None\nexcept Exception as e:\n raise Exception(lineno() + ' Error: UserManagementHostSide::__init__ ' + self.guest_obj.guestname + ' ' + str(e))",
"tr... | <|body_start_0|>
try:
super(UserManagementVmmSide, self).__init__(guest_obj, args)
self.logger.info('function: UserManagementVmmSide::__init__')
self.window_id = None
except Exception as e:
raise Exception(lineno() + ' Error: UserManagementHostSide::__init... | This class is a remote control on the host-side to control a real <userManagement> running on a guest. | UserManagementVmmSide | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserManagementVmmSide:
"""This class is a remote control on the host-side to control a real <userManagement> running on a guest."""
def __init__(self, guest_obj, args):
"""Set default attribute values only. @param guest_obj: The guest on which this application is running. (will be in... | stack_v2_sparse_classes_36k_train_000932 | 13,147 | no_license | [
{
"docstring": "Set default attribute values only. @param guest_obj: The guest on which this application is running. (will be inserted from guest::application()) @param args: containing logger: Logger name for logging.",
"name": "__init__",
"signature": "def __init__(self, guest_obj, args)"
},
{
... | 4 | stack_v2_sparse_classes_30k_train_009299 | Implement the Python class `UserManagementVmmSide` described below.
Class description:
This class is a remote control on the host-side to control a real <userManagement> running on a guest.
Method signatures and docstrings:
- def __init__(self, guest_obj, args): Set default attribute values only. @param guest_obj: Th... | Implement the Python class `UserManagementVmmSide` described below.
Class description:
This class is a remote control on the host-side to control a real <userManagement> running on a guest.
Method signatures and docstrings:
- def __init__(self, guest_obj, args): Set default attribute values only. @param guest_obj: Th... | 925ff53eb0c7a750ae784e3a2c059ed5e8b140e3 | <|skeleton|>
class UserManagementVmmSide:
"""This class is a remote control on the host-side to control a real <userManagement> running on a guest."""
def __init__(self, guest_obj, args):
"""Set default attribute values only. @param guest_obj: The guest on which this application is running. (will be in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserManagementVmmSide:
"""This class is a remote control on the host-side to control a real <userManagement> running on a guest."""
def __init__(self, guest_obj, args):
"""Set default attribute values only. @param guest_obj: The guest on which this application is running. (will be inserted from g... | the_stack_v2_python_sparse | install_tools/build/lib.linux-x86_64-2.7/hystck/application/userManagement.py | dasec/hystck | train | 5 |
6f78ce95b339a5ffc2f02ac2baa1760da9c3a903 | [
"self.opt_filename = None\nself.scanner = None\nself.scan_source = None\nself.scan_mode = None\nself.is_multi_scan = False\nself.is_preview = False\nself.page_size = None\nself.scan_area = None\nself.scan_dir = None\nself.scan_filename = None\nself.scan_filetype = None\nself.depth = None\nself.ext_scan_cmd = None\n... | <|body_start_0|>
self.opt_filename = None
self.scanner = None
self.scan_source = None
self.scan_mode = None
self.is_multi_scan = False
self.is_preview = False
self.page_size = None
self.scan_area = None
self.scan_dir = None
self.scan_filena... | Scan options management class. | iqScanOptions | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class iqScanOptions:
"""Scan options management class."""
def __init__(self):
"""Constructor."""
<|body_0|>
def genOptFileName(self):
"""Generating a parameter file name."""
<|body_1|>
def loadOptions(self, filename=None):
"""Load scan settings fro... | stack_v2_sparse_classes_36k_train_000933 | 28,795 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Generating a parameter file name.",
"name": "genOptFileName",
"signature": "def genOptFileName(self)"
},
{
"docstring": "Load scan settings from the configuration file. :param ... | 5 | null | Implement the Python class `iqScanOptions` described below.
Class description:
Scan options management class.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def genOptFileName(self): Generating a parameter file name.
- def loadOptions(self, filename=None): Load scan settings from the configura... | Implement the Python class `iqScanOptions` described below.
Class description:
Scan options management class.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def genOptFileName(self): Generating a parameter file name.
- def loadOptions(self, filename=None): Load scan settings from the configura... | 7550e242746cb2fb1219474463f8db21f8e3e114 | <|skeleton|>
class iqScanOptions:
"""Scan options management class."""
def __init__(self):
"""Constructor."""
<|body_0|>
def genOptFileName(self):
"""Generating a parameter file name."""
<|body_1|>
def loadOptions(self, filename=None):
"""Load scan settings fro... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class iqScanOptions:
"""Scan options management class."""
def __init__(self):
"""Constructor."""
self.opt_filename = None
self.scanner = None
self.scan_source = None
self.scan_mode = None
self.is_multi_scan = False
self.is_preview = False
self.pag... | the_stack_v2_python_sparse | iq_scanner/scanner/scanner_dlg.py | XHermitOne/iq_framework | train | 1 |
9192d05768c17a08011946c8b55794d195f22761 | [
"self.root = Node()\nfor i, word in enumerate(words):\n longw = word + '#' + word\n for j in range(len(word)):\n cur = self.root\n cur.index = i\n for c in longw[j:]:\n cur = cur[c]\n cur.index = i",
"word = suffix + '#' + prefix\ncur = self.root\nfor c in word:\n ... | <|body_start_0|>
self.root = Node()
for i, word in enumerate(words):
longw = word + '#' + word
for j in range(len(word)):
cur = self.root
cur.index = i
for c in longw[j:]:
cur = cur[c]
cur.ind... | WordFilter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordFilter:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def f(self, prefix, suffix):
""":type prefix: str :type suffix: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.root = Node()
for i, word in ... | stack_v2_sparse_classes_36k_train_000934 | 2,614 | no_license | [
{
"docstring": ":type words: List[str]",
"name": "__init__",
"signature": "def __init__(self, words)"
},
{
"docstring": ":type prefix: str :type suffix: str :rtype: int",
"name": "f",
"signature": "def f(self, prefix, suffix)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002244 | Implement the Python class `WordFilter` described below.
Class description:
Implement the WordFilter class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def f(self, prefix, suffix): :type prefix: str :type suffix: str :rtype: int | Implement the Python class `WordFilter` described below.
Class description:
Implement the WordFilter class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def f(self, prefix, suffix): :type prefix: str :type suffix: str :rtype: int
<|skeleton|>
class WordFilter:
def __in... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class WordFilter:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def f(self, prefix, suffix):
""":type prefix: str :type suffix: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordFilter:
def __init__(self, words):
""":type words: List[str]"""
self.root = Node()
for i, word in enumerate(words):
longw = word + '#' + word
for j in range(len(word)):
cur = self.root
cur.index = i
for c in lo... | the_stack_v2_python_sparse | P/PrefixandSuffixSearch.py | bssrdf/pyleet | train | 2 | |
c3e70a97b5ff3475617ea5fb2bc0534b12341de2 | [
"kwc = kwargs.copy()\nAbstractBase.__init__(self, **kwc)\nself._identifier = identifier\nself.label = label or ''\nself.description = description or ''",
"if self._identifier is None:\n self._identifier = create_guid()\nreturn self._identifier"
] | <|body_start_0|>
kwc = kwargs.copy()
AbstractBase.__init__(self, **kwc)
self._identifier = identifier
self.label = label or ''
self.description = description or ''
<|end_body_0|>
<|body_start_1|>
if self._identifier is None:
self._identifier = create_guid()
... | Base identifiable class for all coverage model objects Provides identifier, label and description attributes | AbstractIdentifiable | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AbstractIdentifiable:
"""Base identifiable class for all coverage model objects Provides identifier, label and description attributes"""
def __init__(self, identifier=None, label=None, description=None, **kwargs):
"""Construct a new AbstractIdentifiable object @param identifier The U... | stack_v2_sparse_classes_36k_train_000935 | 19,016 | no_license | [
{
"docstring": "Construct a new AbstractIdentifiable object @param identifier The UUID of this 'instance' @param label The short description of the object @param description The full description of the object @param **kwargs Additional keyword arguments are copied and the copy is passed up to AbstractBase; see ... | 2 | stack_v2_sparse_classes_30k_train_008554 | Implement the Python class `AbstractIdentifiable` described below.
Class description:
Base identifiable class for all coverage model objects Provides identifier, label and description attributes
Method signatures and docstrings:
- def __init__(self, identifier=None, label=None, description=None, **kwargs): Construct ... | Implement the Python class `AbstractIdentifiable` described below.
Class description:
Base identifiable class for all coverage model objects Provides identifier, label and description attributes
Method signatures and docstrings:
- def __init__(self, identifier=None, label=None, description=None, **kwargs): Construct ... | 9047b834c645d558e5454843dac26cd8bc971b4a | <|skeleton|>
class AbstractIdentifiable:
"""Base identifiable class for all coverage model objects Provides identifier, label and description attributes"""
def __init__(self, identifier=None, label=None, description=None, **kwargs):
"""Construct a new AbstractIdentifiable object @param identifier The U... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AbstractIdentifiable:
"""Base identifiable class for all coverage model objects Provides identifier, label and description attributes"""
def __init__(self, identifier=None, label=None, description=None, **kwargs):
"""Construct a new AbstractIdentifiable object @param identifier The UUID of this '... | the_stack_v2_python_sparse | coverage_model/basic_types.py | caseybryant/coverage-model | train | 0 |
57be333b030d1e339f49d4f8fdf063bd3f4587fc | [
"super(UserClearBouncing, self).AssertBasePermission(mr)\nif mr.auth.user_id == mr.viewed_user_auth.user_id:\n return\nif mr.perms.HasPerm(permissions.EDIT_OTHER_USERS, None, None):\n return\nraise permissions.PermissionException('You cannot edit this user.')",
"viewed_user = mr.viewed_user_auth.user_pb\nif... | <|body_start_0|>
super(UserClearBouncing, self).AssertBasePermission(mr)
if mr.auth.user_id == mr.viewed_user_auth.user_id:
return
if mr.perms.HasPerm(permissions.EDIT_OTHER_USERS, None, None):
return
raise permissions.PermissionException('You cannot edit this use... | Shows a page that can clear a user's bouncing email timestamp. | UserClearBouncing | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserClearBouncing:
"""Shows a page that can clear a user's bouncing email timestamp."""
def AssertBasePermission(self, mr):
"""Check whether the user has any permission to visit this page. Args: mr: commonly used info parsed from the request."""
<|body_0|>
def GatherPage... | stack_v2_sparse_classes_36k_train_000936 | 2,175 | permissive | [
{
"docstring": "Check whether the user has any permission to visit this page. Args: mr: commonly used info parsed from the request.",
"name": "AssertBasePermission",
"signature": "def AssertBasePermission(self, mr)"
},
{
"docstring": "Build up a dictionary of data values to use when rendering th... | 3 | null | Implement the Python class `UserClearBouncing` described below.
Class description:
Shows a page that can clear a user's bouncing email timestamp.
Method signatures and docstrings:
- def AssertBasePermission(self, mr): Check whether the user has any permission to visit this page. Args: mr: commonly used info parsed fr... | Implement the Python class `UserClearBouncing` described below.
Class description:
Shows a page that can clear a user's bouncing email timestamp.
Method signatures and docstrings:
- def AssertBasePermission(self, mr): Check whether the user has any permission to visit this page. Args: mr: commonly used info parsed fr... | b5d4783f99461438ca9e6a477535617fadab6ba3 | <|skeleton|>
class UserClearBouncing:
"""Shows a page that can clear a user's bouncing email timestamp."""
def AssertBasePermission(self, mr):
"""Check whether the user has any permission to visit this page. Args: mr: commonly used info parsed from the request."""
<|body_0|>
def GatherPage... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserClearBouncing:
"""Shows a page that can clear a user's bouncing email timestamp."""
def AssertBasePermission(self, mr):
"""Check whether the user has any permission to visit this page. Args: mr: commonly used info parsed from the request."""
super(UserClearBouncing, self).AssertBasePe... | the_stack_v2_python_sparse | appengine/monorail/sitewide/userclearbouncing.py | xinghun61/infra | train | 2 |
ec8088077279899aaa232eaaed0696e8faeaa525 | [
"super(CustomSchedule, self).__init__()\nself.d_model = model\nself.d_model = tf.cast(self.d_model, tf.float32)\nself.warmup_steps = warmup_steps",
"p1 = tf.math.rsqrt(step)\np2 = step * self.warmup_steps ** (-1.5)\noutput = tf.math.rsqrt(self.d_model) * tf.math.minimum(p1, p2)\nreturn output"
] | <|body_start_0|>
super(CustomSchedule, self).__init__()
self.d_model = model
self.d_model = tf.cast(self.d_model, tf.float32)
self.warmup_steps = warmup_steps
<|end_body_0|>
<|body_start_1|>
p1 = tf.math.rsqrt(step)
p2 = step * self.warmup_steps ** (-1.5)
output ... | Custom Schedule class | CustomSchedule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomSchedule:
"""Custom Schedule class"""
def __init__(self, model, warmup_steps=4000):
"""Class Constructor"""
<|body_0|>
def __call__(self, step):
"""Method call"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(CustomSchedule, self).__i... | stack_v2_sparse_classes_36k_train_000937 | 4,824 | no_license | [
{
"docstring": "Class Constructor",
"name": "__init__",
"signature": "def __init__(self, model, warmup_steps=4000)"
},
{
"docstring": "Method call",
"name": "__call__",
"signature": "def __call__(self, step)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004932 | Implement the Python class `CustomSchedule` described below.
Class description:
Custom Schedule class
Method signatures and docstrings:
- def __init__(self, model, warmup_steps=4000): Class Constructor
- def __call__(self, step): Method call | Implement the Python class `CustomSchedule` described below.
Class description:
Custom Schedule class
Method signatures and docstrings:
- def __init__(self, model, warmup_steps=4000): Class Constructor
- def __call__(self, step): Method call
<|skeleton|>
class CustomSchedule:
"""Custom Schedule class"""
def... | fc2cec306961f7ca2448965ddd3a2f656bbe10c7 | <|skeleton|>
class CustomSchedule:
"""Custom Schedule class"""
def __init__(self, model, warmup_steps=4000):
"""Class Constructor"""
<|body_0|>
def __call__(self, step):
"""Method call"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomSchedule:
"""Custom Schedule class"""
def __init__(self, model, warmup_steps=4000):
"""Class Constructor"""
super(CustomSchedule, self).__init__()
self.d_model = model
self.d_model = tf.cast(self.d_model, tf.float32)
self.warmup_steps = warmup_steps
def ... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/5-train.py | dalexach/holbertonschool-machine_learning | train | 2 |
bce79b656e8e110d7fa115aa2818d32579bc3bdb | [
"len1 = len(s1)\nlen2 = len(s2)\nlen3 = len(s3)\nif len1 == 0 and len2 == 0 and (len3 == 0):\n return True\nif len1 > 0 and len2 > 0 and (s1[0] == s2[0]):\n if len3 > 0 and s1[0] == s3[0]:\n return self.isInterleave(s1[1:], s2, s3[1:]) or self.isInterleave(s1, s2[1:], s3[1:])\n else:\n return... | <|body_start_0|>
len1 = len(s1)
len2 = len(s2)
len3 = len(s3)
if len1 == 0 and len2 == 0 and (len3 == 0):
return True
if len1 > 0 and len2 > 0 and (s1[0] == s2[0]):
if len3 > 0 and s1[0] == s3[0]:
return self.isInterleave(s1[1:], s2, s3[1:]... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isInterleave_recur(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool"""
<|body_0|>
def isInterleave(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_36k_train_000938 | 2,251 | no_license | [
{
"docstring": ":type s1: str :type s2: str :type s3: str :rtype: bool",
"name": "isInterleave_recur",
"signature": "def isInterleave_recur(self, s1, s2, s3)"
},
{
"docstring": ":type s1: str :type s2: str :type s3: str :rtype: bool",
"name": "isInterleave",
"signature": "def isInterleav... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isInterleave_recur(self, s1, s2, s3): :type s1: str :type s2: str :type s3: str :rtype: bool
- def isInterleave(self, s1, s2, s3): :type s1: str :type s2: str :type s3: str :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isInterleave_recur(self, s1, s2, s3): :type s1: str :type s2: str :type s3: str :rtype: bool
- def isInterleave(self, s1, s2, s3): :type s1: str :type s2: str :type s3: str :... | e2028eec2f4354a224c15f80d4f0dbd41b7fcd00 | <|skeleton|>
class Solution:
def isInterleave_recur(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool"""
<|body_0|>
def isInterleave(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isInterleave_recur(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool"""
len1 = len(s1)
len2 = len(s2)
len3 = len(s3)
if len1 == 0 and len2 == 0 and (len3 == 0):
return True
if len1 > 0 and len2 > 0 and (s1[0] ... | the_stack_v2_python_sparse | algorithm/leetcode/97.py | guodafeng/pythondev | train | 0 | |
bb015d3127109792e29780dc42142e8daef1725d | [
"arr_sorted = sorted(arr)\nlength = len(arr)\nrepeatNum = []\nfor i in range(1, length):\n if arr_sorted[i] == arr_sorted[i - 1]:\n repeatNum.append(arr_sorted[i])\nreturn repeatNum",
"hash_map = dict()\nrepeatNum = []\nfor i, val in enumerate(arr):\n if val in hash_map.keys():\n repeatNum.app... | <|body_start_0|>
arr_sorted = sorted(arr)
length = len(arr)
repeatNum = []
for i in range(1, length):
if arr_sorted[i] == arr_sorted[i - 1]:
repeatNum.append(arr_sorted[i])
return repeatNum
<|end_body_0|>
<|body_start_1|>
hash_map = dict()
... | 思路1：先对数组进行排序，扫描排序后的数组，时间复杂度为O(nlogn)； 思路2：利用hash表解决，时间复杂度是O(n)，空间复杂度也为O(n)； 思路3：利用数组中值和其下标的关系 | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""思路1：先对数组进行排序，扫描排序后的数组，时间复杂度为O(nlogn)； 思路2：利用hash表解决，时间复杂度是O(n)，空间复杂度也为O(n)； 思路3：利用数组中值和其下标的关系"""
def duplicate1(self, arr):
"""排序后查找"""
<|body_0|>
def duplicate2(self, arr):
"""hash表"""
<|body_1|>
def duplicate3(self, arr):
"""时间... | stack_v2_sparse_classes_36k_train_000939 | 2,139 | no_license | [
{
"docstring": "排序后查找",
"name": "duplicate1",
"signature": "def duplicate1(self, arr)"
},
{
"docstring": "hash表",
"name": "duplicate2",
"signature": "def duplicate2(self, arr)"
},
{
"docstring": "时间换空间",
"name": "duplicate3",
"signature": "def duplicate3(self, arr)"
}
] | 3 | stack_v2_sparse_classes_30k_train_016139 | Implement the Python class `Solution` described below.
Class description:
思路1：先对数组进行排序，扫描排序后的数组，时间复杂度为O(nlogn)； 思路2：利用hash表解决，时间复杂度是O(n)，空间复杂度也为O(n)； 思路3：利用数组中值和其下标的关系
Method signatures and docstrings:
- def duplicate1(self, arr): 排序后查找
- def duplicate2(self, arr): hash表
- def duplicate3(self, arr): 时间换空间 | Implement the Python class `Solution` described below.
Class description:
思路1：先对数组进行排序，扫描排序后的数组，时间复杂度为O(nlogn)； 思路2：利用hash表解决，时间复杂度是O(n)，空间复杂度也为O(n)； 思路3：利用数组中值和其下标的关系
Method signatures and docstrings:
- def duplicate1(self, arr): 排序后查找
- def duplicate2(self, arr): hash表
- def duplicate3(self, arr): 时间换空间
<|skeleton... | 14fb97af36c5fb1d69439585adb0db0ce9eae45d | <|skeleton|>
class Solution:
"""思路1：先对数组进行排序，扫描排序后的数组，时间复杂度为O(nlogn)； 思路2：利用hash表解决，时间复杂度是O(n)，空间复杂度也为O(n)； 思路3：利用数组中值和其下标的关系"""
def duplicate1(self, arr):
"""排序后查找"""
<|body_0|>
def duplicate2(self, arr):
"""hash表"""
<|body_1|>
def duplicate3(self, arr):
"""时间... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""思路1：先对数组进行排序，扫描排序后的数组，时间复杂度为O(nlogn)； 思路2：利用hash表解决，时间复杂度是O(n)，空间复杂度也为O(n)； 思路3：利用数组中值和其下标的关系"""
def duplicate1(self, arr):
"""排序后查找"""
arr_sorted = sorted(arr)
length = len(arr)
repeatNum = []
for i in range(1, length):
if arr_sorted[i] ==... | the_stack_v2_python_sparse | 数组中的重复数字.py | zhanvwei/targetoffer | train | 0 |
80b8a27145294a30172d1616d5b437c084f758de | [
"logger.info('%s initialization' % obj.name)\nsuper(self.__class__, self).__init__(obj, parent)\nself._kuka_armature = None\nfor child in self.robot_parent.blender_obj.children:\n if str(child) == self.blender_obj['KUKAname']:\n self._kuka_armature = child\n break\nif self._kuka_armature:\n logg... | <|body_start_0|>
logger.info('%s initialization' % obj.name)
super(self.__class__, self).__init__(obj, parent)
self._kuka_armature = None
for child in self.robot_parent.blender_obj.children:
if str(child) == self.blender_obj['KUKAname']:
self._kuka_armature = ... | KUKA posture sensor Reads the position of the KUKA LWR arm with respect to the robot, as well as the angles of each of the segments. | KukaPostureClass | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KukaPostureClass:
"""KUKA posture sensor Reads the position of the KUKA LWR arm with respect to the robot, as well as the angles of each of the segments."""
def __init__(self, obj, parent=None):
"""Constructor method. Receives the reference to the Blender object. The second parameter... | stack_v2_sparse_classes_36k_train_000940 | 3,200 | permissive | [
{
"docstring": "Constructor method. Receives the reference to the Blender object. The second parameter should be the name of the object's parent.",
"name": "__init__",
"signature": "def __init__(self, obj, parent=None)"
},
{
"docstring": "Get the x, y, z, yaw, pitch and roll of the KUKA armature... | 2 | stack_v2_sparse_classes_30k_train_008762 | Implement the Python class `KukaPostureClass` described below.
Class description:
KUKA posture sensor Reads the position of the KUKA LWR arm with respect to the robot, as well as the angles of each of the segments.
Method signatures and docstrings:
- def __init__(self, obj, parent=None): Constructor method. Receives ... | Implement the Python class `KukaPostureClass` described below.
Class description:
KUKA posture sensor Reads the position of the KUKA LWR arm with respect to the robot, as well as the angles of each of the segments.
Method signatures and docstrings:
- def __init__(self, obj, parent=None): Constructor method. Receives ... | 07fcb64fea3b58b258e917eb1aed0a585f863418 | <|skeleton|>
class KukaPostureClass:
"""KUKA posture sensor Reads the position of the KUKA LWR arm with respect to the robot, as well as the angles of each of the segments."""
def __init__(self, obj, parent=None):
"""Constructor method. Receives the reference to the Blender object. The second parameter... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KukaPostureClass:
"""KUKA posture sensor Reads the position of the KUKA LWR arm with respect to the robot, as well as the angles of each of the segments."""
def __init__(self, obj, parent=None):
"""Constructor method. Receives the reference to the Blender object. The second parameter should be th... | the_stack_v2_python_sparse | src/morse/sensors/kuka_posture.py | DefaultUser/morse | train | 2 |
a1d8254c5eac458e2824f9d6a472f098b9fcb162 | [
"add_furniture('invoice.csv', 'Elisa Miles', 'LR04', 'Leather Sofa', 25)\nadd_furniture('invoice.csv', 'Edward Data', 'KT78', 'Kitchen Table', 10)\nadd_furniture('invoice.csv', 'Alex Gonzales', 'BR02', 'Queen Mattress', 17)\nwith open('invoice.csv', 'r') as csvfile:\n rentals = []\n for row in csvfile:\n ... | <|body_start_0|>
add_furniture('invoice.csv', 'Elisa Miles', 'LR04', 'Leather Sofa', 25)
add_furniture('invoice.csv', 'Edward Data', 'KT78', 'Kitchen Table', 10)
add_furniture('invoice.csv', 'Alex Gonzales', 'BR02', 'Queen Mattress', 17)
with open('invoice.csv', 'r') as csvfile:
... | Class to test inventory module. | TestIventory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestIventory:
"""Class to test inventory module."""
def test_add_furniture(self):
"""Function to test add furniture functionality."""
<|body_0|>
def test_single_customer(self):
"""Tests single customer functionality."""
<|body_1|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_36k_train_000941 | 1,649 | no_license | [
{
"docstring": "Function to test add furniture functionality.",
"name": "test_add_furniture",
"signature": "def test_add_furniture(self)"
},
{
"docstring": "Tests single customer functionality.",
"name": "test_single_customer",
"signature": "def test_single_customer(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001264 | Implement the Python class `TestIventory` described below.
Class description:
Class to test inventory module.
Method signatures and docstrings:
- def test_add_furniture(self): Function to test add furniture functionality.
- def test_single_customer(self): Tests single customer functionality. | Implement the Python class `TestIventory` described below.
Class description:
Class to test inventory module.
Method signatures and docstrings:
- def test_add_furniture(self): Function to test add furniture functionality.
- def test_single_customer(self): Tests single customer functionality.
<|skeleton|>
class TestI... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class TestIventory:
"""Class to test inventory module."""
def test_add_furniture(self):
"""Function to test add furniture functionality."""
<|body_0|>
def test_single_customer(self):
"""Tests single customer functionality."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestIventory:
"""Class to test inventory module."""
def test_add_furniture(self):
"""Function to test add furniture functionality."""
add_furniture('invoice.csv', 'Elisa Miles', 'LR04', 'Leather Sofa', 25)
add_furniture('invoice.csv', 'Edward Data', 'KT78', 'Kitchen Table', 10)
... | the_stack_v2_python_sparse | students/N0vA/lesson08/assignment/test_inventory.py | JavaRod/SP_Python220B_2019 | train | 1 |
ec33a05dbb323ad494cc41199be616575efe12d6 | [
"result = {}\nfor i in nums:\n result[i] = result.get(i, 0) + 1\nfor k, v in result.items():\n if v == 1:\n return k",
"res = 0\nfor i in range(3):\n for num in nums:\n res ^= num\nreturn res"
] | <|body_start_0|>
result = {}
for i in nums:
result[i] = result.get(i, 0) + 1
for k, v in result.items():
if v == 1:
return k
<|end_body_0|>
<|body_start_1|>
res = 0
for i in range(3):
for num in nums:
res ^= num... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def singleNumber_1(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def singleNumber_2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = {}
for i in nums:
... | stack_v2_sparse_classes_36k_train_000942 | 582 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "singleNumber_1",
"signature": "def singleNumber_1(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "singleNumber_2",
"signature": "def singleNumber_2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020821 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber_1(self, nums): :type nums: List[int] :rtype: int
- def singleNumber_2(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber_1(self, nums): :type nums: List[int] :rtype: int
- def singleNumber_2(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def singl... | 8a62b397a5fa107c70efc8ea65d0edfb74f8baa7 | <|skeleton|>
class Solution:
def singleNumber_1(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def singleNumber_2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def singleNumber_1(self, nums):
""":type nums: List[int] :rtype: int"""
result = {}
for i in nums:
result[i] = result.get(i, 0) + 1
for k, v in result.items():
if v == 1:
return k
def singleNumber_2(self, nums):
"""... | the_stack_v2_python_sparse | LeetCode-Solution/Algorithms/Single-Number-II.py | LFYG/leetcode-acm-euler-other | train | 0 | |
744b85f377a0d84048fbf5c614a594194706623f | [
"processed = 0\nfor base in queryset:\n base.ResetNames()\n processed += 1\nself.message_user(request, '%s reset.' % GetMessageBit(processed))",
"processed = 0\nfor base in queryset:\n base.stateManaged = 'new'\n base.ResetNames()\n processed += 1\nself.message_user(request, '%s reset and marked as... | <|body_start_0|>
processed = 0
for base in queryset:
base.ResetNames()
processed += 1
self.message_user(request, '%s reset.' % GetMessageBit(processed))
<|end_body_0|>
<|body_start_1|>
processed = 0
for base in queryset:
base.stateManaged = 'n... | XrumerBaseRawAdmin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XrumerBaseRawAdmin:
def ResetNames(self, request, queryset):
"""Сбрасываем имена"""
<|body_0|>
def ResetNamesAndNew(self, request, queryset):
"""Сбрасываем имена и помечаем как новые"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
processed = 0
... | stack_v2_sparse_classes_36k_train_000943 | 29,849 | no_license | [
{
"docstring": "Сбрасываем имена",
"name": "ResetNames",
"signature": "def ResetNames(self, request, queryset)"
},
{
"docstring": "Сбрасываем имена и помечаем как новые",
"name": "ResetNamesAndNew",
"signature": "def ResetNamesAndNew(self, request, queryset)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015610 | Implement the Python class `XrumerBaseRawAdmin` described below.
Class description:
Implement the XrumerBaseRawAdmin class.
Method signatures and docstrings:
- def ResetNames(self, request, queryset): Сбрасываем имена
- def ResetNamesAndNew(self, request, queryset): Сбрасываем имена и помечаем как новые | Implement the Python class `XrumerBaseRawAdmin` described below.
Class description:
Implement the XrumerBaseRawAdmin class.
Method signatures and docstrings:
- def ResetNames(self, request, queryset): Сбрасываем имена
- def ResetNamesAndNew(self, request, queryset): Сбрасываем имена и помечаем как новые
<|skeleton|>... | d2771bf04aa187dda6d468883a5a167237589369 | <|skeleton|>
class XrumerBaseRawAdmin:
def ResetNames(self, request, queryset):
"""Сбрасываем имена"""
<|body_0|>
def ResetNamesAndNew(self, request, queryset):
"""Сбрасываем имена и помечаем как новые"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XrumerBaseRawAdmin:
def ResetNames(self, request, queryset):
"""Сбрасываем имена"""
processed = 0
for base in queryset:
base.ResetNames()
processed += 1
self.message_user(request, '%s reset.' % GetMessageBit(processed))
def ResetNamesAndNew(self, re... | the_stack_v2_python_sparse | doorsadmin/admin.py | cash2one/doorscenter | train | 0 | |
936171128a03de9b1c3fb1b3ceefea5cd224a70e | [
"super(FastGradientMethod, self).__init__(model, sess, dtypestr, **kwargs)\nself.feedable_kwargs = ('eps', 'y', 'y_target', 'clip_min', 'clip_max')\nself.structural_kwargs = ['ord', 'sanity_checks']",
"assert self.parse_params(**kwargs)\nlabels, _nb_classes = self.get_or_guess_labels(x, kwargs)\nreturn fgm(x, sel... | <|body_start_0|>
super(FastGradientMethod, self).__init__(model, sess, dtypestr, **kwargs)
self.feedable_kwargs = ('eps', 'y', 'y_target', 'clip_min', 'clip_max')
self.structural_kwargs = ['ord', 'sanity_checks']
<|end_body_0|>
<|body_start_1|>
assert self.parse_params(**kwargs)
... | This attack was originally implemented by Goodfellow et al. (2014) with the infinity norm (and is known as the "Fast Gradient Sign Method"). This implementation extends the attack to other norms, and is therefore called the Fast Gradient Method. Paper link: https://arxiv.org/abs/1412.6572 :param model: cleverhans.model... | FastGradientMethod | [
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FastGradientMethod:
"""This attack was originally implemented by Goodfellow et al. (2014) with the infinity norm (and is known as the "Fast Gradient Sign Method"). This implementation extends the attack to other norms, and is therefore called the Fast Gradient Method. Paper link: https://arxiv.or... | stack_v2_sparse_classes_36k_train_000944 | 8,992 | permissive | [
{
"docstring": "Create a FastGradientMethod instance. Note: the model parameter should be an instance of the cleverhans.model.Model abstraction provided by CleverHans.",
"name": "__init__",
"signature": "def __init__(self, model, sess=None, dtypestr='float32', **kwargs)"
},
{
"docstring": "Retur... | 3 | stack_v2_sparse_classes_30k_val_000287 | Implement the Python class `FastGradientMethod` described below.
Class description:
This attack was originally implemented by Goodfellow et al. (2014) with the infinity norm (and is known as the "Fast Gradient Sign Method"). This implementation extends the attack to other norms, and is therefore called the Fast Gradie... | Implement the Python class `FastGradientMethod` described below.
Class description:
This attack was originally implemented by Goodfellow et al. (2014) with the infinity norm (and is known as the "Fast Gradient Sign Method"). This implementation extends the attack to other norms, and is therefore called the Fast Gradie... | bbe96757fa7daded0090b1d9a26b9c90d7d87c61 | <|skeleton|>
class FastGradientMethod:
"""This attack was originally implemented by Goodfellow et al. (2014) with the infinity norm (and is known as the "Fast Gradient Sign Method"). This implementation extends the attack to other norms, and is therefore called the Fast Gradient Method. Paper link: https://arxiv.or... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FastGradientMethod:
"""This attack was originally implemented by Goodfellow et al. (2014) with the infinity norm (and is known as the "Fast Gradient Sign Method"). This implementation extends the attack to other norms, and is therefore called the Fast Gradient Method. Paper link: https://arxiv.org/abs/1412.65... | the_stack_v2_python_sparse | cleverhans/attacks/fast_gradient_method.py | yogeshbalaji/InvGAN | train | 17 |
81f3be23bafc0cb0cd1e9c8d2e66e66e8b0cce95 | [
"self.filtered_mails = self._get_filtered_mails(mails, code_and_mail)\nself._get_sorted_mails()\nself.mails = sorted(mails, key=lambda x: x.is_ok)",
"if isinstance(value, int) or isinstance(value, slice):\n return self.mails[value]\nraise IndexError",
"if isinstance(value, Good):\n return self.good_mails\... | <|body_start_0|>
self.filtered_mails = self._get_filtered_mails(mails, code_and_mail)
self._get_sorted_mails()
self.mails = sorted(mails, key=lambda x: x.is_ok)
<|end_body_0|>
<|body_start_1|>
if isinstance(value, int) or isinstance(value, slice):
return self.mails[value]
... | MailFilter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MailFilter:
def __init__(self, mails, code_and_mail, *args, **kwargs):
"""The main class for sorting and filtering the list of emails. Accepts a list of emails, filtering conditions. Performs filtering, sorting and storage of the received data. Args: mails (list): list of unsorted, unfil... | stack_v2_sparse_classes_36k_train_000945 | 3,285 | no_license | [
{
"docstring": "The main class for sorting and filtering the list of emails. Accepts a list of emails, filtering conditions. Performs filtering, sorting and storage of the received data. Args: mails (list): list of unsorted, unfiltered emails code_and_mail (list): List of emails and codes to filter by.",
"n... | 5 | stack_v2_sparse_classes_30k_train_017794 | Implement the Python class `MailFilter` described below.
Class description:
Implement the MailFilter class.
Method signatures and docstrings:
- def __init__(self, mails, code_and_mail, *args, **kwargs): The main class for sorting and filtering the list of emails. Accepts a list of emails, filtering conditions. Perfor... | Implement the Python class `MailFilter` described below.
Class description:
Implement the MailFilter class.
Method signatures and docstrings:
- def __init__(self, mails, code_and_mail, *args, **kwargs): The main class for sorting and filtering the list of emails. Accepts a list of emails, filtering conditions. Perfor... | 65f528d18d7e50ab17eff4fa2abfe8345fda60d1 | <|skeleton|>
class MailFilter:
def __init__(self, mails, code_and_mail, *args, **kwargs):
"""The main class for sorting and filtering the list of emails. Accepts a list of emails, filtering conditions. Performs filtering, sorting and storage of the received data. Args: mails (list): list of unsorted, unfil... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MailFilter:
def __init__(self, mails, code_and_mail, *args, **kwargs):
"""The main class for sorting and filtering the list of emails. Accepts a list of emails, filtering conditions. Performs filtering, sorting and storage of the received data. Args: mails (list): list of unsorted, unfiltered emails c... | the_stack_v2_python_sparse | search_dead_mail/MailFilter.py | dmitriyVasilievich1986/mail_parser | train | 0 | |
1b22e51a104766169a7bf7481defce0a96734664 | [
"caseversions = dict(((unicode(x.id), x) for x in model.CaseVersion.objects.filter(pk__in=self.cleaned_data['caseversions'])))\ntry:\n return [caseversions[x] for x in self.cleaned_data['caseversions']]\nexcept KeyError as e:\n raise ValidationError('Not a valid caseversion for this tag.')",
"user = user or... | <|body_start_0|>
caseversions = dict(((unicode(x.id), x) for x in model.CaseVersion.objects.filter(pk__in=self.cleaned_data['caseversions'])))
try:
return [caseversions[x] for x in self.cleaned_data['caseversions']]
except KeyError as e:
raise ValidationError('Not a valid... | Base form for tags. | TagForm | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TagForm:
"""Base form for tags."""
def clean_caseversions(self):
"""Make sure all the ids for the cases are valid and populate self.cleaned_data with the real objects."""
<|body_0|>
def save(self, user=None):
"""Save the tag and case associations."""
<|bo... | stack_v2_sparse_classes_36k_train_000946 | 4,627 | permissive | [
{
"docstring": "Make sure all the ids for the cases are valid and populate self.cleaned_data with the real objects.",
"name": "clean_caseversions",
"signature": "def clean_caseversions(self)"
},
{
"docstring": "Save the tag and case associations.",
"name": "save",
"signature": "def save(... | 2 | stack_v2_sparse_classes_30k_train_016886 | Implement the Python class `TagForm` described below.
Class description:
Base form for tags.
Method signatures and docstrings:
- def clean_caseversions(self): Make sure all the ids for the cases are valid and populate self.cleaned_data with the real objects.
- def save(self, user=None): Save the tag and case associat... | Implement the Python class `TagForm` described below.
Class description:
Base form for tags.
Method signatures and docstrings:
- def clean_caseversions(self): Make sure all the ids for the cases are valid and populate self.cleaned_data with the real objects.
- def save(self, user=None): Save the tag and case associat... | ee54db2fe8ffbf2216d359b7a093b51f2574878e | <|skeleton|>
class TagForm:
"""Base form for tags."""
def clean_caseversions(self):
"""Make sure all the ids for the cases are valid and populate self.cleaned_data with the real objects."""
<|body_0|>
def save(self, user=None):
"""Save the tag and case associations."""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TagForm:
"""Base form for tags."""
def clean_caseversions(self):
"""Make sure all the ids for the cases are valid and populate self.cleaned_data with the real objects."""
caseversions = dict(((unicode(x.id), x) for x in model.CaseVersion.objects.filter(pk__in=self.cleaned_data['caseversio... | the_stack_v2_python_sparse | moztrap/view/manage/tags/forms.py | isakib/moztrap | train | 1 |
52c8bbd470a30b42bb1da00520ae10a00ae7a648 | [
"super(SoftwareSpriteRenderSystem, self).__init__()\nif isinstance(window, Window):\n self.window = window.window\nelif isinstance(window, video.SDL_Window):\n self.window = window\nelse:\n raise TypeError('unsupported window type')\nself.target = window\nsfc = video.SDL_GetWindowSurface(self.window)\nif n... | <|body_start_0|>
super(SoftwareSpriteRenderSystem, self).__init__()
if isinstance(window, Window):
self.window = window.window
elif isinstance(window, video.SDL_Window):
self.window = window
else:
raise TypeError('unsupported window type')
self... | A rendering system for SoftwareSprite components. The SoftwareSpriteRenderSystem class uses a Window as drawing device to display Sprite surfaces. It uses the Window's internal SDL surface as drawing context, so that GL operations, such as texture handling or using SDL renderers is not possible. | SoftwareSpriteRenderSystem | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SoftwareSpriteRenderSystem:
"""A rendering system for SoftwareSprite components. The SoftwareSpriteRenderSystem class uses a Window as drawing device to display Sprite surfaces. It uses the Window's internal SDL surface as drawing context, so that GL operations, such as texture handling or using ... | stack_v2_sparse_classes_36k_train_000947 | 14,308 | permissive | [
{
"docstring": "Creates a new SoftwareSpriteRenderSystem for a specific Window.",
"name": "__init__",
"signature": "def __init__(self, window)"
},
{
"docstring": "Draws the passed sprites (or sprite) on the Window's surface. x and y are optional arguments that can be used as relative drawing loc... | 2 | stack_v2_sparse_classes_30k_train_016285 | Implement the Python class `SoftwareSpriteRenderSystem` described below.
Class description:
A rendering system for SoftwareSprite components. The SoftwareSpriteRenderSystem class uses a Window as drawing device to display Sprite surfaces. It uses the Window's internal SDL surface as drawing context, so that GL operati... | Implement the Python class `SoftwareSpriteRenderSystem` described below.
Class description:
A rendering system for SoftwareSprite components. The SoftwareSpriteRenderSystem class uses a Window as drawing device to display Sprite surfaces. It uses the Window's internal SDL surface as drawing context, so that GL operati... | 29f79c41cfb49ea5b1dd1bec559795727e868558 | <|skeleton|>
class SoftwareSpriteRenderSystem:
"""A rendering system for SoftwareSprite components. The SoftwareSpriteRenderSystem class uses a Window as drawing device to display Sprite surfaces. It uses the Window's internal SDL surface as drawing context, so that GL operations, such as texture handling or using ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SoftwareSpriteRenderSystem:
"""A rendering system for SoftwareSprite components. The SoftwareSpriteRenderSystem class uses a Window as drawing device to display Sprite surfaces. It uses the Window's internal SDL surface as drawing context, so that GL operations, such as texture handling or using SDL renderers... | the_stack_v2_python_sparse | blimgui/dist/sdl2/ext/spritesystem.py | juso40/bl2sdk_Mods | train | 42 |
a3a0f9b0c8bb55c12b2b8985915613ad842347cf | [
"if 'Sub Path' in data_source_dict:\n merged_path = data_source_dict['Path'] + job_id + data_source_dict['Subpath']\n del data_source_dict['Subpath']\nelse:\n merged_path = data_source_dict['Path']\nreturn merged_path",
"data_source_args = {}\nservice = service_factory.create_service(data_source_dict)\np... | <|body_start_0|>
if 'Sub Path' in data_source_dict:
merged_path = data_source_dict['Path'] + job_id + data_source_dict['Subpath']
del data_source_dict['Subpath']
else:
merged_path = data_source_dict['Path']
return merged_path
<|end_body_0|>
<|body_start_1|>
... | A factory for creating data sources | DataSourceFactory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataSourceFactory:
"""A factory for creating data sources"""
def _merge_path(self, job_id, data_source_dict):
"""Merges the path, job id, and subpath. Returns it as a string"""
<|body_0|>
def create_data_source(self, job_id, data_source_dict):
"""Creates one data... | stack_v2_sparse_classes_36k_train_000948 | 1,577 | no_license | [
{
"docstring": "Merges the path, job id, and subpath. Returns it as a string",
"name": "_merge_path",
"signature": "def _merge_path(self, job_id, data_source_dict)"
},
{
"docstring": "Creates one data source given a job id, and a data_source_dict (from DataSources.json)",
"name": "create_dat... | 3 | stack_v2_sparse_classes_30k_train_009755 | Implement the Python class `DataSourceFactory` described below.
Class description:
A factory for creating data sources
Method signatures and docstrings:
- def _merge_path(self, job_id, data_source_dict): Merges the path, job id, and subpath. Returns it as a string
- def create_data_source(self, job_id, data_source_di... | Implement the Python class `DataSourceFactory` described below.
Class description:
A factory for creating data sources
Method signatures and docstrings:
- def _merge_path(self, job_id, data_source_dict): Merges the path, job id, and subpath. Returns it as a string
- def create_data_source(self, job_id, data_source_di... | c69fe121799d72d5239d2da59577e9c1b7a9c51c | <|skeleton|>
class DataSourceFactory:
"""A factory for creating data sources"""
def _merge_path(self, job_id, data_source_dict):
"""Merges the path, job id, and subpath. Returns it as a string"""
<|body_0|>
def create_data_source(self, job_id, data_source_dict):
"""Creates one data... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataSourceFactory:
"""A factory for creating data sources"""
def _merge_path(self, job_id, data_source_dict):
"""Merges the path, job id, and subpath. Returns it as a string"""
if 'Sub Path' in data_source_dict:
merged_path = data_source_dict['Path'] + job_id + data_source_dic... | the_stack_v2_python_sparse | Factories/data_source_factory.py | McFunston/PythonScheduleTools | train | 1 |
a6cb4bd1c560abaad1a0deaffc2214891c6453fa | [
"super(TemplateAngleEmbedder, self).__init__()\nself.c_out = c_out\nself.c_in = c_in\nself.linear_1 = Linear(self.c_in, self.c_out, init='relu')\nself.relu = nn.ReLU()\nself.linear_2 = Linear(self.c_out, self.c_out, init='relu')",
"x = self.linear_1(x)\nx = self.relu(x)\nx = self.linear_2(x)\nreturn x"
] | <|body_start_0|>
super(TemplateAngleEmbedder, self).__init__()
self.c_out = c_out
self.c_in = c_in
self.linear_1 = Linear(self.c_in, self.c_out, init='relu')
self.relu = nn.ReLU()
self.linear_2 = Linear(self.c_out, self.c_out, init='relu')
<|end_body_0|>
<|body_start_1|>... | Embeds the "template_angle_feat" feature. Implements Algorithm 2, line 7. | TemplateAngleEmbedder | [
"Apache-2.0",
"CC-BY-4.0",
"LicenseRef-scancode-other-permissive",
"CC-BY-NC-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TemplateAngleEmbedder:
"""Embeds the "template_angle_feat" feature. Implements Algorithm 2, line 7."""
def __init__(self, c_in: int, c_out: int, **kwargs):
"""Args: c_in: Final dimension of "template_angle_feat" c_out: Output channel dimension"""
<|body_0|>
def forward(s... | stack_v2_sparse_classes_36k_train_000949 | 9,577 | permissive | [
{
"docstring": "Args: c_in: Final dimension of \"template_angle_feat\" c_out: Output channel dimension",
"name": "__init__",
"signature": "def __init__(self, c_in: int, c_out: int, **kwargs)"
},
{
"docstring": "Args: x: [*, N_templ, N_res, c_in] \"template_angle_feat\" features Returns: x: [*, N... | 2 | stack_v2_sparse_classes_30k_train_016109 | Implement the Python class `TemplateAngleEmbedder` described below.
Class description:
Embeds the "template_angle_feat" feature. Implements Algorithm 2, line 7.
Method signatures and docstrings:
- def __init__(self, c_in: int, c_out: int, **kwargs): Args: c_in: Final dimension of "template_angle_feat" c_out: Output c... | Implement the Python class `TemplateAngleEmbedder` described below.
Class description:
Embeds the "template_angle_feat" feature. Implements Algorithm 2, line 7.
Method signatures and docstrings:
- def __init__(self, c_in: int, c_out: int, **kwargs): Args: c_in: Final dimension of "template_angle_feat" c_out: Output c... | 2134cc09b3994b6280e6e3c569dd7d761e4da7a0 | <|skeleton|>
class TemplateAngleEmbedder:
"""Embeds the "template_angle_feat" feature. Implements Algorithm 2, line 7."""
def __init__(self, c_in: int, c_out: int, **kwargs):
"""Args: c_in: Final dimension of "template_angle_feat" c_out: Output channel dimension"""
<|body_0|>
def forward(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TemplateAngleEmbedder:
"""Embeds the "template_angle_feat" feature. Implements Algorithm 2, line 7."""
def __init__(self, c_in: int, c_out: int, **kwargs):
"""Args: c_in: Final dimension of "template_angle_feat" c_out: Output channel dimension"""
super(TemplateAngleEmbedder, self).__init_... | the_stack_v2_python_sparse | openfold/model/embedders.py | aqlaboratory/openfold | train | 2,033 |
235bea81a7895dc78c4ca7bd704cd9fc6093faec | [
"def check_supported_spec(spec):\n if spec.is_discrete:\n assert np.min(spec.minimum) == np.max(spec.minimum) == 0\n assert np.min(spec.maximum) == np.max(spec.maximum)\nalf.nest.map_structure(check_supported_spec, action_spec)\nself._action_spec = action_spec\nsuper().__init__(input_tensor_spec=ac... | <|body_start_0|>
def check_supported_spec(spec):
if spec.is_discrete:
assert np.min(spec.minimum) == np.max(spec.minimum) == 0
assert np.min(spec.maximum) == np.max(spec.maximum)
alf.nest.map_structure(check_supported_spec, action_spec)
self._action_sp... | A simple encoder for action. It encodes discrete action to one hot representation and use the original continous actions. The output is the concat of all of them after flattening. | SimpleActionEncoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleActionEncoder:
"""A simple encoder for action. It encodes discrete action to one hot representation and use the original continous actions. The output is the concat of all of them after flattening."""
def __init__(self, action_spec):
"""Args: action_spec (nested BoundedTensorSp... | stack_v2_sparse_classes_36k_train_000950 | 2,719 | permissive | [
{
"docstring": "Args: action_spec (nested BoundedTensorSpec): spec for actions",
"name": "__init__",
"signature": "def __init__(self, action_spec)"
},
{
"docstring": "Generate encoded actions. Args: inputs (nested Tensor): action tensors. Returns: nested Tensor with the same structure as inputs.... | 2 | null | Implement the Python class `SimpleActionEncoder` described below.
Class description:
A simple encoder for action. It encodes discrete action to one hot representation and use the original continous actions. The output is the concat of all of them after flattening.
Method signatures and docstrings:
- def __init__(self... | Implement the Python class `SimpleActionEncoder` described below.
Class description:
A simple encoder for action. It encodes discrete action to one hot representation and use the original continous actions. The output is the concat of all of them after flattening.
Method signatures and docstrings:
- def __init__(self... | b00ff2fa5e660de31020338ba340263183fbeaa4 | <|skeleton|>
class SimpleActionEncoder:
"""A simple encoder for action. It encodes discrete action to one hot representation and use the original continous actions. The output is the concat of all of them after flattening."""
def __init__(self, action_spec):
"""Args: action_spec (nested BoundedTensorSp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleActionEncoder:
"""A simple encoder for action. It encodes discrete action to one hot representation and use the original continous actions. The output is the concat of all of them after flattening."""
def __init__(self, action_spec):
"""Args: action_spec (nested BoundedTensorSpec): spec for... | the_stack_v2_python_sparse | alf/networks/action_encoder.py | HorizonRobotics/alf | train | 288 |
df5be540ac69cca4a7b4617e13b9ae3d3c9a4950 | [
"if show_columns is not None:\n self.all_extra_data_columns = show_columns['extra_data']\nelse:\n self.all_extra_data_columns = all_extra_data_columns\nsuper().__init__(instance=instance, data=data, **kwargs)\nif show_columns is not None:\n for field_name in set(self.fields) - set(show_columns['fields']):\... | <|body_start_0|>
if show_columns is not None:
self.all_extra_data_columns = show_columns['extra_data']
else:
self.all_extra_data_columns = all_extra_data_columns
super().__init__(instance=instance, data=data, **kwargs)
if show_columns is not None:
for ... | PropertyStateSerializer | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PropertyStateSerializer:
def __init__(self, instance=None, data=empty, all_extra_data_columns=None, show_columns=None, **kwargs):
"""If show_columns is passed, then all_extra_data_columns is not needed since the extra_data columns are embedded in the show_columns. TODO: remove the use of... | stack_v2_sparse_classes_36k_train_000951 | 26,431 | permissive | [
{
"docstring": "If show_columns is passed, then all_extra_data_columns is not needed since the extra_data columns are embedded in the show_columns. TODO: remove the use of all_extra_data_columns. :param instance: instance to serialize :param data: initial data :param all_extra_data_columns: :param show_columns:... | 2 | null | Implement the Python class `PropertyStateSerializer` described below.
Class description:
Implement the PropertyStateSerializer class.
Method signatures and docstrings:
- def __init__(self, instance=None, data=empty, all_extra_data_columns=None, show_columns=None, **kwargs): If show_columns is passed, then all_extra_d... | Implement the Python class `PropertyStateSerializer` described below.
Class description:
Implement the PropertyStateSerializer class.
Method signatures and docstrings:
- def __init__(self, instance=None, data=empty, all_extra_data_columns=None, show_columns=None, **kwargs): If show_columns is passed, then all_extra_d... | 680b6a2b45f3c568d779d8ac86553a0b08c384c8 | <|skeleton|>
class PropertyStateSerializer:
def __init__(self, instance=None, data=empty, all_extra_data_columns=None, show_columns=None, **kwargs):
"""If show_columns is passed, then all_extra_data_columns is not needed since the extra_data columns are embedded in the show_columns. TODO: remove the use of... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PropertyStateSerializer:
def __init__(self, instance=None, data=empty, all_extra_data_columns=None, show_columns=None, **kwargs):
"""If show_columns is passed, then all_extra_data_columns is not needed since the extra_data columns are embedded in the show_columns. TODO: remove the use of all_extra_dat... | the_stack_v2_python_sparse | seed/serializers/properties.py | SEED-platform/seed | train | 108 | |
2f567c0151d0bbff6c870e7e35b7da609417104c | [
"try:\n paths = GetPath()\n config_file_path = paths.config_path()\n config = configparser.RawConfigParser()\n config.read(config_file_path)\n return config\nexcept Exception as e:\n self.log.error('Error while reading config.ini file' + str(e))\n return None",
"try:\n config = self.get_co... | <|body_start_0|>
try:
paths = GetPath()
config_file_path = paths.config_path()
config = configparser.RawConfigParser()
config.read(config_file_path)
return config
except Exception as e:
self.log.error('Error while reading config.ini... | ReadConfig | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReadConfig:
def get_config_file(self):
"""Get config.ini"""
<|body_0|>
def get_property_value(self, section: str, key: str) -> str:
"""Returns property value for give key. If key is password first it decrypt the encrypted password and return it"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_000952 | 1,542 | no_license | [
{
"docstring": "Get config.ini",
"name": "get_config_file",
"signature": "def get_config_file(self)"
},
{
"docstring": "Returns property value for give key. If key is password first it decrypt the encrypted password and return it",
"name": "get_property_value",
"signature": "def get_prop... | 2 | stack_v2_sparse_classes_30k_train_008870 | Implement the Python class `ReadConfig` described below.
Class description:
Implement the ReadConfig class.
Method signatures and docstrings:
- def get_config_file(self): Get config.ini
- def get_property_value(self, section: str, key: str) -> str: Returns property value for give key. If key is password first it decr... | Implement the Python class `ReadConfig` described below.
Class description:
Implement the ReadConfig class.
Method signatures and docstrings:
- def get_config_file(self): Get config.ini
- def get_property_value(self, section: str, key: str) -> str: Returns property value for give key. If key is password first it decr... | 282ddcc37659fdd65bd35d8c078bf47cc649f5eb | <|skeleton|>
class ReadConfig:
def get_config_file(self):
"""Get config.ini"""
<|body_0|>
def get_property_value(self, section: str, key: str) -> str:
"""Returns property value for give key. If key is password first it decrypt the encrypted password and return it"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReadConfig:
def get_config_file(self):
"""Get config.ini"""
try:
paths = GetPath()
config_file_path = paths.config_path()
config = configparser.RawConfigParser()
config.read(config_file_path)
return config
except Exception as ... | the_stack_v2_python_sparse | utilities/read_config.py | halchal14naresh/MyPython_drone_BS_TestRail | train | 0 | |
505a5f59f61a0cc2900ea99f1e91de466ac2f96d | [
"def helper(root, ret, cache):\n if not root:\n cache.append('')\n return False\n ret += cache\n ret.append(str(root.val))\n return True\nif not root:\n return ''\nqueue = list([root])\nret = list()\ncache = list()\nwhile queue:\n node = queue.pop(0)\n if helper(node, ret, cache):... | <|body_start_0|>
def helper(root, ret, cache):
if not root:
cache.append('')
return False
ret += cache
ret.append(str(root.val))
return True
if not root:
return ''
queue = list([root])
ret = list(... | 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_000953 | 1,855 | 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_val_001147 | 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:... | 73d65512eef07475b5790864cce1fdf3f6f4277a | <|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"""
def helper(root, ret, cache):
if not root:
cache.append('')
return False
ret += cache
ret.append(str(root.val))
... | the_stack_v2_python_sparse | leetcode/serialize-and-deserialize-binary-tree-bfs.py | Jingwu010/Code-Practice | train | 0 | |
1d302630b056840b480fda2d2084d23aac60171e | [
"from .designs_pyx import is_group_divisible_design\nself._lambd = lambd\nIncidenceStructure.__init__(self, points, blocks, copy=copy, check=False, **kwds)\nif groups is None or (copy is False and self._point_to_index is None):\n self._groups = groups\nelif self._point_to_index is None:\n self._groups = [g[:]... | <|body_start_0|>
from .designs_pyx import is_group_divisible_design
self._lambd = lambd
IncidenceStructure.__init__(self, points, blocks, copy=copy, check=False, **kwds)
if groups is None or (copy is False and self._point_to_index is None):
self._groups = groups
elif ... | Group Divisible Design (GDD) Let `K` and `G` be sets of positive integers and let `\\lambda` be a positive integer. A Group Divisible Design of index `\\lambda` and order `v` is a triple `(V,\\mathcal G,\\mathcal B)` where: - `V` is a set of cardinality `v` - `\\mathcal G` is a partition of `V` into groups whose size b... | GroupDivisibleDesign | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GroupDivisibleDesign:
"""Group Divisible Design (GDD) Let `K` and `G` be sets of positive integers and let `\\lambda` be a positive integer. A Group Divisible Design of index `\\lambda` and order `v` is a triple `(V,\\mathcal G,\\mathcal B)` where: - `V` is a set of cardinality `v` - `\\mathcal G... | stack_v2_sparse_classes_36k_train_000954 | 13,086 | no_license | [
{
"docstring": "Constructor function EXAMPLE:: sage: from sage.combinat.designs.group_divisible_designs import GroupDivisibleDesign sage: TD = designs.transversal_design(4,10) sage: groups = [list(range(i*10,(i+1)*10)) for i in range(4)] sage: GDD = GroupDivisibleDesign(40,groups,TD); GDD Group Divisible Design... | 3 | stack_v2_sparse_classes_30k_train_021348 | Implement the Python class `GroupDivisibleDesign` described below.
Class description:
Group Divisible Design (GDD) Let `K` and `G` be sets of positive integers and let `\\lambda` be a positive integer. A Group Divisible Design of index `\\lambda` and order `v` is a triple `(V,\\mathcal G,\\mathcal B)` where: - `V` is ... | Implement the Python class `GroupDivisibleDesign` described below.
Class description:
Group Divisible Design (GDD) Let `K` and `G` be sets of positive integers and let `\\lambda` be a positive integer. A Group Divisible Design of index `\\lambda` and order `v` is a triple `(V,\\mathcal G,\\mathcal B)` where: - `V` is ... | 0d9eacbf74e2acffefde93e39f8bcbec745cdaba | <|skeleton|>
class GroupDivisibleDesign:
"""Group Divisible Design (GDD) Let `K` and `G` be sets of positive integers and let `\\lambda` be a positive integer. A Group Divisible Design of index `\\lambda` and order `v` is a triple `(V,\\mathcal G,\\mathcal B)` where: - `V` is a set of cardinality `v` - `\\mathcal G... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GroupDivisibleDesign:
"""Group Divisible Design (GDD) Let `K` and `G` be sets of positive integers and let `\\lambda` be a positive integer. A Group Divisible Design of index `\\lambda` and order `v` is a triple `(V,\\mathcal G,\\mathcal B)` where: - `V` is a set of cardinality `v` - `\\mathcal G` is a partit... | the_stack_v2_python_sparse | sage/src/sage/combinat/designs/group_divisible_designs.py | bopopescu/geosci | train | 0 |
80faf38a113b2b0d7dbaa76430b3edfbe3d40b68 | [
"self.label_size = FLAGS.num_classes\nself.batch_size = FLAGS.batch_size\nself.num_sampled = FLAGS.num_sampled\nself.sentence_length = FLAGS.sequence_length\nself.vocab_size = FLAGS.vocab_size\nself.embed_size = FLAGS.embed_size\nself.is_training = FLAGS.is_training\nself.learning_rate = FLAGS.lr\nself.sentence = t... | <|body_start_0|>
self.label_size = FLAGS.num_classes
self.batch_size = FLAGS.batch_size
self.num_sampled = FLAGS.num_sampled
self.sentence_length = FLAGS.sequence_length
self.vocab_size = FLAGS.vocab_size
self.embed_size = FLAGS.embed_size
self.is_training = FLAGS... | fastText | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class fastText:
def __init__(self):
"""init all hyperparameter here"""
<|body_0|>
def instantiate_weights(self):
"""define all weights here"""
<|body_1|>
def inference(self):
"""main computation graph here: 1.embedding-->2.average-->3.linear classifier... | stack_v2_sparse_classes_36k_train_000955 | 10,416 | no_license | [
{
"docstring": "init all hyperparameter here",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "define all weights here",
"name": "instantiate_weights",
"signature": "def instantiate_weights(self)"
},
{
"docstring": "main computation graph here: 1.embeddin... | 5 | stack_v2_sparse_classes_30k_train_006800 | Implement the Python class `fastText` described below.
Class description:
Implement the fastText class.
Method signatures and docstrings:
- def __init__(self): init all hyperparameter here
- def instantiate_weights(self): define all weights here
- def inference(self): main computation graph here: 1.embedding-->2.aver... | Implement the Python class `fastText` described below.
Class description:
Implement the fastText class.
Method signatures and docstrings:
- def __init__(self): init all hyperparameter here
- def instantiate_weights(self): define all weights here
- def inference(self): main computation graph here: 1.embedding-->2.aver... | 26f7df35bf5b0185ce774e5e866618e025b0993e | <|skeleton|>
class fastText:
def __init__(self):
"""init all hyperparameter here"""
<|body_0|>
def instantiate_weights(self):
"""define all weights here"""
<|body_1|>
def inference(self):
"""main computation graph here: 1.embedding-->2.average-->3.linear classifier... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class fastText:
def __init__(self):
"""init all hyperparameter here"""
self.label_size = FLAGS.num_classes
self.batch_size = FLAGS.batch_size
self.num_sampled = FLAGS.num_sampled
self.sentence_length = FLAGS.sequence_length
self.vocab_size = FLAGS.vocab_size
s... | the_stack_v2_python_sparse | text_classification/fasttext.py | learnerzhang/ai_algorithm | train | 1 | |
e59a98f363b7bd16c68401ec92bc39925ceeb51e | [
"result = []\nif n < 1:\n return result\nself.bracket_trace(result, '', 0, 0, n)\nreturn result",
"if len(s) == 2 * n:\n result.append(s)\nif left < n:\n self.bracket_trace(result, s + '(', left + 1, right, n)\nif right < left:\n self.bracket_trace(result, s + ')', left, right + 1, n)"
] | <|body_start_0|>
result = []
if n < 1:
return result
self.bracket_trace(result, '', 0, 0, n)
return result
<|end_body_0|>
<|body_start_1|>
if len(s) == 2 * n:
result.append(s)
if left < n:
self.bracket_trace(result, s + '(', left + 1, ... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def generate_parentheses(self, n: int) -> List[str]:
"""按照要求生成括号字符串 Args: n: 括号对长度 Returns: 链表"""
<|body_0|>
def bracket_trace(self, result, s: str, left: int, right: int, n: int):
"""递归遍历括号 Args: result: 结果集 s: 字符串 left: 左边 right: 右边 n: 长度"""
<|bod... | stack_v2_sparse_classes_36k_train_000956 | 2,020 | permissive | [
{
"docstring": "按照要求生成括号字符串 Args: n: 括号对长度 Returns: 链表",
"name": "generate_parentheses",
"signature": "def generate_parentheses(self, n: int) -> List[str]"
},
{
"docstring": "递归遍历括号 Args: result: 结果集 s: 字符串 left: 左边 right: 右边 n: 长度",
"name": "bracket_trace",
"signature": "def bracket_tra... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def generate_parentheses(self, n: int) -> List[str]: 按照要求生成括号字符串 Args: n: 括号对长度 Returns: 链表
- def bracket_trace(self, result, s: str, left: int, right: int, n: int): 递归遍历括号 Args:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def generate_parentheses(self, n: int) -> List[str]: 按照要求生成括号字符串 Args: n: 括号对长度 Returns: 链表
- def bracket_trace(self, result, s: str, left: int, right: int, n: int): 递归遍历括号 Args:... | 50f35eef6a0ad63173efed10df3c835b1dceaa3f | <|skeleton|>
class Solution:
def generate_parentheses(self, n: int) -> List[str]:
"""按照要求生成括号字符串 Args: n: 括号对长度 Returns: 链表"""
<|body_0|>
def bracket_trace(self, result, s: str, left: int, right: int, n: int):
"""递归遍历括号 Args: result: 结果集 s: 字符串 left: 左边 right: 右边 n: 长度"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def generate_parentheses(self, n: int) -> List[str]:
"""按照要求生成括号字符串 Args: n: 括号对长度 Returns: 链表"""
result = []
if n < 1:
return result
self.bracket_trace(result, '', 0, 0, n)
return result
def bracket_trace(self, result, s: str, left: int, righ... | the_stack_v2_python_sparse | src/leetcodepython/string/generate_parenthess_22.py | zhangyu345293721/leetcode | train | 101 | |
1df3f1004f5ead7a853c0db30af098fb1ca64e8b | [
"self.persistence_factory = PersistenceMechanismFactory(bucket_base=bucket_base, key_base=key_base, object_type=object_type, logger=logger)\nself.object_type = object_type\nself.reference_pruner = reference_pruner\nself.dictionary_converter = dictionary_converter\nself.fallback = SerializationFormats.JSON\nself.log... | <|body_start_0|>
self.persistence_factory = PersistenceMechanismFactory(bucket_base=bucket_base, key_base=key_base, object_type=object_type, logger=logger)
self.object_type = object_type
self.reference_pruner = reference_pruner
self.dictionary_converter = dictionary_converter
sel... | Factory class for Persistence implementations. Given: 1. a string specifying PersistenceMechanism type 2. a "persist_dir" passed from the caller (which often is experiment name) 3. a "persist_file" passed from the caller (i.e. file name) ... the create_persistence() method will dish out the correct persistence implemen... | PersistenceFactory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PersistenceFactory:
"""Factory class for Persistence implementations. Given: 1. a string specifying PersistenceMechanism type 2. a "persist_dir" passed from the caller (which often is experiment name) 3. a "persist_file" passed from the caller (i.e. file name) ... the create_persistence() method ... | stack_v2_sparse_classes_36k_train_000957 | 9,147 | no_license | [
{
"docstring": "Constructor. :param bucket_base: The bucket base for S3 storage :param key_base: The key (folder) base for S3 storage :param object_type: A string describing what kind of object is to be persisted. :param reference_pruner: A ReferencePruner implementation to prevent persisting reference data twi... | 5 | stack_v2_sparse_classes_30k_train_012828 | Implement the Python class `PersistenceFactory` described below.
Class description:
Factory class for Persistence implementations. Given: 1. a string specifying PersistenceMechanism type 2. a "persist_dir" passed from the caller (which often is experiment name) 3. a "persist_file" passed from the caller (i.e. file nam... | Implement the Python class `PersistenceFactory` described below.
Class description:
Factory class for Persistence implementations. Given: 1. a string specifying PersistenceMechanism type 2. a "persist_dir" passed from the caller (which often is experiment name) 3. a "persist_file" passed from the caller (i.e. file nam... | 99c2f401d6c4b203ee439ed607985a918d0c3c7e | <|skeleton|>
class PersistenceFactory:
"""Factory class for Persistence implementations. Given: 1. a string specifying PersistenceMechanism type 2. a "persist_dir" passed from the caller (which often is experiment name) 3. a "persist_file" passed from the caller (i.e. file name) ... the create_persistence() method ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PersistenceFactory:
"""Factory class for Persistence implementations. Given: 1. a string specifying PersistenceMechanism type 2. a "persist_dir" passed from the caller (which often is experiment name) 3. a "persist_file" passed from the caller (i.e. file name) ... the create_persistence() method will dish out... | the_stack_v2_python_sparse | servicecommon/persistence/factory/persistence_factory.py | Cognizant-CDB-AIA-BAI-AI-OI/LEAF-ENN-Training-V2 | train | 0 |
b9cd759af62468851fe6265efc2943203b6b5a86 | [
"for i in range(len(numbers)):\n for j in range(len(numbers) - 1, i, -1):\n if target - numbers[i] == numbers[j]:\n return [i, j]",
"head, tail = (0, len(numbers) - 1)\nwhile head < tail:\n if numbers[head] + numbers[tail] == target:\n return [head + 1, tail + 1]\n elif numbers[h... | <|body_start_0|>
for i in range(len(numbers)):
for j in range(len(numbers) - 1, i, -1):
if target - numbers[i] == numbers[j]:
return [i, j]
<|end_body_0|>
<|body_start_1|>
head, tail = (0, len(numbers) - 1)
while head < tail:
if number... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def twoSum(self, numbers, target):
""":type numbers: List[int] :type target: int :rtype: List[int]"""
<|body_0|>
def twoSum2(self, numbers, target):
""":type numbers: List[int] :type target: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|b... | stack_v2_sparse_classes_36k_train_000958 | 2,149 | no_license | [
{
"docstring": ":type numbers: List[int] :type target: int :rtype: List[int]",
"name": "twoSum",
"signature": "def twoSum(self, numbers, target)"
},
{
"docstring": ":type numbers: List[int] :type target: int :rtype: List[int]",
"name": "twoSum2",
"signature": "def twoSum2(self, numbers, ... | 2 | stack_v2_sparse_classes_30k_test_000802 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def twoSum(self, numbers, target): :type numbers: List[int] :type target: int :rtype: List[int]
- def twoSum2(self, numbers, target): :type numbers: List[int] :type target: int :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def twoSum(self, numbers, target): :type numbers: List[int] :type target: int :rtype: List[int]
- def twoSum2(self, numbers, target): :type numbers: List[int] :type target: int :... | edccd4b62719c700713e3db78493564e2dc768c4 | <|skeleton|>
class Solution:
def twoSum(self, numbers, target):
""":type numbers: List[int] :type target: int :rtype: List[int]"""
<|body_0|>
def twoSum2(self, numbers, target):
""":type numbers: List[int] :type target: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def twoSum(self, numbers, target):
""":type numbers: List[int] :type target: int :rtype: List[int]"""
for i in range(len(numbers)):
for j in range(len(numbers) - 1, i, -1):
if target - numbers[i] == numbers[j]:
return [i, j]
def tw... | the_stack_v2_python_sparse | Array/167.twosum2InputArrayIsSorted.py | moonclearner/leetcode | train | 0 | |
d8cb7f29fffb26307813c6a77cb930b4c3e7891d | [
"self.Wf = np.random.normal(size=(i + h, h))\nself.Wu = np.random.normal(size=(i + h, h))\nself.Wc = np.random.normal(size=(i + h, h))\nself.Wo = np.random.normal(size=(i + h, h))\nself.Wy = np.random.normal(size=(h, o))\nself.bf = np.zeros((1, h))\nself.bu = np.zeros((1, h))\nself.bc = np.zeros((1, h))\nself.bo = ... | <|body_start_0|>
self.Wf = np.random.normal(size=(i + h, h))
self.Wu = np.random.normal(size=(i + h, h))
self.Wc = np.random.normal(size=(i + h, h))
self.Wo = np.random.normal(size=(i + h, h))
self.Wy = np.random.normal(size=(h, o))
self.bf = np.zeros((1, h))
self... | LSTM cell class | LSTMCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LSTMCell:
"""LSTM cell class"""
def __init__(self, i, h, o):
"""Constructor"""
<|body_0|>
def forward(self, h_prev, c_prev, x_t):
"""Method that performs forward propagation for one time step"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.... | stack_v2_sparse_classes_36k_train_000959 | 1,302 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, i, h, o)"
},
{
"docstring": "Method that performs forward propagation for one time step",
"name": "forward",
"signature": "def forward(self, h_prev, c_prev, x_t)"
}
] | 2 | null | Implement the Python class `LSTMCell` described below.
Class description:
LSTM cell class
Method signatures and docstrings:
- def __init__(self, i, h, o): Constructor
- def forward(self, h_prev, c_prev, x_t): Method that performs forward propagation for one time step | Implement the Python class `LSTMCell` described below.
Class description:
LSTM cell class
Method signatures and docstrings:
- def __init__(self, i, h, o): Constructor
- def forward(self, h_prev, c_prev, x_t): Method that performs forward propagation for one time step
<|skeleton|>
class LSTMCell:
"""LSTM cell cla... | 131be8fcf61aafb5a4ddc0b3853ba625560eb786 | <|skeleton|>
class LSTMCell:
"""LSTM cell class"""
def __init__(self, i, h, o):
"""Constructor"""
<|body_0|>
def forward(self, h_prev, c_prev, x_t):
"""Method that performs forward propagation for one time step"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LSTMCell:
"""LSTM cell class"""
def __init__(self, i, h, o):
"""Constructor"""
self.Wf = np.random.normal(size=(i + h, h))
self.Wu = np.random.normal(size=(i + h, h))
self.Wc = np.random.normal(size=(i + h, h))
self.Wo = np.random.normal(size=(i + h, h))
se... | the_stack_v2_python_sparse | supervised_learning/0x0D-RNNs/3-lstm_cell.py | zahraaassaad/holbertonschool-machine_learning | train | 1 |
43a9c9d8e3d3b9636750a9b1dcda7b57e388c428 | [
"ar = np.array(ar)\nar0 = ar.reshape(1, -1)\nnf, p1 = ar0.shape\nif p == None:\n p = p1 - 1\nff = np.fft.rfft(ar0, n=2 * p + 2).T ** (-1)\nreturn ff",
"N = len(x)\ne = np.zeros((N, p + 1))\nb = np.zeros((N, p + 1))\nalphal = np.zeros((p, p))\ne[:, 0] = x\nb[:, 0] = x\nk = np.zeros(p)\nk[0] = np.sum(e[p:p + L, ... | <|body_start_0|>
ar = np.array(ar)
ar0 = ar.reshape(1, -1)
nf, p1 = ar0.shape
if p == None:
p = p1 - 1
ff = np.fft.rfft(ar0, n=2 * p + 2).T ** (-1)
return ff
<|end_body_0|>
<|body_start_1|>
N = len(x)
e = np.zeros((N, p + 1))
b = np.ze... | LPC | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LPC:
def lpcar2ff(ar, p=None):
"""Convert AR coefs to complex spectrum FF=(AR,P) :param ar: :param p: :return ff:"""
<|body_0|>
def latticem(x, L, p):
"""用几何平均格型法求出线性预测的系数 :param x: 一帧语音数据 （长度大于等于L+p） :param L: 该帧数据中做格型法处理的长度 :param p: 线性预测的系数 :return E: 最小均方误差 :retu... | stack_v2_sparse_classes_36k_train_000960 | 2,100 | permissive | [
{
"docstring": "Convert AR coefs to complex spectrum FF=(AR,P) :param ar: :param p: :return ff:",
"name": "lpcar2ff",
"signature": "def lpcar2ff(ar, p=None)"
},
{
"docstring": "用几何平均格型法求出线性预测的系数 :param x: 一帧语音数据 （长度大于等于L+p） :param L: 该帧数据中做格型法处理的长度 :param p: 线性预测的系数 :return E: 最小均方误差 :return G: ... | 3 | null | Implement the Python class `LPC` described below.
Class description:
Implement the LPC class.
Method signatures and docstrings:
- def lpcar2ff(ar, p=None): Convert AR coefs to complex spectrum FF=(AR,P) :param ar: :param p: :return ff:
- def latticem(x, L, p): 用几何平均格型法求出线性预测的系数 :param x: 一帧语音数据 （长度大于等于L+p） :param L: ... | Implement the Python class `LPC` described below.
Class description:
Implement the LPC class.
Method signatures and docstrings:
- def lpcar2ff(ar, p=None): Convert AR coefs to complex spectrum FF=(AR,P) :param ar: :param p: :return ff:
- def latticem(x, L, p): 用几何平均格型法求出线性预测的系数 :param x: 一帧语音数据 （长度大于等于L+p） :param L: ... | 0074ad1d519387a75d5eca42c77f4d6966eb0a0e | <|skeleton|>
class LPC:
def lpcar2ff(ar, p=None):
"""Convert AR coefs to complex spectrum FF=(AR,P) :param ar: :param p: :return ff:"""
<|body_0|>
def latticem(x, L, p):
"""用几何平均格型法求出线性预测的系数 :param x: 一帧语音数据 （长度大于等于L+p） :param L: 该帧数据中做格型法处理的长度 :param p: 线性预测的系数 :return E: 最小均方误差 :retu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LPC:
def lpcar2ff(ar, p=None):
"""Convert AR coefs to complex spectrum FF=(AR,P) :param ar: :param p: :return ff:"""
ar = np.array(ar)
ar0 = ar.reshape(1, -1)
nf, p1 = ar0.shape
if p == None:
p = p1 - 1
ff = np.fft.rfft(ar0, n=2 * p + 2).T ** (-1)
... | the_stack_v2_python_sparse | Chapter4_LinearPrediction/LPC.py | BarryZM/Python_Speech_SZY | train | 0 | |
3bb550a265da82351e272a40875182d9508bee27 | [
"self.debug = debug\nself.logger = AntiVirusLogger(__name__, debug=self.debug)\nif config_path:\n self._CONFIG_PATH = config_path\nelse:\n self.logger.log('Configuration file path not found.', logtype='error')\n sys.exit(0)\nself.vt_api_key = vt_api_key\nself.context = Context()\nself.monitor = Monitor.fro... | <|body_start_0|>
self.debug = debug
self.logger = AntiVirusLogger(__name__, debug=self.debug)
if config_path:
self._CONFIG_PATH = config_path
else:
self.logger.log('Configuration file path not found.', logtype='error')
sys.exit(0)
self.vt_api_k... | USBMonitor class. | USBMonitor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class USBMonitor:
"""USBMonitor class."""
def __init__(self, debug=False, config_path=None, vt_api_key=None):
"""Initialize USBMonitor. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): Virus Total API Key Raises: None Returns: Non... | stack_v2_sparse_classes_36k_train_000961 | 7,479 | permissive | [
{
"docstring": "Initialize USBMonitor. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): Virus Total API Key Raises: None Returns: None",
"name": "__init__",
"signature": "def __init__(self, debug=False, config_path=None, vt_api_key=None)"
},
... | 5 | null | Implement the Python class `USBMonitor` described below.
Class description:
USBMonitor class.
Method signatures and docstrings:
- def __init__(self, debug=False, config_path=None, vt_api_key=None): Initialize USBMonitor. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api... | Implement the Python class `USBMonitor` described below.
Class description:
USBMonitor class.
Method signatures and docstrings:
- def __init__(self, debug=False, config_path=None, vt_api_key=None): Initialize USBMonitor. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api... | 43dec187e5848b9ced8a6b4957b6e9028d4d43cd | <|skeleton|>
class USBMonitor:
"""USBMonitor class."""
def __init__(self, debug=False, config_path=None, vt_api_key=None):
"""Initialize USBMonitor. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): Virus Total API Key Raises: None Returns: Non... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class USBMonitor:
"""USBMonitor class."""
def __init__(self, debug=False, config_path=None, vt_api_key=None):
"""Initialize USBMonitor. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): Virus Total API Key Raises: None Returns: None"""
... | the_stack_v2_python_sparse | securetea/lib/antivirus/monitor/usb_monitor.py | rejahrehim/SecureTea-Project | train | 1 |
ad44a7cc99b477f3de6293372420f97dc0731fe8 | [
"if hasattr(obj, 'synContentValues'):\n values = obj.synContentValues()\nelse:\n values = obj.getFolderContents()\nreturn values",
"mtool = getToolByName(self, 'portal_membership')\nif not mtool.checkPermission(ManageProperties, obj):\n raise Unauthorized\nBaseTool.enableSyndication(self, obj)",
"mtool... | <|body_start_0|>
if hasattr(obj, 'synContentValues'):
values = obj.synContentValues()
else:
values = obj.getFolderContents()
return values
<|end_body_0|>
<|body_start_1|>
mtool = getToolByName(self, 'portal_membership')
if not mtool.checkPermission(Manage... | SyndicationTool | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SyndicationTool:
def getSyndicatableContent(self, obj):
"""Use the getFolderContents script, unless an object (like Topic) overrides it"""
<|body_0|>
def enableSyndication(self, obj):
"""Enable syndication for the obj"""
<|body_1|>
def disableSyndication... | stack_v2_sparse_classes_36k_train_000962 | 1,729 | no_license | [
{
"docstring": "Use the getFolderContents script, unless an object (like Topic) overrides it",
"name": "getSyndicatableContent",
"signature": "def getSyndicatableContent(self, obj)"
},
{
"docstring": "Enable syndication for the obj",
"name": "enableSyndication",
"signature": "def enableS... | 3 | null | Implement the Python class `SyndicationTool` described below.
Class description:
Implement the SyndicationTool class.
Method signatures and docstrings:
- def getSyndicatableContent(self, obj): Use the getFolderContents script, unless an object (like Topic) overrides it
- def enableSyndication(self, obj): Enable syndi... | Implement the Python class `SyndicationTool` described below.
Class description:
Implement the SyndicationTool class.
Method signatures and docstrings:
- def getSyndicatableContent(self, obj): Use the getFolderContents script, unless an object (like Topic) overrides it
- def enableSyndication(self, obj): Enable syndi... | e137eb6225cc5e724bee74a892567796166134ac | <|skeleton|>
class SyndicationTool:
def getSyndicatableContent(self, obj):
"""Use the getFolderContents script, unless an object (like Topic) overrides it"""
<|body_0|>
def enableSyndication(self, obj):
"""Enable syndication for the obj"""
<|body_1|>
def disableSyndication... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SyndicationTool:
def getSyndicatableContent(self, obj):
"""Use the getFolderContents script, unless an object (like Topic) overrides it"""
if hasattr(obj, 'synContentValues'):
values = obj.synContentValues()
else:
values = obj.getFolderContents()
return ... | the_stack_v2_python_sparse | eggs/Products.CMFPlone-4.1-py2.7.egg/Products/CMFPlone/SyndicationTool.py | nacho22martin/tesis | train | 0 | |
01627473b422a441a17a979212c1f4becc5f189f | [
"mocker.patch.object(client, 'update_entry', return_value=response)\nreturn_value = update_entry_command(client, 'test_collection', filter=filter, update=update, update_one=update_one, upsert=upsert)\nassert return_value[0] == expected",
"mocker.patch.object(client, 'update_entry', return_value=response)\ntry:\n ... | <|body_start_0|>
mocker.patch.object(client, 'update_entry', return_value=response)
return_value = update_entry_command(client, 'test_collection', filter=filter, update=update, update_one=update_one, upsert=upsert)
assert return_value[0] == expected
<|end_body_0|>
<|body_start_1|>
mocke... | Class for update_query_command UTs. | TestUpdateQueryCommands | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestUpdateQueryCommands:
"""Class for update_query_command UTs."""
def test_update_entry_command(self, mocker, filter, update, update_one, upsert, response, expected, client=client):
"""Given: valid arguments When: running mongodb-update command in XSOAR Then: the expected human read... | stack_v2_sparse_classes_36k_train_000963 | 17,096 | permissive | [
{
"docstring": "Given: valid arguments When: running mongodb-update command in XSOAR Then: the expected human readable is returned",
"name": "test_update_entry_command",
"signature": "def test_update_entry_command(self, mocker, filter, update, update_one, upsert, response, expected, client=client)"
},... | 2 | null | Implement the Python class `TestUpdateQueryCommands` described below.
Class description:
Class for update_query_command UTs.
Method signatures and docstrings:
- def test_update_entry_command(self, mocker, filter, update, update_one, upsert, response, expected, client=client): Given: valid arguments When: running mong... | Implement the Python class `TestUpdateQueryCommands` described below.
Class description:
Class for update_query_command UTs.
Method signatures and docstrings:
- def test_update_entry_command(self, mocker, filter, update, update_one, upsert, response, expected, client=client): Given: valid arguments When: running mong... | 890def5a0e0ae8d6eaa538148249ddbc851dbb6b | <|skeleton|>
class TestUpdateQueryCommands:
"""Class for update_query_command UTs."""
def test_update_entry_command(self, mocker, filter, update, update_one, upsert, response, expected, client=client):
"""Given: valid arguments When: running mongodb-update command in XSOAR Then: the expected human read... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestUpdateQueryCommands:
"""Class for update_query_command UTs."""
def test_update_entry_command(self, mocker, filter, update, update_one, upsert, response, expected, client=client):
"""Given: valid arguments When: running mongodb-update command in XSOAR Then: the expected human readable is retur... | the_stack_v2_python_sparse | Packs/MongoDB/Integrations/MongoDB/MongoDB_test.py | demisto/content | train | 1,023 |
9891c8db00c56edc38905c41ae0d97ad918a0241 | [
"model.preds, score = crf_decode(model.logits, model.transitions, model.input_x_len)\nmodel.score = tf.identity(score, name='score')\nmodel.y_ground_truth = model.input_y\nif model.use_pretrained_model:\n logging.info('initialize_pretrained_model_variables')\n self.initialize_pretrained_model_variables(model.... | <|body_start_0|>
model.preds, score = crf_decode(model.logits, model.transitions, model.input_x_len)
model.score = tf.identity(score, name='score')
model.y_ground_truth = model.input_y
if model.use_pretrained_model:
logging.info('initialize_pretrained_model_variables')
... | Solver for raw tensorflow model. | PretrainRawSeqLabelSolver | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PretrainRawSeqLabelSolver:
"""Solver for raw tensorflow model."""
def build_output(self, model):
"""Build the output of the model. `score` and `input_y` are for loss calculation. `preds` and `y_ground_truth` are for metric calculation."""
<|body_0|>
def build_export_outp... | stack_v2_sparse_classes_36k_train_000964 | 4,189 | permissive | [
{
"docstring": "Build the output of the model. `score` and `input_y` are for loss calculation. `preds` and `y_ground_truth` are for metric calculation.",
"name": "build_output",
"signature": "def build_output(self, model)"
},
{
"docstring": "Build the output of the model. `score` and `input_y` a... | 5 | null | Implement the Python class `PretrainRawSeqLabelSolver` described below.
Class description:
Solver for raw tensorflow model.
Method signatures and docstrings:
- def build_output(self, model): Build the output of the model. `score` and `input_y` are for loss calculation. `preds` and `y_ground_truth` are for metric calc... | Implement the Python class `PretrainRawSeqLabelSolver` described below.
Class description:
Solver for raw tensorflow model.
Method signatures and docstrings:
- def build_output(self, model): Build the output of the model. `score` and `input_y` are for loss calculation. `preds` and `y_ground_truth` are for metric calc... | 7eb4e3be578a680737616efff6858d280595ff48 | <|skeleton|>
class PretrainRawSeqLabelSolver:
"""Solver for raw tensorflow model."""
def build_output(self, model):
"""Build the output of the model. `score` and `input_y` are for loss calculation. `preds` and `y_ground_truth` are for metric calculation."""
<|body_0|>
def build_export_outp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PretrainRawSeqLabelSolver:
"""Solver for raw tensorflow model."""
def build_output(self, model):
"""Build the output of the model. `score` and `input_y` are for loss calculation. `preds` and `y_ground_truth` are for metric calculation."""
model.preds, score = crf_decode(model.logits, mode... | the_stack_v2_python_sparse | delta/utils/solver/raw_pretrain_seq_label_solver.py | luffywalf/delta | train | 1 |
7e456e3ec8443663914cdd91a1caa281180dfc18 | [
"self.case_sensitive = case_sensitive\nself.is_regex = is_regex\nself.source_filter = source_filter",
"if dictionary is None:\n return None\ncase_sensitive = dictionary.get('caseSensitive')\nis_regex = dictionary.get('isRegex')\nsource_filter = dictionary.get('sourceFilter')\nreturn cls(case_sensitive, is_rege... | <|body_start_0|>
self.case_sensitive = case_sensitive
self.is_regex = is_regex
self.source_filter = source_filter
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
case_sensitive = dictionary.get('caseSensitive')
is_regex = dictionary.get('is... | Implementation of the 'SourceFilters_SourceFilter' model. Plain text filter: { source_filter: "TestDatabase", is_regex: false}. Wildcard filter: { source_filter: "Test?Database*", is_regex: false}. Regex filter: { source_filter: "^Test.*Database$", is_regex: true}. Attributes: case_sensitive (bool): Determines if the f... | SourceFilters_SourceFilter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SourceFilters_SourceFilter:
"""Implementation of the 'SourceFilters_SourceFilter' model. Plain text filter: { source_filter: "TestDatabase", is_regex: false}. Wildcard filter: { source_filter: "Test?Database*", is_regex: false}. Regex filter: { source_filter: "^Test.*Database$", is_regex: true}. ... | stack_v2_sparse_classes_36k_train_000965 | 2,428 | permissive | [
{
"docstring": "Constructor for the SourceFilters_SourceFilter class",
"name": "__init__",
"signature": "def __init__(self, case_sensitive=None, is_regex=None, source_filter=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary ... | 2 | null | Implement the Python class `SourceFilters_SourceFilter` described below.
Class description:
Implementation of the 'SourceFilters_SourceFilter' model. Plain text filter: { source_filter: "TestDatabase", is_regex: false}. Wildcard filter: { source_filter: "Test?Database*", is_regex: false}. Regex filter: { source_filter... | Implement the Python class `SourceFilters_SourceFilter` described below.
Class description:
Implementation of the 'SourceFilters_SourceFilter' model. Plain text filter: { source_filter: "TestDatabase", is_regex: false}. Wildcard filter: { source_filter: "Test?Database*", is_regex: false}. Regex filter: { source_filter... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class SourceFilters_SourceFilter:
"""Implementation of the 'SourceFilters_SourceFilter' model. Plain text filter: { source_filter: "TestDatabase", is_regex: false}. Wildcard filter: { source_filter: "Test?Database*", is_regex: false}. Regex filter: { source_filter: "^Test.*Database$", is_regex: true}. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SourceFilters_SourceFilter:
"""Implementation of the 'SourceFilters_SourceFilter' model. Plain text filter: { source_filter: "TestDatabase", is_regex: false}. Wildcard filter: { source_filter: "Test?Database*", is_regex: false}. Regex filter: { source_filter: "^Test.*Database$", is_regex: true}. Attributes: c... | the_stack_v2_python_sparse | cohesity_management_sdk/models/source_filters_source_filter.py | cohesity/management-sdk-python | train | 24 |
3a38abf1f9be341e3507e9174e8fa6c4dd0e8036 | [
"if len(fname) < 4 or fname[-4:] != '.omf':\n fname = fname + '.omf'\nself.fname = fname\nwith open(fname, 'wb') as fopen:\n self.initialize_header(fopen, project.uid)\n self.project_json = project.serialize(open_file=fopen)\n self.update_header(fopen)\n fopen.write(json.dumps(self.project_json).enco... | <|body_start_0|>
if len(fname) < 4 or fname[-4:] != '.omf':
fname = fname + '.omf'
self.fname = fname
with open(fname, 'wb') as fopen:
self.initialize_header(fopen, project.uid)
self.project_json = project.serialize(open_file=fopen)
self.update_hea... | OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :code:`'OMF-v0.9.0'` (other bytes empty) * 16 byte project uid (in little-endi... | OMFWriter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OMFWriter:
"""OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :code:`'OMF-v0.9.0'` (other bytes empty) ... | stack_v2_sparse_classes_36k_train_000966 | 6,061 | permissive | [
{
"docstring": "Project serialization is performed on OMFWriter init Binary data is written during project serialization",
"name": "__init__",
"signature": "def __init__(self, project, fname)"
},
{
"docstring": "Write magic number, version string, project uid, and zero bytes Total header length ... | 3 | stack_v2_sparse_classes_30k_val_000368 | Implement the Python class `OMFWriter` described below.
Class description:
OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :c... | Implement the Python class `OMFWriter` described below.
Class description:
OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :c... | d35f04c8ab8f007384a7bea4d4997572daf38553 | <|skeleton|>
class OMFWriter:
"""OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :code:`'OMF-v0.9.0'` (other bytes empty) ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OMFWriter:
"""OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :code:`'OMF-v0.9.0'` (other bytes empty) * 16 byte pro... | the_stack_v2_python_sparse | omf/fileio.py | jmoraga-mines/omf | train | 0 |
9c18ee9ce67227fb788e208810093d626cd3b475 | [
"self.maxlength = capacity\nself.array = {}\nself.array_list = []",
"value = self.array.get(key)\nif value is not None and self.array_list[0] is not key:\n index = self.array_list.index(key)\n self.array_list.pop(index)\n self.array_list.insert(0, key)\nvalue = value if value is not None else -1\nreturn ... | <|body_start_0|>
self.maxlength = capacity
self.array = {}
self.array_list = []
<|end_body_0|>
<|body_start_1|>
value = self.array.get(key)
if value is not None and self.array_list[0] is not key:
index = self.array_list.index(key)
self.array_list.pop(inde... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k_train_000967 | 2,420 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: void",
"name": "pu... | 3 | stack_v2_sparse_classes_30k_train_020775 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void
<|sk... | b613718bf69982535b7c3c9f329a47d5741d8a9e | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.maxlength = capacity
self.array = {}
self.array_list = []
def get(self, key):
""":type key: int :rtype: int"""
value = self.array.get(key)
if value is not None and self.array_lis... | the_stack_v2_python_sparse | Python3/LRU Cache.py | liuyuhang791034063/LeetCode | train | 12 | |
4f961ead6cdc2942555f78f1326cb4e396ffce2f | [
"if not s:\n return 0\nres = 0\ncurrent_letters = set()\nleft = 0\nfor right in range(len(s)):\n if not s[right] in current_letters:\n current_letters.add(s[right])\n if len(current_letters) > res:\n res = len(current_letters)\n else:\n while s[left] != s[right]:\n ... | <|body_start_0|>
if not s:
return 0
res = 0
current_letters = set()
left = 0
for right in range(len(s)):
if not s[right] in current_letters:
current_letters.add(s[right])
if len(current_letters) > res:
re... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def len_longest_substring(self, s: str) -> int:
"""eager storage. current_letters is always accurate."""
<|body_0|>
def len_longest_substring_with_dict(self, s: str) -> int:
"""lazy storage. need to check with current left point when retrieving the index.""... | stack_v2_sparse_classes_36k_train_000968 | 1,358 | no_license | [
{
"docstring": "eager storage. current_letters is always accurate.",
"name": "len_longest_substring",
"signature": "def len_longest_substring(self, s: str) -> int"
},
{
"docstring": "lazy storage. need to check with current left point when retrieving the index.",
"name": "len_longest_substri... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def len_longest_substring(self, s: str) -> int: eager storage. current_letters is always accurate.
- def len_longest_substring_with_dict(self, s: str) -> int: lazy storage. need ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def len_longest_substring(self, s: str) -> int: eager storage. current_letters is always accurate.
- def len_longest_substring_with_dict(self, s: str) -> int: lazy storage. need ... | 5625e6396b746255f3343253c75447ead95879c7 | <|skeleton|>
class Solution:
def len_longest_substring(self, s: str) -> int:
"""eager storage. current_letters is always accurate."""
<|body_0|>
def len_longest_substring_with_dict(self, s: str) -> int:
"""lazy storage. need to check with current left point when retrieving the index.""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def len_longest_substring(self, s: str) -> int:
"""eager storage. current_letters is always accurate."""
if not s:
return 0
res = 0
current_letters = set()
left = 0
for right in range(len(s)):
if not s[right] in current_letters:... | the_stack_v2_python_sparse | 3_longest_substring_without_repeating_characters/solution.py | FluffyFu/Leetcode | train | 0 | |
12c80f0717d8304b1fb9d3b0466ff9ed0c072302 | [
"res = []\nqueue = deque([root])\nwhile queue:\n node = queue.popleft()\n res.append(str(node.val) if node else 'null')\n if node:\n queue.append(node.left)\n queue.append(node.right)\nwhile res and res[-1] == 'null':\n res.pop()\nreturn '[' + ','.join(res) + ']'",
"def walk(n, i):\n ... | <|body_start_0|>
res = []
queue = deque([root])
while queue:
node = queue.popleft()
res.append(str(node.val) if node else 'null')
if node:
queue.append(node.left)
queue.append(node.right)
while res and res[-1] == 'null':... | Codec1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec1:
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_000969 | 2,379 | 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_005888 | Implement the Python class `Codec1` described below.
Class description:
Implement the Codec1 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 :rtyp... | Implement the Python class `Codec1` described below.
Class description:
Implement the Codec1 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 :rtyp... | a08b44323b04fc7d488708b0ffbe94dafc47eb18 | <|skeleton|>
class Codec1:
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 Codec1:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
res = []
queue = deque([root])
while queue:
node = queue.popleft()
res.append(str(node.val) if node else 'null')
if node:
... | the_stack_v2_python_sparse | Tree/serilizeAndDeserilizeBT.py | DiracSea/LC | train | 0 | |
f086918ec178c3c537664642139a4ab3518bd5aa | [
"self.X = X_init\nself.Y = Y_init\nself.l = l\nself.sigma_f = sigma_f\nself.K = self.kernel(X_init, X_init)",
"first = np.sum(X1 ** 2, axis=1).reshape(-1, 1)\nsecond = np.sum(X2 ** 2, axis=1)\nthird = -2 * np.dot(X1, X2.T)\nsqdist = first + second + third\nkernel_1 = self.sigma_f ** 2\nkernel_2 = np.exp(-0.5 / se... | <|body_start_0|>
self.X = X_init
self.Y = Y_init
self.l = l
self.sigma_f = sigma_f
self.K = self.kernel(X_init, X_init)
<|end_body_0|>
<|body_start_1|>
first = np.sum(X1 ** 2, axis=1).reshape(-1, 1)
second = np.sum(X2 ** 2, axis=1)
third = -2 * np.dot(X1,... | Gaussian Process | GaussianProcess | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianProcess:
"""Gaussian Process"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""* X_init is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box function * Y_init is a numpy.ndarray of shape (t, 1) representing the outputs of the b... | stack_v2_sparse_classes_36k_train_000970 | 2,959 | no_license | [
{
"docstring": "* X_init is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box function * Y_init is a numpy.ndarray of shape (t, 1) representing the outputs of the black-box function for each input in X_init - t is the number of initial samples * l is the length parameter... | 3 | stack_v2_sparse_classes_30k_train_015746 | Implement the Python class `GaussianProcess` described below.
Class description:
Gaussian Process
Method signatures and docstrings:
- def __init__(self, X_init, Y_init, l=1, sigma_f=1): * X_init is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box function * Y_init is a numpy.... | Implement the Python class `GaussianProcess` described below.
Class description:
Gaussian Process
Method signatures and docstrings:
- def __init__(self, X_init, Y_init, l=1, sigma_f=1): * X_init is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box function * Y_init is a numpy.... | 8ad4c2594ff78b345dbd92e9d54d2a143ac4071a | <|skeleton|>
class GaussianProcess:
"""Gaussian Process"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""* X_init is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box function * Y_init is a numpy.ndarray of shape (t, 1) representing the outputs of the b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GaussianProcess:
"""Gaussian Process"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""* X_init is a numpy.ndarray of shape (t, 1) representing the inputs already sampled with the black-box function * Y_init is a numpy.ndarray of shape (t, 1) representing the outputs of the black-box func... | the_stack_v2_python_sparse | unsupervised_learning/0x03-hyperparameter_tuning/1-gp.py | jorgezafra94/holbertonschool-machine_learning | train | 1 |
1ff5cf19221fcaf3017c0cc3f48325da8afe2ce5 | [
"try:\n db.show_by_id(show_id, session=session)\nexcept NoResultFound:\n raise NotFoundError('show with ID %s not found' % show_id)\ntry:\n db.episode_by_id(ep_id, session)\nexcept NoResultFound:\n raise NotFoundError('episode with ID %s not found' % ep_id)\ntry:\n release = db.episode_release_by_id(... | <|body_start_0|>
try:
db.show_by_id(show_id, session=session)
except NoResultFound:
raise NotFoundError('show with ID %s not found' % show_id)
try:
db.episode_by_id(ep_id, session)
except NoResultFound:
raise NotFoundError('episode with ID ... | SeriesEpisodeReleaseAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SeriesEpisodeReleaseAPI:
def get(self, show_id, ep_id, rel_id, session):
"""Get episode release by show ID, episode ID and release ID"""
<|body_0|>
def delete(self, show_id, ep_id, rel_id, session):
"""Delete episode release by show ID, episode ID and release ID"""
... | stack_v2_sparse_classes_36k_train_000971 | 47,001 | permissive | [
{
"docstring": "Get episode release by show ID, episode ID and release ID",
"name": "get",
"signature": "def get(self, show_id, ep_id, rel_id, session)"
},
{
"docstring": "Delete episode release by show ID, episode ID and release ID",
"name": "delete",
"signature": "def delete(self, show... | 3 | stack_v2_sparse_classes_30k_train_011408 | Implement the Python class `SeriesEpisodeReleaseAPI` described below.
Class description:
Implement the SeriesEpisodeReleaseAPI class.
Method signatures and docstrings:
- def get(self, show_id, ep_id, rel_id, session): Get episode release by show ID, episode ID and release ID
- def delete(self, show_id, ep_id, rel_id,... | Implement the Python class `SeriesEpisodeReleaseAPI` described below.
Class description:
Implement the SeriesEpisodeReleaseAPI class.
Method signatures and docstrings:
- def get(self, show_id, ep_id, rel_id, session): Get episode release by show ID, episode ID and release ID
- def delete(self, show_id, ep_id, rel_id,... | ea95ff60041beaea9aacbc2d93549e3a6b981dc5 | <|skeleton|>
class SeriesEpisodeReleaseAPI:
def get(self, show_id, ep_id, rel_id, session):
"""Get episode release by show ID, episode ID and release ID"""
<|body_0|>
def delete(self, show_id, ep_id, rel_id, session):
"""Delete episode release by show ID, episode ID and release ID"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SeriesEpisodeReleaseAPI:
def get(self, show_id, ep_id, rel_id, session):
"""Get episode release by show ID, episode ID and release ID"""
try:
db.show_by_id(show_id, session=session)
except NoResultFound:
raise NotFoundError('show with ID %s not found' % show_id)... | the_stack_v2_python_sparse | flexget/components/series/api.py | BrutuZ/Flexget | train | 1 | |
7aab5d06b9051c5bc2a3c681ac33ecb36fe78273 | [
"self.paths = PhenoXPaths(outprefix)\nself.query_str = query_str\nself.email = email",
"mesh = MeshSearcher(self.paths.outprefix)\nmesh_entry = mesh.lookup(self.query_str)\nreturn (mesh_entry, [mesh.mesh[c]['name'] for c in mesh_entry['children']])",
"sys.stdout.write('Retrieving matching GEO datasets...\\n')\n... | <|body_start_0|>
self.paths = PhenoXPaths(outprefix)
self.query_str = query_str
self.email = email
<|end_body_0|>
<|body_start_1|>
mesh = MeshSearcher(self.paths.outprefix)
mesh_entry = mesh.lookup(self.query_str)
return (mesh_entry, [mesh.mesh[c]['name'] for c in mesh_e... | PhenoX | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PhenoX:
def __init__(self, email: str, query_str: str, outprefix: str) -> None:
"""Initialize class :param gene_list:"""
<|body_0|>
def _get_best_mesh_term(self) -> Tuple:
"""Retrieve the best MeSH term from search query :return:"""
<|body_1|>
def _get_g... | stack_v2_sparse_classes_36k_train_000972 | 5,879 | permissive | [
{
"docstring": "Initialize class :param gene_list:",
"name": "__init__",
"signature": "def __init__(self, email: str, query_str: str, outprefix: str) -> None"
},
{
"docstring": "Retrieve the best MeSH term from search query :return:",
"name": "_get_best_mesh_term",
"signature": "def _get... | 6 | stack_v2_sparse_classes_30k_train_017165 | Implement the Python class `PhenoX` described below.
Class description:
Implement the PhenoX class.
Method signatures and docstrings:
- def __init__(self, email: str, query_str: str, outprefix: str) -> None: Initialize class :param gene_list:
- def _get_best_mesh_term(self) -> Tuple: Retrieve the best MeSH term from ... | Implement the Python class `PhenoX` described below.
Class description:
Implement the PhenoX class.
Method signatures and docstrings:
- def __init__(self, email: str, query_str: str, outprefix: str) -> None: Initialize class :param gene_list:
- def _get_best_mesh_term(self) -> Tuple: Retrieve the best MeSH term from ... | a7912e2cf3cc14fa7c6cb666767e0fdb6be78114 | <|skeleton|>
class PhenoX:
def __init__(self, email: str, query_str: str, outprefix: str) -> None:
"""Initialize class :param gene_list:"""
<|body_0|>
def _get_best_mesh_term(self) -> Tuple:
"""Retrieve the best MeSH term from search query :return:"""
<|body_1|>
def _get_g... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PhenoX:
def __init__(self, email: str, query_str: str, outprefix: str) -> None:
"""Initialize class :param gene_list:"""
self.paths = PhenoXPaths(outprefix)
self.query_str = query_str
self.email = email
def _get_best_mesh_term(self) -> Tuple:
"""Retrieve the best M... | the_stack_v2_python_sparse | phenox/phenox.py | NCBI-Hackathons/phenotypeXpression | train | 13 | |
951508147bd5ffdaf7799bdd1625eaf5a5d81dad | [
"def flip(k):\n arr[:] = arr[:k][::-1] + arr[k:]\n\ndef find_max_num_index(top_index):\n max = float('inf')\n idx = -1\n for i in range(top_index):\n if arr[i] > max:\n max = arr[i]\n idx = i\n return idx\nfor i in range(len(arr), 0, -1):\n biggest = find_max_num_index... | <|body_start_0|>
def flip(k):
arr[:] = arr[:k][::-1] + arr[k:]
def find_max_num_index(top_index):
max = float('inf')
idx = -1
for i in range(top_index):
if arr[i] > max:
max = arr[i]
idx = i
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def pancakeSort(self, arr):
""":type A: List[int] :rtype: List[int]"""
<|body_0|>
def pancakeSort(self, arr):
""":type A: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def flip(k):
arr[:] = arr[:k]... | stack_v2_sparse_classes_36k_train_000973 | 2,159 | no_license | [
{
"docstring": ":type A: List[int] :rtype: List[int]",
"name": "pancakeSort",
"signature": "def pancakeSort(self, arr)"
},
{
"docstring": ":type A: List[int] :rtype: List[int]",
"name": "pancakeSort",
"signature": "def pancakeSort(self, arr)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006137 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def pancakeSort(self, arr): :type A: List[int] :rtype: List[int]
- def pancakeSort(self, arr): :type A: List[int] :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def pancakeSort(self, arr): :type A: List[int] :rtype: List[int]
- def pancakeSort(self, arr): :type A: List[int] :rtype: List[int]
<|skeleton|>
class Solution:
def pancake... | 844f502da4d6fb9cd69cf0a1ef71da3385a4d2b4 | <|skeleton|>
class Solution:
def pancakeSort(self, arr):
""":type A: List[int] :rtype: List[int]"""
<|body_0|>
def pancakeSort(self, arr):
""":type A: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def pancakeSort(self, arr):
""":type A: List[int] :rtype: List[int]"""
def flip(k):
arr[:] = arr[:k][::-1] + arr[k:]
def find_max_num_index(top_index):
max = float('inf')
idx = -1
for i in range(top_index):
if a... | the_stack_v2_python_sparse | 969-pancake_sorting.py | stevestar888/leetcode-problems | train | 2 | |
5b3fd235aa5fee6d8d90209c226fdf6f45968453 | [
"super().__init__(db, version, uuid)\nself.columns = get_table_columns(self.conn, 'ZDETECTEDFACE')\nself.table_name = 'ZDETECTEDFACE'",
"conn, cursor = self.db.get_db_connection()\nsql = f' SELECT ZDETECTEDFACE.*\\n FROM ZDETECTEDFACE\\n JOIN {self.asset_table} ON {self.asse... | <|body_start_0|>
super().__init__(db, version, uuid)
self.columns = get_table_columns(self.conn, 'ZDETECTEDFACE')
self.table_name = 'ZDETECTEDFACE'
<|end_body_0|>
<|body_start_1|>
conn, cursor = self.db.get_db_connection()
sql = f' SELECT ZDETECTEDFACE.*\n FR... | ZDETECTEDFACE table. | DetectedFaceTable | [
"MIT",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DetectedFaceTable:
"""ZDETECTEDFACE table."""
def __init__(self, db: osxphotos.PhotosDB, version: int, uuid: str):
"""Create a Table object."""
<|body_0|>
def rows(self) -> list[tuple[Any]]:
"""Return rows for this photo from the ZDETECTEDFACE table."""
<... | stack_v2_sparse_classes_36k_train_000974 | 8,828 | permissive | [
{
"docstring": "Create a Table object.",
"name": "__init__",
"signature": "def __init__(self, db: osxphotos.PhotosDB, version: int, uuid: str)"
},
{
"docstring": "Return rows for this photo from the ZDETECTEDFACE table.",
"name": "rows",
"signature": "def rows(self) -> list[tuple[Any]]"
... | 3 | null | Implement the Python class `DetectedFaceTable` described below.
Class description:
ZDETECTEDFACE table.
Method signatures and docstrings:
- def __init__(self, db: osxphotos.PhotosDB, version: int, uuid: str): Create a Table object.
- def rows(self) -> list[tuple[Any]]: Return rows for this photo from the ZDETECTEDFAC... | Implement the Python class `DetectedFaceTable` described below.
Class description:
ZDETECTEDFACE table.
Method signatures and docstrings:
- def __init__(self, db: osxphotos.PhotosDB, version: int, uuid: str): Create a Table object.
- def rows(self) -> list[tuple[Any]]: Return rows for this photo from the ZDETECTEDFAC... | 2cb5a4d18a27be6ccf68f5f35abd39418d238016 | <|skeleton|>
class DetectedFaceTable:
"""ZDETECTEDFACE table."""
def __init__(self, db: osxphotos.PhotosDB, version: int, uuid: str):
"""Create a Table object."""
<|body_0|>
def rows(self) -> list[tuple[Any]]:
"""Return rows for this photo from the ZDETECTEDFACE table."""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DetectedFaceTable:
"""ZDETECTEDFACE table."""
def __init__(self, db: osxphotos.PhotosDB, version: int, uuid: str):
"""Create a Table object."""
super().__init__(db, version, uuid)
self.columns = get_table_columns(self.conn, 'ZDETECTEDFACE')
self.table_name = 'ZDETECTEDFACE... | the_stack_v2_python_sparse | osxphotos/phototables.py | RhetTbull/osxphotos | train | 1,287 |
1e6d33b3d2b4bdf7607f156c538ec5bca16e5041 | [
"sql = 'INSERT INTO %(tab)s (detract, period, people, amount, time, target_ct_id, target_id)\\n SELECT 1, %(pe)s, SUM(people), SUM(amount), DATE(time), target_ct_id, target_id\\n FROM %(tab)s\\n WHERE time <= %%(li)s and detract = 0\\n GROUP BY target_... | <|body_start_0|>
sql = 'INSERT INTO %(tab)s (detract, period, people, amount, time, target_ct_id, target_id)\n SELECT 1, %(pe)s, SUM(people), SUM(amount), DATE(time), target_ct_id, target_id\n FROM %(tab)s\n WHERE time <= %%(li)s and detract = 0\n ... | AggManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AggManager:
def copy_agg_to_agg(self, time_limit, time_format, time_period):
"""Coppy aggregated Agg data to table Agg time_limit: limit for time of transfering data time_format: format for destiny DATE_FORMAT time_period: is a period of aggregation data"""
<|body_0|>
def ag... | stack_v2_sparse_classes_36k_train_000975 | 15,410 | no_license | [
{
"docstring": "Coppy aggregated Agg data to table Agg time_limit: limit for time of transfering data time_format: format for destiny DATE_FORMAT time_period: is a period of aggregation data",
"name": "copy_agg_to_agg",
"signature": "def copy_agg_to_agg(self, time_limit, time_format, time_period)"
},
... | 3 | null | Implement the Python class `AggManager` described below.
Class description:
Implement the AggManager class.
Method signatures and docstrings:
- def copy_agg_to_agg(self, time_limit, time_format, time_period): Coppy aggregated Agg data to table Agg time_limit: limit for time of transfering data time_format: format for... | Implement the Python class `AggManager` described below.
Class description:
Implement the AggManager class.
Method signatures and docstrings:
- def copy_agg_to_agg(self, time_limit, time_format, time_period): Coppy aggregated Agg data to table Agg time_limit: limit for time of transfering data time_format: format for... | edcae8ac03816631cf8fbae98b7730479f4c41b6 | <|skeleton|>
class AggManager:
def copy_agg_to_agg(self, time_limit, time_format, time_period):
"""Coppy aggregated Agg data to table Agg time_limit: limit for time of transfering data time_format: format for destiny DATE_FORMAT time_period: is a period of aggregation data"""
<|body_0|>
def ag... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AggManager:
def copy_agg_to_agg(self, time_limit, time_format, time_period):
"""Coppy aggregated Agg data to table Agg time_limit: limit for time of transfering data time_format: format for destiny DATE_FORMAT time_period: is a period of aggregation data"""
sql = 'INSERT INTO %(tab)s (detract,... | the_stack_v2_python_sparse | ella/ratings/models.py | majerm/ella | train | 1 | |
8b132c2e94fbb59c32a2f6ee9d8f424dce93b12e | [
"ImageProcessor.__init__(self, **kwargs)\nself.sigma = sigma\nself.box_size = box_size\nself.filter_size = filter_size",
"from photutils.background import Background2D, MedianBackground\nsigma_clip = SigmaClip(sigma=self.sigma)\nbkg_estimator = MedianBackground()\nbkg = Background2D(image.data, self.box_size, fil... | <|body_start_0|>
ImageProcessor.__init__(self, **kwargs)
self.sigma = sigma
self.box_size = box_size
self.filter_size = filter_size
<|end_body_0|>
<|body_start_1|>
from photutils.background import Background2D, MedianBackground
sigma_clip = SigmaClip(sigma=self.sigma)
... | Remove background from image. | RemoveBackground | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemoveBackground:
"""Remove background from image."""
def __init__(self, sigma: float=3.0, box_size: Tuple[int, int]=(50, 50), filter_size: Tuple[int, int]=(3, 3), **kwargs: Any):
"""Init an image processor that removes background from image. Args: sigma: Sigma for clipping box_size:... | stack_v2_sparse_classes_36k_train_000976 | 1,681 | permissive | [
{
"docstring": "Init an image processor that removes background from image. Args: sigma: Sigma for clipping box_size: Box size for bkg estimation. filter_size: Size of filter.",
"name": "__init__",
"signature": "def __init__(self, sigma: float=3.0, box_size: Tuple[int, int]=(50, 50), filter_size: Tuple[... | 2 | null | Implement the Python class `RemoveBackground` described below.
Class description:
Remove background from image.
Method signatures and docstrings:
- def __init__(self, sigma: float=3.0, box_size: Tuple[int, int]=(50, 50), filter_size: Tuple[int, int]=(3, 3), **kwargs: Any): Init an image processor that removes backgro... | Implement the Python class `RemoveBackground` described below.
Class description:
Remove background from image.
Method signatures and docstrings:
- def __init__(self, sigma: float=3.0, box_size: Tuple[int, int]=(50, 50), filter_size: Tuple[int, int]=(3, 3), **kwargs: Any): Init an image processor that removes backgro... | 2d7a06e5485b61b6ca7e51d99b08651ea6021086 | <|skeleton|>
class RemoveBackground:
"""Remove background from image."""
def __init__(self, sigma: float=3.0, box_size: Tuple[int, int]=(50, 50), filter_size: Tuple[int, int]=(3, 3), **kwargs: Any):
"""Init an image processor that removes background from image. Args: sigma: Sigma for clipping box_size:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RemoveBackground:
"""Remove background from image."""
def __init__(self, sigma: float=3.0, box_size: Tuple[int, int]=(50, 50), filter_size: Tuple[int, int]=(3, 3), **kwargs: Any):
"""Init an image processor that removes background from image. Args: sigma: Sigma for clipping box_size: Box size for... | the_stack_v2_python_sparse | pyobs/images/processors/misc/removebackground.py | pyobs/pyobs-core | train | 9 |
042847acb7e926eac9a7c327ae6a4b3bb5af1d8c | [
"super().__init__(grad=False, **kwargs)\nself.key_mapping = key_mapping\nself.props_key = props_key",
"props = data[self.props_key]\nfor source, target in self.key_mapping.items():\n data[target] = [p[source] for p in props]\nreturn data"
] | <|body_start_0|>
super().__init__(grad=False, **kwargs)
self.key_mapping = key_mapping
self.props_key = props_key
<|end_body_0|>
<|body_start_1|>
props = data[self.props_key]
for source, target in self.key_mapping.items():
data[target] = [p[source] for p in props]
... | AddProps2Data | [
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AddProps2Data:
def __init__(self, props_key: str, key_mapping: Mapping[str, str], **kwargs):
"""Move properties from property dict to data dict Args props_key: key where properties and :param:`map_key` key is located; key_mapping: maps properties(key) to new keys in data dict(item)"""
... | stack_v2_sparse_classes_36k_train_000977 | 2,296 | permissive | [
{
"docstring": "Move properties from property dict to data dict Args props_key: key where properties and :param:`map_key` key is located; key_mapping: maps properties(key) to new keys in data dict(item)",
"name": "__init__",
"signature": "def __init__(self, props_key: str, key_mapping: Mapping[str, str]... | 2 | stack_v2_sparse_classes_30k_train_012597 | Implement the Python class `AddProps2Data` described below.
Class description:
Implement the AddProps2Data class.
Method signatures and docstrings:
- def __init__(self, props_key: str, key_mapping: Mapping[str, str], **kwargs): Move properties from property dict to data dict Args props_key: key where properties and :... | Implement the Python class `AddProps2Data` described below.
Class description:
Implement the AddProps2Data class.
Method signatures and docstrings:
- def __init__(self, props_key: str, key_mapping: Mapping[str, str], **kwargs): Move properties from property dict to data dict Args props_key: key where properties and :... | 4f41faa7536dcef8fca7b647dcdca25360e5b58a | <|skeleton|>
class AddProps2Data:
def __init__(self, props_key: str, key_mapping: Mapping[str, str], **kwargs):
"""Move properties from property dict to data dict Args props_key: key where properties and :param:`map_key` key is located; key_mapping: maps properties(key) to new keys in data dict(item)"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AddProps2Data:
def __init__(self, props_key: str, key_mapping: Mapping[str, str], **kwargs):
"""Move properties from property dict to data dict Args props_key: key where properties and :param:`map_key` key is located; key_mapping: maps properties(key) to new keys in data dict(item)"""
super().... | the_stack_v2_python_sparse | nndet/io/transforms/utils.py | dboun/nnDetection | train | 1 | |
ea9e14053c3cbd5b90c142ceff775c96ba8707ab | [
"i = 0\nfor num in nums:\n if num != val:\n nums[i] = num\n i += 1\nreturn i",
"for i in range(len(nums) - 1, -1, -1):\n if nums[i] == val:\n nums.pop(i)\nreturn len(nums)"
] | <|body_start_0|>
i = 0
for num in nums:
if num != val:
nums[i] = num
i += 1
return i
<|end_body_0|>
<|body_start_1|>
for i in range(len(nums) - 1, -1, -1):
if nums[i] == val:
nums.pop(i)
return len(nums)
<|e... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeElement_MK1(self, nums: List[int], val: int) -> int:
"""双指针法 Approach 1: Two Pointers"""
<|body_0|>
def removeElement_MK2(self, nums: List[int], val: int) -> int:
"""改进的双指针法，当要删除的元素很少时，速度更快 Approach 2: Two Pointers - when elements to remove are ra... | stack_v2_sparse_classes_36k_train_000978 | 692 | no_license | [
{
"docstring": "双指针法 Approach 1: Two Pointers",
"name": "removeElement_MK1",
"signature": "def removeElement_MK1(self, nums: List[int], val: int) -> int"
},
{
"docstring": "改进的双指针法，当要删除的元素很少时，速度更快 Approach 2: Two Pointers - when elements to remove are rare",
"name": "removeElement_MK2",
... | 2 | stack_v2_sparse_classes_30k_train_018657 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeElement_MK1(self, nums: List[int], val: int) -> int: 双指针法 Approach 1: Two Pointers
- def removeElement_MK2(self, nums: List[int], val: int) -> int: 改进的双指针法，当要删除的元素很少时，速... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeElement_MK1(self, nums: List[int], val: int) -> int: 双指针法 Approach 1: Two Pointers
- def removeElement_MK2(self, nums: List[int], val: int) -> int: 改进的双指针法，当要删除的元素很少时，速... | d7ba416d22becfa8f2a2ae4eee04c86617cd9332 | <|skeleton|>
class Solution:
def removeElement_MK1(self, nums: List[int], val: int) -> int:
"""双指针法 Approach 1: Two Pointers"""
<|body_0|>
def removeElement_MK2(self, nums: List[int], val: int) -> int:
"""改进的双指针法，当要删除的元素很少时，速度更快 Approach 2: Two Pointers - when elements to remove are ra... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def removeElement_MK1(self, nums: List[int], val: int) -> int:
"""双指针法 Approach 1: Two Pointers"""
i = 0
for num in nums:
if num != val:
nums[i] = num
i += 1
return i
def removeElement_MK2(self, nums: List[int], val: in... | the_stack_v2_python_sparse | 0027. Remove Element/Solution.py | faterazer/LeetCode | train | 4 | |
5662a5946d92b0dd126207f65ac366b9433538ce | [
"activation = self.params.get('attentionActivation', None)\nif activation == 'None':\n activation = None\nfeature_vector_size = K.int_shape(merged_input)[-1]\natt_layer = layers.TimeDistributed(layers.Dense(feature_vector_size, activation=activation), name='attention_matrix_score')(merged_input)\natt_layer = lay... | <|body_start_0|>
activation = self.params.get('attentionActivation', None)
if activation == 'None':
activation = None
feature_vector_size = K.int_shape(merged_input)[-1]
att_layer = layers.TimeDistributed(layers.Dense(feature_vector_size, activation=activation), name='attenti... | Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer. | TimePreAttArgBiLSTM | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TimePreAttArgBiLSTM:
"""Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer."""
def addPreAttentionLayer(self, merged_input):
"""Add attention mechanisms to the tensor merged_input. Args: merged_input: 3-dimensional Tensor, wh... | stack_v2_sparse_classes_36k_train_000979 | 12,548 | no_license | [
{
"docstring": "Add attention mechanisms to the tensor merged_input. Args: merged_input: 3-dimensional Tensor, where the first dimension corresponds to the batch size, the second to the sequence timesteps and the last one to the concatenation of features. Retruns: 3-dimensional Tensor of the same dimension as m... | 4 | stack_v2_sparse_classes_30k_val_000244 | Implement the Python class `TimePreAttArgBiLSTM` described below.
Class description:
Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer.
Method signatures and docstrings:
- def addPreAttentionLayer(self, merged_input): Add attention mechanisms to the tensor m... | Implement the Python class `TimePreAttArgBiLSTM` described below.
Class description:
Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer.
Method signatures and docstrings:
- def addPreAttentionLayer(self, merged_input): Add attention mechanisms to the tensor m... | 74a9a09194bebefd8581cfee0676ed7d6bceaf14 | <|skeleton|>
class TimePreAttArgBiLSTM:
"""Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer."""
def addPreAttentionLayer(self, merged_input):
"""Add attention mechanisms to the tensor merged_input. Args: merged_input: 3-dimensional Tensor, wh... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TimePreAttArgBiLSTM:
"""Bidirectional RNN with an attention mechanism. The attention is applied timestep wise before the BiLSTM layer."""
def addPreAttentionLayer(self, merged_input):
"""Add attention mechanisms to the tensor merged_input. Args: merged_input: 3-dimensional Tensor, where the first... | the_stack_v2_python_sparse | models/att_arg_bilstm.py | mit0110/argument_mining | train | 1 |
736a9edef206569582a0f1b0c5097a6b87112433 | [
"video_idx, frames_idx = self.get_clip_location(idx)\nmask_file = self.mask_paths[video_idx]\nif mask_file == '':\n return None\nframes = self.clips[video_idx][frames_idx]\nvid_masks = np.load(mask_file)\nmasks = np.take(vid_masks, frames, 0)\nreturn masks",
"self.video_pts = []\nfor video_path in self.video_p... | <|body_start_0|>
video_idx, frames_idx = self.get_clip_location(idx)
mask_file = self.mask_paths[video_idx]
if mask_file == '':
return None
frames = self.clips[video_idx][frames_idx]
vid_masks = np.load(mask_file)
masks = np.take(vid_masks, frames, 0)
... | Clips indexer for the test set of the ShanghaiTech Campus dataset. The train and test subsets of the ShanghaiTech dataset use different file formats, so separate clips indexer implementations are needed. | ShanghaiTechTestClipsIndexer | [
"CC-BY-SA-4.0",
"CC-BY-SA-3.0",
"CC-BY-NC-SA-4.0",
"Python-2.0",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShanghaiTechTestClipsIndexer:
"""Clips indexer for the test set of the ShanghaiTech Campus dataset. The train and test subsets of the ShanghaiTech dataset use different file formats, so separate clips indexer implementations are needed."""
def get_mask(self, idx: int) -> Tensor | None:
... | stack_v2_sparse_classes_36k_train_000980 | 14,449 | permissive | [
{
"docstring": "Retrieve the masks from the file system.",
"name": "get_mask",
"signature": "def get_mask(self, idx: int) -> Tensor | None"
},
{
"docstring": "Retrieve the number of frames in each video.",
"name": "_compute_frame_pts",
"signature": "def _compute_frame_pts(self) -> None"
... | 3 | stack_v2_sparse_classes_30k_test_000755 | Implement the Python class `ShanghaiTechTestClipsIndexer` described below.
Class description:
Clips indexer for the test set of the ShanghaiTech Campus dataset. The train and test subsets of the ShanghaiTech dataset use different file formats, so separate clips indexer implementations are needed.
Method signatures an... | Implement the Python class `ShanghaiTechTestClipsIndexer` described below.
Class description:
Clips indexer for the test set of the ShanghaiTech Campus dataset. The train and test subsets of the ShanghaiTech dataset use different file formats, so separate clips indexer implementations are needed.
Method signatures an... | 4abfa93dcfcb98771bc768b334c929ff9a02ce8b | <|skeleton|>
class ShanghaiTechTestClipsIndexer:
"""Clips indexer for the test set of the ShanghaiTech Campus dataset. The train and test subsets of the ShanghaiTech dataset use different file formats, so separate clips indexer implementations are needed."""
def get_mask(self, idx: int) -> Tensor | None:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ShanghaiTechTestClipsIndexer:
"""Clips indexer for the test set of the ShanghaiTech Campus dataset. The train and test subsets of the ShanghaiTech dataset use different file formats, so separate clips indexer implementations are needed."""
def get_mask(self, idx: int) -> Tensor | None:
"""Retriev... | the_stack_v2_python_sparse | src/anomalib/data/shanghaitech.py | openvinotoolkit/anomalib | train | 2,325 |
17cbd3eefb6242eb7d1385db4ee44a74404e087b | [
"self.loop = loop_param\nself.sleep = sleep_param\nself.mqttClient = MqttClientConnector.MqttClientConnector()\nself.mqttClient.connectSensorData()\nself.pUtil = PersistenceUtil.PersistenceUtil()\nself.TempSensor = TempSensorAdapterTask.TempSensorAdapterTask(loop_param, sleep_param, self.pUtil, self.mqttClient)",
... | <|body_start_0|>
self.loop = loop_param
self.sleep = sleep_param
self.mqttClient = MqttClientConnector.MqttClientConnector()
self.mqttClient.connectSensorData()
self.pUtil = PersistenceUtil.PersistenceUtil()
self.TempSensor = TempSensorAdapterTask.TempSensorAdapterTask(lo... | Method to run TempSensorAdapterTask takes in sleeptime and looptime in the constructors has a bunch of settings to control the program behavior with | MultiSensorAdapter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiSensorAdapter:
"""Method to run TempSensorAdapterTask takes in sleeptime and looptime in the constructors has a bunch of settings to control the program behavior with"""
def __init__(self, loop_param=10, sleep_param=1):
"""Constructor Initializing both the sensor tasks and a dat... | stack_v2_sparse_classes_36k_train_000981 | 3,006 | no_license | [
{
"docstring": "Constructor Initializing both the sensor tasks and a data manager.",
"name": "__init__",
"signature": "def __init__(self, loop_param=10, sleep_param=1)"
},
{
"docstring": "Initialize threads",
"name": "__init_threads__",
"signature": "def __init_threads__(self)"
},
{
... | 3 | null | Implement the Python class `MultiSensorAdapter` described below.
Class description:
Method to run TempSensorAdapterTask takes in sleeptime and looptime in the constructors has a bunch of settings to control the program behavior with
Method signatures and docstrings:
- def __init__(self, loop_param=10, sleep_param=1):... | Implement the Python class `MultiSensorAdapter` described below.
Class description:
Method to run TempSensorAdapterTask takes in sleeptime and looptime in the constructors has a bunch of settings to control the program behavior with
Method signatures and docstrings:
- def __init__(self, loop_param=10, sleep_param=1):... | dfd5fd8c757cae8b1306ae3e4eb2cfc9bf124fee | <|skeleton|>
class MultiSensorAdapter:
"""Method to run TempSensorAdapterTask takes in sleeptime and looptime in the constructors has a bunch of settings to control the program behavior with"""
def __init__(self, loop_param=10, sleep_param=1):
"""Constructor Initializing both the sensor tasks and a dat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiSensorAdapter:
"""Method to run TempSensorAdapterTask takes in sleeptime and looptime in the constructors has a bunch of settings to control the program behavior with"""
def __init__(self, loop_param=10, sleep_param=1):
"""Constructor Initializing both the sensor tasks and a data manager."""... | the_stack_v2_python_sparse | apps/labs/module06/MultiSensorAdapter.py | mnk400/iot-device | train | 0 |
6973e814e8bd0ed9c172ed148e16416b9727b213 | [
"output_json = dict()\noutput_json['AuthenticationDetails'] = request.data['AuthenticationDetails']\noutput_json['SessionDetails'] = request.data['SessionDetails']\noutput_json['Payload'] = dict(zip(['user_ip'], [None]))\nreturn Response(output_json)",
"try:\n input_json = request.data\n if 'APIParams' not ... | <|body_start_0|>
output_json = dict()
output_json['AuthenticationDetails'] = request.data['AuthenticationDetails']
output_json['SessionDetails'] = request.data['SessionDetails']
output_json['Payload'] = dict(zip(['user_ip'], [None]))
return Response(output_json)
<|end_body_0|>
<... | This API gets user's ip | GetUserIPAPI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetUserIPAPI:
"""This API gets user's ip"""
def post(self, request):
"""Post function to get user's ip"""
<|body_0|>
def get_user_ip_json(self, request):
"""json function to get user's ip. This is an unusual json function as it takes request object (request) inst... | stack_v2_sparse_classes_36k_train_000982 | 2,283 | no_license | [
{
"docstring": "Post function to get user's ip",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "json function to get user's ip. This is an unusual json function as it takes request object (request) instead of json object (request.data) as input",
"name": "get_user_... | 2 | null | Implement the Python class `GetUserIPAPI` described below.
Class description:
This API gets user's ip
Method signatures and docstrings:
- def post(self, request): Post function to get user's ip
- def get_user_ip_json(self, request): json function to get user's ip. This is an unusual json function as it takes request ... | Implement the Python class `GetUserIPAPI` described below.
Class description:
This API gets user's ip
Method signatures and docstrings:
- def post(self, request): Post function to get user's ip
- def get_user_ip_json(self, request): json function to get user's ip. This is an unusual json function as it takes request ... | 36eb9931f330e64902354c6fc471be2adf4b7049 | <|skeleton|>
class GetUserIPAPI:
"""This API gets user's ip"""
def post(self, request):
"""Post function to get user's ip"""
<|body_0|>
def get_user_ip_json(self, request):
"""json function to get user's ip. This is an unusual json function as it takes request object (request) inst... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetUserIPAPI:
"""This API gets user's ip"""
def post(self, request):
"""Post function to get user's ip"""
output_json = dict()
output_json['AuthenticationDetails'] = request.data['AuthenticationDetails']
output_json['SessionDetails'] = request.data['SessionDetails']
... | the_stack_v2_python_sparse | Generic/common/location/api/get_user_ip/views_get_user_ip.py | archiemb303/common_backend_django | train | 0 |
91a8bd58b62134b8b25b058b92931d651fcc1c80 | [
"if Projection.projection is None:\n sys.stderr.write('Warning: Projection.getProjection() called before Projection.initProjection()\\n')\nreturn Projection.projection",
"if Projection.projection is None:\n sys.stderr.write('Warning: Projection.getProjection() called before Projection.initProjection()\\n')\... | <|body_start_0|>
if Projection.projection is None:
sys.stderr.write('Warning: Projection.getProjection() called before Projection.initProjection()\n')
return Projection.projection
<|end_body_0|>
<|body_start_1|>
if Projection.projection is None:
sys.stderr.write('Warning... | Class defining the projection used. | Projection | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Projection:
"""Class defining the projection used."""
def getProjection():
"""Return the projection used."""
<|body_0|>
def getProjectionString():
"""Return the projection used as a string."""
<|body_1|>
def project(long, lat):
"""Return a pr... | stack_v2_sparse_classes_36k_train_000983 | 2,522 | permissive | [
{
"docstring": "Return the projection used.",
"name": "getProjection",
"signature": "def getProjection()"
},
{
"docstring": "Return the projection used as a string.",
"name": "getProjectionString",
"signature": "def getProjectionString()"
},
{
"docstring": "Return a projected coo... | 4 | stack_v2_sparse_classes_30k_train_020699 | Implement the Python class `Projection` described below.
Class description:
Class defining the projection used.
Method signatures and docstrings:
- def getProjection(): Return the projection used.
- def getProjectionString(): Return the projection used as a string.
- def project(long, lat): Return a projected coordin... | Implement the Python class `Projection` described below.
Class description:
Class defining the projection used.
Method signatures and docstrings:
- def getProjection(): Return the projection used.
- def getProjectionString(): Return the projection used as a string.
- def project(long, lat): Return a projected coordin... | 8aba6eaae76989facf3442305c8089d3cc366bcf | <|skeleton|>
class Projection:
"""Class defining the projection used."""
def getProjection():
"""Return the projection used."""
<|body_0|>
def getProjectionString():
"""Return the projection used as a string."""
<|body_1|>
def project(long, lat):
"""Return a pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Projection:
"""Class defining the projection used."""
def getProjection():
"""Return the projection used."""
if Projection.projection is None:
sys.stderr.write('Warning: Projection.getProjection() called before Projection.initProjection()\n')
return Projection.projecti... | the_stack_v2_python_sparse | resources/osm_importer/projection.py | cyberbotics/webots | train | 2,495 |
6b2c4143a80df5931e14f92675e9c4bdb2ab2741 | [
"super(Transformer, self).__init__()\nself.embedding = fc_block(input_dim, output_dim, activation=activation)\nself.act = activation\nlayers = []\ndims = [output_dim] + [output_dim] * layer_num\nself.dropout = nn.Dropout(dropout_ratio)\nfor i in range(layer_num):\n layers.append(TransformerLayer(dims[i], head_di... | <|body_start_0|>
super(Transformer, self).__init__()
self.embedding = fc_block(input_dim, output_dim, activation=activation)
self.act = activation
layers = []
dims = [output_dim] + [output_dim] * layer_num
self.dropout = nn.Dropout(dropout_ratio)
for i in range(la... | Overview: Transformer implementation .. note:: For details refer to Attention is all you need: http://arxiv.org/abs/1706.03762 | Transformer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Transformer:
"""Overview: Transformer implementation .. note:: For details refer to Attention is all you need: http://arxiv.org/abs/1706.03762"""
def __init__(self, input_dim: int, head_dim: int=128, hidden_dim: int=1024, output_dim: int=256, head_num: int=2, mlp_num: int=2, layer_num: int=3... | stack_v2_sparse_classes_36k_train_000984 | 8,556 | permissive | [
{
"docstring": "Overview: Init transformer Arguments: - input_dim (:obj:`int`): dimension of input - head_dim (:obj:`int`): dimension of each head - hidden_dim (:obj:`int`): dimension of hidden layer in mlp - output_dim (:obj:`int`): dimension of output - head_num (:obj:`int`): number of heads for multihead att... | 2 | null | Implement the Python class `Transformer` described below.
Class description:
Overview: Transformer implementation .. note:: For details refer to Attention is all you need: http://arxiv.org/abs/1706.03762
Method signatures and docstrings:
- def __init__(self, input_dim: int, head_dim: int=128, hidden_dim: int=1024, ou... | Implement the Python class `Transformer` described below.
Class description:
Overview: Transformer implementation .. note:: For details refer to Attention is all you need: http://arxiv.org/abs/1706.03762
Method signatures and docstrings:
- def __init__(self, input_dim: int, head_dim: int=128, hidden_dim: int=1024, ou... | eb483fa6e46602d58c8e7d2ca1e566adca28e703 | <|skeleton|>
class Transformer:
"""Overview: Transformer implementation .. note:: For details refer to Attention is all you need: http://arxiv.org/abs/1706.03762"""
def __init__(self, input_dim: int, head_dim: int=128, hidden_dim: int=1024, output_dim: int=256, head_num: int=2, mlp_num: int=2, layer_num: int=3... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Transformer:
"""Overview: Transformer implementation .. note:: For details refer to Attention is all you need: http://arxiv.org/abs/1706.03762"""
def __init__(self, input_dim: int, head_dim: int=128, hidden_dim: int=1024, output_dim: int=256, head_num: int=2, mlp_num: int=2, layer_num: int=3, dropout_rat... | the_stack_v2_python_sparse | ding/torch_utils/network/transformer.py | shengxuesun/DI-engine | train | 1 |
eec1fe6596707cec2607002c0eaefec805e6f255 | [
"self.config = config_\nself.logger = logging.getLogger('cuda_logger')\ndata_provider = DataProvider(self.config)\nfilename = self.config['city_state_creator'].get('filename', 'city_states.dill')\nself.city_states = data_provider.read_city_states(filename)\nself.reg_models = data_provider.read_regression_models()",... | <|body_start_0|>
self.config = config_
self.logger = logging.getLogger('cuda_logger')
data_provider = DataProvider(self.config)
filename = self.config['city_state_creator'].get('filename', 'city_states.dill')
self.city_states = data_provider.read_city_states(filename)
sel... | Fills up sparse city state matrices | SparseMatrixFiller | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SparseMatrixFiller:
"""Fills up sparse city state matrices"""
def __init__(self, config_):
"""Constructor :param config_: :return:"""
<|body_0|>
def fill_matrices(self):
"""Fills up sparse matrices :param: :return:"""
<|body_1|>
def fill_rewards_matr... | stack_v2_sparse_classes_36k_train_000985 | 5,073 | no_license | [
{
"docstring": "Constructor :param config_: :return:",
"name": "__init__",
"signature": "def __init__(self, config_)"
},
{
"docstring": "Fills up sparse matrices :param: :return:",
"name": "fill_matrices",
"signature": "def fill_matrices(self)"
},
{
"docstring": "Fills missing en... | 6 | null | Implement the Python class `SparseMatrixFiller` described below.
Class description:
Fills up sparse city state matrices
Method signatures and docstrings:
- def __init__(self, config_): Constructor :param config_: :return:
- def fill_matrices(self): Fills up sparse matrices :param: :return:
- def fill_rewards_matrix(s... | Implement the Python class `SparseMatrixFiller` described below.
Class description:
Fills up sparse city state matrices
Method signatures and docstrings:
- def __init__(self, config_): Constructor :param config_: :return:
- def fill_matrices(self): Fills up sparse matrices :param: :return:
- def fill_rewards_matrix(s... | f7fcd2cc1d6ba18b199d176d4d39193f025ee281 | <|skeleton|>
class SparseMatrixFiller:
"""Fills up sparse city state matrices"""
def __init__(self, config_):
"""Constructor :param config_: :return:"""
<|body_0|>
def fill_matrices(self):
"""Fills up sparse matrices :param: :return:"""
<|body_1|>
def fill_rewards_matr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SparseMatrixFiller:
"""Fills up sparse city state matrices"""
def __init__(self, config_):
"""Constructor :param config_: :return:"""
self.config = config_
self.logger = logging.getLogger('cuda_logger')
data_provider = DataProvider(self.config)
filename = self.conf... | the_stack_v2_python_sparse | learn_to_earn_framework/sparse_matrices/fill_sparse_matrices.py | transparent-framework/optimize-ride-sharing-earnings | train | 7 |
5672a433858beb1d00ae7120d445c84f9a949de8 | [
"self.maze = maze\nself.x1 = x1\nself.x2 = x2\nself.y1 = y1\nself.y2 = y2\nself.queue = deque()\nself.position = [lambda x, y: (x + 1, y), lambda x, y: (x - 1, y), lambda x, y: (x, y + 1), lambda x, y: (x, y - 1)]",
"self.queue.append((self.x1, self.y1, -1))\npath = []\nwhile len(self.queue) > 0:\n cur_node = ... | <|body_start_0|>
self.maze = maze
self.x1 = x1
self.x2 = x2
self.y1 = y1
self.y2 = y2
self.queue = deque()
self.position = [lambda x, y: (x + 1, y), lambda x, y: (x - 1, y), lambda x, y: (x, y + 1), lambda x, y: (x, y - 1)]
<|end_body_0|>
<|body_start_1|>
... | 队列实现 广度优先 | QueueMazePath | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QueueMazePath:
"""队列实现 广度优先"""
def __init__(self, maze, x1, y1, x2, y2):
"""初始化数据 :param maze: 二维列表 :param x1: 起始坐标x :param y1: 起始坐标y :param x2: 终点坐标x :param y2: 终点坐标y"""
<|body_0|>
def maze_alt(self):
"""实现算法 :return:"""
<|body_1|>
<|end_skeleton|>
<|b... | stack_v2_sparse_classes_36k_train_000986 | 4,700 | no_license | [
{
"docstring": "初始化数据 :param maze: 二维列表 :param x1: 起始坐标x :param y1: 起始坐标y :param x2: 终点坐标x :param y2: 终点坐标y",
"name": "__init__",
"signature": "def __init__(self, maze, x1, y1, x2, y2)"
},
{
"docstring": "实现算法 :return:",
"name": "maze_alt",
"signature": "def maze_alt(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018877 | Implement the Python class `QueueMazePath` described below.
Class description:
队列实现 广度优先
Method signatures and docstrings:
- def __init__(self, maze, x1, y1, x2, y2): 初始化数据 :param maze: 二维列表 :param x1: 起始坐标x :param y1: 起始坐标y :param x2: 终点坐标x :param y2: 终点坐标y
- def maze_alt(self): 实现算法 :return: | Implement the Python class `QueueMazePath` described below.
Class description:
队列实现 广度优先
Method signatures and docstrings:
- def __init__(self, maze, x1, y1, x2, y2): 初始化数据 :param maze: 二维列表 :param x1: 起始坐标x :param y1: 起始坐标y :param x2: 终点坐标x :param y2: 终点坐标y
- def maze_alt(self): 实现算法 :return:
<|skeleton|>
class Que... | 894137bacf0305b8afdd74302f416b2715e216fd | <|skeleton|>
class QueueMazePath:
"""队列实现 广度优先"""
def __init__(self, maze, x1, y1, x2, y2):
"""初始化数据 :param maze: 二维列表 :param x1: 起始坐标x :param y1: 起始坐标y :param x2: 终点坐标x :param y2: 终点坐标y"""
<|body_0|>
def maze_alt(self):
"""实现算法 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QueueMazePath:
"""队列实现 广度优先"""
def __init__(self, maze, x1, y1, x2, y2):
"""初始化数据 :param maze: 二维列表 :param x1: 起始坐标x :param y1: 起始坐标y :param x2: 终点坐标x :param y2: 终点坐标y"""
self.maze = maze
self.x1 = x1
self.x2 = x2
self.y1 = y1
self.y2 = y2
self.queu... | the_stack_v2_python_sparse | data_struct/stack_queue_demo.py | dxc13762525628/concurrent | train | 0 |
aeb8478cc684637b28720155b71014c7b9b81c1d | [
"self.nums = nums\nN = len(nums)\nBIT = [0] * (N + 1)\nfor i in range(N + 1):\n BIT[i] = sum(nums[i - (i & -i):i])\nself.BIT = BIT",
"if not self.nums:\n return\nd = val - self.nums[i]\nself.nums[i] = val\nx = i + 1\nwhile x < len(self.BIT):\n self.BIT[x] += d\n x += x & -x",
"if not self.nums or i ... | <|body_start_0|>
self.nums = nums
N = len(nums)
BIT = [0] * (N + 1)
for i in range(N + 1):
BIT[i] = sum(nums[i - (i & -i):i])
self.BIT = BIT
<|end_body_0|>
<|body_start_1|>
if not self.nums:
return
d = val - self.nums[i]
self.nums[... | NumArray | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def update(self, i, val):
""":type i: int :type val: int :rtype: void"""
<|body_1|>
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
<|body_2|... | stack_v2_sparse_classes_36k_train_000987 | 3,955 | permissive | [
{
"docstring": ":type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": ":type i: int :type val: int :rtype: void",
"name": "update",
"signature": "def update(self, i, val)"
},
{
"docstring": ":type i: int :type j: int :rtype: int",
... | 3 | null | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def update(self, i, val): :type i: int :type val: int :rtype: void
- def sumRange(self, i, j): :type i: int :type j: int :rtype:... | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def update(self, i, val): :type i: int :type val: int :rtype: void
- def sumRange(self, i, j): :type i: int :type j: int :rtype:... | 2830c7e2ada8dfd3dcdda7c06846116d4f944a27 | <|skeleton|>
class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def update(self, i, val):
""":type i: int :type val: int :rtype: void"""
<|body_1|>
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
<|body_2|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
self.nums = nums
N = len(nums)
BIT = [0] * (N + 1)
for i in range(N + 1):
BIT[i] = sum(nums[i - (i & -i):i])
self.BIT = BIT
def update(self, i, val):
""":type i: int :type v... | the_stack_v2_python_sparse | leetcode/medium/Range_Sum_Query_Mutable.py | shhuan/algorithms | train | 0 | |
b891a1d58d4a863f443a35cd5c3803828e8b3b29 | [
"self.ebvs = {}\nself.ebvs['young'] = float(self.parameters['E_BVs_young'])\nself.ebvs_old_factor = float(self.parameters['E_BVs_old_factor'])\nself.ebvs['old'] = self.ebvs_old_factor * self.ebvs['young']\nself.uv_bump_wavelength = float(self.parameters['uv_bump_wavelength'])\nself.uv_bump_width = float(self.parame... | <|body_start_0|>
self.ebvs = {}
self.ebvs['young'] = float(self.parameters['E_BVs_young'])
self.ebvs_old_factor = float(self.parameters['E_BVs_old_factor'])
self.ebvs['old'] = self.ebvs_old_factor * self.ebvs['young']
self.uv_bump_wavelength = float(self.parameters['uv_bump_wavel... | Calzetti + Leitherer attenuation module This module computes the dust attenuation using the formulae from Calzetti et al. (2000) and Leitherer et al. (2002). The attenuation can be computed on the whole spectrum or on a specific contribution and is added to the SED as a negative contribution. | CalzLeit | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CalzLeit:
"""Calzetti + Leitherer attenuation module This module computes the dust attenuation using the formulae from Calzetti et al. (2000) and Leitherer et al. (2002). The attenuation can be computed on the whole spectrum or on a specific contribution and is added to the SED as a negative cont... | stack_v2_sparse_classes_36k_train_000988 | 11,143 | no_license | [
{
"docstring": "Get the filters from the database",
"name": "_init_code",
"signature": "def _init_code(self)"
},
{
"docstring": "Add the CCM dust attenuation to the SED. Parameters ---------- sed: pcigale.sed.SED object",
"name": "process",
"signature": "def process(self, sed)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016517 | Implement the Python class `CalzLeit` described below.
Class description:
Calzetti + Leitherer attenuation module This module computes the dust attenuation using the formulae from Calzetti et al. (2000) and Leitherer et al. (2002). The attenuation can be computed on the whole spectrum or on a specific contribution and... | Implement the Python class `CalzLeit` described below.
Class description:
Calzetti + Leitherer attenuation module This module computes the dust attenuation using the formulae from Calzetti et al. (2000) and Leitherer et al. (2002). The attenuation can be computed on the whole spectrum or on a specific contribution and... | 9ef9b99425537350b8706fddfe90ed47301107a5 | <|skeleton|>
class CalzLeit:
"""Calzetti + Leitherer attenuation module This module computes the dust attenuation using the formulae from Calzetti et al. (2000) and Leitherer et al. (2002). The attenuation can be computed on the whole spectrum or on a specific contribution and is added to the SED as a negative cont... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CalzLeit:
"""Calzetti + Leitherer attenuation module This module computes the dust attenuation using the formulae from Calzetti et al. (2000) and Leitherer et al. (2002). The attenuation can be computed on the whole spectrum or on a specific contribution and is added to the SED as a negative contribution."""
... | the_stack_v2_python_sparse | pcigale/sed_modules/dustatt_calzleit.py | JohannesBuchner/cigale | train | 5 |
1116992dc5dd5909f642ee759e3b607fa8a21d41 | [
"assert textcoords.startswith('offset '), \"*textcoords* must be 'offset points' or 'offset pixels'\"\nself.line = line\nself.xytext = xytext\nxs, ys = line.get_data()\nif type(xs) == au.quantity.Quantity:\n xs = xs.value\nif type(ys) == au.quantity.Quantity:\n ys = ys.value\n\ndef neighbours(x, xs, ys, try_i... | <|body_start_0|>
assert textcoords.startswith('offset '), "*textcoords* must be 'offset points' or 'offset pixels'"
self.line = line
self.xytext = xytext
xs, ys = line.get_data()
if type(xs) == au.quantity.Quantity:
xs = xs.value
if type(ys) == au.quantity.Qua... | A sloped annotation to *line* at position *x* with *text* Optionally an arrow pointing from the text to the graph at *x* can be drawn. Usage ----- fig, ax = plt.subplots() x = np.linspace(0, 2*pi) line, = ax.plot(x, sin(x)) ax.add_artist(LineAnnotation("text", line, 1.5)) | LineAnnotation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LineAnnotation:
"""A sloped annotation to *line* at position *x* with *text* Optionally an arrow pointing from the text to the graph at *x* can be drawn. Usage ----- fig, ax = plt.subplots() x = np.linspace(0, 2*pi) line, = ax.plot(x, sin(x)) ax.add_artist(LineAnnotation("text", line, 1.5))"""
... | stack_v2_sparse_classes_36k_train_000989 | 4,212 | permissive | [
{
"docstring": "Annotate the point at *x* of the graph *line* with text *text*. By default, the text is displayed with the same rotation as the slope of the graph at a relative position *xytext* above it (perpendicularly above). An arrow pointing from the text to the annotated point *xy* can be added by definin... | 3 | null | Implement the Python class `LineAnnotation` described below.
Class description:
A sloped annotation to *line* at position *x* with *text* Optionally an arrow pointing from the text to the graph at *x* can be drawn. Usage ----- fig, ax = plt.subplots() x = np.linspace(0, 2*pi) line, = ax.plot(x, sin(x)) ax.add_artist(L... | Implement the Python class `LineAnnotation` described below.
Class description:
A sloped annotation to *line* at position *x* with *text* Optionally an arrow pointing from the text to the graph at *x* can be drawn. Usage ----- fig, ax = plt.subplots() x = np.linspace(0, 2*pi) line, = ax.plot(x, sin(x)) ax.add_artist(L... | 000631c0e643cc3d68a7c97658573a931ea745cb | <|skeleton|>
class LineAnnotation:
"""A sloped annotation to *line* at position *x* with *text* Optionally an arrow pointing from the text to the graph at *x* can be drawn. Usage ----- fig, ax = plt.subplots() x = np.linspace(0, 2*pi) line, = ax.plot(x, sin(x)) ax.add_artist(LineAnnotation("text", line, 1.5))"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LineAnnotation:
"""A sloped annotation to *line* at position *x* with *text* Optionally an arrow pointing from the text to the graph at *x* can be drawn. Usage ----- fig, ax = plt.subplots() x = np.linspace(0, 2*pi) line, = ax.plot(x, sin(x)) ax.add_artist(LineAnnotation("text", line, 1.5))"""
def __init... | the_stack_v2_python_sparse | pyathena/plt_tools/line_annotation.py | jeonggyukim/pyathena | train | 3 |
c41fc58f338093078e3aa771f9a7fa301d4955c4 | [
"self.drive.find_element_by_xpath(\".//*[@id='app']/div[3]/div[2]/div/div[2]/div/div/div[3]/form/div[1]/div/div/label[2]/span[1]/span\").click()\nusername = self.drive.find_element_by_xpath(\".//*[@id='app']/div[3]/div[2]/div/div[2]/div/div/div[3]/form/div[2]/div/div/input\")\npassword = self.drive.find_element_by_... | <|body_start_0|>
self.drive.find_element_by_xpath(".//*[@id='app']/div[3]/div[2]/div/div[2]/div/div/div[3]/form/div[1]/div/div/label[2]/span[1]/span").click()
username = self.drive.find_element_by_xpath(".//*[@id='app']/div[3]/div[2]/div/div[2]/div/div/div[3]/form/div[2]/div/div/input")
password... | portal_fabu_001 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class portal_fabu_001:
def test_login_001(self):
"""登录"""
<|body_0|>
def test_fabu_001(self):
"""流转发布第一步"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.drive.find_element_by_xpath(".//*[@id='app']/div[3]/div[2]/div/div[2]/div/div/div[3]/form/div[1]/... | stack_v2_sparse_classes_36k_train_000990 | 3,377 | no_license | [
{
"docstring": "登录",
"name": "test_login_001",
"signature": "def test_login_001(self)"
},
{
"docstring": "流转发布第一步",
"name": "test_fabu_001",
"signature": "def test_fabu_001(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017252 | Implement the Python class `portal_fabu_001` described below.
Class description:
Implement the portal_fabu_001 class.
Method signatures and docstrings:
- def test_login_001(self): 登录
- def test_fabu_001(self): 流转发布第一步 | Implement the Python class `portal_fabu_001` described below.
Class description:
Implement the portal_fabu_001 class.
Method signatures and docstrings:
- def test_login_001(self): 登录
- def test_fabu_001(self): 流转发布第一步
<|skeleton|>
class portal_fabu_001:
def test_login_001(self):
"""登录"""
<|body_... | 87d713a5c8d3763b3dfa191cd7a00933899679b9 | <|skeleton|>
class portal_fabu_001:
def test_login_001(self):
"""登录"""
<|body_0|>
def test_fabu_001(self):
"""流转发布第一步"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class portal_fabu_001:
def test_login_001(self):
"""登录"""
self.drive.find_element_by_xpath(".//*[@id='app']/div[3]/div[2]/div/div[2]/div/div/div[3]/form/div[1]/div/div/label[2]/span[1]/span").click()
username = self.drive.find_element_by_xpath(".//*[@id='app']/div[3]/div[2]/div/div[2]/div/di... | the_stack_v2_python_sparse | por/portalA.py | Zhaokun-max/workspaces | train | 0 | |
bb0c5155111e0c6ad0be0dcc95af68e9fde7e24b | [
"response = self.client.get(reverse('education:demographic_detail', args=('XYZ',)))\nself.assertEqual(response.status_code, 200)\nself.assertEqual(response.context.get('json_rate_data'), None)\nself.assertNotEqual(response.context.get('message'), None)\nself.assertContains(response, 'Home')\nself.assertContains(res... | <|body_start_0|>
response = self.client.get(reverse('education:demographic_detail', args=('XYZ',)))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.context.get('json_rate_data'), None)
self.assertNotEqual(response.context.get('message'), None)
self.assertCon... | EducationDemographicDetailsViewTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EducationDemographicDetailsViewTest:
def test_fake_group(self):
"""Make sure the page gives an error message if a group is specified that does not actually exist."""
<|body_0|>
def test_no_data(self):
"""Make sure all demographic pages render even when there is no da... | stack_v2_sparse_classes_36k_train_000991 | 9,266 | no_license | [
{
"docstring": "Make sure the page gives an error message if a group is specified that does not actually exist.",
"name": "test_fake_group",
"signature": "def test_fake_group(self)"
},
{
"docstring": "Make sure all demographic pages render even when there is no data in the database.",
"name"... | 3 | stack_v2_sparse_classes_30k_train_017010 | Implement the Python class `EducationDemographicDetailsViewTest` described below.
Class description:
Implement the EducationDemographicDetailsViewTest class.
Method signatures and docstrings:
- def test_fake_group(self): Make sure the page gives an error message if a group is specified that does not actually exist.
-... | Implement the Python class `EducationDemographicDetailsViewTest` described below.
Class description:
Implement the EducationDemographicDetailsViewTest class.
Method signatures and docstrings:
- def test_fake_group(self): Make sure the page gives an error message if a group is specified that does not actually exist.
-... | 2a8e2dc4e9b3cb92d4d437b37e61940a9486b81f | <|skeleton|>
class EducationDemographicDetailsViewTest:
def test_fake_group(self):
"""Make sure the page gives an error message if a group is specified that does not actually exist."""
<|body_0|>
def test_no_data(self):
"""Make sure all demographic pages render even when there is no da... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EducationDemographicDetailsViewTest:
def test_fake_group(self):
"""Make sure the page gives an error message if a group is specified that does not actually exist."""
response = self.client.get(reverse('education:demographic_detail', args=('XYZ',)))
self.assertEqual(response.status_code... | the_stack_v2_python_sparse | education/tests.py | smeds1/mysite | train | 1 | |
ba5a4e19bc1af156ac0e7caf39cf21953f8089f7 | [
"super().__init__()\nself._encoder = FCNNEncoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension)\nself._decoder = FCNNDecoder(layer_spec_target_res, layer_spec_input_res, kernel_size, filters_cap, initial_filters, channels)",
"encoding = self._encoder(in... | <|body_start_0|>
super().__init__()
self._encoder = FCNNEncoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension)
self._decoder = FCNNDecoder(layer_spec_target_res, layer_spec_input_res, kernel_size, filters_cap, initial_filters, channels... | Primitive Model for all fully convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = FCNNAutoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_dimension=100, channels=3, ) encoding, reconstruction = autoencoder(t... | FCNNAutoencoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FCNNAutoencoder:
"""Primitive Model for all fully convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = FCNNAutoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_dimension=100, channels=3,... | stack_v2_sparse_classes_36k_train_000992 | 6,511 | permissive | [
{
"docstring": "Instantiate the :py:class:`FCNNBaseAutoEncoder`. Args: layer_spec_input_res (:obj:`tuple` of (:obj:`int`, :obj:`int`)): Shape of the input tensors. layer_spec_target_res: (:obj:`tuple` of (:obj:`int`, :obj:`int`)): Shape of tensor desired as output of :func:`_get_layer_spec`. kernel_size (int): ... | 2 | stack_v2_sparse_classes_30k_val_000904 | Implement the Python class `FCNNAutoencoder` described below.
Class description:
Primitive Model for all fully convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = FCNNAutoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=1... | Implement the Python class `FCNNAutoencoder` described below.
Class description:
Primitive Model for all fully convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = FCNNAutoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=1... | 92ac86fb0c962854e0d80c44165e0e7ff126b3c1 | <|skeleton|>
class FCNNAutoencoder:
"""Primitive Model for all fully convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = FCNNAutoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_dimension=100, channels=3,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FCNNAutoencoder:
"""Primitive Model for all fully convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = FCNNAutoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_dimension=100, channels=3, ) encoding, ... | the_stack_v2_python_sparse | src/ashpy/models/convolutional/autoencoders.py | zurutech/ashpy | train | 89 |
4c5950ddf6f2c8b2bd1b805712ea1fb4d0c9fb83 | [
"for key, val in cls.param.items():\n if not isinstance(val, (list, Quantity)):\n cls.param[key] = [val]\n elif isinstance(val, Quantity) and val.size == 1:\n try:\n iter(val.value)\n except:\n cls.param[key] = [val.value] * val.unit\ncombos = tuple((dict(zip(cls.par... | <|body_start_0|>
for key, val in cls.param.items():
if not isinstance(val, (list, Quantity)):
cls.param[key] = [val]
elif isinstance(val, Quantity) and val.size == 1:
try:
iter(val.value)
except:
cls.... | Base class for supernova model classes that initialise from physics parameters. | _RegistryModel | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _RegistryModel:
"""Base class for supernova model classes that initialise from physics parameters."""
def get_param_combinations(cls):
"""Returns all valid combinations of parameters for a given SNEWPY register model. Subclasses can provide a Callable `cls._param_validator` that take... | stack_v2_sparse_classes_36k_train_000993 | 18,095 | permissive | [
{
"docstring": "Returns all valid combinations of parameters for a given SNEWPY register model. Subclasses can provide a Callable `cls._param_validator` that takes a combination of parameters as an argument and returns True if a particular combinations of parameters is valid. If None is provided, all combinatio... | 2 | stack_v2_sparse_classes_30k_train_000207 | Implement the Python class `_RegistryModel` described below.
Class description:
Base class for supernova model classes that initialise from physics parameters.
Method signatures and docstrings:
- def get_param_combinations(cls): Returns all valid combinations of parameters for a given SNEWPY register model. Subclasse... | Implement the Python class `_RegistryModel` described below.
Class description:
Base class for supernova model classes that initialise from physics parameters.
Method signatures and docstrings:
- def get_param_combinations(cls): Returns all valid combinations of parameters for a given SNEWPY register model. Subclasse... | feb3a6c46d7dc4e999446994025001de77768e1d | <|skeleton|>
class _RegistryModel:
"""Base class for supernova model classes that initialise from physics parameters."""
def get_param_combinations(cls):
"""Returns all valid combinations of parameters for a given SNEWPY register model. Subclasses can provide a Callable `cls._param_validator` that take... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _RegistryModel:
"""Base class for supernova model classes that initialise from physics parameters."""
def get_param_combinations(cls):
"""Returns all valid combinations of parameters for a given SNEWPY register model. Subclasses can provide a Callable `cls._param_validator` that takes a combinati... | the_stack_v2_python_sparse | python/snewpy/models/base.py | SNEWS2/snewpy | train | 22 |
2b9790055970f0e55c898e7d0da41adc6e3a16d6 | [
"curent_file_path = os.path.realpath(__file__)\ndir_name = os.path.dirname(curent_file_path)\ndir_name = os.path.dirname(dir_name)\nreturn dir_name + '\\\\'",
"real_file_path = self.get_project_path() + file_path\nconfig = configparser.ConfigParser()\nconfig.read(real_file_path)\nvalue = config.get('env', key)\nr... | <|body_start_0|>
curent_file_path = os.path.realpath(__file__)
dir_name = os.path.dirname(curent_file_path)
dir_name = os.path.dirname(dir_name)
return dir_name + '\\'
<|end_body_0|>
<|body_start_1|>
real_file_path = self.get_project_path() + file_path
config = configpar... | DataRead | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataRead:
def get_project_path(self):
"""获取当前工程路径 :return:"""
<|body_0|>
def read_ini(self, file_path, key):
"""读取ini文件，返回key 返回的value :param file_path: 文件路径 :param key: :return: key 对应的value"""
<|body_1|>
def read_yaml(self, file_path):
"""读取yam... | stack_v2_sparse_classes_36k_train_000994 | 1,600 | no_license | [
{
"docstring": "获取当前工程路径 :return:",
"name": "get_project_path",
"signature": "def get_project_path(self)"
},
{
"docstring": "读取ini文件，返回key 返回的value :param file_path: 文件路径 :param key: :return: key 对应的value",
"name": "read_ini",
"signature": "def read_ini(self, file_path, key)"
},
{
... | 3 | stack_v2_sparse_classes_30k_train_004265 | Implement the Python class `DataRead` described below.
Class description:
Implement the DataRead class.
Method signatures and docstrings:
- def get_project_path(self): 获取当前工程路径 :return:
- def read_ini(self, file_path, key): 读取ini文件，返回key 返回的value :param file_path: 文件路径 :param key: :return: key 对应的value
- def read_yam... | Implement the Python class `DataRead` described below.
Class description:
Implement the DataRead class.
Method signatures and docstrings:
- def get_project_path(self): 获取当前工程路径 :return:
- def read_ini(self, file_path, key): 读取ini文件，返回key 返回的value :param file_path: 文件路径 :param key: :return: key 对应的value
- def read_yam... | d281e1438260746d27627326b76f5e4f10b74a5b | <|skeleton|>
class DataRead:
def get_project_path(self):
"""获取当前工程路径 :return:"""
<|body_0|>
def read_ini(self, file_path, key):
"""读取ini文件，返回key 返回的value :param file_path: 文件路径 :param key: :return: key 对应的value"""
<|body_1|>
def read_yaml(self, file_path):
"""读取yam... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataRead:
def get_project_path(self):
"""获取当前工程路径 :return:"""
curent_file_path = os.path.realpath(__file__)
dir_name = os.path.dirname(curent_file_path)
dir_name = os.path.dirname(dir_name)
return dir_name + '\\'
def read_ini(self, file_path, key):
"""读取ini... | the_stack_v2_python_sparse | jinrongxiangmuzonghe/caw/DataRead.py | dA123Ea/ApiAutoTest | train | 0 | |
cf91167ae80ee39a766aa708cf7e50c09b485a1c | [
"wmin = 2.0 * fmin / sr\nwmax = 2.0 * fmax / sr\nite = np.arange(-(win_size // 2), (win_size + 1) // 2)\nself.filter = wmin * np.sinc(wmin * ite) - wmax * np.sinc(wmax * ite)\nself.filter[win_size // 2] += 1.0\nself.filter *= np.hamming(win_size)\nself.status = np.zeros(win_size)\nself.win_size = win_size",
"if i... | <|body_start_0|>
wmin = 2.0 * fmin / sr
wmax = 2.0 * fmax / sr
ite = np.arange(-(win_size // 2), (win_size + 1) // 2)
self.filter = wmin * np.sinc(wmin * ite) - wmax * np.sinc(wmax * ite)
self.filter[win_size // 2] += 1.0
self.filter *= np.hamming(win_size)
self.s... | BSF | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BSF:
def __init__(self, fmin, fmax, win_size, sr):
"""Args: fmin (int):the lowest frequency of the band stopped fmax (int):the highest frequency of the band stopped win_size(int):sizeof window applied to filter sr (int):sampling rate of the signal"""
<|body_0|>
def __call__(... | stack_v2_sparse_classes_36k_train_000995 | 5,321 | no_license | [
{
"docstring": "Args: fmin (int):the lowest frequency of the band stopped fmax (int):the highest frequency of the band stopped win_size(int):sizeof window applied to filter sr (int):sampling rate of the signal",
"name": "__init__",
"signature": "def __init__(self, fmin, fmax, win_size, sr)"
},
{
... | 2 | null | Implement the Python class `BSF` described below.
Class description:
Implement the BSF class.
Method signatures and docstrings:
- def __init__(self, fmin, fmax, win_size, sr): Args: fmin (int):the lowest frequency of the band stopped fmax (int):the highest frequency of the band stopped win_size(int):sizeof window app... | Implement the Python class `BSF` described below.
Class description:
Implement the BSF class.
Method signatures and docstrings:
- def __init__(self, fmin, fmax, win_size, sr): Args: fmin (int):the lowest frequency of the band stopped fmax (int):the highest frequency of the band stopped win_size(int):sizeof window app... | 11edb5540f57429019ece8ddd60ed439f337b186 | <|skeleton|>
class BSF:
def __init__(self, fmin, fmax, win_size, sr):
"""Args: fmin (int):the lowest frequency of the band stopped fmax (int):the highest frequency of the band stopped win_size(int):sizeof window applied to filter sr (int):sampling rate of the signal"""
<|body_0|>
def __call__(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BSF:
def __init__(self, fmin, fmax, win_size, sr):
"""Args: fmin (int):the lowest frequency of the band stopped fmax (int):the highest frequency of the band stopped win_size(int):sizeof window applied to filter sr (int):sampling rate of the signal"""
wmin = 2.0 * fmin / sr
wmax = 2.0 *... | the_stack_v2_python_sparse | ex_2/a_miyashita/main.py | shin04/B4Lecture-2021 | train | 0 | |
df43e17021f7ff51c390f323ea8afa2d08784064 | [
"if not attrs['password'] == attrs['password2']:\n raise ValidationError({'password': \"Password fields didn't match.\"})\nreturn attrs",
"user = User.objects.create(username=validated_data['username'], email=validated_data['email'], name=validated_data['name'])\nuser.set_password(validated_data['password'])\n... | <|body_start_0|>
if not attrs['password'] == attrs['password2']:
raise ValidationError({'password': "Password fields didn't match."})
return attrs
<|end_body_0|>
<|body_start_1|>
user = User.objects.create(username=validated_data['username'], email=validated_data['email'], name=vali... | --Register Serializer-- | RegisterSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegisterSerializer:
"""--Register Serializer--"""
def validate(self, attrs):
"""--Validate override-- 1) Checks to make sure both passwords match. Returns attribute dict or ValidationError."""
<|body_0|>
def create(self, validated_data):
"""--Create function over... | stack_v2_sparse_classes_36k_train_000996 | 9,847 | permissive | [
{
"docstring": "--Validate override-- 1) Checks to make sure both passwords match. Returns attribute dict or ValidationError.",
"name": "validate",
"signature": "def validate(self, attrs)"
},
{
"docstring": "--Create function override-- :returns: User object. 1) Creates new user with validated d... | 2 | stack_v2_sparse_classes_30k_train_003361 | Implement the Python class `RegisterSerializer` described below.
Class description:
--Register Serializer--
Method signatures and docstrings:
- def validate(self, attrs): --Validate override-- 1) Checks to make sure both passwords match. Returns attribute dict or ValidationError.
- def create(self, validated_data): -... | Implement the Python class `RegisterSerializer` described below.
Class description:
--Register Serializer--
Method signatures and docstrings:
- def validate(self, attrs): --Validate override-- 1) Checks to make sure both passwords match. Returns attribute dict or ValidationError.
- def create(self, validated_data): -... | 8d984ee3f9b2b7ea35a847013743f236a1a67fdb | <|skeleton|>
class RegisterSerializer:
"""--Register Serializer--"""
def validate(self, attrs):
"""--Validate override-- 1) Checks to make sure both passwords match. Returns attribute dict or ValidationError."""
<|body_0|>
def create(self, validated_data):
"""--Create function over... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegisterSerializer:
"""--Register Serializer--"""
def validate(self, attrs):
"""--Validate override-- 1) Checks to make sure both passwords match. Returns attribute dict or ValidationError."""
if not attrs['password'] == attrs['password2']:
raise ValidationError({'password': "... | the_stack_v2_python_sparse | blog_api/users/api/serializers.py | beasyx0/blog_api | train | 0 |
b698ac2e1cdb958c5f3ad6fe55f31909450611e5 | [
"http_method = str(request_raw['method']).lower()\ntemp_url = str(request_raw['url']).strip()\ntemp_headers = header_to_lowercase(json.loads(request_raw['headers']))\nif http_method == HttpMethod.GET:\n parameters = self.get_parser_class(request_raw).get_parameter(url=temp_url, data=None, http_method=HttpMethod.... | <|body_start_0|>
http_method = str(request_raw['method']).lower()
temp_url = str(request_raw['url']).strip()
temp_headers = header_to_lowercase(json.loads(request_raw['headers']))
if http_method == HttpMethod.GET:
parameters = self.get_parser_class(request_raw).get_parameter(... | Checker | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Checker:
def check_logic(self, request_raw):
"""检测逻辑，jsonp 水坑攻击只会存在于GET请求中，检测方式，看能否自定义设置CALLBACK :param request_raw: :return:"""
<|body_0|>
def init_plugin_info(self):
"""初插件始化信息 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
http_method =... | stack_v2_sparse_classes_36k_train_000997 | 3,308 | permissive | [
{
"docstring": "检测逻辑，jsonp 水坑攻击只会存在于GET请求中，检测方式，看能否自定义设置CALLBACK :param request_raw: :return:",
"name": "check_logic",
"signature": "def check_logic(self, request_raw)"
},
{
"docstring": "初插件始化信息 :return:",
"name": "init_plugin_info",
"signature": "def init_plugin_info(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013793 | Implement the Python class `Checker` described below.
Class description:
Implement the Checker class.
Method signatures and docstrings:
- def check_logic(self, request_raw): 检测逻辑，jsonp 水坑攻击只会存在于GET请求中，检测方式，看能否自定义设置CALLBACK :param request_raw: :return:
- def init_plugin_info(self): 初插件始化信息 :return: | Implement the Python class `Checker` described below.
Class description:
Implement the Checker class.
Method signatures and docstrings:
- def check_logic(self, request_raw): 检测逻辑，jsonp 水坑攻击只会存在于GET请求中，检测方式，看能否自定义设置CALLBACK :param request_raw: :return:
- def init_plugin_info(self): 初插件始化信息 :return:
<|skeleton|>
class... | 4ee5cca8dc5fc5d7e631e935517bd0f493c30a37 | <|skeleton|>
class Checker:
def check_logic(self, request_raw):
"""检测逻辑，jsonp 水坑攻击只会存在于GET请求中，检测方式，看能否自定义设置CALLBACK :param request_raw: :return:"""
<|body_0|>
def init_plugin_info(self):
"""初插件始化信息 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Checker:
def check_logic(self, request_raw):
"""检测逻辑，jsonp 水坑攻击只会存在于GET请求中，检测方式，看能否自定义设置CALLBACK :param request_raw: :return:"""
http_method = str(request_raw['method']).lower()
temp_url = str(request_raw['url']).strip()
temp_headers = header_to_lowercase(json.loads(request_raw... | the_stack_v2_python_sparse | HunterAdminApi/plugins/owasp/jsonp_hijacking.py | a1kaid/hunter | train | 0 | |
89a228e893e87b20035e03a43537bcf5d1fcf929 | [
"super(Masker, self).__init__()\nself.rnn_enc = _rnn_enc.RNNEnc(input_dim=rnn_enc_input_dim, context_length=context_length)\nself.rnn_dec = _rnn_dec.RNNDec(input_dim=rnn_dec_input_dim)\nself.fnn = _fnn.FNNMasker(input_dim=rnn_dec_input_dim, output_dim=original_input_dim, context_length=context_length)\nself.output ... | <|body_start_0|>
super(Masker, self).__init__()
self.rnn_enc = _rnn_enc.RNNEnc(input_dim=rnn_enc_input_dim, context_length=context_length)
self.rnn_dec = _rnn_dec.RNNDec(input_dim=rnn_dec_input_dim)
self.fnn = _fnn.FNNMasker(input_dim=rnn_dec_input_dim, output_dim=original_input_dim, con... | Masker | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Masker:
def __init__(self, rnn_enc_input_dim, rnn_dec_input_dim, context_length, original_input_dim):
"""The Masker module of the MaD TwinNet. :param rnn_enc_input_dim: The input dimensionality for the RNN encoder. :type rnn_enc_input_dim: int :param rnn_dec_input_dim: The input dimensio... | stack_v2_sparse_classes_36k_train_000998 | 16,858 | no_license | [
{
"docstring": "The Masker module of the MaD TwinNet. :param rnn_enc_input_dim: The input dimensionality for the RNN encoder. :type rnn_enc_input_dim: int :param rnn_dec_input_dim: The input dimensionality for the RNN decoder. :type rnn_dec_input_dim: int :param context_length: The amount of time steps used for... | 2 | null | Implement the Python class `Masker` described below.
Class description:
Implement the Masker class.
Method signatures and docstrings:
- def __init__(self, rnn_enc_input_dim, rnn_dec_input_dim, context_length, original_input_dim): The Masker module of the MaD TwinNet. :param rnn_enc_input_dim: The input dimensionality... | Implement the Python class `Masker` described below.
Class description:
Implement the Masker class.
Method signatures and docstrings:
- def __init__(self, rnn_enc_input_dim, rnn_dec_input_dim, context_length, original_input_dim): The Masker module of the MaD TwinNet. :param rnn_enc_input_dim: The input dimensionality... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class Masker:
def __init__(self, rnn_enc_input_dim, rnn_dec_input_dim, context_length, original_input_dim):
"""The Masker module of the MaD TwinNet. :param rnn_enc_input_dim: The input dimensionality for the RNN encoder. :type rnn_enc_input_dim: int :param rnn_dec_input_dim: The input dimensio... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Masker:
def __init__(self, rnn_enc_input_dim, rnn_dec_input_dim, context_length, original_input_dim):
"""The Masker module of the MaD TwinNet. :param rnn_enc_input_dim: The input dimensionality for the RNN encoder. :type rnn_enc_input_dim: int :param rnn_dec_input_dim: The input dimensionality for the... | the_stack_v2_python_sparse | generated/test_dr_costas_mad_twinnet.py | jansel/pytorch-jit-paritybench | train | 35 | |
8d46ebc4d5c8160b8f352f3d26cd960d0422e4fe | [
"if file_resources is None:\n file_resources = {}\n file_resources['data_v2017.txt'] = 'data_v2017.txt'\nself.species = species\nsuper().__init__(path, file_resources, col_rename=col_rename, **kwargs)",
"df = pd.read_csv(self.file_resources['data_v2017.txt'], header=None, sep='\\t', encoding='unicode_escape... | <|body_start_0|>
if file_resources is None:
file_resources = {}
file_resources['data_v2017.txt'] = 'data_v2017.txt'
self.species = species
super().__init__(path, file_resources, col_rename=col_rename, **kwargs)
<|end_body_0|>
<|body_start_1|>
df = pd.read_csv(sel... | LncRNADisease | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LncRNADisease:
def __init__(self, path='http://www.cuilab.cn/files/images/ldd/', file_resources=None, species='Human', col_rename=COLUMNS_RENAME_DICT, **kwargs):
"""Args: path: file_resources: species: col_rename: **kwargs:"""
<|body_0|>
def load_dataframe(self, file_resourc... | stack_v2_sparse_classes_36k_train_000999 | 6,117 | permissive | [
{
"docstring": "Args: path: file_resources: species: col_rename: **kwargs:",
"name": "__init__",
"signature": "def __init__(self, path='http://www.cuilab.cn/files/images/ldd/', file_resources=None, species='Human', col_rename=COLUMNS_RENAME_DICT, **kwargs)"
},
{
"docstring": "Args: file_resource... | 2 | stack_v2_sparse_classes_30k_test_000822 | Implement the Python class `LncRNADisease` described below.
Class description:
Implement the LncRNADisease class.
Method signatures and docstrings:
- def __init__(self, path='http://www.cuilab.cn/files/images/ldd/', file_resources=None, species='Human', col_rename=COLUMNS_RENAME_DICT, **kwargs): Args: path: file_reso... | Implement the Python class `LncRNADisease` described below.
Class description:
Implement the LncRNADisease class.
Method signatures and docstrings:
- def __init__(self, path='http://www.cuilab.cn/files/images/ldd/', file_resources=None, species='Human', col_rename=COLUMNS_RENAME_DICT, **kwargs): Args: path: file_reso... | 35a0e00964c9b308f831263936f9507a69f52613 | <|skeleton|>
class LncRNADisease:
def __init__(self, path='http://www.cuilab.cn/files/images/ldd/', file_resources=None, species='Human', col_rename=COLUMNS_RENAME_DICT, **kwargs):
"""Args: path: file_resources: species: col_rename: **kwargs:"""
<|body_0|>
def load_dataframe(self, file_resourc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LncRNADisease:
def __init__(self, path='http://www.cuilab.cn/files/images/ldd/', file_resources=None, species='Human', col_rename=COLUMNS_RENAME_DICT, **kwargs):
"""Args: path: file_resources: species: col_rename: **kwargs:"""
if file_resources is None:
file_resources = {}
... | the_stack_v2_python_sparse | openomics/database/disease.py | JonnyTran/OpenOmics | train | 8 |
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