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dd5e067bdd138e92ad3cb25a5410c843c1b94202 | [
"self.scanner_name = 'container-capabilities-scanner'\nself.full_scanner_name = 'registry.centos.org/pipeline-images/container-capabilities-scanner'\nself.scan_types = ['check-capabilities']",
"logs = []\nsuper(ContainerCapabilities, self).__init__(image_under_test=image_under_test, scanner_name=self.scanner_name... | <|body_start_0|>
self.scanner_name = 'container-capabilities-scanner'
self.full_scanner_name = 'registry.centos.org/pipeline-images/container-capabilities-scanner'
self.scan_types = ['check-capabilities']
<|end_body_0|>
<|body_start_1|>
logs = []
super(ContainerCapabilities, sel... | Container Capabilities scan. | ContainerCapabilities | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ContainerCapabilities:
"""Container Capabilities scan."""
def __init__(self):
"""Scanner name and types."""
<|body_0|>
def scan(self, image_under_test):
"""Run the scanner on image under test."""
<|body_1|>
def process_output(self, logs):
"""... | stack_v2_sparse_classes_36k_train_033400 | 2,140 | no_license | [
{
"docstring": "Scanner name and types.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Run the scanner on image under test.",
"name": "scan",
"signature": "def scan(self, image_under_test)"
},
{
"docstring": "Process the output logs to send to other wo... | 3 | stack_v2_sparse_classes_30k_train_021663 | Implement the Python class `ContainerCapabilities` described below.
Class description:
Container Capabilities scan.
Method signatures and docstrings:
- def __init__(self): Scanner name and types.
- def scan(self, image_under_test): Run the scanner on image under test.
- def process_output(self, logs): Process the out... | Implement the Python class `ContainerCapabilities` described below.
Class description:
Container Capabilities scan.
Method signatures and docstrings:
- def __init__(self): Scanner name and types.
- def scan(self, image_under_test): Run the scanner on image under test.
- def process_output(self, logs): Process the out... | 4b59184c3453ae706d5e352306fe9e551c90dc41 | <|skeleton|>
class ContainerCapabilities:
"""Container Capabilities scan."""
def __init__(self):
"""Scanner name and types."""
<|body_0|>
def scan(self, image_under_test):
"""Run the scanner on image under test."""
<|body_1|>
def process_output(self, logs):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ContainerCapabilities:
"""Container Capabilities scan."""
def __init__(self):
"""Scanner name and types."""
self.scanner_name = 'container-capabilities-scanner'
self.full_scanner_name = 'registry.centos.org/pipeline-images/container-capabilities-scanner'
self.scan_types = ... | the_stack_v2_python_sparse | container_pipeline/scanners/container_capabilities.py | eupraxialabs/container-pipeline-service | train | 0 |
6f1bcea99af09f0bd30fab0cc486c8e6fe043792 | [
"self.center = center\nself.indices = indices\nself.score = score\nself.left = None\nself.right = None",
"self.left = _BisectingTree(indices=self.indices[labels == 0], center=centers[0], score=scores[0])\nself.right = _BisectingTree(indices=self.indices[labels == 1], center=centers[1], score=scores[1])\nself.indi... | <|body_start_0|>
self.center = center
self.indices = indices
self.score = score
self.left = None
self.right = None
<|end_body_0|>
<|body_start_1|>
self.left = _BisectingTree(indices=self.indices[labels == 0], center=centers[0], score=scores[0])
self.right = _Bise... | Tree structure representing the hierarchical clusters of BisectingKMeans. | _BisectingTree | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _BisectingTree:
"""Tree structure representing the hierarchical clusters of BisectingKMeans."""
def __init__(self, center, indices, score):
"""Create a new cluster node in the tree. The node holds the center of this cluster and the indices of the data points that belong to it."""
... | stack_v2_sparse_classes_36k_train_033401 | 18,882 | permissive | [
{
"docstring": "Create a new cluster node in the tree. The node holds the center of this cluster and the indices of the data points that belong to it.",
"name": "__init__",
"signature": "def __init__(self, center, indices, score)"
},
{
"docstring": "Split the cluster node into two subclusters.",... | 4 | stack_v2_sparse_classes_30k_train_000369 | Implement the Python class `_BisectingTree` described below.
Class description:
Tree structure representing the hierarchical clusters of BisectingKMeans.
Method signatures and docstrings:
- def __init__(self, center, indices, score): Create a new cluster node in the tree. The node holds the center of this cluster and... | Implement the Python class `_BisectingTree` described below.
Class description:
Tree structure representing the hierarchical clusters of BisectingKMeans.
Method signatures and docstrings:
- def __init__(self, center, indices, score): Create a new cluster node in the tree. The node holds the center of this cluster and... | 061f8777b48e5491b0c57bb8e0bc7067c103079d | <|skeleton|>
class _BisectingTree:
"""Tree structure representing the hierarchical clusters of BisectingKMeans."""
def __init__(self, center, indices, score):
"""Create a new cluster node in the tree. The node holds the center of this cluster and the indices of the data points that belong to it."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _BisectingTree:
"""Tree structure representing the hierarchical clusters of BisectingKMeans."""
def __init__(self, center, indices, score):
"""Create a new cluster node in the tree. The node holds the center of this cluster and the indices of the data points that belong to it."""
self.cen... | the_stack_v2_python_sparse | sklearn/cluster/_bisect_k_means.py | scikit-learn/scikit-learn | train | 58,456 |
f2089bd9188880443dd919bfc65fef2ac84b5987 | [
"if area_m2 is None:\n area = 1000000.0 * self.sample.farea[i]\nelse:\n area = area_m2\nhflux_kW = 1000.0 * rad2conv * self.sample.pow[i] / area\nptop = 1.0\nu = self.sample.u[i]\nv = self.sample.v[i]\nT = self.sample.t[i]\nq = self.sample.qv[i]\ndelp = self.sample.delp[i]\nif delp.min() <= 0 or T.min() <= 0:... | <|body_start_0|>
if area_m2 is None:
area = 1000000.0 * self.sample.farea[i]
else:
area = area_m2
hflux_kW = 1000.0 * rad2conv * self.sample.pow[i] / area
ptop = 1.0
u = self.sample.u[i]
v = self.sample.v[i]
T = self.sample.t[i]
q =... | Extension of the MxD14,IGBP and DOZIER classes, adding the Plume Rise functionality. This class handles non-gridded, observation location fires. | PLUME_L2 | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PLUME_L2:
"""Extension of the MxD14,IGBP and DOZIER classes, adding the Plume Rise functionality. This class handles non-gridded, observation location fires."""
def getPlume1(self, i, Verbose=False, rad2conv=5, area_m2=None):
"""Compute plume height for the ith fire."""
<|bod... | stack_v2_sparse_classes_36k_train_033402 | 46,607 | permissive | [
{
"docstring": "Compute plume height for the ith fire.",
"name": "getPlume1",
"signature": "def getPlume1(self, i, Verbose=False, rad2conv=5, area_m2=None)"
},
{
"docstring": "Runs the Plume Rise extension to compute the extent of the plume for each fire. It is assumed that the necessary met fie... | 3 | stack_v2_sparse_classes_30k_train_016712 | Implement the Python class `PLUME_L2` described below.
Class description:
Extension of the MxD14,IGBP and DOZIER classes, adding the Plume Rise functionality. This class handles non-gridded, observation location fires.
Method signatures and docstrings:
- def getPlume1(self, i, Verbose=False, rad2conv=5, area_m2=None)... | Implement the Python class `PLUME_L2` described below.
Class description:
Extension of the MxD14,IGBP and DOZIER classes, adding the Plume Rise functionality. This class handles non-gridded, observation location fires.
Method signatures and docstrings:
- def getPlume1(self, i, Verbose=False, rad2conv=5, area_m2=None)... | dff1f2ed36189f6879409375d241be40f18c5666 | <|skeleton|>
class PLUME_L2:
"""Extension of the MxD14,IGBP and DOZIER classes, adding the Plume Rise functionality. This class handles non-gridded, observation location fires."""
def getPlume1(self, i, Verbose=False, rad2conv=5, area_m2=None):
"""Compute plume height for the ith fire."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PLUME_L2:
"""Extension of the MxD14,IGBP and DOZIER classes, adding the Plume Rise functionality. This class handles non-gridded, observation location fires."""
def getPlume1(self, i, Verbose=False, rad2conv=5, area_m2=None):
"""Compute plume height for the ith fire."""
if area_m2 is None... | the_stack_v2_python_sparse | src/Components/qfed/qfed/PlumeRise.py | GEOS-ESM/AeroApps | train | 4 |
95381f61022132a181a0b5399a1854c8f40fbb40 | [
"AssessmentResults.__init__(self, controller, **kwargs)\nself._lst_labels.append(u'π<sub>C</sub>:')\nself._lblModel.set_tooltip_markup(_(u\"The assessment model used to calculate the inductive device's failure rate.\"))\nself.txtPiC = ramstk.RAMSTKEntry(width=125, editable=False, bold=True, tooltip=_(u'The construc... | <|body_start_0|>
AssessmentResults.__init__(self, controller, **kwargs)
self._lst_labels.append(u'π<sub>C</sub>:')
self._lblModel.set_tooltip_markup(_(u"The assessment model used to calculate the inductive device's failure rate."))
self.txtPiC = ramstk.RAMSTKEntry(width=125, editable=Fal... | Display Inductor assessment results attribute data in the RAMSTK Work Book. The Inductor assessment result view displays all the assessment results for the selected inductor. This includes, currently, results for MIL-HDBK-217FN2 parts count and part stress methods. The attributes of an Inductor assessment result view a... | InductorAssessmentResults | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InductorAssessmentResults:
"""Display Inductor assessment results attribute data in the RAMSTK Work Book. The Inductor assessment result view displays all the assessment results for the selected inductor. This includes, currently, results for MIL-HDBK-217FN2 parts count and part stress methods. T... | stack_v2_sparse_classes_36k_train_033403 | 20,499 | permissive | [
{
"docstring": "Initialize an instance of the Inductor assessment result view. :param controller: the hardware data controller instance. :type controller: :class:`ramstk.hardware.Controller.HardwareBoMDataController` :param int hardware_id: the hardware ID of the currently selected inductor. :param int subcateg... | 5 | stack_v2_sparse_classes_30k_train_010546 | Implement the Python class `InductorAssessmentResults` described below.
Class description:
Display Inductor assessment results attribute data in the RAMSTK Work Book. The Inductor assessment result view displays all the assessment results for the selected inductor. This includes, currently, results for MIL-HDBK-217FN2... | Implement the Python class `InductorAssessmentResults` described below.
Class description:
Display Inductor assessment results attribute data in the RAMSTK Work Book. The Inductor assessment result view displays all the assessment results for the selected inductor. This includes, currently, results for MIL-HDBK-217FN2... | 488ffed8b842399ddcae93007de6c6f1dda23d05 | <|skeleton|>
class InductorAssessmentResults:
"""Display Inductor assessment results attribute data in the RAMSTK Work Book. The Inductor assessment result view displays all the assessment results for the selected inductor. This includes, currently, results for MIL-HDBK-217FN2 parts count and part stress methods. T... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InductorAssessmentResults:
"""Display Inductor assessment results attribute data in the RAMSTK Work Book. The Inductor assessment result view displays all the assessment results for the selected inductor. This includes, currently, results for MIL-HDBK-217FN2 parts count and part stress methods. The attributes... | the_stack_v2_python_sparse | src/ramstk/gui/gtk/workviews/components/Inductor.py | JmiXIII/ramstk | train | 0 |
866459d62327a5df7621afee0713a5cee04b036e | [
"self.lowest_list_price = lowest_list_price\nself.highest_list_price = highest_list_price\nself.lowest_sale_price = lowest_sale_price\nself.highest_sale_price = highest_sale_price",
"if dictionary is None:\n return None\nlowest_list_price = awsecommerceservice.models.price.Price.from_dictionary(dictionary.get(... | <|body_start_0|>
self.lowest_list_price = lowest_list_price
self.highest_list_price = highest_list_price
self.lowest_sale_price = lowest_sale_price
self.highest_sale_price = highest_sale_price
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
... | Implementation of the 'CollectionSummary' model. TODO: type model description here. Attributes: lowest_list_price (Price): TODO: type description here. highest_list_price (Price): TODO: type description here. lowest_sale_price (Price): TODO: type description here. highest_sale_price (Price): TODO: type description here... | CollectionSummary | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CollectionSummary:
"""Implementation of the 'CollectionSummary' model. TODO: type model description here. Attributes: lowest_list_price (Price): TODO: type description here. highest_list_price (Price): TODO: type description here. lowest_sale_price (Price): TODO: type description here. highest_sa... | stack_v2_sparse_classes_36k_train_033404 | 2,826 | permissive | [
{
"docstring": "Constructor for the CollectionSummary class",
"name": "__init__",
"signature": "def __init__(self, lowest_list_price=None, highest_list_price=None, lowest_sale_price=None, highest_sale_price=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dicti... | 2 | stack_v2_sparse_classes_30k_train_008524 | Implement the Python class `CollectionSummary` described below.
Class description:
Implementation of the 'CollectionSummary' model. TODO: type model description here. Attributes: lowest_list_price (Price): TODO: type description here. highest_list_price (Price): TODO: type description here. lowest_sale_price (Price): ... | Implement the Python class `CollectionSummary` described below.
Class description:
Implementation of the 'CollectionSummary' model. TODO: type model description here. Attributes: lowest_list_price (Price): TODO: type description here. highest_list_price (Price): TODO: type description here. lowest_sale_price (Price): ... | 26ea1019115a1de3b1b37a4b830525e164ac55ce | <|skeleton|>
class CollectionSummary:
"""Implementation of the 'CollectionSummary' model. TODO: type model description here. Attributes: lowest_list_price (Price): TODO: type description here. highest_list_price (Price): TODO: type description here. lowest_sale_price (Price): TODO: type description here. highest_sa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CollectionSummary:
"""Implementation of the 'CollectionSummary' model. TODO: type model description here. Attributes: lowest_list_price (Price): TODO: type description here. highest_list_price (Price): TODO: type description here. lowest_sale_price (Price): TODO: type description here. highest_sale_price (Pri... | the_stack_v2_python_sparse | awsecommerceservice/models/collection_summary.py | nidaizamir/Test-PY | train | 0 |
c929e66ccb7582ca2e8427ddf32fee9206e4664c | [
"self.F = F\nself.F0 = B\nself.Q = chol_ext(len(B))",
"def getA(i, j):\n n = len(x)\n return self.F0[i, j] - sum((self.F[k][i, j] * x[k] for k in range(n)))\nif self.Q.factor(getA):\n return None\nep = self.Q.witness()\ng = np.array([self.Q.sym_quad(Fk) for Fk in self.F])\nreturn (g, ep)"
] | <|body_start_0|>
self.F = F
self.F0 = B
self.Q = chol_ext(len(B))
<|end_body_0|>
<|body_start_1|>
def getA(i, j):
n = len(x)
return self.F0[i, j] - sum((self.F[k][i, j] * x[k] for k in range(n)))
if self.Q.factor(getA):
return None
ep ... | Oracle for Linear Matrix Inequality constraint. This oracle solves the following feasibility problem: find x s.t. (B − F * x) ⪰ 0 | lmi_oracle | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class lmi_oracle:
"""Oracle for Linear Matrix Inequality constraint. This oracle solves the following feasibility problem: find x s.t. (B − F * x) ⪰ 0"""
def __init__(self, F, B):
"""Construct a new lmi oracle object Arguments: F (List[Arr]): [description] B (Arr): [description]"""
... | stack_v2_sparse_classes_36k_train_033405 | 1,216 | permissive | [
{
"docstring": "Construct a new lmi oracle object Arguments: F (List[Arr]): [description] B (Arr): [description]",
"name": "__init__",
"signature": "def __init__(self, F, B)"
},
{
"docstring": "[summary] Arguments: x (Arr): [description] Returns: Optional[Cut]: [description]",
"name": "__cal... | 2 | stack_v2_sparse_classes_30k_train_017317 | Implement the Python class `lmi_oracle` described below.
Class description:
Oracle for Linear Matrix Inequality constraint. This oracle solves the following feasibility problem: find x s.t. (B − F * x) ⪰ 0
Method signatures and docstrings:
- def __init__(self, F, B): Construct a new lmi oracle object Arguments: F (Li... | Implement the Python class `lmi_oracle` described below.
Class description:
Oracle for Linear Matrix Inequality constraint. This oracle solves the following feasibility problem: find x s.t. (B − F * x) ⪰ 0
Method signatures and docstrings:
- def __init__(self, F, B): Construct a new lmi oracle object Arguments: F (Li... | 4cf885a8656b6aac1bb08040e3e1bf00c74ac6a8 | <|skeleton|>
class lmi_oracle:
"""Oracle for Linear Matrix Inequality constraint. This oracle solves the following feasibility problem: find x s.t. (B − F * x) ⪰ 0"""
def __init__(self, F, B):
"""Construct a new lmi oracle object Arguments: F (List[Arr]): [description] B (Arr): [description]"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class lmi_oracle:
"""Oracle for Linear Matrix Inequality constraint. This oracle solves the following feasibility problem: find x s.t. (B − F * x) ⪰ 0"""
def __init__(self, F, B):
"""Construct a new lmi oracle object Arguments: F (List[Arr]): [description] B (Arr): [description]"""
self.F = F
... | the_stack_v2_python_sparse | src/ellpy/oracles/lmi_oracle.py | luk036/ellpy | train | 10 |
f1ba499debd71c1f75cc9519fad89284ae9dea20 | [
"def predict_fn(model_config: ml_collections.FrozenConfigDict, model_params: Dict[str, Any], model_vars: Dict[str, Any], batch: Dict[str, Any]) -> Dict[str, Array]:\n \"\"\"Model-specific prediction function.\n\n Args:\n model_config: contains model config hyperparameters.\n model_params: cont... | <|body_start_0|>
def predict_fn(model_config: ml_collections.FrozenConfigDict, model_params: Dict[str, Any], model_vars: Dict[str, Any], batch: Dict[str, Any]) -> Dict[str, Array]:
"""Model-specific prediction function.
Args:
model_config: contains model config hyperpa... | Task that generates memory from the corpus using an encoder. | MemoryGenerationTask | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MemoryGenerationTask:
"""Task that generates memory from the corpus using an encoder."""
def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]:
"""Creates task prediction function for inference."""
<|body_0|>
def make_preproces... | stack_v2_sparse_classes_36k_train_033406 | 14,047 | permissive | [
{
"docstring": "Creates task prediction function for inference.",
"name": "make_prediction_fn",
"signature": "def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]"
},
{
"docstring": "Produces function to preprocess samples. See BaseTask. Here we add a... | 5 | stack_v2_sparse_classes_30k_train_018291 | Implement the Python class `MemoryGenerationTask` described below.
Class description:
Task that generates memory from the corpus using an encoder.
Method signatures and docstrings:
- def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]: Creates task prediction function for... | Implement the Python class `MemoryGenerationTask` described below.
Class description:
Task that generates memory from the corpus using an encoder.
Method signatures and docstrings:
- def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]: Creates task prediction function for... | ac9447064195e06de48cc91ff642f7fffa28ffe8 | <|skeleton|>
class MemoryGenerationTask:
"""Task that generates memory from the corpus using an encoder."""
def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]:
"""Creates task prediction function for inference."""
<|body_0|>
def make_preproces... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MemoryGenerationTask:
"""Task that generates memory from the corpus using an encoder."""
def make_prediction_fn(cls, config: ml_collections.ConfigDict) -> Callable[..., Dict[str, Array]]:
"""Creates task prediction function for inference."""
def predict_fn(model_config: ml_collections.Fro... | the_stack_v2_python_sparse | language/mentionmemory/tasks/memory_generation_task.py | google-research/language | train | 1,567 |
549a2ef77f7024d0387f8b9c57ac04f637af5f8f | [
"for asset_name in AVAILABLE_STAGES:\n if asset_name != BASE_CLASS_NAME:\n AVAILABLE_STAGES[asset_name] = {}\nself.pipeline_name = ''\nif isinstance(pipeline_input, list):\n self.stages = pipeline_input\nif isinstance(pipeline_input, str):\n self.pipeline_name = \" '\" + pipeline_input + \"'\"\n ... | <|body_start_0|>
for asset_name in AVAILABLE_STAGES:
if asset_name != BASE_CLASS_NAME:
AVAILABLE_STAGES[asset_name] = {}
self.pipeline_name = ''
if isinstance(pipeline_input, list):
self.stages = pipeline_input
if isinstance(pipeline_input, str):
... | PipelineManager | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PipelineManager:
def __init__(self, run_id, pipeline_input, _input: IOManager, _output: IOManager, config, *args, **kwargs):
""":param pipeline_input: the pipeline name string or list of strings :param _input: IOmanager instance with input to the pipeline :param _output: IOmanager instan... | stack_v2_sparse_classes_36k_train_033407 | 7,804 | permissive | [
{
"docstring": ":param pipeline_input: the pipeline name string or list of strings :param _input: IOmanager instance with input to the pipeline :param _output: IOmanager instance to store the outputs of the pipelines to be saved externally :param config: config string of the pipeline :param args: :param kwargs:... | 5 | null | Implement the Python class `PipelineManager` described below.
Class description:
Implement the PipelineManager class.
Method signatures and docstrings:
- def __init__(self, run_id, pipeline_input, _input: IOManager, _output: IOManager, config, *args, **kwargs): :param pipeline_input: the pipeline name string or list ... | Implement the Python class `PipelineManager` described below.
Class description:
Implement the PipelineManager class.
Method signatures and docstrings:
- def __init__(self, run_id, pipeline_input, _input: IOManager, _output: IOManager, config, *args, **kwargs): :param pipeline_input: the pipeline name string or list ... | db34927e4c45df93438e2b7129f01388f1a34753 | <|skeleton|>
class PipelineManager:
def __init__(self, run_id, pipeline_input, _input: IOManager, _output: IOManager, config, *args, **kwargs):
""":param pipeline_input: the pipeline name string or list of strings :param _input: IOmanager instance with input to the pipeline :param _output: IOmanager instan... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PipelineManager:
def __init__(self, run_id, pipeline_input, _input: IOManager, _output: IOManager, config, *args, **kwargs):
""":param pipeline_input: the pipeline name string or list of strings :param _input: IOmanager instance with input to the pipeline :param _output: IOmanager instance to store th... | the_stack_v2_python_sparse | mlapp/managers/pipeline_manager.py | ghas-results/mlapp | train | 0 | |
18374e3a0394f568e6ee8f47cc759e82eb8b5893 | [
"self.input = input.clone()\noutput = input.tanh()\nreturn output",
"grad_input = 1 - self.input.tanh() ** 2\ngrad_input = grad_output * grad_input\nreturn grad_input"
] | <|body_start_0|>
self.input = input.clone()
output = input.tanh()
return output
<|end_body_0|>
<|body_start_1|>
grad_input = 1 - self.input.tanh() ** 2
grad_input = grad_output * grad_input
return grad_input
<|end_body_1|>
| Class representing the hyperbolic tangent activation function. | Tanh | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Tanh:
"""Class representing the hyperbolic tangent activation function."""
def forward(self, input):
"""Applies the hyperbolic tangent to the input. Args: input -- tensor of size (N, *) Returns: output -- tensor of same size as input"""
<|body_0|>
def backward(self, grad... | stack_v2_sparse_classes_36k_train_033408 | 2,186 | permissive | [
{
"docstring": "Applies the hyperbolic tangent to the input. Args: input -- tensor of size (N, *) Returns: output -- tensor of same size as input",
"name": "forward",
"signature": "def forward(self, input)"
},
{
"docstring": "Given the gradient w.r.t. to the output of the activation, computes th... | 2 | stack_v2_sparse_classes_30k_train_002663 | Implement the Python class `Tanh` described below.
Class description:
Class representing the hyperbolic tangent activation function.
Method signatures and docstrings:
- def forward(self, input): Applies the hyperbolic tangent to the input. Args: input -- tensor of size (N, *) Returns: output -- tensor of same size as... | Implement the Python class `Tanh` described below.
Class description:
Class representing the hyperbolic tangent activation function.
Method signatures and docstrings:
- def forward(self, input): Applies the hyperbolic tangent to the input. Args: input -- tensor of size (N, *) Returns: output -- tensor of same size as... | 056b1be878b77c5a7dd5cff8d29ecb390be8b5de | <|skeleton|>
class Tanh:
"""Class representing the hyperbolic tangent activation function."""
def forward(self, input):
"""Applies the hyperbolic tangent to the input. Args: input -- tensor of size (N, *) Returns: output -- tensor of same size as input"""
<|body_0|>
def backward(self, grad... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Tanh:
"""Class representing the hyperbolic tangent activation function."""
def forward(self, input):
"""Applies the hyperbolic tangent to the input. Args: input -- tensor of size (N, *) Returns: output -- tensor of same size as input"""
self.input = input.clone()
output = input.ta... | the_stack_v2_python_sparse | Proj2/modules/Activations.py | jouvemax/DeepLearning | train | 0 |
b346fc6cb43b2bfc84761f27bbec5042c8222a53 | [
"super().__init__()\nconfig = AutoConfig.from_pretrained(pretrained_model_name, num_labels=num_classes)\nself.distilbert = AutoModel.from_pretrained(pretrained_model_name, config=config)\nself.pre_classifier = nn.Linear(config.dim, config.dim)\nself.classifier = nn.Sequential(nn.ReLU(), nn.Dropout(config.seq_classi... | <|body_start_0|>
super().__init__()
config = AutoConfig.from_pretrained(pretrained_model_name, num_labels=num_classes)
self.distilbert = AutoModel.from_pretrained(pretrained_model_name, config=config)
self.pre_classifier = nn.Linear(config.dim, config.dim)
self.classifier = nn.Se... | Simplified version of the same class by HuggingFace. See ``transformers/modeling_distilbert.py`` in the transformers repository. | BertClassifier | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BertClassifier:
"""Simplified version of the same class by HuggingFace. See ``transformers/modeling_distilbert.py`` in the transformers repository."""
def __init__(self, pretrained_model_name: str, num_classes: Optional[int]=None):
"""Args: pretrained_model_name (str): HuggingFace mo... | stack_v2_sparse_classes_36k_train_033409 | 2,717 | permissive | [
{
"docstring": "Args: pretrained_model_name (str): HuggingFace model name. See transformers/modeling_auto.py num_classes (int, optional): the number of class labels in the classification task",
"name": "__init__",
"signature": "def __init__(self, pretrained_model_name: str, num_classes: Optional[int]=No... | 2 | null | Implement the Python class `BertClassifier` described below.
Class description:
Simplified version of the same class by HuggingFace. See ``transformers/modeling_distilbert.py`` in the transformers repository.
Method signatures and docstrings:
- def __init__(self, pretrained_model_name: str, num_classes: Optional[int]... | Implement the Python class `BertClassifier` described below.
Class description:
Simplified version of the same class by HuggingFace. See ``transformers/modeling_distilbert.py`` in the transformers repository.
Method signatures and docstrings:
- def __init__(self, pretrained_model_name: str, num_classes: Optional[int]... | a35297ecab8d1a6c2f00b6435ea1d6d37ec9f441 | <|skeleton|>
class BertClassifier:
"""Simplified version of the same class by HuggingFace. See ``transformers/modeling_distilbert.py`` in the transformers repository."""
def __init__(self, pretrained_model_name: str, num_classes: Optional[int]=None):
"""Args: pretrained_model_name (str): HuggingFace mo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BertClassifier:
"""Simplified version of the same class by HuggingFace. See ``transformers/modeling_distilbert.py`` in the transformers repository."""
def __init__(self, pretrained_model_name: str, num_classes: Optional[int]=None):
"""Args: pretrained_model_name (str): HuggingFace model name. See... | the_stack_v2_python_sparse | catalyst/contrib/models/nlp/classification/bert.py | saswat0/catalyst | train | 2 |
e0880581d64c978acf267852b35dd723a4c6b0f3 | [
"super().__init__()\nself.session = requests.Session()\nlogin_page = self.session.get('https://ers.cr.usgs.gov/login/')\nhtml_root = html.fromstring(login_page.content)\ncsrf, = html_root.xpath('//*[@id=\"csrf_token\"]')\nncforminfo, = html_root.xpath('//*[@id=\"loginForm\"]/input[2]')\ncsrf_token = csrf.get('value... | <|body_start_0|>
super().__init__()
self.session = requests.Session()
login_page = self.session.get('https://ers.cr.usgs.gov/login/')
html_root = html.fromstring(login_page.content)
csrf, = html_root.xpath('//*[@id="csrf_token"]')
ncforminfo, = html_root.xpath('//*[@id="l... | USGSCrawler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class USGSCrawler:
def __init__(self):
"""login is required to download files from USGS. we are simulating a login with session here"""
<|body_0|>
def crawl(self, target_date: date) -> Optional[str]:
"""this func will download a single file :param target_date: date :return... | stack_v2_sparse_classes_36k_train_033410 | 3,922 | no_license | [
{
"docstring": "login is required to download files from USGS. we are simulating a login with session here",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "this func will download a single file :param target_date: date :return: full-path of downloaded file. None if not ... | 2 | stack_v2_sparse_classes_30k_train_007821 | Implement the Python class `USGSCrawler` described below.
Class description:
Implement the USGSCrawler class.
Method signatures and docstrings:
- def __init__(self): login is required to download files from USGS. we are simulating a login with session here
- def crawl(self, target_date: date) -> Optional[str]: this f... | Implement the Python class `USGSCrawler` described below.
Class description:
Implement the USGSCrawler class.
Method signatures and docstrings:
- def __init__(self): login is required to download files from USGS. we are simulating a login with session here
- def crawl(self, target_date: date) -> Optional[str]: this f... | 9d0dc17e0e5a60fc0507475cd5ef0975beb8b397 | <|skeleton|>
class USGSCrawler:
def __init__(self):
"""login is required to download files from USGS. we are simulating a login with session here"""
<|body_0|>
def crawl(self, target_date: date) -> Optional[str]:
"""this func will download a single file :param target_date: date :return... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class USGSCrawler:
def __init__(self):
"""login is required to download files from USGS. we are simulating a login with session here"""
super().__init__()
self.session = requests.Session()
login_page = self.session.get('https://ers.cr.usgs.gov/login/')
html_root = html.fromst... | the_stack_v2_python_sparse | backend/data_preparation/crawler/usgs_crawler.py | totemprotocol/Wildfires | train | 0 | |
e6883236df27c4ad73472e5c6f6567a84768d900 | [
"server = 'guess-api/guessActivity/betting'\nplatformPassword = '111111'\nheader = [{'access-auth-token': 'd809615e96176085a1152f74c8a47b78800141539678969628'}, {'access-auth-token': 'f2794f54e17720d34aa505e3556724f3'}, {'access-auth-token': '492675514ec5f4d8ee249f26b15a4746'}]\nfor i in range(0, 20):\n for hd i... | <|body_start_0|>
server = 'guess-api/guessActivity/betting'
platformPassword = '111111'
header = [{'access-auth-token': 'd809615e96176085a1152f74c8a47b78800141539678969628'}, {'access-auth-token': 'f2794f54e17720d34aa505e3556724f3'}, {'access-auth-token': '492675514ec5f4d8ee249f26b15a4746'}]
... | 疯狂的BTMC游戏 | reqApi_crazy_BTGame_1_3 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class reqApi_crazy_BTGame_1_3:
"""疯狂的BTMC游戏"""
def guess_api_guessActivity_betting(self):
"""投票活动 :return:"""
<|body_0|>
def guess_api_guessActivity_receiveAward(self):
"""中奖，领取奖励 :return:"""
<|body_1|>
def guess_api_guessActivity_openRedPacket(self):
... | stack_v2_sparse_classes_36k_train_033411 | 1,952 | no_license | [
{
"docstring": "投票活动 :return:",
"name": "guess_api_guessActivity_betting",
"signature": "def guess_api_guessActivity_betting(self)"
},
{
"docstring": "中奖，领取奖励 :return:",
"name": "guess_api_guessActivity_receiveAward",
"signature": "def guess_api_guessActivity_receiveAward(self)"
},
{... | 3 | stack_v2_sparse_classes_30k_train_011389 | Implement the Python class `reqApi_crazy_BTGame_1_3` described below.
Class description:
疯狂的BTMC游戏
Method signatures and docstrings:
- def guess_api_guessActivity_betting(self): 投票活动 :return:
- def guess_api_guessActivity_receiveAward(self): 中奖，领取奖励 :return:
- def guess_api_guessActivity_openRedPacket(self): 开启红包 :re... | Implement the Python class `reqApi_crazy_BTGame_1_3` described below.
Class description:
疯狂的BTMC游戏
Method signatures and docstrings:
- def guess_api_guessActivity_betting(self): 投票活动 :return:
- def guess_api_guessActivity_receiveAward(self): 中奖，领取奖励 :return:
- def guess_api_guessActivity_openRedPacket(self): 开启红包 :re... | 260bddce56a72a780dc305ca4805cfd176756e7d | <|skeleton|>
class reqApi_crazy_BTGame_1_3:
"""疯狂的BTMC游戏"""
def guess_api_guessActivity_betting(self):
"""投票活动 :return:"""
<|body_0|>
def guess_api_guessActivity_receiveAward(self):
"""中奖，领取奖励 :return:"""
<|body_1|>
def guess_api_guessActivity_openRedPacket(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class reqApi_crazy_BTGame_1_3:
"""疯狂的BTMC游戏"""
def guess_api_guessActivity_betting(self):
"""投票活动 :return:"""
server = 'guess-api/guessActivity/betting'
platformPassword = '111111'
header = [{'access-auth-token': 'd809615e96176085a1152f74c8a47b78800141539678969628'}, {'access-au... | the_stack_v2_python_sparse | requestApi/reqApi_crazy_BTGame_1_3.py | chenshl/DKTest | train | 0 |
9e4b268afffe19e25278214c982f35b872917655 | [
"self.m = m\nself.k = k\nself.hashers = [HashFunction(seed=i, length=m) for i in range(len(hist_cols)) for j in range(self.k)]",
"output = []\nfor idx, vc in enumerate(value_counts):\n hashers = self.hashers[idx * self.k:(idx + 1) * self.k]\n for h in hashers:\n hist = [0 for i in range(2 ** self.m)]... | <|body_start_0|>
self.m = m
self.k = k
self.hashers = [HashFunction(seed=i, length=m) for i in range(len(hist_cols)) for j in range(self.k)]
<|end_body_0|>
<|body_start_1|>
output = []
for idx, vc in enumerate(value_counts):
hashers = self.hashers[idx * self.k:(idx +... | HistogramClones | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HistogramClones:
def __init__(self, m, k):
"""Arguments: m {int} -- hash function length (2 ** m) k {int} -- number of clones"""
<|body_0|>
def value_counts_to_hists(self, value_counts):
"""convert value counts of columns to histogram clones Arguments: value_counts {... | stack_v2_sparse_classes_36k_train_033412 | 4,897 | no_license | [
{
"docstring": "Arguments: m {int} -- hash function length (2 ** m) k {int} -- number of clones",
"name": "__init__",
"signature": "def __init__(self, m, k)"
},
{
"docstring": "convert value counts of columns to histogram clones Arguments: value_counts {list of Counters} Returns: [type] -- [desc... | 3 | stack_v2_sparse_classes_30k_train_012253 | Implement the Python class `HistogramClones` described below.
Class description:
Implement the HistogramClones class.
Method signatures and docstrings:
- def __init__(self, m, k): Arguments: m {int} -- hash function length (2 ** m) k {int} -- number of clones
- def value_counts_to_hists(self, value_counts): convert v... | Implement the Python class `HistogramClones` described below.
Class description:
Implement the HistogramClones class.
Method signatures and docstrings:
- def __init__(self, m, k): Arguments: m {int} -- hash function length (2 ** m) k {int} -- number of clones
- def value_counts_to_hists(self, value_counts): convert v... | aa46c84169b8c6c4fb0deefb453e5d4d9e80dc0f | <|skeleton|>
class HistogramClones:
def __init__(self, m, k):
"""Arguments: m {int} -- hash function length (2 ** m) k {int} -- number of clones"""
<|body_0|>
def value_counts_to_hists(self, value_counts):
"""convert value counts of columns to histogram clones Arguments: value_counts {... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HistogramClones:
def __init__(self, m, k):
"""Arguments: m {int} -- hash function length (2 ** m) k {int} -- number of clones"""
self.m = m
self.k = k
self.hashers = [HashFunction(seed=i, length=m) for i in range(len(hist_cols)) for j in range(self.k)]
def value_counts_to_... | the_stack_v2_python_sparse | histogram/compute_kl.py | Narkle/UGR_Experiments | train | 0 | |
860c6fa3102d6a5afca1f901ee9ed69c36cad7a7 | [
"if method == 'deeplearning':\n nltkInitialize(dataSettings['datasets']['nltk_sources'])\n ' \\n\\t\\t\\tLoads model runners according to the selected DL model (defined in settings.ini)\\n\\t\\t\\t'\n if dataSettings['DLmodel']['model'] == 'biowordvec_bilstm':\n from models.Embedding_BiLstmCRF.model... | <|body_start_0|>
if method == 'deeplearning':
nltkInitialize(dataSettings['datasets']['nltk_sources'])
' \n\t\t\tLoads model runners according to the selected DL model (defined in settings.ini)\n\t\t\t'
if dataSettings['DLmodel']['model'] == 'biowordvec_bilstm':
... | Orchestrator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Orchestrator:
def processTask1(files, XMLAnnotations, dictionaries, dataSettings, method=None, show=False):
"""Method to handle with task 1 . :param files: dictionary containing the clinical reports (key: filename) :.... returns tuple(dictionary containing family members (key: filename, ... | stack_v2_sparse_classes_36k_train_033413 | 4,860 | permissive | [
{
"docstring": "Method to handle with task 1 . :param files: dictionary containing the clinical reports (key: filename) :.... returns tuple(dictionary containing family members (key: filename, value: list of tuples ((fm, fs), sentence), dictionary containing observations (key: filename, value: list of tuples (o... | 3 | stack_v2_sparse_classes_30k_train_004880 | Implement the Python class `Orchestrator` described below.
Class description:
Implement the Orchestrator class.
Method signatures and docstrings:
- def processTask1(files, XMLAnnotations, dictionaries, dataSettings, method=None, show=False): Method to handle with task 1 . :param files: dictionary containing the clini... | Implement the Python class `Orchestrator` described below.
Class description:
Implement the Orchestrator class.
Method signatures and docstrings:
- def processTask1(files, XMLAnnotations, dictionaries, dataSettings, method=None, show=False): Method to handle with task 1 . :param files: dictionary containing the clini... | 0c03d587eb2cf2d26e7834ff879f9c0131f2d5ac | <|skeleton|>
class Orchestrator:
def processTask1(files, XMLAnnotations, dictionaries, dataSettings, method=None, show=False):
"""Method to handle with task 1 . :param files: dictionary containing the clinical reports (key: filename) :.... returns tuple(dictionary containing family members (key: filename, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Orchestrator:
def processTask1(files, XMLAnnotations, dictionaries, dataSettings, method=None, show=False):
"""Method to handle with task 1 . :param files: dictionary containing the clinical reports (key: filename) :.... returns tuple(dictionary containing family members (key: filename, value: list of... | the_stack_v2_python_sparse | src/Orchestrator.py | odnodn/PatientFM | train | 0 | |
0390e3c8a3692ad1811c00b225c0c24536fc1574 | [
"from torchvision import datasets\ndset = datasets.MNIST('/tmp', download=True)\nrows = [{'mnist_id': i, 'image': np.array(im), 'split': 'train', 'label': label} for i, (im, label) in enumerate(dset, start=1)]\nDigits.insert(rows)\ndset = datasets.MNIST('/tmp', train=False, download=True)\nrows = [{'mnist_id': i, '... | <|body_start_0|>
from torchvision import datasets
dset = datasets.MNIST('/tmp', download=True)
rows = [{'mnist_id': i, 'image': np.array(im), 'split': 'train', 'label': label} for i, (im, label) in enumerate(dset, start=1)]
Digits.insert(rows)
dset = datasets.MNIST('/tmp', train=... | Digits | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Digits:
def fill():
"""Download MNIST dataset and fill this table."""
<|body_0|>
def fill_stimulus(self):
"""Inserts some digits as stimulus. It rotates the image 90 degrees counter-clockwise, enlarges it to 144 x 144 and pads 56 pixels to the left and right to make ... | stack_v2_sparse_classes_36k_train_033414 | 14,690 | no_license | [
{
"docstring": "Download MNIST dataset and fill this table.",
"name": "fill",
"signature": "def fill()"
},
{
"docstring": "Inserts some digits as stimulus. It rotates the image 90 degrees counter-clockwise, enlarges it to 144 x 144 and pads 56 pixels to the left and right to make a 144 x 256 (16... | 2 | stack_v2_sparse_classes_30k_train_011627 | Implement the Python class `Digits` described below.
Class description:
Implement the Digits class.
Method signatures and docstrings:
- def fill(): Download MNIST dataset and fill this table.
- def fill_stimulus(self): Inserts some digits as stimulus. It rotates the image 90 degrees counter-clockwise, enlarges it to ... | Implement the Python class `Digits` described below.
Class description:
Implement the Digits class.
Method signatures and docstrings:
- def fill(): Download MNIST dataset and fill this table.
- def fill_stimulus(self): Inserts some digits as stimulus. It rotates the image 90 degrees counter-clockwise, enlarges it to ... | e3086381e8da3c0698f3beefe4c067c4716cb654 | <|skeleton|>
class Digits:
def fill():
"""Download MNIST dataset and fill this table."""
<|body_0|>
def fill_stimulus(self):
"""Inserts some digits as stimulus. It rotates the image 90 degrees counter-clockwise, enlarges it to 144 x 144 and pads 56 pixels to the left and right to make ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Digits:
def fill():
"""Download MNIST dataset and fill this table."""
from torchvision import datasets
dset = datasets.MNIST('/tmp', download=True)
rows = [{'mnist_id': i, 'image': np.array(im), 'split': 'train', 'label': label} for i, (im, label) in enumerate(dset, start=1)]
... | the_stack_v2_python_sparse | brainreader/mnist.py | ecobost/brainreader | train | 0 | |
2874baaa24f1c2cdafe8577cd7b68d50be0329b1 | [
"nums = [1, 2, 3]\nlets = ['a', 'b', 'c']\nn, m = zip(*[random_product(nums, lets) for _ in range(100)])\nn, m = (set(n), set(m))\neq_(n, set(nums))\neq_(m, set(lets))\neq_(len(n), len(nums))\neq_(len(m), len(lets))",
"nums = [1, 2, 3]\nlets = ['a', 'b', 'c']\nr = list(random_product(nums, lets, repeat=100))\neq_... | <|body_start_0|>
nums = [1, 2, 3]
lets = ['a', 'b', 'c']
n, m = zip(*[random_product(nums, lets) for _ in range(100)])
n, m = (set(n), set(m))
eq_(n, set(nums))
eq_(m, set(lets))
eq_(len(n), len(nums))
eq_(len(m), len(lets))
<|end_body_0|>
<|body_start_1|... | Tests for ``random_product()`` Since random.choice() has different results with the same seed across python versions 2.x and 3.x, these tests use highly probably events to create predictable outcomes across platforms. | RandomProductTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomProductTests:
"""Tests for ``random_product()`` Since random.choice() has different results with the same seed across python versions 2.x and 3.x, these tests use highly probably events to create predictable outcomes across platforms."""
def test_simple_lists(self):
"""Ensure t... | stack_v2_sparse_classes_36k_train_033415 | 47,145 | no_license | [
{
"docstring": "Ensure that one item is chosen from each list in each pair. Also ensure that each item from each list eventually appears in the chosen combinations. Odds are roughly 1 in 7.1 * 10e16 that one item from either list will not be chosen after 100 samplings of one item from each list. Just to be safe... | 2 | null | Implement the Python class `RandomProductTests` described below.
Class description:
Tests for ``random_product()`` Since random.choice() has different results with the same seed across python versions 2.x and 3.x, these tests use highly probably events to create predictable outcomes across platforms.
Method signature... | Implement the Python class `RandomProductTests` described below.
Class description:
Tests for ``random_product()`` Since random.choice() has different results with the same seed across python versions 2.x and 3.x, these tests use highly probably events to create predictable outcomes across platforms.
Method signature... | 0ac6653219c2701c13c508c5c4fc9bc3437eea06 | <|skeleton|>
class RandomProductTests:
"""Tests for ``random_product()`` Since random.choice() has different results with the same seed across python versions 2.x and 3.x, these tests use highly probably events to create predictable outcomes across platforms."""
def test_simple_lists(self):
"""Ensure t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomProductTests:
"""Tests for ``random_product()`` Since random.choice() has different results with the same seed across python versions 2.x and 3.x, these tests use highly probably events to create predictable outcomes across platforms."""
def test_simple_lists(self):
"""Ensure that one item ... | the_stack_v2_python_sparse | repoData/erikrose-more-itertools/allPythonContent.py | aCoffeeYin/pyreco | train | 0 |
d6afcc23d03e1975bdc65ebe37b80153f54ddd14 | [
"if picking.purchase_id:\n return picking.purchase_id.company_id.currency_id.id\nreturn super(stock_picking, self).get_currency_id(cursor, user, picking)",
"if move_line.purchase_line_id:\n return [x.id for x in move_line.purchase_line_id.order_id.taxes_id]\nreturn super(stock_picking, self)._get_taxes_invo... | <|body_start_0|>
if picking.purchase_id:
return picking.purchase_id.company_id.currency_id.id
return super(stock_picking, self).get_currency_id(cursor, user, picking)
<|end_body_0|>
<|body_start_1|>
if move_line.purchase_line_id:
return [x.id for x in move_line.purchase_... | To let functions read data from company and purchase order and remove price list | stock_picking | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class stock_picking:
"""To let functions read data from company and purchase order and remove price list"""
def get_currency_id(self, cursor, user, picking):
"""Get currency from company instaed of pricelist. @param picking: The picking id @return: returns currency id"""
<|body_0|>... | stack_v2_sparse_classes_36k_train_033416 | 5,611 | no_license | [
{
"docstring": "Get currency from company instaed of pricelist. @param picking: The picking id @return: returns currency id",
"name": "get_currency_id",
"signature": "def get_currency_id(self, cursor, user, picking)"
},
{
"docstring": "To get taxes from purchase_order instead of getting them fro... | 3 | stack_v2_sparse_classes_30k_train_019279 | Implement the Python class `stock_picking` described below.
Class description:
To let functions read data from company and purchase order and remove price list
Method signatures and docstrings:
- def get_currency_id(self, cursor, user, picking): Get currency from company instaed of pricelist. @param picking: The pick... | Implement the Python class `stock_picking` described below.
Class description:
To let functions read data from company and purchase order and remove price list
Method signatures and docstrings:
- def get_currency_id(self, cursor, user, picking): Get currency from company instaed of pricelist. @param picking: The pick... | 0b997095c260d58b026440967fea3a202bef7efb | <|skeleton|>
class stock_picking:
"""To let functions read data from company and purchase order and remove price list"""
def get_currency_id(self, cursor, user, picking):
"""Get currency from company instaed of pricelist. @param picking: The picking id @return: returns currency id"""
<|body_0|>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class stock_picking:
"""To let functions read data from company and purchase order and remove price list"""
def get_currency_id(self, cursor, user, picking):
"""Get currency from company instaed of pricelist. @param picking: The picking id @return: returns currency id"""
if picking.purchase_id:... | the_stack_v2_python_sparse | v_7/GDS/shamil_v3/purchase_no_pricelist/stock.py | musabahmed/baba | train | 0 |
1cef4cc27c1e96a21a1162e8c7e9b363f8d390f6 | [
"candidates.sort()\nres = []\nself.DFS(candidates, target, 0, res, [])\nreturn res",
"if target == 0:\n res.append(intermedia)\n return\nfor i in range(start, len(candidates)):\n if target < candidates[i]:\n return\n self.DFS(candidates, target - candidates[i], i, res, intermedia + [candidates[... | <|body_start_0|>
candidates.sort()
res = []
self.DFS(candidates, target, 0, res, [])
return res
<|end_body_0|>
<|body_start_1|>
if target == 0:
res.append(intermedia)
return
for i in range(start, len(candidates)):
if target < candidate... | @param candidates, a list of integers @param target, integer @return a list of lists of integers | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""@param candidates, a list of integers @param target, integer @return a list of lists of integers"""
def func(self, candidates, target):
"""solution func"""
<|body_0|>
def DFS(self, candidates, target, start, res, intermedia):
"""solution dfs"""
... | stack_v2_sparse_classes_36k_train_033417 | 984 | permissive | [
{
"docstring": "solution func",
"name": "func",
"signature": "def func(self, candidates, target)"
},
{
"docstring": "solution dfs",
"name": "DFS",
"signature": "def DFS(self, candidates, target, start, res, intermedia)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012681 | Implement the Python class `Solution` described below.
Class description:
@param candidates, a list of integers @param target, integer @return a list of lists of integers
Method signatures and docstrings:
- def func(self, candidates, target): solution func
- def DFS(self, candidates, target, start, res, intermedia): ... | Implement the Python class `Solution` described below.
Class description:
@param candidates, a list of integers @param target, integer @return a list of lists of integers
Method signatures and docstrings:
- def func(self, candidates, target): solution func
- def DFS(self, candidates, target, start, res, intermedia): ... | 869ee24c50c08403b170e8f7868699185e9dfdd1 | <|skeleton|>
class Solution:
"""@param candidates, a list of integers @param target, integer @return a list of lists of integers"""
def func(self, candidates, target):
"""solution func"""
<|body_0|>
def DFS(self, candidates, target, start, res, intermedia):
"""solution dfs"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""@param candidates, a list of integers @param target, integer @return a list of lists of integers"""
def func(self, candidates, target):
"""solution func"""
candidates.sort()
res = []
self.DFS(candidates, target, 0, res, [])
return res
def DFS(self... | the_stack_v2_python_sparse | 39.Combination.Sum/2.py | cerebrumaize/leetcode | train | 0 |
b0ee622d09dd2b8053933fb34f60185cb025608a | [
"self.callbacks = callbacks\nself.model = model\nself.loss = loss\nself.optimizer = optimizer",
"if self.model is None:\n raise RuntimeError('You must compile the trainer first!')\nself.loss.reset_avg()\nfor callback in self.callbacks:\n callback.on_epoch_begin(dataloader, phase, epoch)\nvariables = self.mo... | <|body_start_0|>
self.callbacks = callbacks
self.model = model
self.loss = loss
self.optimizer = optimizer
<|end_body_0|>
<|body_start_1|>
if self.model is None:
raise RuntimeError('You must compile the trainer first!')
self.loss.reset_avg()
for callb... | SupervisedTrainer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SupervisedTrainer:
def __init__(self, model: Module, loss: Loss, optimizer: Optimizer, callbacks: Iterable[BaseCallback]=DEFAULT_CALLBACKS):
"""Create a trainer for supervised training scenarios. The fit function is very basic and can be vastly extended by using callbacks. The default be... | stack_v2_sparse_classes_36k_train_033418 | 5,957 | permissive | [
{
"docstring": "Create a trainer for supervised training scenarios. The fit function is very basic and can be vastly extended by using callbacks. The default behaviour can be changed by changing not passing the DEFAULT_CALLBACKS but a modified set of callbacks (only do this if you know what you are doing). A no... | 3 | stack_v2_sparse_classes_30k_train_012875 | Implement the Python class `SupervisedTrainer` described below.
Class description:
Implement the SupervisedTrainer class.
Method signatures and docstrings:
- def __init__(self, model: Module, loss: Loss, optimizer: Optimizer, callbacks: Iterable[BaseCallback]=DEFAULT_CALLBACKS): Create a trainer for supervised traini... | Implement the Python class `SupervisedTrainer` described below.
Class description:
Implement the SupervisedTrainer class.
Method signatures and docstrings:
- def __init__(self, model: Module, loss: Loss, optimizer: Optimizer, callbacks: Iterable[BaseCallback]=DEFAULT_CALLBACKS): Create a trainer for supervised traini... | d3b1dd7c38a9de8f1e553cc5c0b2dfa62fe25c27 | <|skeleton|>
class SupervisedTrainer:
def __init__(self, model: Module, loss: Loss, optimizer: Optimizer, callbacks: Iterable[BaseCallback]=DEFAULT_CALLBACKS):
"""Create a trainer for supervised training scenarios. The fit function is very basic and can be vastly extended by using callbacks. The default be... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SupervisedTrainer:
def __init__(self, model: Module, loss: Loss, optimizer: Optimizer, callbacks: Iterable[BaseCallback]=DEFAULT_CALLBACKS):
"""Create a trainer for supervised training scenarios. The fit function is very basic and can be vastly extended by using callbacks. The default behaviour can be... | the_stack_v2_python_sparse | babilim/training/supervised.py | penguinmenac3/babilim | train | 1 | |
e5644e3db89bc29be04231d23f5a64f329b91bec | [
"pygame.init()\npygame.joystick.init()\nself.controller = pygame.joystick.Joystick(0)\nself.controller.init()",
"if not self.axis_data:\n self.axis_data = {}\nif not self.button_data:\n self.button_data = {}\n for i in range(self.controller.get_numbuttons()):\n self.button_data[i] = False\nif not ... | <|body_start_0|>
pygame.init()
pygame.joystick.init()
self.controller = pygame.joystick.Joystick(0)
self.controller.init()
<|end_body_0|>
<|body_start_1|>
if not self.axis_data:
self.axis_data = {}
if not self.button_data:
self.button_data = {}
... | Class representing the PS4 controller. Pretty straightforward functionality. | PS4Controller | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PS4Controller:
"""Class representing the PS4 controller. Pretty straightforward functionality."""
def init(self):
"""Initialize the joystick components"""
<|body_0|>
def listen(self):
"""Listen for events to happen"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_033419 | 2,441 | permissive | [
{
"docstring": "Initialize the joystick components",
"name": "init",
"signature": "def init(self)"
},
{
"docstring": "Listen for events to happen",
"name": "listen",
"signature": "def listen(self)"
}
] | 2 | null | Implement the Python class `PS4Controller` described below.
Class description:
Class representing the PS4 controller. Pretty straightforward functionality.
Method signatures and docstrings:
- def init(self): Initialize the joystick components
- def listen(self): Listen for events to happen | Implement the Python class `PS4Controller` described below.
Class description:
Class representing the PS4 controller. Pretty straightforward functionality.
Method signatures and docstrings:
- def init(self): Initialize the joystick components
- def listen(self): Listen for events to happen
<|skeleton|>
class PS4Cont... | 665d39a2bd82543d5196555f0801ef8fd4a3ee48 | <|skeleton|>
class PS4Controller:
"""Class representing the PS4 controller. Pretty straightforward functionality."""
def init(self):
"""Initialize the joystick components"""
<|body_0|>
def listen(self):
"""Listen for events to happen"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PS4Controller:
"""Class representing the PS4 controller. Pretty straightforward functionality."""
def init(self):
"""Initialize the joystick components"""
pygame.init()
pygame.joystick.init()
self.controller = pygame.joystick.Joystick(0)
self.controller.init()
... | the_stack_v2_python_sparse | all-gists/028386b860b75e4f5472/snippet.py | gistable/gistable | train | 76 |
1451ccf5ff0951b9c8a222db4384a22ec0166fec | [
"super(ConvolutionModule, self).__init__()\nassert (depthwise_kernel_size - 1) % 2 == 0, \"kernel_size should be a odd number for 'SAME' padding\"\nself.layer_norm = LayerNorm(embed_dim, export=export)\nself.pointwise_conv1 = torch.nn.Conv1d(embed_dim, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias)\ns... | <|body_start_0|>
super(ConvolutionModule, self).__init__()
assert (depthwise_kernel_size - 1) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding"
self.layer_norm = LayerNorm(embed_dim, export=export)
self.pointwise_conv1 = torch.nn.Conv1d(embed_dim, 2 * channels, kernel_siz... | Convolution block used in the conformer block | ConvolutionModule | [
"LicenseRef-scancode-unknown-license-reference",
"MIT",
"LGPL-2.1-or-later",
"LicenseRef-scancode-free-unknown",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConvolutionModule:
"""Convolution block used in the conformer block"""
def __init__(self, embed_dim, channels, depthwise_kernel_size, dropout, activation_fn='swish', bias=False, export=False):
"""Args: embed_dim: Embedding dimension channels: Number of channels in depthwise conv laye... | stack_v2_sparse_classes_36k_train_033420 | 9,087 | permissive | [
{
"docstring": "Args: embed_dim: Embedding dimension channels: Number of channels in depthwise conv layers depthwise_kernel_size: Depthwise conv layer kernel size dropout: dropout value activation_fn: Activation function to use after depthwise convolution kernel bias: If bias should be added to conv layers expo... | 2 | stack_v2_sparse_classes_30k_train_014781 | Implement the Python class `ConvolutionModule` described below.
Class description:
Convolution block used in the conformer block
Method signatures and docstrings:
- def __init__(self, embed_dim, channels, depthwise_kernel_size, dropout, activation_fn='swish', bias=False, export=False): Args: embed_dim: Embedding dime... | Implement the Python class `ConvolutionModule` described below.
Class description:
Convolution block used in the conformer block
Method signatures and docstrings:
- def __init__(self, embed_dim, channels, depthwise_kernel_size, dropout, activation_fn='swish', bias=False, export=False): Args: embed_dim: Embedding dime... | b60c741f746877293bb85eed6806736fc8fa0ffd | <|skeleton|>
class ConvolutionModule:
"""Convolution block used in the conformer block"""
def __init__(self, embed_dim, channels, depthwise_kernel_size, dropout, activation_fn='swish', bias=False, export=False):
"""Args: embed_dim: Embedding dimension channels: Number of channels in depthwise conv laye... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConvolutionModule:
"""Convolution block used in the conformer block"""
def __init__(self, embed_dim, channels, depthwise_kernel_size, dropout, activation_fn='swish', bias=False, export=False):
"""Args: embed_dim: Embedding dimension channels: Number of channels in depthwise conv layers depthwise_... | the_stack_v2_python_sparse | kosmos-2/fairseq/fairseq/modules/conformer_layer.py | microsoft/unilm | train | 15,313 |
beb38cc997755610caf12d1abca076ead5e0cf6b | [
"super().__init__()\nself.blocks = ModuleList()\nfor in_feat_os in in_feature_output_strides:\n num_upsample = int(math.log2(int(in_feat_os))) - int(math.log2(int(out_feature_output_stride)))\n num_layers = num_upsample if num_upsample != 0 else 1\n self.blocks.append(Sequential(*[Sequential(Conv2d(in_chan... | <|body_start_0|>
super().__init__()
self.blocks = ModuleList()
for in_feat_os in in_feature_output_strides:
num_upsample = int(math.log2(int(in_feat_os))) - int(math.log2(int(out_feature_output_stride)))
num_layers = num_upsample if num_upsample != 0 else 1
se... | Light Weight Decoder. | _LightWeightDecoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _LightWeightDecoder:
"""Light Weight Decoder."""
def __init__(self, in_channels: int, out_channels: int, num_classes: int, in_feature_output_strides: list[int]=[4, 8, 16, 32], out_feature_output_stride: int=4) -> None:
"""Initialize the _LightWeightDecoder module. Args: in_channels: ... | stack_v2_sparse_classes_36k_train_033421 | 8,033 | permissive | [
{
"docstring": "Initialize the _LightWeightDecoder module. Args: in_channels: number of channels of input feature maps out_channels: number of channels of output feature maps num_classes: number of output segmentation classes in_feature_output_strides: output stride of input feature maps at different levels out... | 2 | null | Implement the Python class `_LightWeightDecoder` described below.
Class description:
Light Weight Decoder.
Method signatures and docstrings:
- def __init__(self, in_channels: int, out_channels: int, num_classes: int, in_feature_output_strides: list[int]=[4, 8, 16, 32], out_feature_output_stride: int=4) -> None: Initi... | Implement the Python class `_LightWeightDecoder` described below.
Class description:
Light Weight Decoder.
Method signatures and docstrings:
- def __init__(self, in_channels: int, out_channels: int, num_classes: int, in_feature_output_strides: list[int]=[4, 8, 16, 32], out_feature_output_stride: int=4) -> None: Initi... | 29985861614b3b93f9ef5389469ebb98570de7dd | <|skeleton|>
class _LightWeightDecoder:
"""Light Weight Decoder."""
def __init__(self, in_channels: int, out_channels: int, num_classes: int, in_feature_output_strides: list[int]=[4, 8, 16, 32], out_feature_output_stride: int=4) -> None:
"""Initialize the _LightWeightDecoder module. Args: in_channels: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _LightWeightDecoder:
"""Light Weight Decoder."""
def __init__(self, in_channels: int, out_channels: int, num_classes: int, in_feature_output_strides: list[int]=[4, 8, 16, 32], out_feature_output_stride: int=4) -> None:
"""Initialize the _LightWeightDecoder module. Args: in_channels: number of cha... | the_stack_v2_python_sparse | torchgeo/models/farseg.py | microsoft/torchgeo | train | 1,724 |
bf2d31d8dd13d7c1bc6c3cbef8dd6300cb327961 | [
"if data_type == 'mel' or data_type == 'scatter':\n self.data_type = data_type\nelse:\n raise ValueError(\"data_type must be 'mel' or 'scatter'.\")",
"if self.data_type == 'mel':\n mean = 2.3779549598693848\nelif self.data_type == 'scatter':\n mean = 0.21285544335842133\nif 'data' in sample:\n key ... | <|body_start_0|>
if data_type == 'mel' or data_type == 'scatter':
self.data_type = data_type
else:
raise ValueError("data_type must be 'mel' or 'scatter'.")
<|end_body_0|>
<|body_start_1|>
if self.data_type == 'mel':
mean = 2.3779549598693848
elif sel... | Subtract mean from audio input. | SubtractMean | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubtractMean:
"""Subtract mean from audio input."""
def __init__(self, data_type):
"""Initialize SubtractMean."""
<|body_0|>
def __call__(self, sample):
"""Subtract the appropriate mean from the sample data."""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_36k_train_033422 | 1,433 | no_license | [
{
"docstring": "Initialize SubtractMean.",
"name": "__init__",
"signature": "def __init__(self, data_type)"
},
{
"docstring": "Subtract the appropriate mean from the sample data.",
"name": "__call__",
"signature": "def __call__(self, sample)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013309 | Implement the Python class `SubtractMean` described below.
Class description:
Subtract mean from audio input.
Method signatures and docstrings:
- def __init__(self, data_type): Initialize SubtractMean.
- def __call__(self, sample): Subtract the appropriate mean from the sample data. | Implement the Python class `SubtractMean` described below.
Class description:
Subtract mean from audio input.
Method signatures and docstrings:
- def __init__(self, data_type): Initialize SubtractMean.
- def __call__(self, sample): Subtract the appropriate mean from the sample data.
<|skeleton|>
class SubtractMean:
... | 55a62c62d26534f3f1a0d7d529cc79d4796680a1 | <|skeleton|>
class SubtractMean:
"""Subtract mean from audio input."""
def __init__(self, data_type):
"""Initialize SubtractMean."""
<|body_0|>
def __call__(self, sample):
"""Subtract the appropriate mean from the sample data."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubtractMean:
"""Subtract mean from audio input."""
def __init__(self, data_type):
"""Initialize SubtractMean."""
if data_type == 'mel' or data_type == 'scatter':
self.data_type = data_type
else:
raise ValueError("data_type must be 'mel' or 'scatter'.")
... | the_stack_v2_python_sparse | dc/datasets/transforms.py | yamato2199/DeepContentRecommenders | train | 1 |
5fa62ecdfde88d6ec041563490ced6f5da083bf1 | [
"l1, l2 = (len(word1) + 1, len(word2) + 1)\ndp = [[0 for _ in range(l2)] for _ in range(l1)]\nfor i in range(l1):\n dp[i][0] = i\nfor j in range(l2):\n dp[0][j] = j\nfor i in range(1, l1):\n for j in range(1, l2):\n dp[i][j] = min(dp[i - 1][j] + 1, dp[i][j - 1] + 1, dp[i - 1][j - 1] + (word1[i - 1] ... | <|body_start_0|>
l1, l2 = (len(word1) + 1, len(word2) + 1)
dp = [[0 for _ in range(l2)] for _ in range(l1)]
for i in range(l1):
dp[i][0] = i
for j in range(l2):
dp[0][j] = j
for i in range(1, l1):
for j in range(1, l2):
dp[i][j]... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minDistance(self, word1, word2):
""":type word1: str :type word2: str :rtype: int O(m*n) space beats 54.30%"""
<|body_0|>
def minDistance1(self, word1, word2):
""":param word1: :param word2: :return: O(n) space with rolling array beats 87.02%"""
... | stack_v2_sparse_classes_36k_train_033423 | 1,190 | no_license | [
{
"docstring": ":type word1: str :type word2: str :rtype: int O(m*n) space beats 54.30%",
"name": "minDistance",
"signature": "def minDistance(self, word1, word2)"
},
{
"docstring": ":param word1: :param word2: :return: O(n) space with rolling array beats 87.02%",
"name": "minDistance1",
... | 2 | stack_v2_sparse_classes_30k_train_016471 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDistance(self, word1, word2): :type word1: str :type word2: str :rtype: int O(m*n) space beats 54.30%
- def minDistance1(self, word1, word2): :param word1: :param word2: :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDistance(self, word1, word2): :type word1: str :type word2: str :rtype: int O(m*n) space beats 54.30%
- def minDistance1(self, word1, word2): :param word1: :param word2: :... | 7e0e917c15d3e35f49da3a00ef395bd5ff180d79 | <|skeleton|>
class Solution:
def minDistance(self, word1, word2):
""":type word1: str :type word2: str :rtype: int O(m*n) space beats 54.30%"""
<|body_0|>
def minDistance1(self, word1, word2):
""":param word1: :param word2: :return: O(n) space with rolling array beats 87.02%"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minDistance(self, word1, word2):
""":type word1: str :type word2: str :rtype: int O(m*n) space beats 54.30%"""
l1, l2 = (len(word1) + 1, len(word2) + 1)
dp = [[0 for _ in range(l2)] for _ in range(l1)]
for i in range(l1):
dp[i][0] = i
for j in ... | the_stack_v2_python_sparse | LeetCode/072_edit_distance.py | yao23/Machine_Learning_Playground | train | 12 | |
0bb1206ec28a05d04314cd05801171a84bf787c0 | [
"if jwthandler.authorize_action(self, 1) == False:\n return None\nbody_categories = {'node_id': 0, 'link_id': 0}\nmetadata_dict = errorutil.check_fields(self.request.arguments, body_categories, self)\nif metadata_dict == False:\n self.set_status(400)\n self.write({'message': 'Empty get request'})\n retu... | <|body_start_0|>
if jwthandler.authorize_action(self, 1) == False:
return None
body_categories = {'node_id': 0, 'link_id': 0}
metadata_dict = errorutil.check_fields(self.request.arguments, body_categories, self)
if metadata_dict == False:
self.set_status(400)
... | Class to handle metadata API requests Functions: get, post, put, delete | Metadata | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Metadata:
"""Class to handle metadata API requests Functions: get, post, put, delete"""
def get(self):
"""Function to get metadata or a single metadata record Inputs: Tornado web request Output: Metadata data Caveats: Authentication needs to be passed"""
<|body_0|>
def p... | stack_v2_sparse_classes_36k_train_033424 | 5,025 | no_license | [
{
"docstring": "Function to get metadata or a single metadata record Inputs: Tornado web request Output: Metadata data Caveats: Authentication needs to be passed",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Function to create a metadata record Inputs: Tornado web request Outp... | 4 | stack_v2_sparse_classes_30k_train_000849 | Implement the Python class `Metadata` described below.
Class description:
Class to handle metadata API requests Functions: get, post, put, delete
Method signatures and docstrings:
- def get(self): Function to get metadata or a single metadata record Inputs: Tornado web request Output: Metadata data Caveats: Authentic... | Implement the Python class `Metadata` described below.
Class description:
Class to handle metadata API requests Functions: get, post, put, delete
Method signatures and docstrings:
- def get(self): Function to get metadata or a single metadata record Inputs: Tornado web request Output: Metadata data Caveats: Authentic... | ee812db479ccd65bb319c1d5e268cd119952e2f0 | <|skeleton|>
class Metadata:
"""Class to handle metadata API requests Functions: get, post, put, delete"""
def get(self):
"""Function to get metadata or a single metadata record Inputs: Tornado web request Output: Metadata data Caveats: Authentication needs to be passed"""
<|body_0|>
def p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Metadata:
"""Class to handle metadata API requests Functions: get, post, put, delete"""
def get(self):
"""Function to get metadata or a single metadata record Inputs: Tornado web request Output: Metadata data Caveats: Authentication needs to be passed"""
if jwthandler.authorize_action(sel... | the_stack_v2_python_sparse | src/handlers/api/metadata.py | FedoraTipper/AMS-Project | train | 0 |
7610424e221559b22fed52aaaee6fb8c679f4f3e | [
"self._model = model\nself._model.build_graph()\nself._batch_reader = batch_reader\nself._hps = hps\nself._vocab = vocab\nself._saver = tf.train.Saver()\nself._decode_io = DecodeIO(FLAGS.decode_dir)",
"sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))\nstep = 0\nwhile step < FLAGS.max_decode_ste... | <|body_start_0|>
self._model = model
self._model.build_graph()
self._batch_reader = batch_reader
self._hps = hps
self._vocab = vocab
self._saver = tf.train.Saver()
self._decode_io = DecodeIO(FLAGS.decode_dir)
<|end_body_0|>
<|body_start_1|>
sess = tf.Sess... | Beam search decoder. | BSDecoder | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BSDecoder:
"""Beam search decoder."""
def __init__(self, model, batch_reader, hps, vocab):
"""Beam search decoding. Args: model: The seq2seq attentional model. batch_reader: The batch data reader. hps: Hyperparamters. vocab: Vocabulary"""
<|body_0|>
def DecodeLoop(self):... | stack_v2_sparse_classes_36k_train_033425 | 5,579 | permissive | [
{
"docstring": "Beam search decoding. Args: model: The seq2seq attentional model. batch_reader: The batch data reader. hps: Hyperparamters. vocab: Vocabulary",
"name": "__init__",
"signature": "def __init__(self, model, batch_reader, hps, vocab)"
},
{
"docstring": "Decoding loop for long running... | 4 | stack_v2_sparse_classes_30k_train_003525 | Implement the Python class `BSDecoder` described below.
Class description:
Beam search decoder.
Method signatures and docstrings:
- def __init__(self, model, batch_reader, hps, vocab): Beam search decoding. Args: model: The seq2seq attentional model. batch_reader: The batch data reader. hps: Hyperparamters. vocab: Vo... | Implement the Python class `BSDecoder` described below.
Class description:
Beam search decoder.
Method signatures and docstrings:
- def __init__(self, model, batch_reader, hps, vocab): Beam search decoding. Args: model: The seq2seq attentional model. batch_reader: The batch data reader. hps: Hyperparamters. vocab: Vo... | 92ec5ec3efeee852aec5c057798298cd3a8e58ae | <|skeleton|>
class BSDecoder:
"""Beam search decoder."""
def __init__(self, model, batch_reader, hps, vocab):
"""Beam search decoding. Args: model: The seq2seq attentional model. batch_reader: The batch data reader. hps: Hyperparamters. vocab: Vocabulary"""
<|body_0|>
def DecodeLoop(self):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BSDecoder:
"""Beam search decoder."""
def __init__(self, model, batch_reader, hps, vocab):
"""Beam search decoding. Args: model: The seq2seq attentional model. batch_reader: The batch data reader. hps: Hyperparamters. vocab: Vocabulary"""
self._model = model
self._model.build_grap... | the_stack_v2_python_sparse | model_zoo/models/textsum/seq2seq_attention_decode.py | coderSkyChen/Action_Recognition_Zoo | train | 246 |
377c54471b3906b2b620013d5b17d09e7549a10b | [
"gazettes_data = requests.get(self.BASE_URL).json()\nnumber_of_documents = gazettes_data['response']['numFound']\nurl = f'{self.BASE_URL}?start=0&rows={number_of_documents}'\nyield scrapy.Request(url=url, callback=self.parse)",
"data = json.loads(response.body)['response']\nfor gazette_data in data['docs']:\n ... | <|body_start_0|>
gazettes_data = requests.get(self.BASE_URL).json()
number_of_documents = gazettes_data['response']['numFound']
url = f'{self.BASE_URL}?start=0&rows={number_of_documents}'
yield scrapy.Request(url=url, callback=self.parse)
<|end_body_0|>
<|body_start_1|>
data = j... | PaBelemSpider | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PaBelemSpider:
def start_requests(self):
"""Requests the gazette to get the total of documents and use it as a query param @url https://sistemas.belem.pa.gov.br/diario-consulta-api/diarios @returns requests 1"""
<|body_0|>
def parse(self, response):
"""@url https://s... | stack_v2_sparse_classes_36k_train_033426 | 1,742 | permissive | [
{
"docstring": "Requests the gazette to get the total of documents and use it as a query param @url https://sistemas.belem.pa.gov.br/diario-consulta-api/diarios @returns requests 1",
"name": "start_requests",
"signature": "def start_requests(self)"
},
{
"docstring": "@url https://sistemas.belem.... | 2 | stack_v2_sparse_classes_30k_train_020490 | Implement the Python class `PaBelemSpider` described below.
Class description:
Implement the PaBelemSpider class.
Method signatures and docstrings:
- def start_requests(self): Requests the gazette to get the total of documents and use it as a query param @url https://sistemas.belem.pa.gov.br/diario-consulta-api/diari... | Implement the Python class `PaBelemSpider` described below.
Class description:
Implement the PaBelemSpider class.
Method signatures and docstrings:
- def start_requests(self): Requests the gazette to get the total of documents and use it as a query param @url https://sistemas.belem.pa.gov.br/diario-consulta-api/diari... | feef1d36d540b052ec0b178015872a215352ba80 | <|skeleton|>
class PaBelemSpider:
def start_requests(self):
"""Requests the gazette to get the total of documents and use it as a query param @url https://sistemas.belem.pa.gov.br/diario-consulta-api/diarios @returns requests 1"""
<|body_0|>
def parse(self, response):
"""@url https://s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PaBelemSpider:
def start_requests(self):
"""Requests the gazette to get the total of documents and use it as a query param @url https://sistemas.belem.pa.gov.br/diario-consulta-api/diarios @returns requests 1"""
gazettes_data = requests.get(self.BASE_URL).json()
number_of_documents = g... | the_stack_v2_python_sparse | data_collection/gazette/spiders/pa_belem.py | tiagofer/querido-diario | train | 1 | |
f98f2bd04367b414fb00d1f1b863858066055ce0 | [
"self.df = df\nself.col_name = col_name\nself.threshold = threshold\nself.relative_error = relative_error\nself.upper_bound, self.lower_bound = dict_filter(self.whiskers(), ['upper_bound', 'lower_bound'])\nsuper().__init__(df, col_name)",
"mad_value = self.df.cols.mad(self.col_name, self.relative_error, more=True... | <|body_start_0|>
self.df = df
self.col_name = col_name
self.threshold = threshold
self.relative_error = relative_error
self.upper_bound, self.lower_bound = dict_filter(self.whiskers(), ['upper_bound', 'lower_bound'])
super().__init__(df, col_name)
<|end_body_0|>
<|body_s... | Handle outliers using mad | MAD | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MAD:
"""Handle outliers using mad"""
def __init__(self, df, col_name, threshold: int, relative_error: int=RELATIVE_ERROR):
""":param df: :param col_name: :type threshold: object :type relative_error: object"""
<|body_0|>
def whiskers(self):
"""Get the wisker used... | stack_v2_sparse_classes_36k_train_033427 | 1,930 | permissive | [
{
"docstring": ":param df: :param col_name: :type threshold: object :type relative_error: object",
"name": "__init__",
"signature": "def __init__(self, df, col_name, threshold: int, relative_error: int=RELATIVE_ERROR)"
},
{
"docstring": "Get the wisker used to defined outliers :return:",
"na... | 3 | null | Implement the Python class `MAD` described below.
Class description:
Handle outliers using mad
Method signatures and docstrings:
- def __init__(self, df, col_name, threshold: int, relative_error: int=RELATIVE_ERROR): :param df: :param col_name: :type threshold: object :type relative_error: object
- def whiskers(self)... | Implement the Python class `MAD` described below.
Class description:
Handle outliers using mad
Method signatures and docstrings:
- def __init__(self, df, col_name, threshold: int, relative_error: int=RELATIVE_ERROR): :param df: :param col_name: :type threshold: object :type relative_error: object
- def whiskers(self)... | 13e7b180f0970addae77cafe128bd2a93be138a2 | <|skeleton|>
class MAD:
"""Handle outliers using mad"""
def __init__(self, df, col_name, threshold: int, relative_error: int=RELATIVE_ERROR):
""":param df: :param col_name: :type threshold: object :type relative_error: object"""
<|body_0|>
def whiskers(self):
"""Get the wisker used... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MAD:
"""Handle outliers using mad"""
def __init__(self, df, col_name, threshold: int, relative_error: int=RELATIVE_ERROR):
""":param df: :param col_name: :type threshold: object :type relative_error: object"""
self.df = df
self.col_name = col_name
self.threshold = threshol... | the_stack_v2_python_sparse | optimus/outliers/mad.py | XD-DENG/Optimus | train | 1 |
cacb6ec8145bcd9298c91b9adfea0e97439b6fc1 | [
"config_properties = config_domain.Registry.get_config_property_schemas()\nconfig_prop_for_blog_admin = {'max_number_of_tags_assigned_to_blog_post': config_properties['max_number_of_tags_assigned_to_blog_post']}\nrole_to_action = role_services.get_role_actions()\nself.render_json({'config_properties': config_prop_f... | <|body_start_0|>
config_properties = config_domain.Registry.get_config_property_schemas()
config_prop_for_blog_admin = {'max_number_of_tags_assigned_to_blog_post': config_properties['max_number_of_tags_assigned_to_blog_post']}
role_to_action = role_services.get_role_actions()
self.render... | Handler for the blog admin page. | BlogAdminHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BlogAdminHandler:
"""Handler for the blog admin page."""
def get(self) -> None:
"""Handles GET requests."""
<|body_0|>
def post(self) -> None:
"""Handles POST requests."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
config_properties = config_d... | stack_v2_sparse_classes_36k_train_033428 | 8,179 | permissive | [
{
"docstring": "Handles GET requests.",
"name": "get",
"signature": "def get(self) -> None"
},
{
"docstring": "Handles POST requests.",
"name": "post",
"signature": "def post(self) -> None"
}
] | 2 | null | Implement the Python class `BlogAdminHandler` described below.
Class description:
Handler for the blog admin page.
Method signatures and docstrings:
- def get(self) -> None: Handles GET requests.
- def post(self) -> None: Handles POST requests. | Implement the Python class `BlogAdminHandler` described below.
Class description:
Handler for the blog admin page.
Method signatures and docstrings:
- def get(self) -> None: Handles GET requests.
- def post(self) -> None: Handles POST requests.
<|skeleton|>
class BlogAdminHandler:
"""Handler for the blog admin p... | d16fdf23d790eafd63812bd7239532256e30a21d | <|skeleton|>
class BlogAdminHandler:
"""Handler for the blog admin page."""
def get(self) -> None:
"""Handles GET requests."""
<|body_0|>
def post(self) -> None:
"""Handles POST requests."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BlogAdminHandler:
"""Handler for the blog admin page."""
def get(self) -> None:
"""Handles GET requests."""
config_properties = config_domain.Registry.get_config_property_schemas()
config_prop_for_blog_admin = {'max_number_of_tags_assigned_to_blog_post': config_properties['max_num... | the_stack_v2_python_sparse | core/controllers/blog_admin.py | oppia/oppia | train | 6,172 |
39700ebad682933b18916046daa1d43242ff6df5 | [
"return_type = AppPrincipalCredential(context)\npayload = {'symmetricKey': symmetric_key, 'notBefore': not_before.isoformat(), 'notAfter': not_after.isoformat()}\nqry = ServiceOperationQuery(return_type, 'CreateFromSymmetricKey', None, payload, None, return_type)\nqry.static = True\ncontext.add_query(qry)\nreturn r... | <|body_start_0|>
return_type = AppPrincipalCredential(context)
payload = {'symmetricKey': symmetric_key, 'notBefore': not_before.isoformat(), 'notAfter': not_after.isoformat()}
qry = ServiceOperationQuery(return_type, 'CreateFromSymmetricKey', None, payload, None, return_type)
qry.static... | Represents a credential belonging to an app principal. | AppPrincipalCredential | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppPrincipalCredential:
"""Represents a credential belonging to an app principal."""
def create_from_symmetric_key(context, symmetric_key, not_before, not_after=None):
"""Create an instance of SP.AppPrincipalCredential that wraps a symmetric key. :type context: office365.sharepoint.c... | stack_v2_sparse_classes_36k_train_033429 | 1,836 | permissive | [
{
"docstring": "Create an instance of SP.AppPrincipalCredential that wraps a symmetric key. :type context: office365.sharepoint.client_context.ClientContext :param str symmetric_key: The symmetric key of the app principal credential. :param datetime.datetime not_before: The earliest time when the key is valid. ... | 2 | null | Implement the Python class `AppPrincipalCredential` described below.
Class description:
Represents a credential belonging to an app principal.
Method signatures and docstrings:
- def create_from_symmetric_key(context, symmetric_key, not_before, not_after=None): Create an instance of SP.AppPrincipalCredential that wra... | Implement the Python class `AppPrincipalCredential` described below.
Class description:
Represents a credential belonging to an app principal.
Method signatures and docstrings:
- def create_from_symmetric_key(context, symmetric_key, not_before, not_after=None): Create an instance of SP.AppPrincipalCredential that wra... | cbd245d1af8d69e013c469cfc2a9851f51c91417 | <|skeleton|>
class AppPrincipalCredential:
"""Represents a credential belonging to an app principal."""
def create_from_symmetric_key(context, symmetric_key, not_before, not_after=None):
"""Create an instance of SP.AppPrincipalCredential that wraps a symmetric key. :type context: office365.sharepoint.c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AppPrincipalCredential:
"""Represents a credential belonging to an app principal."""
def create_from_symmetric_key(context, symmetric_key, not_before, not_after=None):
"""Create an instance of SP.AppPrincipalCredential that wraps a symmetric key. :type context: office365.sharepoint.client_context... | the_stack_v2_python_sparse | office365/sharepoint/appprincipal/credential.py | vgrem/Office365-REST-Python-Client | train | 1,006 |
fc6f3d3e273a3a8e83b797f47ce4f6f7a506c5af | [
"data = self.request.get('data', {})\nuser = self.request.app['models']['user']\ncompany = self.request.app['models']['company']\nself_id = data['self_id']\nu = await user.get_user(self_id)\ncontacts = await user.get_users(u['contacts'])\ncompanys = await company.get_companys_by_user(self_id)\ndata.update({'contact... | <|body_start_0|>
data = self.request.get('data', {})
user = self.request.app['models']['user']
company = self.request.app['models']['company']
self_id = data['self_id']
u = await user.get_user(self_id)
contacts = await user.get_users(u['contacts'])
companys = awai... | Contacts | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Contacts:
async def get(self):
"""Контакты, с которыми есть чат или добавлены в контакты"""
<|body_0|>
async def put(self):
"""Добавить пользователя в контакты"""
<|body_1|>
async def delete(self):
"""Удалить пользователя из списка контактов"""
... | stack_v2_sparse_classes_36k_train_033430 | 19,248 | no_license | [
{
"docstring": "Контакты, с которыми есть чат или добавлены в контакты",
"name": "get",
"signature": "async def get(self)"
},
{
"docstring": "Добавить пользователя в контакты",
"name": "put",
"signature": "async def put(self)"
},
{
"docstring": "Удалить пользователя из списка кон... | 3 | stack_v2_sparse_classes_30k_train_003078 | Implement the Python class `Contacts` described below.
Class description:
Implement the Contacts class.
Method signatures and docstrings:
- async def get(self): Контакты, с которыми есть чат или добавлены в контакты
- async def put(self): Добавить пользователя в контакты
- async def delete(self): Удалить пользователя... | Implement the Python class `Contacts` described below.
Class description:
Implement the Contacts class.
Method signatures and docstrings:
- async def get(self): Контакты, с которыми есть чат или добавлены в контакты
- async def put(self): Добавить пользователя в контакты
- async def delete(self): Удалить пользователя... | c8726ad77079b981453c11d5c7fc39bc838eec67 | <|skeleton|>
class Contacts:
async def get(self):
"""Контакты, с которыми есть чат или добавлены в контакты"""
<|body_0|>
async def put(self):
"""Добавить пользователя в контакты"""
<|body_1|>
async def delete(self):
"""Удалить пользователя из списка контактов"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Contacts:
async def get(self):
"""Контакты, с которыми есть чат или добавлены в контакты"""
data = self.request.get('data', {})
user = self.request.app['models']['user']
company = self.request.app['models']['company']
self_id = data['self_id']
u = await user.get... | the_stack_v2_python_sparse | chat/views.py | ArtemZaitsev1994/chat | train | 0 | |
c8b00059b1df5aeb6eee78c16eaf76442ecb53c8 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | TodoServiceServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TodoServiceServicer:
"""Missing associated documentation comment in .proto file."""
def CreateTodo(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def GetTodos(self, request, context):
"""Missing associated docum... | stack_v2_sparse_classes_36k_train_033431 | 6,980 | no_license | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "CreateTodo",
"signature": "def CreateTodo(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "GetTodos",
"signature": "def GetTodos(self, reques... | 4 | null | Implement the Python class `TodoServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def CreateTodo(self, request, context): Missing associated documentation comment in .proto file.
- def GetTodos(self, request, context): Mis... | Implement the Python class `TodoServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def CreateTodo(self, request, context): Missing associated documentation comment in .proto file.
- def GetTodos(self, request, context): Mis... | f2f5418d8a7674e8f91de443b3bf72a419589f9f | <|skeleton|>
class TodoServiceServicer:
"""Missing associated documentation comment in .proto file."""
def CreateTodo(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def GetTodos(self, request, context):
"""Missing associated docum... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TodoServiceServicer:
"""Missing associated documentation comment in .proto file."""
def CreateTodo(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')... | the_stack_v2_python_sparse | grpc-main-service/todo/todo_pb2_grpc.py | Jprichard314/python-workbook | train | 0 |
bcff8fe1b938a68a11a8edc2a4ecc82e6f2f2256 | [
"self.fields = list()\nfor k in argsdict.keys():\n setattr(self, k, argsdict[k])\n self.fields.append(k)",
"exp = dict()\nfor key in self.fields:\n exp[key] = self.__dict__.get(key)\nreturn exp"
] | <|body_start_0|>
self.fields = list()
for k in argsdict.keys():
setattr(self, k, argsdict[k])
self.fields.append(k)
<|end_body_0|>
<|body_start_1|>
exp = dict()
for key in self.fields:
exp[key] = self.__dict__.get(key)
return exp
<|end_body_1|... | fields = collections.OrderedDict([ ('id', int), ('complete', bool), ('result', bool), ('size', int), ('hashes', [str]), ('time', int) ]) | CheckResult | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CheckResult:
"""fields = collections.OrderedDict([ ('id', int), ('complete', bool), ('result', bool), ('size', int), ('hashes', [str]), ('time', int) ])"""
def __init__(self, argsdict):
"""self.id = argsdict.get('id') self.complete = argsdict.get('id') self.result = argsdict.get('res... | stack_v2_sparse_classes_36k_train_033432 | 5,265 | permissive | [
{
"docstring": "self.id = argsdict.get('id') self.complete = argsdict.get('id') self.result = argsdict.get('result') self.size = argsdict.get('size') self.hashes = argsdict.get('hashes') self.time = argsdict.get('time')",
"name": "__init__",
"signature": "def __init__(self, argsdict)"
},
{
"docs... | 2 | stack_v2_sparse_classes_30k_train_015571 | Implement the Python class `CheckResult` described below.
Class description:
fields = collections.OrderedDict([ ('id', int), ('complete', bool), ('result', bool), ('size', int), ('hashes', [str]), ('time', int) ])
Method signatures and docstrings:
- def __init__(self, argsdict): self.id = argsdict.get('id') self.comp... | Implement the Python class `CheckResult` described below.
Class description:
fields = collections.OrderedDict([ ('id', int), ('complete', bool), ('result', bool), ('size', int), ('hashes', [str]), ('time', int) ])
Method signatures and docstrings:
- def __init__(self, argsdict): self.id = argsdict.get('id') self.comp... | b363a298e8a7d2918eb57a686f5db153099cb6fc | <|skeleton|>
class CheckResult:
"""fields = collections.OrderedDict([ ('id', int), ('complete', bool), ('result', bool), ('size', int), ('hashes', [str]), ('time', int) ])"""
def __init__(self, argsdict):
"""self.id = argsdict.get('id') self.complete = argsdict.get('id') self.result = argsdict.get('res... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CheckResult:
"""fields = collections.OrderedDict([ ('id', int), ('complete', bool), ('result', bool), ('size', int), ('hashes', [str]), ('time', int) ])"""
def __init__(self, argsdict):
"""self.id = argsdict.get('id') self.complete = argsdict.get('id') self.result = argsdict.get('result') self.si... | the_stack_v2_python_sparse | epastack/shared/check.py | PPerfLab/EPARIMM-Release | train | 8 |
911e0dbed9ecdcf7e1976979180f42495b73d0e7 | [
"all_databases = database_services.get_all_database_names()\ndefault_database = database_services.get_default_database_name()\nall_succeeded = True\ndata = {}\nfor item in all_databases:\n name = item\n if item == default_database:\n name = None\n inspection_data, succeeded = self._test_database(nam... | <|body_start_0|>
all_databases = database_services.get_all_database_names()
default_database = database_services.get_default_database_name()
all_succeeded = True
data = {}
for item in all_databases:
name = item
if item == default_database:
... | database audit manager class. | DatabaseAuditManager | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatabaseAuditManager:
"""database audit manager class."""
def inspect(self, **options):
"""inspects the status of available databases. it returns a tuple of two values. first value is a dict containing the inspection data. and the second value is a bool value indicating that inspecti... | stack_v2_sparse_classes_36k_train_033433 | 3,572 | permissive | [
{
"docstring": "inspects the status of available databases. it returns a tuple of two values. first value is a dict containing the inspection data. and the second value is a bool value indicating that inspection has been succeeded or failed. :keyword bool traceback: specifies that on failure report, it must inc... | 2 | null | Implement the Python class `DatabaseAuditManager` described below.
Class description:
database audit manager class.
Method signatures and docstrings:
- def inspect(self, **options): inspects the status of available databases. it returns a tuple of two values. first value is a dict containing the inspection data. and ... | Implement the Python class `DatabaseAuditManager` described below.
Class description:
database audit manager class.
Method signatures and docstrings:
- def inspect(self, **options): inspects the status of available databases. it returns a tuple of two values. first value is a dict containing the inspection data. and ... | 9d4776498225de4f3d16a4600b5b19212abe8562 | <|skeleton|>
class DatabaseAuditManager:
"""database audit manager class."""
def inspect(self, **options):
"""inspects the status of available databases. it returns a tuple of two values. first value is a dict containing the inspection data. and the second value is a bool value indicating that inspecti... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatabaseAuditManager:
"""database audit manager class."""
def inspect(self, **options):
"""inspects the status of available databases. it returns a tuple of two values. first value is a dict containing the inspection data. and the second value is a bool value indicating that inspection has been s... | the_stack_v2_python_sparse | src/pyrin/database/audit/manager.py | mononobi/pyrin | train | 20 |
9796a40d6b3946ffeeb4989b72535ce12509c877 | [
"super(AlexNet, self).__init__()\nself.n_bins = n_bins\nself.dropout_rate = dropout_rate\nself.conv1 = nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)\nself.relu = nn.ReLU(inplace=True)\nself.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\nself.conv2 = nn.Conv2d(64, 192, kernel_size=5, stride=2)\nself.pool2 = ... | <|body_start_0|>
super(AlexNet, self).__init__()
self.n_bins = n_bins
self.dropout_rate = dropout_rate
self.conv1 = nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)
self.relu = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.conv... | Implements AlexNet, laid out as a HopeNet which classifies Euler angles in bins. Regression is then used on the output to output the expected value. | AlexNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlexNet:
"""Implements AlexNet, laid out as a HopeNet which classifies Euler angles in bins. Regression is then used on the output to output the expected value."""
def __init__(self, n_bins, dropout_rate=0.5):
"""Instantiates an AlexNet object. Parameters ---------- n_bins : int The ... | stack_v2_sparse_classes_36k_train_033434 | 9,784 | no_license | [
{
"docstring": "Instantiates an AlexNet object. Parameters ---------- n_bins : int The number of bins, which are output by the network. dropout_rate : float, optional The dropout rate passed on to ``nn.Dropout()``, by default 0.5. Returns ------- None",
"name": "__init__",
"signature": "def __init__(sel... | 2 | stack_v2_sparse_classes_30k_train_014700 | Implement the Python class `AlexNet` described below.
Class description:
Implements AlexNet, laid out as a HopeNet which classifies Euler angles in bins. Regression is then used on the output to output the expected value.
Method signatures and docstrings:
- def __init__(self, n_bins, dropout_rate=0.5): Instantiates a... | Implement the Python class `AlexNet` described below.
Class description:
Implements AlexNet, laid out as a HopeNet which classifies Euler angles in bins. Regression is then used on the output to output the expected value.
Method signatures and docstrings:
- def __init__(self, n_bins, dropout_rate=0.5): Instantiates a... | a7c30481822ecb945e3ff6ad184d104361a40ed1 | <|skeleton|>
class AlexNet:
"""Implements AlexNet, laid out as a HopeNet which classifies Euler angles in bins. Regression is then used on the output to output the expected value."""
def __init__(self, n_bins, dropout_rate=0.5):
"""Instantiates an AlexNet object. Parameters ---------- n_bins : int The ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AlexNet:
"""Implements AlexNet, laid out as a HopeNet which classifies Euler angles in bins. Regression is then used on the output to output the expected value."""
def __init__(self, n_bins, dropout_rate=0.5):
"""Instantiates an AlexNet object. Parameters ---------- n_bins : int The number of bin... | the_stack_v2_python_sparse | cheapfake/hopenet/models.py | hu-simon/cheapfake | train | 0 |
4a11ad8a0bf8b8772aa39bfa39c7ba5a9acccff5 | [
"mats = cmds.ls(materials=1)\nmats.remove('lambert1')\nmats.remove('particleCloud1')\nreturn mats",
"rv = []\nSS = cmds.shadingNode('surfaceShader', asShader=1, n=name)\nSLCode = cmds.shadingNode('SLCodeNode', asUtility=1, n=name)\nmel.eval('source \"//file-cluster/GDC/Resource/Support/AnimalLogic/mayaman2.0.7/me... | <|body_start_0|>
mats = cmds.ls(materials=1)
mats.remove('lambert1')
mats.remove('particleCloud1')
return mats
<|end_body_0|>
<|body_start_1|>
rv = []
SS = cmds.shadingNode('surfaceShader', asShader=1, n=name)
SLCode = cmds.shadingNode('SLCodeNode', asUtility=1, ... | Materials | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Materials:
def ListMats(self):
"""list all materials except lamber1 and particleCloud1"""
<|body_0|>
def CreateZdpShader(self, name):
"""return value===>[surfaceShader,SLCode,]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
mats = cmds.ls(materials... | stack_v2_sparse_classes_36k_train_033435 | 14,297 | no_license | [
{
"docstring": "list all materials except lamber1 and particleCloud1",
"name": "ListMats",
"signature": "def ListMats(self)"
},
{
"docstring": "return value===>[surfaceShader,SLCode,]",
"name": "CreateZdpShader",
"signature": "def CreateZdpShader(self, name)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006716 | Implement the Python class `Materials` described below.
Class description:
Implement the Materials class.
Method signatures and docstrings:
- def ListMats(self): list all materials except lamber1 and particleCloud1
- def CreateZdpShader(self, name): return value===>[surfaceShader,SLCode,] | Implement the Python class `Materials` described below.
Class description:
Implement the Materials class.
Method signatures and docstrings:
- def ListMats(self): list all materials except lamber1 and particleCloud1
- def CreateZdpShader(self, name): return value===>[surfaceShader,SLCode,]
<|skeleton|>
class Material... | c11f715996a435396c28ffb4c20f11f8e3c1a681 | <|skeleton|>
class Materials:
def ListMats(self):
"""list all materials except lamber1 and particleCloud1"""
<|body_0|>
def CreateZdpShader(self, name):
"""return value===>[surfaceShader,SLCode,]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Materials:
def ListMats(self):
"""list all materials except lamber1 and particleCloud1"""
mats = cmds.ls(materials=1)
mats.remove('lambert1')
mats.remove('particleCloud1')
return mats
def CreateZdpShader(self, name):
"""return value===>[surfaceShader,SLCode... | the_stack_v2_python_sparse | OLD/idmt/maya/ROMA/wxII_RenderTools.py | Bn-com/myProj_octv | train | 1 | |
e872d5529ef84268616f7ebc4fb78c9c704916c7 | [
"regular_attrs = kwargs.pop('regular_attrs', None)\nextra_attrs = kwargs.pop('extra_attrs', None)\npreserve_order = kwargs.pop('preserve_order', False)\nspecific_order = kwargs.pop('specific_order', None)\nsuper(DictFormatter, self).__init__(*args, **kwargs)\nif regular_attrs is None:\n self.regular_attrs = copy... | <|body_start_0|>
regular_attrs = kwargs.pop('regular_attrs', None)
extra_attrs = kwargs.pop('extra_attrs', None)
preserve_order = kwargs.pop('preserve_order', False)
specific_order = kwargs.pop('specific_order', None)
super(DictFormatter, self).__init__(*args, **kwargs)
i... | Used for formatting log records into a dict. | DictFormatter | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DictFormatter:
"""Used for formatting log records into a dict."""
def __init__(self, *args, **kwargs):
""":param list regular_attrs: A list of strings specifying built-in python logging args that should be included in each output dict. If not specified, all args will be used. Setting... | stack_v2_sparse_classes_36k_train_033436 | 4,802 | permissive | [
{
"docstring": ":param list regular_attrs: A list of strings specifying built-in python logging args that should be included in each output dict. If not specified, all args will be used. Setting to an empty list will disable regular args. :param list extra_attrs: A list of strings specifying additional argument... | 2 | null | Implement the Python class `DictFormatter` described below.
Class description:
Used for formatting log records into a dict.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): :param list regular_attrs: A list of strings specifying built-in python logging args that should be included in each outp... | Implement the Python class `DictFormatter` described below.
Class description:
Used for formatting log records into a dict.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): :param list regular_attrs: A list of strings specifying built-in python logging args that should be included in each outp... | 2b5f3562584137c8c9f5392265db1ab8ee8acf75 | <|skeleton|>
class DictFormatter:
"""Used for formatting log records into a dict."""
def __init__(self, *args, **kwargs):
""":param list regular_attrs: A list of strings specifying built-in python logging args that should be included in each output dict. If not specified, all args will be used. Setting... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DictFormatter:
"""Used for formatting log records into a dict."""
def __init__(self, *args, **kwargs):
""":param list regular_attrs: A list of strings specifying built-in python logging args that should be included in each output dict. If not specified, all args will be used. Setting to an empty ... | the_stack_v2_python_sparse | bluebottle/utils/formatters.py | onepercentclub/bluebottle | train | 15 |
93e833a7df5203491a5dc5f8b6315c63468f5fcf | [
"devices = i2c.scan()\nassert slave_addr in devices, 'Did not find slave %d in scan: %s' % (slave_addr, devices)\nself.i2c = i2c\nself.addr = slave_addr\nself.fmt = '>2B'\nsleep(0.015)\nsetup_data = 1 << 4\ndata = bytearray(3)\ndata[0] = CONF_REG\ndata[1] = setup_data\ni2c.writeto(self.addr, data)",
"data = bytea... | <|body_start_0|>
devices = i2c.scan()
assert slave_addr in devices, 'Did not find slave %d in scan: %s' % (slave_addr, devices)
self.i2c = i2c
self.addr = slave_addr
self.fmt = '>2B'
sleep(0.015)
setup_data = 1 << 4
data = bytearray(3)
data[0] = CO... | HDC1080 | [
"LicenseRef-scancode-free-unknown",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HDC1080:
def __init__(self, i2c, slave_addr=64):
"""Initialize a HDC1080 temperature and humidity sensor. Keyword arguments: i2c -- The i2c object (driver) used to interact through device addresses. slave_addr -- The slave address of the sensor (default 64 or 0x40)."""
<|body_0|>... | stack_v2_sparse_classes_36k_train_033437 | 3,281 | permissive | [
{
"docstring": "Initialize a HDC1080 temperature and humidity sensor. Keyword arguments: i2c -- The i2c object (driver) used to interact through device addresses. slave_addr -- The slave address of the sensor (default 64 or 0x40).",
"name": "__init__",
"signature": "def __init__(self, i2c, slave_addr=64... | 3 | null | Implement the Python class `HDC1080` described below.
Class description:
Implement the HDC1080 class.
Method signatures and docstrings:
- def __init__(self, i2c, slave_addr=64): Initialize a HDC1080 temperature and humidity sensor. Keyword arguments: i2c -- The i2c object (driver) used to interact through device addr... | Implement the Python class `HDC1080` described below.
Class description:
Implement the HDC1080 class.
Method signatures and docstrings:
- def __init__(self, i2c, slave_addr=64): Initialize a HDC1080 temperature and humidity sensor. Keyword arguments: i2c -- The i2c object (driver) used to interact through device addr... | 5366302af8073fd3d122865272f92215b363f3a6 | <|skeleton|>
class HDC1080:
def __init__(self, i2c, slave_addr=64):
"""Initialize a HDC1080 temperature and humidity sensor. Keyword arguments: i2c -- The i2c object (driver) used to interact through device addresses. slave_addr -- The slave address of the sensor (default 64 or 0x40)."""
<|body_0|>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HDC1080:
def __init__(self, i2c, slave_addr=64):
"""Initialize a HDC1080 temperature and humidity sensor. Keyword arguments: i2c -- The i2c object (driver) used to interact through device addresses. slave_addr -- The slave address of the sensor (default 64 or 0x40)."""
devices = i2c.scan()
... | the_stack_v2_python_sparse | lib/sensor/hdc1080/hdc1080.py | digidotcom/xbee-micropython | train | 72 | |
78eafb97ba6c2ea8c173a9cea7f4d2afaabe6633 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn PlannerBucket()",
"from .entity import Entity\nfrom .planner_task import PlannerTask\nfrom .entity import Entity\nfrom .planner_task import PlannerTask\nfields: Dict[str, Callable[[Any], None]] = {'name': lambda n: setattr(self, 'name'... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return PlannerBucket()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .planner_task import PlannerTask
from .entity import Entity
from .planner_task import Plan... | PlannerBucket | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlannerBucket:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerBucket:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns... | stack_v2_sparse_classes_36k_train_033438 | 2,863 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: PlannerBucket",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value... | 3 | stack_v2_sparse_classes_30k_train_002117 | Implement the Python class `PlannerBucket` described below.
Class description:
Implement the PlannerBucket class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerBucket: Creates a new instance of the appropriate class based on discriminator value... | Implement the Python class `PlannerBucket` described below.
Class description:
Implement the PlannerBucket class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerBucket: Creates a new instance of the appropriate class based on discriminator value... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class PlannerBucket:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerBucket:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlannerBucket:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerBucket:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: PlannerBucke... | the_stack_v2_python_sparse | msgraph/generated/models/planner_bucket.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
e33aa5bf9d7ade33b15f8024897bc2855f694c11 | [
"descriptions = descriptions or list(utils.generate_ids(count=count))\nchassis_list = []\n_chassis_descriptions = {}\nfor description in descriptions:\n chassis = self._client.create(description=description)\n _chassis_descriptions[chassis.uuid] = description\n chassis_list.append(chassis)\nif check:\n ... | <|body_start_0|>
descriptions = descriptions or list(utils.generate_ids(count=count))
chassis_list = []
_chassis_descriptions = {}
for description in descriptions:
chassis = self._client.create(description=description)
_chassis_descriptions[chassis.uuid] = descrip... | Chassis steps. | IronicChassisSteps | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IronicChassisSteps:
"""Chassis steps."""
def create_ironic_chassis(self, descriptions=None, count=1, check=True):
"""Step to create a ironic chassis. Args: descriptions (list): descriptions of created chassis, if not specified one chassis description will be generate count (int): cou... | stack_v2_sparse_classes_36k_train_033439 | 4,645 | no_license | [
{
"docstring": "Step to create a ironic chassis. Args: descriptions (list): descriptions of created chassis, if not specified one chassis description will be generate count (int): count of created chassis, it's ignored if chassis_descriptions are specified; one chassis is created if both args are missing check ... | 4 | stack_v2_sparse_classes_30k_train_013369 | Implement the Python class `IronicChassisSteps` described below.
Class description:
Chassis steps.
Method signatures and docstrings:
- def create_ironic_chassis(self, descriptions=None, count=1, check=True): Step to create a ironic chassis. Args: descriptions (list): descriptions of created chassis, if not specified ... | Implement the Python class `IronicChassisSteps` described below.
Class description:
Chassis steps.
Method signatures and docstrings:
- def create_ironic_chassis(self, descriptions=None, count=1, check=True): Step to create a ironic chassis. Args: descriptions (list): descriptions of created chassis, if not specified ... | e7583444cd24893ec6ae237b47db7c605b99b0c5 | <|skeleton|>
class IronicChassisSteps:
"""Chassis steps."""
def create_ironic_chassis(self, descriptions=None, count=1, check=True):
"""Step to create a ironic chassis. Args: descriptions (list): descriptions of created chassis, if not specified one chassis description will be generate count (int): cou... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IronicChassisSteps:
"""Chassis steps."""
def create_ironic_chassis(self, descriptions=None, count=1, check=True):
"""Step to create a ironic chassis. Args: descriptions (list): descriptions of created chassis, if not specified one chassis description will be generate count (int): count of created... | the_stack_v2_python_sparse | stepler/baremetal/steps/chassis.py | Mirantis/stepler | train | 16 |
8c120896a5a268336a4ad3e61acf430a521d019e | [
"_data = {BGPStream_Website_Event_Types.HIJACK.value: Hijack(self.csv_dir), BGPStream_Website_Event_Types.LEAK.value: Leak(self.csv_dir), BGPStream_Website_Event_Types.OUTAGE.value: Outage(self.csv_dir)}\nself._data = {k: v for k, v in _data.items() if k in data_types}\nknown_events = self._generate_known_events()\... | <|body_start_0|>
_data = {BGPStream_Website_Event_Types.HIJACK.value: Hijack(self.csv_dir), BGPStream_Website_Event_Types.LEAK.value: Leak(self.csv_dir), BGPStream_Website_Event_Types.OUTAGE.value: Outage(self.csv_dir)}
self._data = {k: v for k, v in _data.items() if k in data_types}
known_event... | This class parses bgpstream.com information into a database. For a more in depth explanation, read the top of the file. | BGPStream_Website_Parser | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BGPStream_Website_Parser:
"""This class parses bgpstream.com information into a database. For a more in depth explanation, read the top of the file."""
def _run(self, row_limit: int=None, IPV4=True, IPV6=True, data_types: list=BGPStream_Website_Event_Types.list_values(), refresh=False):
... | stack_v2_sparse_classes_36k_train_033440 | 5,973 | permissive | [
{
"docstring": "Parses rows in the bgpstream website. row_limit is for testing purposes only, to run a small subset. IPV4 and IPV6 are the prefixes that should be included if true. The possible values for data_types are anything in BGPStream_Website_Event_Types, these are the values that will be parsed, everyth... | 5 | null | Implement the Python class `BGPStream_Website_Parser` described below.
Class description:
This class parses bgpstream.com information into a database. For a more in depth explanation, read the top of the file.
Method signatures and docstrings:
- def _run(self, row_limit: int=None, IPV4=True, IPV6=True, data_types: li... | Implement the Python class `BGPStream_Website_Parser` described below.
Class description:
This class parses bgpstream.com information into a database. For a more in depth explanation, read the top of the file.
Method signatures and docstrings:
- def _run(self, row_limit: int=None, IPV4=True, IPV6=True, data_types: li... | 91c92584b31bd128d818c7fee86c738367c0712e | <|skeleton|>
class BGPStream_Website_Parser:
"""This class parses bgpstream.com information into a database. For a more in depth explanation, read the top of the file."""
def _run(self, row_limit: int=None, IPV4=True, IPV6=True, data_types: list=BGPStream_Website_Event_Types.list_values(), refresh=False):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BGPStream_Website_Parser:
"""This class parses bgpstream.com information into a database. For a more in depth explanation, read the top of the file."""
def _run(self, row_limit: int=None, IPV4=True, IPV6=True, data_types: list=BGPStream_Website_Event_Types.list_values(), refresh=False):
"""Parses... | the_stack_v2_python_sparse | lib_bgp_data/collectors/bgpstream_website/bgpstream_website_parser.py | jfuruness/lib_bgp_data | train | 16 |
874cc24f7a574cfd5868862487d3c795e46a64a0 | [
"logic = AppraisingScoreLogic(self.auth, sid, aid)\nparams = ParamsParser(request.GET)\nlimit = params.int('limit', desc='每页最大渲染数', require=False, default=10)\npage = params.int('page', desc='当前页数', require=False, default=1)\nscore = AppraisingScore.objects.values('id', 'update_time').filter(association=logic.assoc... | <|body_start_0|>
logic = AppraisingScoreLogic(self.auth, sid, aid)
params = ParamsParser(request.GET)
limit = params.int('limit', desc='每页最大渲染数', require=False, default=10)
page = params.int('page', desc='当前页数', require=False, default=1)
score = AppraisingScore.objects.values('id... | AppraisingScoreInfo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppraisingScoreInfo:
def get(self, request, sid, aid):
"""获取评分列表 :param request: :param sid: :param aid: :return:"""
<|body_0|>
def post(self, request, sid, aid):
"""批量获取评分信息 :param request: :param sid: :param aid: :return:"""
<|body_1|>
<|end_skeleton|>
<|... | stack_v2_sparse_classes_36k_train_033441 | 6,147 | no_license | [
{
"docstring": "获取评分列表 :param request: :param sid: :param aid: :return:",
"name": "get",
"signature": "def get(self, request, sid, aid)"
},
{
"docstring": "批量获取评分信息 :param request: :param sid: :param aid: :return:",
"name": "post",
"signature": "def post(self, request, sid, aid)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004065 | Implement the Python class `AppraisingScoreInfo` described below.
Class description:
Implement the AppraisingScoreInfo class.
Method signatures and docstrings:
- def get(self, request, sid, aid): 获取评分列表 :param request: :param sid: :param aid: :return:
- def post(self, request, sid, aid): 批量获取评分信息 :param request: :par... | Implement the Python class `AppraisingScoreInfo` described below.
Class description:
Implement the AppraisingScoreInfo class.
Method signatures and docstrings:
- def get(self, request, sid, aid): 获取评分列表 :param request: :param sid: :param aid: :return:
- def post(self, request, sid, aid): 批量获取评分信息 :param request: :par... | a0553be3c259712de1fe5517b06317ad5756f79d | <|skeleton|>
class AppraisingScoreInfo:
def get(self, request, sid, aid):
"""获取评分列表 :param request: :param sid: :param aid: :return:"""
<|body_0|>
def post(self, request, sid, aid):
"""批量获取评分信息 :param request: :param sid: :param aid: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AppraisingScoreInfo:
def get(self, request, sid, aid):
"""获取评分列表 :param request: :param sid: :param aid: :return:"""
logic = AppraisingScoreLogic(self.auth, sid, aid)
params = ParamsParser(request.GET)
limit = params.int('limit', desc='每页最大渲染数', require=False, default=10)
... | the_stack_v2_python_sparse | LittlePigHoHo/server/appraising/views/score.py | shoogoome/hoho | train | 1 | |
4bd4ec6862a16cd5348d284673bbe52e44be01ba | [
"s = session()\ngoals = Goal.query.all()\nfor goal in goals:\n print(goal.old_numbered, type(goal.old_numbered))\n for each in goal.old_numbered:\n gs = GoalStep(each, goal.id)\n s.add(gs)\n s.commit()",
"rv = []\ntokens = nltk.word_tokenize(data)\nfor token in tokens:\n if token.low... | <|body_start_0|>
s = session()
goals = Goal.query.all()
for goal in goals:
print(goal.old_numbered, type(goal.old_numbered))
for each in goal.old_numbered:
gs = GoalStep(each, goal.id)
s.add(gs)
s.commit()
<|end_body_0|>
<|... | Represent patient/caregiver goal. | Goal | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Goal:
"""Represent patient/caregiver goal."""
def goal_step_extractor():
"""Decompose goal instances into goal-steps."""
<|body_0|>
def handle_proper_nouns(data):
"""Parameters ---------- data"""
<|body_1|>
def __init__(self, data, source):
"... | stack_v2_sparse_classes_36k_train_033442 | 5,825 | permissive | [
{
"docstring": "Decompose goal instances into goal-steps.",
"name": "goal_step_extractor",
"signature": "def goal_step_extractor()"
},
{
"docstring": "Parameters ---------- data",
"name": "handle_proper_nouns",
"signature": "def handle_proper_nouns(data)"
},
{
"docstring": "Goal ... | 3 | stack_v2_sparse_classes_30k_train_009596 | Implement the Python class `Goal` described below.
Class description:
Represent patient/caregiver goal.
Method signatures and docstrings:
- def goal_step_extractor(): Decompose goal instances into goal-steps.
- def handle_proper_nouns(data): Parameters ---------- data
- def __init__(self, data, source): Goal construc... | Implement the Python class `Goal` described below.
Class description:
Represent patient/caregiver goal.
Method signatures and docstrings:
- def goal_step_extractor(): Decompose goal instances into goal-steps.
- def handle_proper_nouns(data): Parameters ---------- data
- def __init__(self, data, source): Goal construc... | 96935bb06f71b509f97ca426afe14713d5830e46 | <|skeleton|>
class Goal:
"""Represent patient/caregiver goal."""
def goal_step_extractor():
"""Decompose goal instances into goal-steps."""
<|body_0|>
def handle_proper_nouns(data):
"""Parameters ---------- data"""
<|body_1|>
def __init__(self, data, source):
"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Goal:
"""Represent patient/caregiver goal."""
def goal_step_extractor():
"""Decompose goal instances into goal-steps."""
s = session()
goals = Goal.query.all()
for goal in goals:
print(goal.old_numbered, type(goal.old_numbered))
for each in goal.old... | the_stack_v2_python_sparse | tcas/abstract/model/goal.py | mishrasushruti99/TransitionalCareAnalyticsServer | train | 0 |
0a7bb81c88338d5bcebdb82e6c6931febb20808c | [
"super().__init__()\nself._use_condition = use_condition\nself._model = tf.keras.Sequential([tf.keras.layers.Conv2D(64, [5, 5], strides=2, padding='same'), tf.keras.layers.BatchNormalization(), tf.keras.layers.LeakyReLU(), tf.keras.layers.Conv2D(128, [5, 5], strides=2, padding='same'), tf.keras.layers.BatchNormaliz... | <|body_start_0|>
super().__init__()
self._use_condition = use_condition
self._model = tf.keras.Sequential([tf.keras.layers.Conv2D(64, [5, 5], strides=2, padding='same'), tf.keras.layers.BatchNormalization(), tf.keras.layers.LeakyReLU(), tf.keras.layers.Conv2D(128, [5, 5], strides=2, padding='sam... | Class conditioned encoder. This encoder is used by MNIST and FMNIST dataset. Attributes: _use_condition: _model: _mu_layer: _logvar_layer: | ClassConditionedEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassConditionedEncoder:
"""Class conditioned encoder. This encoder is used by MNIST and FMNIST dataset. Attributes: _use_condition: _model: _mu_layer: _logvar_layer:"""
def __init__(self, use_condition, noise_size):
"""Initializes the object. Args: use_condition: noise_size:"""
... | stack_v2_sparse_classes_36k_train_033443 | 10,560 | no_license | [
{
"docstring": "Initializes the object. Args: use_condition: noise_size:",
"name": "__init__",
"signature": "def __init__(self, use_condition, noise_size)"
},
{
"docstring": "Apples the model to the inputs. Args: image: embedding: Returns:",
"name": "call",
"signature": "def call(self, i... | 2 | stack_v2_sparse_classes_30k_train_016420 | Implement the Python class `ClassConditionedEncoder` described below.
Class description:
Class conditioned encoder. This encoder is used by MNIST and FMNIST dataset. Attributes: _use_condition: _model: _mu_layer: _logvar_layer:
Method signatures and docstrings:
- def __init__(self, use_condition, noise_size): Initial... | Implement the Python class `ClassConditionedEncoder` described below.
Class description:
Class conditioned encoder. This encoder is used by MNIST and FMNIST dataset. Attributes: _use_condition: _model: _mu_layer: _logvar_layer:
Method signatures and docstrings:
- def __init__(self, use_condition, noise_size): Initial... | 6d04861ef87ba2ba2a4182ad36f3b322fcf47cfa | <|skeleton|>
class ClassConditionedEncoder:
"""Class conditioned encoder. This encoder is used by MNIST and FMNIST dataset. Attributes: _use_condition: _model: _mu_layer: _logvar_layer:"""
def __init__(self, use_condition, noise_size):
"""Initializes the object. Args: use_condition: noise_size:"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassConditionedEncoder:
"""Class conditioned encoder. This encoder is used by MNIST and FMNIST dataset. Attributes: _use_condition: _model: _mu_layer: _logvar_layer:"""
def __init__(self, use_condition, noise_size):
"""Initializes the object. Args: use_condition: noise_size:"""
super()._... | the_stack_v2_python_sparse | vae.py | gaotianxiang/text-to-image-synthesis | train | 0 |
fffed213ed11b43a5328b06caca1320208cf87be | [
"queryset = self.get_queryset()\nslug = self.kwargs.get(self.slug_url_kwarg)\nif slug is not None:\n slug_field = self.get_slug_field()\n queryset = queryset.filter(**{slug_field: slug})\n try:\n part = queryset.get()\n return part\n except queryset.model.MultipleObjectsReturned:\n ... | <|body_start_0|>
queryset = self.get_queryset()
slug = self.kwargs.get(self.slug_url_kwarg)
if slug is not None:
slug_field = self.get_slug_field()
queryset = queryset.filter(**{slug_field: slug})
try:
part = queryset.get()
retu... | Part detail view using the IPN (internal part number) of the Part as the lookup field | PartDetailFromIPN | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PartDetailFromIPN:
"""Part detail view using the IPN (internal part number) of the Part as the lookup field"""
def get_object(self):
"""Return Part object which IPN field matches the slug value."""
<|body_0|>
def get(self, request, *args, **kwargs):
"""Attempt to... | stack_v2_sparse_classes_36k_train_033444 | 28,283 | permissive | [
{
"docstring": "Return Part object which IPN field matches the slug value.",
"name": "get_object",
"signature": "def get_object(self)"
},
{
"docstring": "Attempt to match slug to a Part, else redirect to PartIndex view.",
"name": "get",
"signature": "def get(self, request, *args, **kwarg... | 2 | stack_v2_sparse_classes_30k_train_018783 | Implement the Python class `PartDetailFromIPN` described below.
Class description:
Part detail view using the IPN (internal part number) of the Part as the lookup field
Method signatures and docstrings:
- def get_object(self): Return Part object which IPN field matches the slug value.
- def get(self, request, *args, ... | Implement the Python class `PartDetailFromIPN` described below.
Class description:
Part detail view using the IPN (internal part number) of the Part as the lookup field
Method signatures and docstrings:
- def get_object(self): Return Part object which IPN field matches the slug value.
- def get(self, request, *args, ... | 5a08ef908dd5344b4433436a4679d122f7f99e41 | <|skeleton|>
class PartDetailFromIPN:
"""Part detail view using the IPN (internal part number) of the Part as the lookup field"""
def get_object(self):
"""Return Part object which IPN field matches the slug value."""
<|body_0|>
def get(self, request, *args, **kwargs):
"""Attempt to... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PartDetailFromIPN:
"""Part detail view using the IPN (internal part number) of the Part as the lookup field"""
def get_object(self):
"""Return Part object which IPN field matches the slug value."""
queryset = self.get_queryset()
slug = self.kwargs.get(self.slug_url_kwarg)
... | the_stack_v2_python_sparse | InvenTree/part/views.py | onurtatli/InvenTree | train | 0 |
61e555092a5fbd7720819b61a81a895a1a4dbe7f | [
"super().pre_craft(**kwargs)\ncrafter = self.crafter\nfor skill_name, min_value in self.skill_requirements:\n skill_value = crafter.attributes.get(skill_name)\n if skill_value is None or skill_value < min_value:\n self.msg(self.error_too_low_skill_level.format(skill_name=skill_name, spell=self.name))\n... | <|body_start_0|>
super().pre_craft(**kwargs)
crafter = self.crafter
for skill_name, min_value in self.skill_requirements:
skill_value = crafter.attributes.get(skill_name)
if skill_value is None or skill_value < min_value:
self.msg(self.error_too_low_skill_... | A base 'recipe' to represent magical spells. We *could* treat this just like the sword above - by combining the wand and spellbook to make a fireball object that the user can then throw with another command. For this example we instead generate 'magical effects' as strings+values that we would then supposedly inject in... | _MagicRecipe | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _MagicRecipe:
"""A base 'recipe' to represent magical spells. We *could* treat this just like the sword above - by combining the wand and spellbook to make a fireball object that the user can then throw with another command. For this example we instead generate 'magical effects' as strings+values... | stack_v2_sparse_classes_36k_train_033445 | 17,892 | permissive | [
{
"docstring": "This is where we do input validation. We want to do the normal validation of the tools, but also check for a skill on the crafter. This must set the result on `self.validated_inputs`. We also set the crafter's relevant skill value on `self.skill_roll_value`. Args: **kwargs: Any optional extra kw... | 3 | stack_v2_sparse_classes_30k_train_010547 | Implement the Python class `_MagicRecipe` described below.
Class description:
A base 'recipe' to represent magical spells. We *could* treat this just like the sword above - by combining the wand and spellbook to make a fireball object that the user can then throw with another command. For this example we instead gener... | Implement the Python class `_MagicRecipe` described below.
Class description:
A base 'recipe' to represent magical spells. We *could* treat this just like the sword above - by combining the wand and spellbook to make a fireball object that the user can then throw with another command. For this example we instead gener... | b3ca58b5c1325a3bf57051dfe23560a08d2947b7 | <|skeleton|>
class _MagicRecipe:
"""A base 'recipe' to represent magical spells. We *could* treat this just like the sword above - by combining the wand and spellbook to make a fireball object that the user can then throw with another command. For this example we instead generate 'magical effects' as strings+values... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _MagicRecipe:
"""A base 'recipe' to represent magical spells. We *could* treat this just like the sword above - by combining the wand and spellbook to make a fireball object that the user can then throw with another command. For this example we instead generate 'magical effects' as strings+values that we woul... | the_stack_v2_python_sparse | evennia/contrib/game_systems/crafting/example_recipes.py | evennia/evennia | train | 1,781 |
c24df6a4b42c4c985c974f2f04df2f2ef7ce7de7 | [
"super().__init__(gbd_round_id=gbd_round_id)\nself.process_version_id = process_version_id\nself.cause_id = cause_id\nself.demographics = demographics\nself.decomp_step = decomp_step\nself.gbd_round_id = gbd_round_id\nself.raw = None",
"if self.cause_id:\n LOG.info(f'Getting CSMR from process version ID {self.... | <|body_start_0|>
super().__init__(gbd_round_id=gbd_round_id)
self.process_version_id = process_version_id
self.cause_id = cause_id
self.demographics = demographics
self.decomp_step = decomp_step
self.gbd_round_id = gbd_round_id
self.raw = None
<|end_body_0|>
<|bo... | CSMR | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CSMR:
def __init__(self, process_version_id, cause_id, demographics, decomp_step, gbd_round_id):
"""Get cause-specific mortality rate for demographic groups from a specific CodCorrect output version. :param process_version_id: (int) :param cause_id: (int) :param demographics (cascade_at.... | stack_v2_sparse_classes_36k_train_033446 | 3,808 | permissive | [
{
"docstring": "Get cause-specific mortality rate for demographic groups from a specific CodCorrect output version. :param process_version_id: (int) :param cause_id: (int) :param demographics (cascade_at.inputs.demographics.Demographics) :param decomp_step: (str) :param gbd_round_id: (int)",
"name": "__init... | 4 | null | Implement the Python class `CSMR` described below.
Class description:
Implement the CSMR class.
Method signatures and docstrings:
- def __init__(self, process_version_id, cause_id, demographics, decomp_step, gbd_round_id): Get cause-specific mortality rate for demographic groups from a specific CodCorrect output vers... | Implement the Python class `CSMR` described below.
Class description:
Implement the CSMR class.
Method signatures and docstrings:
- def __init__(self, process_version_id, cause_id, demographics, decomp_step, gbd_round_id): Get cause-specific mortality rate for demographic groups from a specific CodCorrect output vers... | b495ee82db416c9edabe992822763a9a71f60808 | <|skeleton|>
class CSMR:
def __init__(self, process_version_id, cause_id, demographics, decomp_step, gbd_round_id):
"""Get cause-specific mortality rate for demographic groups from a specific CodCorrect output version. :param process_version_id: (int) :param cause_id: (int) :param demographics (cascade_at.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CSMR:
def __init__(self, process_version_id, cause_id, demographics, decomp_step, gbd_round_id):
"""Get cause-specific mortality rate for demographic groups from a specific CodCorrect output version. :param process_version_id: (int) :param cause_id: (int) :param demographics (cascade_at.inputs.demogra... | the_stack_v2_python_sparse | src/cascade_at/inputs/csmr.py | bmiltz/cascade-at | train | 0 | |
1b2213f9a2d8af807a4f1fa066d4e902809b182e | [
"self.sensor = Sensor('127.0.0.1', 8000)\nself.pump = P('127.0.0.1', 8000)\nself.pump.set_state = MagicMock(return_value=True)",
"controller = Controller(self.sensor, self.pump, Decider(200, 0.1))\nself.sensor.measure = MagicMock(return_value=250)\nself.pump.get_state = MagicMock(return_value=P.PUMP_OFF)\ncontrol... | <|body_start_0|>
self.sensor = Sensor('127.0.0.1', 8000)
self.pump = P('127.0.0.1', 8000)
self.pump.set_state = MagicMock(return_value=True)
<|end_body_0|>
<|body_start_1|>
controller = Controller(self.sensor, self.pump, Decider(200, 0.1))
self.sensor.measure = MagicMock(return_... | Module tests for the water-regulation module | ModuleTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModuleTests:
"""Module tests for the water-regulation module"""
def setUp(self):
"""Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method."""
<|body_0|>
def test_run_water_regulator1(self):
"""Run the sensor, p... | stack_v2_sparse_classes_36k_train_033447 | 3,148 | no_license | [
{
"docstring": "Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Run the sensor, pump, and controller against random real-life situations.",
"name": "test_run_water... | 3 | stack_v2_sparse_classes_30k_train_013100 | Implement the Python class `ModuleTests` described below.
Class description:
Module tests for the water-regulation module
Method signatures and docstrings:
- def setUp(self): Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method.
- def test_run_water_regulator1(sel... | Implement the Python class `ModuleTests` described below.
Class description:
Module tests for the water-regulation module
Method signatures and docstrings:
- def setUp(self): Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method.
- def test_run_water_regulator1(sel... | b1fea0309b3495b3e1dc167d7029bc9e4b6f00f1 | <|skeleton|>
class ModuleTests:
"""Module tests for the water-regulation module"""
def setUp(self):
"""Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method."""
<|body_0|>
def test_run_water_regulator1(self):
"""Run the sensor, p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModuleTests:
"""Module tests for the water-regulation module"""
def setUp(self):
"""Declare the sensor and pump objects for each test, and declare the mock for the pump's state setter method."""
self.sensor = Sensor('127.0.0.1', 8000)
self.pump = P('127.0.0.1', 8000)
self.... | the_stack_v2_python_sparse | students/Craig_Morton/Lesson06/water-regulation/waterregulation/integrationtest.py | UWPCE-PythonCert-ClassRepos/SP_Online_Course2_2018 | train | 4 |
95fc9d1d592eafa4ad8ba4b6448c65e6c605343a | [
"if not root:\n return []\nstack = [root]\ndata = []\nwhile stack:\n levelstack = []\n for node in stack:\n if node:\n data.append(node.val)\n levelstack.append(node.left)\n levelstack.append(node.right)\n else:\n data.append('#')\n stack = level... | <|body_start_0|>
if not root:
return []
stack = [root]
data = []
while stack:
levelstack = []
for node in stack:
if node:
data.append(node.val)
levelstack.append(node.left)
lev... | 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_033448 | 1,844 | 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_003709 | 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:... | 6fd7b1bea597867889b7a4ababfb54fa649a717c | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return []
stack = [root]
data = []
while stack:
levelstack = []
for node in stack:
if node:
... | the_stack_v2_python_sparse | python/251-300/297. Serialize and Deserialize Binary Tree.py | CrazyCoder4Carrot/leetcode | train | 3 | |
4ffec53d436820497022e21fa24af0fe4eebe568 | [
"conv1 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2)\nconv2 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2, padding=0)\nconv3 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2, padding=0)\nreturn nn.Sequential(conv1, activation_constructor(Cin, False), conv2, activation_constructor(Cin, False), conv3)",
"conv1 = nn.C... | <|body_start_0|>
conv1 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2)
conv2 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2, padding=0)
conv3 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2, padding=0)
return nn.Sequential(conv1, activation_constructor(Cin, False), conv2, activation_construc... | DownUp1D | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DownUp1D:
def downsample1(activation_constructor, Cin, channel_small):
"""First RAB downsample"""
<|body_0|>
def upsample1(activation_constructor, Cin, channel_small):
"""First RAB upsample"""
<|body_1|>
def downsample2(activation_constructor, Cin, chann... | stack_v2_sparse_classes_36k_train_033449 | 17,767 | permissive | [
{
"docstring": "First RAB downsample",
"name": "downsample1",
"signature": "def downsample1(activation_constructor, Cin, channel_small)"
},
{
"docstring": "First RAB upsample",
"name": "upsample1",
"signature": "def upsample1(activation_constructor, Cin, channel_small)"
},
{
"doc... | 4 | stack_v2_sparse_classes_30k_train_003477 | Implement the Python class `DownUp1D` described below.
Class description:
Implement the DownUp1D class.
Method signatures and docstrings:
- def downsample1(activation_constructor, Cin, channel_small): First RAB downsample
- def upsample1(activation_constructor, Cin, channel_small): First RAB upsample
- def downsample... | Implement the Python class `DownUp1D` described below.
Class description:
Implement the DownUp1D class.
Method signatures and docstrings:
- def downsample1(activation_constructor, Cin, channel_small): First RAB downsample
- def upsample1(activation_constructor, Cin, channel_small): First RAB upsample
- def downsample... | b54bd53540c11aa1b70e5160751905141f463217 | <|skeleton|>
class DownUp1D:
def downsample1(activation_constructor, Cin, channel_small):
"""First RAB downsample"""
<|body_0|>
def upsample1(activation_constructor, Cin, channel_small):
"""First RAB upsample"""
<|body_1|>
def downsample2(activation_constructor, Cin, chann... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DownUp1D:
def downsample1(activation_constructor, Cin, channel_small):
"""First RAB downsample"""
conv1 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2)
conv2 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2, padding=0)
conv3 = nn.Conv1d(Cin, Cin, kernel_size=3, stride=2, padding=0)... | the_stack_v2_python_sparse | UnstructuredMesh/Tucodec1D.py | MaximeRedstone/UnstructuredCAE-DA | train | 0 | |
59127335de0806c64425342931cbfd5edf9890b0 | [
"self.bc_file = bc_file\nself.beta = []\nself.code = []\nself.load_bc()",
"array = np.loadtxt(self.bc_file)\nself.beta = array[:, 0]\nself.code = array[:, 1].astype(int)"
] | <|body_start_0|>
self.bc_file = bc_file
self.beta = []
self.code = []
self.load_bc()
<|end_body_0|>
<|body_start_1|>
array = np.loadtxt(self.bc_file)
self.beta = array[:, 0]
self.code = array[:, 1].astype(int)
<|end_body_1|>
| Class for object that represents a beta code. beta, code (corresponding to OPER Case Matrix) | BETA_CODE | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BETA_CODE:
"""Class for object that represents a beta code. beta, code (corresponding to OPER Case Matrix)"""
def __init__(self, bc_file):
"""Method to initialize BETA_CODE class."""
<|body_0|>
def load_bc(self):
"""Method to load the beta code file."""
<... | stack_v2_sparse_classes_36k_train_033450 | 3,260 | no_license | [
{
"docstring": "Method to initialize BETA_CODE class.",
"name": "__init__",
"signature": "def __init__(self, bc_file)"
},
{
"docstring": "Method to load the beta code file.",
"name": "load_bc",
"signature": "def load_bc(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009524 | Implement the Python class `BETA_CODE` described below.
Class description:
Class for object that represents a beta code. beta, code (corresponding to OPER Case Matrix)
Method signatures and docstrings:
- def __init__(self, bc_file): Method to initialize BETA_CODE class.
- def load_bc(self): Method to load the beta co... | Implement the Python class `BETA_CODE` described below.
Class description:
Class for object that represents a beta code. beta, code (corresponding to OPER Case Matrix)
Method signatures and docstrings:
- def __init__(self, bc_file): Method to initialize BETA_CODE class.
- def load_bc(self): Method to load the beta co... | 6b37842203ff4318a48dbf0258cbe2b253436e7d | <|skeleton|>
class BETA_CODE:
"""Class for object that represents a beta code. beta, code (corresponding to OPER Case Matrix)"""
def __init__(self, bc_file):
"""Method to initialize BETA_CODE class."""
<|body_0|>
def load_bc(self):
"""Method to load the beta code file."""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BETA_CODE:
"""Class for object that represents a beta code. beta, code (corresponding to OPER Case Matrix)"""
def __init__(self, bc_file):
"""Method to initialize BETA_CODE class."""
self.bc_file = bc_file
self.beta = []
self.code = []
self.load_bc()
def load_... | the_stack_v2_python_sparse | thermal/beta_code.py | tslowery78/PyLnD | train | 0 |
d3fceccb65a52320447e9be139261e875a1506a7 | [
"global tree_level, current_module, module_type, return_report, last_text\ntext = bpy.context.space_data.text\nif text:\n if text.name != 'api_doc_':\n last_text = bpy.context.space_data.text.name\n elif bpy.data.texts.__len__() < 2:\n last_text = None\nelse:\n last_text = None\nbpy.context.w... | <|body_start_0|>
global tree_level, current_module, module_type, return_report, last_text
text = bpy.context.space_data.text
if text:
if text.name != 'api_doc_':
last_text = bpy.context.space_data.text.name
elif bpy.data.texts.__len__() < 2:
... | Parent class for API Navigator | ApiNavigator | [
"GPL-3.0-only",
"Font-exception-2.0",
"GPL-3.0-or-later",
"Apache-2.0",
"LicenseRef-scancode-public-domain",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-public-domain-disclaimer",
"Bitstream-Vera",
"LicenseRef-scancode-blender-2010",
"LGPL-2.1-or-later",
"GPL-2.0-or-lat... | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApiNavigator:
"""Parent class for API Navigator"""
def generate_global_values():
"""Populate the level attributes to display the panel buttons and the documentation"""
<|body_0|>
def generate_api_doc():
"""Format the doc string for API Navigator"""
<|body... | stack_v2_sparse_classes_36k_train_033451 | 23,528 | permissive | [
{
"docstring": "Populate the level attributes to display the panel buttons and the documentation",
"name": "generate_global_values",
"signature": "def generate_global_values()"
},
{
"docstring": "Format the doc string for API Navigator",
"name": "generate_api_doc",
"signature": "def gene... | 3 | null | Implement the Python class `ApiNavigator` described below.
Class description:
Parent class for API Navigator
Method signatures and docstrings:
- def generate_global_values(): Populate the level attributes to display the panel buttons and the documentation
- def generate_api_doc(): Format the doc string for API Naviga... | Implement the Python class `ApiNavigator` described below.
Class description:
Parent class for API Navigator
Method signatures and docstrings:
- def generate_global_values(): Populate the level attributes to display the panel buttons and the documentation
- def generate_api_doc(): Format the doc string for API Naviga... | f7d23a489c2b4bcc3c1961ac955926484ff8b8d9 | <|skeleton|>
class ApiNavigator:
"""Parent class for API Navigator"""
def generate_global_values():
"""Populate the level attributes to display the panel buttons and the documentation"""
<|body_0|>
def generate_api_doc():
"""Format the doc string for API Navigator"""
<|body... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApiNavigator:
"""Parent class for API Navigator"""
def generate_global_values():
"""Populate the level attributes to display the panel buttons and the documentation"""
global tree_level, current_module, module_type, return_report, last_text
text = bpy.context.space_data.text
... | the_stack_v2_python_sparse | engine/2.80/scripts/addons/development_api_navigator.py | byteinc/Phasor | train | 3 |
a661bd2bb893b8243363024ff444a2e2514b8755 | [
"validator = UserCreateSchema()\ntry:\n loaded_data = validator.load(data)\nexcept ValidationError as error:\n raise UserControllerException(error.messages)\nloaded_data['password'] = hashpw(loaded_data['password'].encode('utf8'), gensalt())\nloaded_data.pop('confirm_password')\nuser = User(public_id=str(uuid... | <|body_start_0|>
validator = UserCreateSchema()
try:
loaded_data = validator.load(data)
except ValidationError as error:
raise UserControllerException(error.messages)
loaded_data['password'] = hashpw(loaded_data['password'].encode('utf8'), gensalt())
loade... | Controller class for user related data manipulations. | UserController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserController:
"""Controller class for user related data manipulations."""
def create_user(self, data):
"""Creates a new user record in the database. Raises: - UserControllerError: if validation fails; if is unable to save the new record into the database due to an integrity error. ... | stack_v2_sparse_classes_36k_train_033452 | 3,200 | no_license | [
{
"docstring": "Creates a new user record in the database. Raises: - UserControllerError: if validation fails; if is unable to save the new record into the database due to an integrity error. Args: - data (dict): Map of user data to be validated and further processed as a User instance. Returns: - user (app.mod... | 3 | stack_v2_sparse_classes_30k_train_001656 | Implement the Python class `UserController` described below.
Class description:
Controller class for user related data manipulations.
Method signatures and docstrings:
- def create_user(self, data): Creates a new user record in the database. Raises: - UserControllerError: if validation fails; if is unable to save the... | Implement the Python class `UserController` described below.
Class description:
Controller class for user related data manipulations.
Method signatures and docstrings:
- def create_user(self, data): Creates a new user record in the database. Raises: - UserControllerError: if validation fails; if is unable to save the... | fc16ecc301c38271767f5a581d917ec6196ff14a | <|skeleton|>
class UserController:
"""Controller class for user related data manipulations."""
def create_user(self, data):
"""Creates a new user record in the database. Raises: - UserControllerError: if validation fails; if is unable to save the new record into the database due to an integrity error. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserController:
"""Controller class for user related data manipulations."""
def create_user(self, data):
"""Creates a new user record in the database. Raises: - UserControllerError: if validation fails; if is unable to save the new record into the database due to an integrity error. Args: - data ... | the_stack_v2_python_sparse | app/controllers/user.py | rqroz/obd-dashboard | train | 3 |
797fcfcc5fe254c36afd45793fa578c0bca407a6 | [
"import heapq\nself.k = k\nself.h = nums\nheapq.heapify(self.h)\nwhile len(self.h) > k:\n heapq.heappop(self.h)\nprint(self.h)",
"import heapq\nif len(self.h) < self.k:\n heapq.heappush(self.h, val)\nelse:\n heapq.heappushpop(self.h, val)\nreturn self.h[0]"
] | <|body_start_0|>
import heapq
self.k = k
self.h = nums
heapq.heapify(self.h)
while len(self.h) > k:
heapq.heappop(self.h)
print(self.h)
<|end_body_0|>
<|body_start_1|>
import heapq
if len(self.h) < self.k:
heapq.heappush(self.h, va... | KthLargest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
<|body_0|>
def add(self, val):
""":type val: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
import heapq
self.k = k
self.h = nums
... | stack_v2_sparse_classes_36k_train_033453 | 778 | no_license | [
{
"docstring": ":type k: int :type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, k, nums)"
},
{
"docstring": ":type val: int :rtype: int",
"name": "add",
"signature": "def add(self, val)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006977 | Implement the Python class `KthLargest` described below.
Class description:
Implement the KthLargest class.
Method signatures and docstrings:
- def __init__(self, k, nums): :type k: int :type nums: List[int]
- def add(self, val): :type val: int :rtype: int | Implement the Python class `KthLargest` described below.
Class description:
Implement the KthLargest class.
Method signatures and docstrings:
- def __init__(self, k, nums): :type k: int :type nums: List[int]
- def add(self, val): :type val: int :rtype: int
<|skeleton|>
class KthLargest:
def __init__(self, k, nu... | 2e1751263f484709102f7f2caf18776a004c8230 | <|skeleton|>
class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
<|body_0|>
def add(self, val):
""":type val: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
import heapq
self.k = k
self.h = nums
heapq.heapify(self.h)
while len(self.h) > k:
heapq.heappop(self.h)
print(self.h)
def add(self, val):
""":type... | the_stack_v2_python_sparse | Python/Leetcode Daily Practice/Heap/703. Kth Largest Element in a Stream.py | YaqianQi/Algorithm-and-Data-Structure | train | 1 | |
752b94e67fa092bee6c61f74d3ff74d27bf6eb16 | [
"point_cloud_1 = [[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]]\npoint_cloud_2 = [[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]]\ntf_point_cloud_1 = tf.constant(point_cloud_1)\ntf_point_cloud_2 = tf.constant(point_cloud_2)\nmatch = tf_approxmatch.approx_match(tf_point_cloud_1, tf_point_cloud_2)\ndistan... | <|body_start_0|>
point_cloud_1 = [[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]]
point_cloud_2 = [[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]]
tf_point_cloud_1 = tf.constant(point_cloud_1)
tf_point_cloud_2 = tf.constant(point_cloud_2)
match = tf_approxmatch.approx_m... | ApproxMatchTest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApproxMatchTest:
def test_emd(self):
"""Test for the approximate algorithm for computing the distance where loss should be zero."""
<|body_0|>
def test_emd_2(self):
"""Test for the approximate algorithm for computing the Earth Mover's Distance to see if match selects... | stack_v2_sparse_classes_36k_train_033454 | 3,587 | permissive | [
{
"docstring": "Test for the approximate algorithm for computing the distance where loss should be zero.",
"name": "test_emd",
"signature": "def test_emd(self)"
},
{
"docstring": "Test for the approximate algorithm for computing the Earth Mover's Distance to see if match selects closest point, a... | 4 | stack_v2_sparse_classes_30k_train_000836 | Implement the Python class `ApproxMatchTest` described below.
Class description:
Implement the ApproxMatchTest class.
Method signatures and docstrings:
- def test_emd(self): Test for the approximate algorithm for computing the distance where loss should be zero.
- def test_emd_2(self): Test for the approximate algori... | Implement the Python class `ApproxMatchTest` described below.
Class description:
Implement the ApproxMatchTest class.
Method signatures and docstrings:
- def test_emd(self): Test for the approximate algorithm for computing the distance where loss should be zero.
- def test_emd_2(self): Test for the approximate algori... | f3cb31909666012dfcf38e5fe0b0f6feb3801560 | <|skeleton|>
class ApproxMatchTest:
def test_emd(self):
"""Test for the approximate algorithm for computing the distance where loss should be zero."""
<|body_0|>
def test_emd_2(self):
"""Test for the approximate algorithm for computing the Earth Mover's Distance to see if match selects... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApproxMatchTest:
def test_emd(self):
"""Test for the approximate algorithm for computing the distance where loss should be zero."""
point_cloud_1 = [[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]]
point_cloud_2 = [[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]]
tf_poi... | the_stack_v2_python_sparse | src/tf_ops/approxmatch/tf_approxmatch_test.py | minghanz/monopsr | train | 0 | |
9efb07e8c6460fa338f4b4901896d80f3adf9afa | [
"if isinstance(size, (str, unicode)):\n size = int(size)\nreturn numpy.ones((size, 1)) * numpy.array([1.0, 0.0])",
"if isinstance(start_idx, (str, unicode)):\n start_idx = int(start_idx)\nif isinstance(end_idx, (str, unicode)):\n end_idx = int(end_idx)\nsize = end_idx - start_idx\nresult = numpy.transpos... | <|body_start_0|>
if isinstance(size, (str, unicode)):
size = int(size)
return numpy.ones((size, 1)) * numpy.array([1.0, 0.0])
<|end_body_0|>
<|body_start_1|>
if isinstance(start_idx, (str, unicode)):
start_idx = int(start_idx)
if isinstance(end_idx, (str, unicode... | Framework methods regarding RegionMapping DataType. | RegionMappingFramework | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegionMappingFramework:
"""Framework methods regarding RegionMapping DataType."""
def get_alpha_array(size):
"""Compute alpha weights. When displaying region-based results, we need to compute color for each surface vertex based on a gradient of the neighbor region(s). Currently only ... | stack_v2_sparse_classes_36k_train_033455 | 25,280 | no_license | [
{
"docstring": "Compute alpha weights. When displaying region-based results, we need to compute color for each surface vertex based on a gradient of the neighbor region(s). Currently only one vertex is used for determining color (the one indicated by the RegionMapping). :return: NumPy array with [[1, 0], [1, 0]... | 3 | null | Implement the Python class `RegionMappingFramework` described below.
Class description:
Framework methods regarding RegionMapping DataType.
Method signatures and docstrings:
- def get_alpha_array(size): Compute alpha weights. When displaying region-based results, we need to compute color for each surface vertex based... | Implement the Python class `RegionMappingFramework` described below.
Class description:
Framework methods regarding RegionMapping DataType.
Method signatures and docstrings:
- def get_alpha_array(size): Compute alpha weights. When displaying region-based results, we need to compute color for each surface vertex based... | dd4beb028719abaa70c639f64c97ba23bd4a1f3a | <|skeleton|>
class RegionMappingFramework:
"""Framework methods regarding RegionMapping DataType."""
def get_alpha_array(size):
"""Compute alpha weights. When displaying region-based results, we need to compute color for each surface vertex based on a gradient of the neighbor region(s). Currently only ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegionMappingFramework:
"""Framework methods regarding RegionMapping DataType."""
def get_alpha_array(size):
"""Compute alpha weights. When displaying region-based results, we need to compute color for each surface vertex based on a gradient of the neighbor region(s). Currently only one vertex is... | the_stack_v2_python_sparse | tvb/datatypes/surfaces_framework.py | HuifangWang/the-virtual-brain-website | train | 0 |
fce9fc5746de5011cdde9b9413abc67f84a3dceb | [
"super(self.__class__, self).__init__(graph, seed)\nself.available_statuses = {'Susceptible': 0, 'Infected': 1}\nself.name = 'Voter'",
"self.clean_initial_status(self.available_statuses.values())\nif self.actual_iteration == 0:\n self.actual_iteration += 1\n delta, node_count, status_delta = self.status_del... | <|body_start_0|>
super(self.__class__, self).__init__(graph, seed)
self.available_statuses = {'Susceptible': 0, 'Infected': 1}
self.name = 'Voter'
<|end_body_0|>
<|body_start_1|>
self.clean_initial_status(self.available_statuses.values())
if self.actual_iteration == 0:
... | VoterModel | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VoterModel:
def __init__(self, graph, seed=None):
"""Model Constructor :param graph: A networkx graph object"""
<|body_0|>
def iteration(self, node_status=True):
"""Execute a single model iteration :return: Iteration_id, Incremental node status (dictionary node->stat... | stack_v2_sparse_classes_36k_train_033456 | 3,326 | permissive | [
{
"docstring": "Model Constructor :param graph: A networkx graph object",
"name": "__init__",
"signature": "def __init__(self, graph, seed=None)"
},
{
"docstring": "Execute a single model iteration :return: Iteration_id, Incremental node status (dictionary node->status)",
"name": "iteration"... | 2 | stack_v2_sparse_classes_30k_train_018738 | Implement the Python class `VoterModel` described below.
Class description:
Implement the VoterModel class.
Method signatures and docstrings:
- def __init__(self, graph, seed=None): Model Constructor :param graph: A networkx graph object
- def iteration(self, node_status=True): Execute a single model iteration :retur... | Implement the Python class `VoterModel` described below.
Class description:
Implement the VoterModel class.
Method signatures and docstrings:
- def __init__(self, graph, seed=None): Model Constructor :param graph: A networkx graph object
- def iteration(self, node_status=True): Execute a single model iteration :retur... | 900cb3727795c97a73e59fdb736aa736c4d17157 | <|skeleton|>
class VoterModel:
def __init__(self, graph, seed=None):
"""Model Constructor :param graph: A networkx graph object"""
<|body_0|>
def iteration(self, node_status=True):
"""Execute a single model iteration :return: Iteration_id, Incremental node status (dictionary node->stat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VoterModel:
def __init__(self, graph, seed=None):
"""Model Constructor :param graph: A networkx graph object"""
super(self.__class__, self).__init__(graph, seed)
self.available_statuses = {'Susceptible': 0, 'Infected': 1}
self.name = 'Voter'
def iteration(self, node_status... | the_stack_v2_python_sparse | ndlib/models/opinions/VoterModel.py | GiulioRossetti/ndlib | train | 265 | |
a06de08ba3b067d7648888faf37ce00055fccd46 | [
"if self.kwargs.get(self.lookup_field, None) is None:\n raise ParseError('Expected URL keyword argument `%s`.' % self.lookup_field)\nqueryset = self.filter_queryset(self.get_queryset())\nfilter_kwargs = {}\nserializer = self.get_serializer()\nlookup_field = self.lookup_field\nif self.lookup_field in serializer.g... | <|body_start_0|>
if self.kwargs.get(self.lookup_field, None) is None:
raise ParseError('Expected URL keyword argument `%s`.' % self.lookup_field)
queryset = self.filter_queryset(self.get_queryset())
filter_kwargs = {}
serializer = self.get_serializer()
lookup_field = ... | ObjectLookupMixin | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObjectLookupMixin:
def get_object(self):
"""Incase the lookup is on an object that has been hyperlinked then update the queryset filter appropriately"""
<|body_0|>
def pre_save(self, obj):
"""Set any attributes on the object that are implicit in the request."""
... | stack_v2_sparse_classes_36k_train_033457 | 2,712 | permissive | [
{
"docstring": "Incase the lookup is on an object that has been hyperlinked then update the queryset filter appropriately",
"name": "get_object",
"signature": "def get_object(self)"
},
{
"docstring": "Set any attributes on the object that are implicit in the request.",
"name": "pre_save",
... | 2 | null | Implement the Python class `ObjectLookupMixin` described below.
Class description:
Implement the ObjectLookupMixin class.
Method signatures and docstrings:
- def get_object(self): Incase the lookup is on an object that has been hyperlinked then update the queryset filter appropriately
- def pre_save(self, obj): Set a... | Implement the Python class `ObjectLookupMixin` described below.
Class description:
Implement the ObjectLookupMixin class.
Method signatures and docstrings:
- def get_object(self): Incase the lookup is on an object that has been hyperlinked then update the queryset filter appropriately
- def pre_save(self, obj): Set a... | b8d93d4da649f323af111cf7247206554be7c8b1 | <|skeleton|>
class ObjectLookupMixin:
def get_object(self):
"""Incase the lookup is on an object that has been hyperlinked then update the queryset filter appropriately"""
<|body_0|>
def pre_save(self, obj):
"""Set any attributes on the object that are implicit in the request."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ObjectLookupMixin:
def get_object(self):
"""Incase the lookup is on an object that has been hyperlinked then update the queryset filter appropriately"""
if self.kwargs.get(self.lookup_field, None) is None:
raise ParseError('Expected URL keyword argument `%s`.' % self.lookup_field)
... | the_stack_v2_python_sparse | onadata/libs/mixins/object_lookup_mixin.py | kobotoolbox/kobocat | train | 101 | |
da1c16b31a3b1e82d070848835f4754146eec7ff | [
"nodes = [('const_node', {'type': 'Const', 'kind': 'op'}), ('const_data', {'kind': 'data', 'value': np.array(5)}), ('result_node', {'type': 'Result', 'kind': 'op'}), ('placeholder_1', {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'}), ('placeholder_1_data', {'kind': 'data'}), ('relu_1', {'type': 'ReLU', 'kind... | <|body_start_0|>
nodes = [('const_node', {'type': 'Const', 'kind': 'op'}), ('const_data', {'kind': 'data', 'value': np.array(5)}), ('result_node', {'type': 'Result', 'kind': 'op'}), ('placeholder_1', {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'}), ('placeholder_1_data', {'kind': 'data'}), ('relu_1', {'... | RemoveConstToResultReplacementTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemoveConstToResultReplacementTest:
def test_only_consumer(self):
"""Result node is only consumer of Const data node"""
<|body_0|>
def test_two_consumers(self):
"""Const data node has two consumers: Result and ReLu"""
<|body_1|>
<|end_skeleton|>
<|body_star... | stack_v2_sparse_classes_36k_train_033458 | 8,443 | permissive | [
{
"docstring": "Result node is only consumer of Const data node",
"name": "test_only_consumer",
"signature": "def test_only_consumer(self)"
},
{
"docstring": "Const data node has two consumers: Result and ReLu",
"name": "test_two_consumers",
"signature": "def test_two_consumers(self)"
... | 2 | null | Implement the Python class `RemoveConstToResultReplacementTest` described below.
Class description:
Implement the RemoveConstToResultReplacementTest class.
Method signatures and docstrings:
- def test_only_consumer(self): Result node is only consumer of Const data node
- def test_two_consumers(self): Const data node ... | Implement the Python class `RemoveConstToResultReplacementTest` described below.
Class description:
Implement the RemoveConstToResultReplacementTest class.
Method signatures and docstrings:
- def test_only_consumer(self): Result node is only consumer of Const data node
- def test_two_consumers(self): Const data node ... | 2e6c95f389b195f6d3ff8597147d1f817433cfb3 | <|skeleton|>
class RemoveConstToResultReplacementTest:
def test_only_consumer(self):
"""Result node is only consumer of Const data node"""
<|body_0|>
def test_two_consumers(self):
"""Const data node has two consumers: Result and ReLu"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RemoveConstToResultReplacementTest:
def test_only_consumer(self):
"""Result node is only consumer of Const data node"""
nodes = [('const_node', {'type': 'Const', 'kind': 'op'}), ('const_data', {'kind': 'data', 'value': np.array(5)}), ('result_node', {'type': 'Result', 'kind': 'op'}), ('placeho... | the_stack_v2_python_sparse | model-optimizer/extensions/back/SpecialNodesFinalization_test.py | 0xF6/openvino | train | 2 | |
5bb530eae97a1c0fb0e554ddc5ded860c896aa8a | [
"self.local_view_box_id = local_view_box_id\nself.local_view_box_name = local_view_box_name\nself.remote_view_box_id = remote_view_box_id\nself.remote_view_box_name = remote_view_box_name",
"if dictionary is None:\n return None\nlocal_view_box_id = dictionary.get('localViewBoxId')\nlocal_view_box_name = dictio... | <|body_start_0|>
self.local_view_box_id = local_view_box_id
self.local_view_box_name = local_view_box_name
self.remote_view_box_id = remote_view_box_id
self.remote_view_box_name = remote_view_box_name
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None... | Implementation of the 'ViewBoxPairInfo' model. Specifies a pairing between a Storage Domain (View Box) on local Cluster with a Storage Domain (View Box) on a remote Cluster. When replication is configured between a local Cluster and a remote Cluster, the Snapshots are replicated from the specified Storage Domain (View ... | ViewBoxPairInfo | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ViewBoxPairInfo:
"""Implementation of the 'ViewBoxPairInfo' model. Specifies a pairing between a Storage Domain (View Box) on local Cluster with a Storage Domain (View Box) on a remote Cluster. When replication is configured between a local Cluster and a remote Cluster, the Snapshots are replicat... | stack_v2_sparse_classes_36k_train_033459 | 2,996 | permissive | [
{
"docstring": "Constructor for the ViewBoxPairInfo class",
"name": "__init__",
"signature": "def __init__(self, local_view_box_id=None, local_view_box_name=None, remote_view_box_id=None, remote_view_box_name=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dic... | 2 | stack_v2_sparse_classes_30k_test_000190 | Implement the Python class `ViewBoxPairInfo` described below.
Class description:
Implementation of the 'ViewBoxPairInfo' model. Specifies a pairing between a Storage Domain (View Box) on local Cluster with a Storage Domain (View Box) on a remote Cluster. When replication is configured between a local Cluster and a rem... | Implement the Python class `ViewBoxPairInfo` described below.
Class description:
Implementation of the 'ViewBoxPairInfo' model. Specifies a pairing between a Storage Domain (View Box) on local Cluster with a Storage Domain (View Box) on a remote Cluster. When replication is configured between a local Cluster and a rem... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class ViewBoxPairInfo:
"""Implementation of the 'ViewBoxPairInfo' model. Specifies a pairing between a Storage Domain (View Box) on local Cluster with a Storage Domain (View Box) on a remote Cluster. When replication is configured between a local Cluster and a remote Cluster, the Snapshots are replicat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ViewBoxPairInfo:
"""Implementation of the 'ViewBoxPairInfo' model. Specifies a pairing between a Storage Domain (View Box) on local Cluster with a Storage Domain (View Box) on a remote Cluster. When replication is configured between a local Cluster and a remote Cluster, the Snapshots are replicated from the s... | the_stack_v2_python_sparse | cohesity_management_sdk/models/view_box_pair_info.py | cohesity/management-sdk-python | train | 24 |
6b16751ca552c52d250410ac9fdb84c5d266b58d | [
"qs, target_qs = self._get_q_values(s_batch, a_batch, r_batch, sp_batch, done_mask)\nloss = nn.functional.smooth_l1_loss(qs, target_qs, reduction='none')\nreturn loss",
"s_batch, a_batch, r_batch, sp_batch, done_mask_batch, weights = sampled_batch\nself.optimizer.zero_grad()\nelement_wise_loss = self._calc_loss(s... | <|body_start_0|>
qs, target_qs = self._get_q_values(s_batch, a_batch, r_batch, sp_batch, done_mask)
loss = nn.functional.smooth_l1_loss(qs, target_qs, reduction='none')
return loss
<|end_body_0|>
<|body_start_1|>
s_batch, a_batch, r_batch, sp_batch, done_mask_batch, weights = sampled_ba... | PerDblDqn | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PerDblDqn:
def _calc_loss(self, s_batch: Tensor, a_batch: Tensor, r_batch: Tensor, sp_batch: Tensor, done_mask: Tensor) -> Tensor:
"""Calculate the Huber loss (SmoothL1Loss) of this step. loss = SmoothL1Loss(Q_sample(s) - Q(s, a)) :param s_batch: state batch (batch_size, n_channel, image... | stack_v2_sparse_classes_36k_train_033460 | 2,456 | no_license | [
{
"docstring": "Calculate the Huber loss (SmoothL1Loss) of this step. loss = SmoothL1Loss(Q_sample(s) - Q(s, a)) :param s_batch: state batch (batch_size, n_channel, image_height, image_width) :param sp_batch: next state batch (batch_size, n_channel, image_height, image_width) :param a_batch: The action the agen... | 2 | stack_v2_sparse_classes_30k_train_007563 | Implement the Python class `PerDblDqn` described below.
Class description:
Implement the PerDblDqn class.
Method signatures and docstrings:
- def _calc_loss(self, s_batch: Tensor, a_batch: Tensor, r_batch: Tensor, sp_batch: Tensor, done_mask: Tensor) -> Tensor: Calculate the Huber loss (SmoothL1Loss) of this step. lo... | Implement the Python class `PerDblDqn` described below.
Class description:
Implement the PerDblDqn class.
Method signatures and docstrings:
- def _calc_loss(self, s_batch: Tensor, a_batch: Tensor, r_batch: Tensor, sp_batch: Tensor, done_mask: Tensor) -> Tensor: Calculate the Huber loss (SmoothL1Loss) of this step. lo... | c9421d5058d5144aec855f4be66673830652845b | <|skeleton|>
class PerDblDqn:
def _calc_loss(self, s_batch: Tensor, a_batch: Tensor, r_batch: Tensor, sp_batch: Tensor, done_mask: Tensor) -> Tensor:
"""Calculate the Huber loss (SmoothL1Loss) of this step. loss = SmoothL1Loss(Q_sample(s) - Q(s, a)) :param s_batch: state batch (batch_size, n_channel, image... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PerDblDqn:
def _calc_loss(self, s_batch: Tensor, a_batch: Tensor, r_batch: Tensor, sp_batch: Tensor, done_mask: Tensor) -> Tensor:
"""Calculate the Huber loss (SmoothL1Loss) of this step. loss = SmoothL1Loss(Q_sample(s) - Q(s, a)) :param s_batch: state batch (batch_size, n_channel, image_height, image... | the_stack_v2_python_sparse | core/ml/dqn/model/per_dbl_dqn.py | XiaoMutt/qingting | train | 1 | |
6e985ea5213d2d82bbf233f1ed04fd79f2dec8f5 | [
"from lib.httplib2 import Http\nself.login = login\nself.access_token = access_token\nself.api_key = api_key\nself.connector = Http()",
"from urllib import urlencode\ndata = {'format': 'json', 'longUrl': url, 'login': self.login, 'apiKey': self.api_key}\nresp, content = self.connector.request(self.shorten_url + '... | <|body_start_0|>
from lib.httplib2 import Http
self.login = login
self.access_token = access_token
self.api_key = api_key
self.connector = Http()
<|end_body_0|>
<|body_start_1|>
from urllib import urlencode
data = {'format': 'json', 'longUrl': url, 'login': self.... | Interface to the bit.ly API; incomplete, containing only what is needed by the socialfeeder application. | api | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class api:
"""Interface to the bit.ly API; incomplete, containing only what is needed by the socialfeeder application."""
def __init__(self, login, access_token, api_key):
"""@param login: bit.ly username @param access_token: bit.ly user access token @param api_key: bit.ly user API key"""
... | stack_v2_sparse_classes_36k_train_033461 | 3,225 | no_license | [
{
"docstring": "@param login: bit.ly username @param access_token: bit.ly user access token @param api_key: bit.ly user API key",
"name": "__init__",
"signature": "def __init__(self, login, access_token, api_key)"
},
{
"docstring": "Shortens an URL with the given bit.ly account @param url: URL t... | 2 | stack_v2_sparse_classes_30k_train_021127 | Implement the Python class `api` described below.
Class description:
Interface to the bit.ly API; incomplete, containing only what is needed by the socialfeeder application.
Method signatures and docstrings:
- def __init__(self, login, access_token, api_key): @param login: bit.ly username @param access_token: bit.ly ... | Implement the Python class `api` described below.
Class description:
Interface to the bit.ly API; incomplete, containing only what is needed by the socialfeeder application.
Method signatures and docstrings:
- def __init__(self, login, access_token, api_key): @param login: bit.ly username @param access_token: bit.ly ... | 31d9a1892c23ae99b1b5259332fbfc93156c07ed | <|skeleton|>
class api:
"""Interface to the bit.ly API; incomplete, containing only what is needed by the socialfeeder application."""
def __init__(self, login, access_token, api_key):
"""@param login: bit.ly username @param access_token: bit.ly user access token @param api_key: bit.ly user API key"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class api:
"""Interface to the bit.ly API; incomplete, containing only what is needed by the socialfeeder application."""
def __init__(self, login, access_token, api_key):
"""@param login: bit.ly username @param access_token: bit.ly user access token @param api_key: bit.ly user API key"""
from ... | the_stack_v2_python_sparse | src/sfdr/modules/bitly.py | CVi/socialfeeder-2 | train | 0 |
0b4356b0175a7e926165e032baa3cdb4df7a8d7c | [
"ret = 0\nbuy = sys.maxint\nfor i, e in enumerate(prices):\n if e > buy:\n if e - buy > 0:\n ret += e - buy\n buy = e\n else:\n buy = e\nreturn ret",
"if not prices:\n return 0\nres = 0\nfor i in range(1, len(prices)):\n if prices[i] > prices[i - 1]:\n res +=... | <|body_start_0|>
ret = 0
buy = sys.maxint
for i, e in enumerate(prices):
if e > buy:
if e - buy > 0:
ret += e - buy
buy = e
else:
buy = e
return ret
<|end_body_0|>
<|body_start_1|>
if... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit1(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit(self, prices):
"""贪婪法 :type prices: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ret = 0
buy = sys.maxint
... | stack_v2_sparse_classes_36k_train_033462 | 1,130 | no_license | [
{
"docstring": ":type prices: List[int] :rtype: int",
"name": "maxProfit1",
"signature": "def maxProfit1(self, prices)"
},
{
"docstring": "贪婪法 :type prices: List[int] :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, prices)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit1(self, prices): :type prices: List[int] :rtype: int
- def maxProfit(self, prices): 贪婪法 :type prices: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit1(self, prices): :type prices: List[int] :rtype: int
- def maxProfit(self, prices): 贪婪法 :type prices: List[int] :rtype: int
<|skeleton|>
class Solution:
def ma... | fabe435f366477ec3526add84accec0b4ac38919 | <|skeleton|>
class Solution:
def maxProfit1(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit(self, prices):
"""贪婪法 :type prices: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit1(self, prices):
""":type prices: List[int] :rtype: int"""
ret = 0
buy = sys.maxint
for i, e in enumerate(prices):
if e > buy:
if e - buy > 0:
ret += e - buy
buy = e
else:
... | the_stack_v2_python_sparse | algorithm/leetcode/122_best-time-to-buy-and-sell-stock-ii.py | icejoywoo/toys | train | 1 | |
9ce241e2decc994a049d65e5351e4f982f3d37fb | [
"j = 0\nn = len(nums)\nfor i in range(n):\n if nums[i]:\n nums[j] = nums[i]\n j += 1\nfor i in range(j, n):\n nums[i] = 0",
"j = 0\nfor i in range(len(nums)):\n if not nums[i]:\n continue\n if i > j:\n nums[j] = nums[i]\n nums[i] = 0\n j += 1"
] | <|body_start_0|>
j = 0
n = len(nums)
for i in range(n):
if nums[i]:
nums[j] = nums[i]
j += 1
for i in range(j, n):
nums[i] = 0
<|end_body_0|>
<|body_start_1|>
j = 0
for i in range(len(nums)):
if not nums... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def moveZeroes1(self, nums: List[int]) -> None:
"""直接填充"""
<|body_0|>
def moveZeros2(self, nums: List[int]) -> None:
"""双指针 一个指针用于遍历数组 另一个指针用于指向数组中的 0"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
j = 0
n = len(nums)
for ... | stack_v2_sparse_classes_36k_train_033463 | 1,121 | no_license | [
{
"docstring": "直接填充",
"name": "moveZeroes1",
"signature": "def moveZeroes1(self, nums: List[int]) -> None"
},
{
"docstring": "双指针 一个指针用于遍历数组 另一个指针用于指向数组中的 0",
"name": "moveZeros2",
"signature": "def moveZeros2(self, nums: List[int]) -> None"
}
] | 2 | stack_v2_sparse_classes_30k_train_011653 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def moveZeroes1(self, nums: List[int]) -> None: 直接填充
- def moveZeros2(self, nums: List[int]) -> None: 双指针 一个指针用于遍历数组 另一个指针用于指向数组中的 0 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def moveZeroes1(self, nums: List[int]) -> None: 直接填充
- def moveZeros2(self, nums: List[int]) -> None: 双指针 一个指针用于遍历数组 另一个指针用于指向数组中的 0
<|skeleton|>
class Solution:
def moveZe... | 52756b30e9d51794591aca030bc918e707f473f1 | <|skeleton|>
class Solution:
def moveZeroes1(self, nums: List[int]) -> None:
"""直接填充"""
<|body_0|>
def moveZeros2(self, nums: List[int]) -> None:
"""双指针 一个指针用于遍历数组 另一个指针用于指向数组中的 0"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def moveZeroes1(self, nums: List[int]) -> None:
"""直接填充"""
j = 0
n = len(nums)
for i in range(n):
if nums[i]:
nums[j] = nums[i]
j += 1
for i in range(j, n):
nums[i] = 0
def moveZeros2(self, nums: Lis... | the_stack_v2_python_sparse | 283.移动零/solution.py | QtTao/daily_leetcode | train | 0 | |
7003e587afb757d626b1d9a2a00c2c0af4c083ad | [
"_query_builder = Configuration.base_uri\n_query_builder += '/task/cancel'\n_query_url = APIHelper.clean_url(_query_builder)\n_headers = {'accept': 'application/json', 'content-type': 'application/json; charset=utf-8'}\n_request = self.http_client.post(_query_url, headers=_headers, parameters=APIHelper.json_seriali... | <|body_start_0|>
_query_builder = Configuration.base_uri
_query_builder += '/task/cancel'
_query_url = APIHelper.clean_url(_query_builder)
_headers = {'accept': 'application/json', 'content-type': 'application/json; charset=utf-8'}
_request = self.http_client.post(_query_url, hea... | A Controller to access Endpoints in the ontraportlib API. | TasksController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TasksController:
"""A Controller to access Endpoints in the ontraportlib API."""
def create_task_cancel(self, criteria):
"""Does a POST request to /task/cancel. To affect a single Task or list of specific Tasks, use the <strong>ids</strong> array in the <strong>criteria</strong> para... | stack_v2_sparse_classes_36k_train_033464 | 3,861 | permissive | [
{
"docstring": "Does a POST request to /task/cancel. To affect a single Task or list of specific Tasks, use the <strong>ids</strong> array in the <strong>criteria</strong> parameter. Otherwise, you should use <strong>performAll</strong> and other criteria to select a Group of Tasks to cancel. Args: criteria (Cr... | 2 | stack_v2_sparse_classes_30k_train_008694 | Implement the Python class `TasksController` described below.
Class description:
A Controller to access Endpoints in the ontraportlib API.
Method signatures and docstrings:
- def create_task_cancel(self, criteria): Does a POST request to /task/cancel. To affect a single Task or list of specific Tasks, use the <strong... | Implement the Python class `TasksController` described below.
Class description:
A Controller to access Endpoints in the ontraportlib API.
Method signatures and docstrings:
- def create_task_cancel(self, criteria): Does a POST request to /task/cancel. To affect a single Task or list of specific Tasks, use the <strong... | fb4834e89b897dce3475c89c7e6c34bf8756880e | <|skeleton|>
class TasksController:
"""A Controller to access Endpoints in the ontraportlib API."""
def create_task_cancel(self, criteria):
"""Does a POST request to /task/cancel. To affect a single Task or list of specific Tasks, use the <strong>ids</strong> array in the <strong>criteria</strong> para... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TasksController:
"""A Controller to access Endpoints in the ontraportlib API."""
def create_task_cancel(self, criteria):
"""Does a POST request to /task/cancel. To affect a single Task or list of specific Tasks, use the <strong>ids</strong> array in the <strong>criteria</strong> parameter. Otherw... | the_stack_v2_python_sparse | ontraportlib/controllers/tasks_controller.py | LifePosts/ontraportlib | train | 0 |
9bd2da744ef351f2627f43339abd16cfebaae8b9 | [
"self.rare_depths = range(min, max + 1, step)\nself.num_reps = num_reps\nself.otu_table = self.getBiomData(otu_path)\nself.max_num_taxa = -1\ntmp = -1\nfor val in self.otu_table.iterObservationData():\n if val.sum() > tmp:\n tmp = val.sum()\nself.max_num_taxa = tmp",
"if not include_lineages:\n for v... | <|body_start_0|>
self.rare_depths = range(min, max + 1, step)
self.num_reps = num_reps
self.otu_table = self.getBiomData(otu_path)
self.max_num_taxa = -1
tmp = -1
for val in self.otu_table.iterObservationData():
if val.sum() > tmp:
tmp = val.su... | RarefactionMaker | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RarefactionMaker:
def __init__(self, otu_path, min, max, step, num_reps):
"""init a rarefactionmaker otu_path is path to .biom otu table we just ignore any rarefaction levels beyond any sample in the data"""
<|body_0|>
def rarefy_to_files(self, output_dir, small_included=Fal... | stack_v2_sparse_classes_36k_train_033465 | 7,880 | no_license | [
{
"docstring": "init a rarefactionmaker otu_path is path to .biom otu table we just ignore any rarefaction levels beyond any sample in the data",
"name": "__init__",
"signature": "def __init__(self, otu_path, min, max, step, num_reps)"
},
{
"docstring": "computes rarefied otu tables and writes t... | 4 | stack_v2_sparse_classes_30k_val_000940 | Implement the Python class `RarefactionMaker` described below.
Class description:
Implement the RarefactionMaker class.
Method signatures and docstrings:
- def __init__(self, otu_path, min, max, step, num_reps): init a rarefactionmaker otu_path is path to .biom otu table we just ignore any rarefaction levels beyond a... | Implement the Python class `RarefactionMaker` described below.
Class description:
Implement the RarefactionMaker class.
Method signatures and docstrings:
- def __init__(self, otu_path, min, max, step, num_reps): init a rarefactionmaker otu_path is path to .biom otu table we just ignore any rarefaction levels beyond a... | afb3eb6531badeb74fc69ae4c9e698d3e9cbe70e | <|skeleton|>
class RarefactionMaker:
def __init__(self, otu_path, min, max, step, num_reps):
"""init a rarefactionmaker otu_path is path to .biom otu table we just ignore any rarefaction levels beyond any sample in the data"""
<|body_0|>
def rarefy_to_files(self, output_dir, small_included=Fal... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RarefactionMaker:
def __init__(self, otu_path, min, max, step, num_reps):
"""init a rarefactionmaker otu_path is path to .biom otu table we just ignore any rarefaction levels beyond any sample in the data"""
self.rare_depths = range(min, max + 1, step)
self.num_reps = num_reps
... | the_stack_v2_python_sparse | qiime/rarefaction.py | rob-knight/qiime | train | 2 | |
66ef5932c9f2120ba3baf0d040c9361e9ca09163 | [
"self.params = {}\nself._space = {}\nself._rank = defaultdict(lambda: [])",
"self.params[name] = np.random.choice([True, False]) if default is None else default\nif name not in self._space:\n self._space[name] = {'mode': 'Boolean', 'default': default, 'values': [True, False]}\n self._rank[rank].append(name)... | <|body_start_0|>
self.params = {}
self._space = {}
self._rank = defaultdict(lambda: [])
<|end_body_0|>
<|body_start_1|>
self.params[name] = np.random.choice([True, False]) if default is None else default
if name not in self._space:
self._space[name] = {'mode': 'Boole... | HyperParametersGrid | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HyperParametersGrid:
def __init__(self):
"""Container for both a hyperparameter space, and current values."""
<|body_0|>
def Boolean(self, name, default=None, rank=0):
"""Choice between True and False. Arguments: name: Str. Name of parameter. Must be unique. default:... | stack_v2_sparse_classes_36k_train_033466 | 7,676 | permissive | [
{
"docstring": "Container for both a hyperparameter space, and current values.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Choice between True and False. Arguments: name: Str. Name of parameter. Must be unique. default: Default value to return for the parameter. If... | 6 | stack_v2_sparse_classes_30k_train_019043 | Implement the Python class `HyperParametersGrid` described below.
Class description:
Implement the HyperParametersGrid class.
Method signatures and docstrings:
- def __init__(self): Container for both a hyperparameter space, and current values.
- def Boolean(self, name, default=None, rank=0): Choice between True and ... | Implement the Python class `HyperParametersGrid` described below.
Class description:
Implement the HyperParametersGrid class.
Method signatures and docstrings:
- def __init__(self): Container for both a hyperparameter space, and current values.
- def Boolean(self, name, default=None, rank=0): Choice between True and ... | ae965a487b4f94f05aa794000401ccc5e3be7446 | <|skeleton|>
class HyperParametersGrid:
def __init__(self):
"""Container for both a hyperparameter space, and current values."""
<|body_0|>
def Boolean(self, name, default=None, rank=0):
"""Choice between True and False. Arguments: name: Str. Name of parameter. Must be unique. default:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HyperParametersGrid:
def __init__(self):
"""Container for both a hyperparameter space, and current values."""
self.params = {}
self._space = {}
self._rank = defaultdict(lambda: [])
def Boolean(self, name, default=None, rank=0):
"""Choice between True and False. Arg... | the_stack_v2_python_sparse | linora/param_search/_HyperParameters.py | NoraXie/linora | train | 1 | |
76f40273c454274ed9aeb54653585056a9a46025 | [
"self.fig = plt.figure('Trajectories')\nself.ax_arr = [None] * (num_traj * 2)\nfor k in range(num_traj * 2):\n num = 2 * 100 + num_traj * 10 + (k + 1)\n self.ax_arr[k] = self.fig.add_subplot(num, projection='3d')\nself.trajectories_in = np.zeros((0, 0, num_traj))\nself.trajectories_out = np.zeros((0, 0, num_t... | <|body_start_0|>
self.fig = plt.figure('Trajectories')
self.ax_arr = [None] * (num_traj * 2)
for k in range(num_traj * 2):
num = 2 * 100 + num_traj * 10 + (k + 1)
self.ax_arr[k] = self.fig.add_subplot(num, projection='3d')
self.trajectories_in = np.zeros((0, 0, nu... | TrajectoryPlot | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrajectoryPlot:
def __init__(self, num_traj):
""":param num_traj: The amount of training and validation trajectories that are plotted"""
<|body_0|>
def update_trajectories(self, trajectories_in: np.ndarray, trajectories_out: np.ndarray, prediction_out: np.ndarray) -> None:
... | stack_v2_sparse_classes_36k_train_033467 | 2,678 | permissive | [
{
"docstring": ":param num_traj: The amount of training and validation trajectories that are plotted",
"name": "__init__",
"signature": "def __init__(self, num_traj)"
},
{
"docstring": "Updates the trajectories that are plotted in the next call to plot. :param trajectories_in: The trajectories p... | 3 | stack_v2_sparse_classes_30k_train_013743 | Implement the Python class `TrajectoryPlot` described below.
Class description:
Implement the TrajectoryPlot class.
Method signatures and docstrings:
- def __init__(self, num_traj): :param num_traj: The amount of training and validation trajectories that are plotted
- def update_trajectories(self, trajectories_in: np... | Implement the Python class `TrajectoryPlot` described below.
Class description:
Implement the TrajectoryPlot class.
Method signatures and docstrings:
- def __init__(self, num_traj): :param num_traj: The amount of training and validation trajectories that are plotted
- def update_trajectories(self, trajectories_in: np... | d07d1b0b54222f1b01624444591f2884b49462b0 | <|skeleton|>
class TrajectoryPlot:
def __init__(self, num_traj):
""":param num_traj: The amount of training and validation trajectories that are plotted"""
<|body_0|>
def update_trajectories(self, trajectories_in: np.ndarray, trajectories_out: np.ndarray, prediction_out: np.ndarray) -> None:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrajectoryPlot:
def __init__(self, num_traj):
""":param num_traj: The amount of training and validation trajectories that are plotted"""
self.fig = plt.figure('Trajectories')
self.ax_arr = [None] * (num_traj * 2)
for k in range(num_traj * 2):
num = 2 * 100 + num_tra... | the_stack_v2_python_sparse | src/plots/TrajectoryPlot.py | kosmitive/rnn-tetherball-dynamics | train | 0 | |
d0119e71b71fcb02867ef7f5013710853c1b6f41 | [
"serialized_function = cloudpickle.dumps(function)\nself.mode = mode\nif mode == 'file':\n with tempfile.NamedTemporaryFile(delete=False) as f:\n f.write(serialized_function)\n self._cache_key = f.name\nelif mode == 'memory':\n self._cache_key = serialized_function\nelif mode == 'random_id':\n ... | <|body_start_0|>
serialized_function = cloudpickle.dumps(function)
self.mode = mode
if mode == 'file':
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write(serialized_function)
self._cache_key = f.name
elif mode == 'memory':
... | A wrapper to allow `cloudpickle.load`ed functions with `ProcessPoolExecutor`. A wrapper around a serialized function that handles deserialization and caches the deserialized function in the worker process. Parameters ---------- function The function to be serialized and wrapped. mode All of the options avoids sending t... | WrappedFunction | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WrappedFunction:
"""A wrapper to allow `cloudpickle.load`ed functions with `ProcessPoolExecutor`. A wrapper around a serialized function that handles deserialization and caches the deserialized function in the worker process. Parameters ---------- function The function to be serialized and wrappe... | stack_v2_sparse_classes_36k_train_033468 | 36,470 | permissive | [
{
"docstring": "Initialize WrappedFunction.",
"name": "__init__",
"signature": "def __init__(self, function: Callable[..., Any], *, mode: Literal['memory', 'random_id', 'file']='random_id') -> None"
},
{
"docstring": "Call the wrapped function. Retrieves the deserialized function from the global... | 2 | stack_v2_sparse_classes_30k_train_013165 | Implement the Python class `WrappedFunction` described below.
Class description:
A wrapper to allow `cloudpickle.load`ed functions with `ProcessPoolExecutor`. A wrapper around a serialized function that handles deserialization and caches the deserialized function in the worker process. Parameters ---------- function T... | Implement the Python class `WrappedFunction` described below.
Class description:
A wrapper to allow `cloudpickle.load`ed functions with `ProcessPoolExecutor`. A wrapper around a serialized function that handles deserialization and caches the deserialized function in the worker process. Parameters ---------- function T... | 75e83b9d645b03fe6c345e0cbedafb0b86a3568d | <|skeleton|>
class WrappedFunction:
"""A wrapper to allow `cloudpickle.load`ed functions with `ProcessPoolExecutor`. A wrapper around a serialized function that handles deserialization and caches the deserialized function in the worker process. Parameters ---------- function The function to be serialized and wrappe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WrappedFunction:
"""A wrapper to allow `cloudpickle.load`ed functions with `ProcessPoolExecutor`. A wrapper around a serialized function that handles deserialization and caches the deserialized function in the worker process. Parameters ---------- function The function to be serialized and wrapped. mode All o... | the_stack_v2_python_sparse | adaptive_scheduler/utils.py | basnijholt/adaptive-scheduler | train | 24 |
4d4e5337c55330fd7332f95f996fd795cdb63d52 | [
"if not prices:\n return 0\nn = len(prices)\ndp = [[0] * 2 for _ in range(n)]\ndp[0][0] = 0\ndp[0][1] = -prices[0]\nfor i in range(1, n):\n dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i])\n dp[i][1] = max(dp[i - 1][1], -prices[i])\nreturn dp[n - 1][0]",
"if not prices:\n return 0\nn = len(price... | <|body_start_0|>
if not prices:
return 0
n = len(prices)
dp = [[0] * 2 for _ in range(n)]
dp[0][0] = 0
dp[0][1] = -prices[0]
for i in range(1, n):
dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i])
dp[i][1] = max(dp[i - 1][1], -pric... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_1|>
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
... | stack_v2_sparse_classes_36k_train_033469 | 1,963 | no_license | [
{
"docstring": ":type prices: List[int] :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, prices)"
},
{
"docstring": ":type prices: List[int] :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, prices)"
},
{
"docstring": ":type prices: List[int... | 4 | stack_v2_sparse_classes_30k_train_014243 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices): :type prices: List[int] :rtype: int
- def maxProfit(self, prices): :type prices: List[int] :rtype: int
- def maxProfit(self, prices): :type prices: L... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices): :type prices: List[int] :rtype: int
- def maxProfit(self, prices): :type prices: List[int] :rtype: int
- def maxProfit(self, prices): :type prices: L... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_1|>
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
if not prices:
return 0
n = len(prices)
dp = [[0] * 2 for _ in range(n)]
dp[0][0] = 0
dp[0][1] = -prices[0]
for i in range(1, n):
dp[i][0] = max(dp[i... | the_stack_v2_python_sparse | 0121_Best_Time_to_Buy_and_Sell_Stock.py | bingli8802/leetcode | train | 0 | |
0d5ec1c2973d425d971a4623ee667629dfa88f0e | [
"assert loss_choice in ['joint', 'ctc']\nself.loss_choice = loss_choice\nif loss_choice == 'joint':\n self.xe_loss_fn = nn.CrossEntropyLoss(weights=ce_weights)\n self.ctc_loss_fn = CTCLoss()\nif loss_choice == 'ctc':\n self.ctc_loss_fn = CTCLoss()\nself.averaged = averaged",
"if self.loss_choice == 'join... | <|body_start_0|>
assert loss_choice in ['joint', 'ctc']
self.loss_choice = loss_choice
if loss_choice == 'joint':
self.xe_loss_fn = nn.CrossEntropyLoss(weights=ce_weights)
self.ctc_loss_fn = CTCLoss()
if loss_choice == 'ctc':
self.ctc_loss_fn = CTCLoss... | Wrapper class for loss functions. | Loss | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Loss:
"""Wrapper class for loss functions."""
def __init__(self, loss_choice, ce_weights=None, joint_balance=None, averaged=True):
"""Construct a loss function wrapper."""
<|body_0|>
def calculate(self, signal, signal_pred, transcription_seq, target_seq, target_lengths):... | stack_v2_sparse_classes_36k_train_033470 | 2,637 | no_license | [
{
"docstring": "Construct a loss function wrapper.",
"name": "__init__",
"signature": "def __init__(self, loss_choice, ce_weights=None, joint_balance=None, averaged=True)"
},
{
"docstring": "Returns loss values after computing. If loss choice is 'joint', return a tuple `(xe_loss, ctc_loss)`. If ... | 2 | null | Implement the Python class `Loss` described below.
Class description:
Wrapper class for loss functions.
Method signatures and docstrings:
- def __init__(self, loss_choice, ce_weights=None, joint_balance=None, averaged=True): Construct a loss function wrapper.
- def calculate(self, signal, signal_pred, transcription_s... | Implement the Python class `Loss` described below.
Class description:
Wrapper class for loss functions.
Method signatures and docstrings:
- def __init__(self, loss_choice, ce_weights=None, joint_balance=None, averaged=True): Construct a loss function wrapper.
- def calculate(self, signal, signal_pred, transcription_s... | 7ad943d9cc7a6872a14bba5239a99755f70db4cd | <|skeleton|>
class Loss:
"""Wrapper class for loss functions."""
def __init__(self, loss_choice, ce_weights=None, joint_balance=None, averaged=True):
"""Construct a loss function wrapper."""
<|body_0|>
def calculate(self, signal, signal_pred, transcription_seq, target_seq, target_lengths):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Loss:
"""Wrapper class for loss functions."""
def __init__(self, loss_choice, ce_weights=None, joint_balance=None, averaged=True):
"""Construct a loss function wrapper."""
assert loss_choice in ['joint', 'ctc']
self.loss_choice = loss_choice
if loss_choice == 'joint':
... | the_stack_v2_python_sparse | Loss.py | paultsw/wavenet-speech | train | 0 |
6b4e90c1c0ab2cabdea74d5c6b7f1d52a126cfcb | [
"if not self.burst_count:\n self.burst_count = dos.current_rate(self.principal.email, self.MAX_BURST_LIMIT, 60)\nif not self.daily_count:\n self.daily_count = dos.current_rate(self.principal.email, self.MAX_DAILY_LIMIT, 3600 * 24)\nreturn super(RateLimitMixin, self)._pre_put_hook()",
"if super(RateLimitMixi... | <|body_start_0|>
if not self.burst_count:
self.burst_count = dos.current_rate(self.principal.email, self.MAX_BURST_LIMIT, 60)
if not self.daily_count:
self.daily_count = dos.current_rate(self.principal.email, self.MAX_DAILY_LIMIT, 3600 * 24)
return super(RateLimitMixin, s... | A RateLimitMixin ensures that the given model will only be created a fixed number of times per minute. If the rate limit is violated, the entity is treated as though the principal creating it is unviolated. >>> from caravel.model.moderation import ModeratedMixin >>> from caravel.model.principal import PrincipalMixin >>... | RateLimitMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RateLimitMixin:
"""A RateLimitMixin ensures that the given model will only be created a fixed number of times per minute. If the rate limit is violated, the entity is treated as though the principal creating it is unviolated. >>> from caravel.model.moderation import ModeratedMixin >>> from carave... | stack_v2_sparse_classes_36k_train_033471 | 2,339 | permissive | [
{
"docstring": "Compute the current rate and store it in the burst/daily limits.",
"name": "_pre_put_hook",
"signature": "def _pre_put_hook(self)"
},
{
"docstring": "Ensure that listings must be manually approved.",
"name": "approved",
"signature": "def approved(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019589 | Implement the Python class `RateLimitMixin` described below.
Class description:
A RateLimitMixin ensures that the given model will only be created a fixed number of times per minute. If the rate limit is violated, the entity is treated as though the principal creating it is unviolated. >>> from caravel.model.moderatio... | Implement the Python class `RateLimitMixin` described below.
Class description:
A RateLimitMixin ensures that the given model will only be created a fixed number of times per minute. If the rate limit is violated, the entity is treated as though the principal creating it is unviolated. >>> from caravel.model.moderatio... | 375840db7fc1d2f00e986d305283dcc542592311 | <|skeleton|>
class RateLimitMixin:
"""A RateLimitMixin ensures that the given model will only be created a fixed number of times per minute. If the rate limit is violated, the entity is treated as though the principal creating it is unviolated. >>> from caravel.model.moderation import ModeratedMixin >>> from carave... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RateLimitMixin:
"""A RateLimitMixin ensures that the given model will only be created a fixed number of times per minute. If the rate limit is violated, the entity is treated as though the principal creating it is unviolated. >>> from caravel.model.moderation import ModeratedMixin >>> from caravel.model.princ... | the_stack_v2_python_sparse | caravel/model/rate_limits.py | uchicago-sg/caravel | train | 17 |
4a0aa13bd980dbcbcb25f9e0977ba83b5eba7382 | [
"res = [-1] * len(nums1)\nfor i, item in enumerate(nums1):\n if item in nums2:\n idx = nums2.index(item)\n next = idx + 1\n while next <= len(nums2) - 1:\n if item < nums2[next]:\n res[i] = nums2[next]\n break\n else:\n next ... | <|body_start_0|>
res = [-1] * len(nums1)
for i, item in enumerate(nums1):
if item in nums2:
idx = nums2.index(item)
next = idx + 1
while next <= len(nums2) - 1:
if item < nums2[next]:
res[i] = nums2[n... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def nextGreaterElement(self, nums1, nums2):
"""TC - O(m*n) Space Complexity - O(m)"""
<|body_0|>
def next_greater_elem_hash(self, nums1, nums2):
"""Time Complexity - O(m*n) Space Complexity - O(m+n)"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_033472 | 4,264 | no_license | [
{
"docstring": "TC - O(m*n) Space Complexity - O(m)",
"name": "nextGreaterElement",
"signature": "def nextGreaterElement(self, nums1, nums2)"
},
{
"docstring": "Time Complexity - O(m*n) Space Complexity - O(m+n)",
"name": "next_greater_elem_hash",
"signature": "def next_greater_elem_hash... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextGreaterElement(self, nums1, nums2): TC - O(m*n) Space Complexity - O(m)
- def next_greater_elem_hash(self, nums1, nums2): Time Complexity - O(m*n) Space Complexity - O(m+... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextGreaterElement(self, nums1, nums2): TC - O(m*n) Space Complexity - O(m)
- def next_greater_elem_hash(self, nums1, nums2): Time Complexity - O(m*n) Space Complexity - O(m+... | f51caae9b764837ff9107d8b3d116637cdc102b0 | <|skeleton|>
class Solution:
def nextGreaterElement(self, nums1, nums2):
"""TC - O(m*n) Space Complexity - O(m)"""
<|body_0|>
def next_greater_elem_hash(self, nums1, nums2):
"""Time Complexity - O(m*n) Space Complexity - O(m+n)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def nextGreaterElement(self, nums1, nums2):
"""TC - O(m*n) Space Complexity - O(m)"""
res = [-1] * len(nums1)
for i, item in enumerate(nums1):
if item in nums2:
idx = nums2.index(item)
next = idx + 1
while next <= le... | the_stack_v2_python_sparse | Leetcode/next_greater_element.py | madhuri-majety/IK | train | 0 | |
4f7fd58a1115af7eef8c88d59605bbdca1177874 | [
"super(FeedForwardSiamese, self).__init__()\nself.linear1 = torch.nn.Linear(INPUT_DIM, args.ff_hidden_dim)\nself.linear2 = torch.nn.Linear(args.ff_hidden_dim, 1)\nself.relu = ReLU()\nself.sigmoid = Sigmoid()\nself.ff2 = torch.nn.Sequential(torch.nn.Linear(INPUT_DIM, args.ff_hidden_dim), ReLU(), torch.nn.Linear(args... | <|body_start_0|>
super(FeedForwardSiamese, self).__init__()
self.linear1 = torch.nn.Linear(INPUT_DIM, args.ff_hidden_dim)
self.linear2 = torch.nn.Linear(args.ff_hidden_dim, 1)
self.relu = ReLU()
self.sigmoid = Sigmoid()
self.ff2 = torch.nn.Sequential(torch.nn.Linear(INPUT... | FeedForwardSiamese | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeedForwardSiamese:
def __init__(self, args):
"""In the constructor we instantiate two nn.Linear modules and assign them as member variables."""
<|body_0|>
def forward(self, x1, x2):
"""In the forward function we accept a Tensor of input data and we must return a Ten... | stack_v2_sparse_classes_36k_train_033473 | 2,388 | no_license | [
{
"docstring": "In the constructor we instantiate two nn.Linear modules and assign them as member variables.",
"name": "__init__",
"signature": "def __init__(self, args)"
},
{
"docstring": "In the forward function we accept a Tensor of input data and we must return a Tensor of output data. We ca... | 2 | stack_v2_sparse_classes_30k_train_009055 | Implement the Python class `FeedForwardSiamese` described below.
Class description:
Implement the FeedForwardSiamese class.
Method signatures and docstrings:
- def __init__(self, args): In the constructor we instantiate two nn.Linear modules and assign them as member variables.
- def forward(self, x1, x2): In the for... | Implement the Python class `FeedForwardSiamese` described below.
Class description:
Implement the FeedForwardSiamese class.
Method signatures and docstrings:
- def __init__(self, args): In the constructor we instantiate two nn.Linear modules and assign them as member variables.
- def forward(self, x1, x2): In the for... | c39a4145fa2f45d824f437193a59b1aa60c31f38 | <|skeleton|>
class FeedForwardSiamese:
def __init__(self, args):
"""In the constructor we instantiate two nn.Linear modules and assign them as member variables."""
<|body_0|>
def forward(self, x1, x2):
"""In the forward function we accept a Tensor of input data and we must return a Ten... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeedForwardSiamese:
def __init__(self, args):
"""In the constructor we instantiate two nn.Linear modules and assign them as member variables."""
super(FeedForwardSiamese, self).__init__()
self.linear1 = torch.nn.Linear(INPUT_DIM, args.ff_hidden_dim)
self.linear2 = torch.nn.Line... | the_stack_v2_python_sparse | ML_Project_Template/ranking_model.py | saiful1105020/park_motor | train | 1 | |
1d8efd8103ca82623309c647404db76c406b5436 | [
"q = [root]\nwhile q:\n pp = []\n node = q.pop(0)\n while node:\n if not node.left and node.right:\n return False\n Nochild = False\n if not node.right:\n if node.left:\n pp.append(node.left)\n Nochild = True\n else:\n i... | <|body_start_0|>
q = [root]
while q:
pp = []
node = q.pop(0)
while node:
if not node.left and node.right:
return False
Nochild = False
if not node.right:
if node.left:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isCompleteTree(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def isCompleteTree2(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
q = [root]
while q:
... | stack_v2_sparse_classes_36k_train_033474 | 1,903 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isCompleteTree",
"signature": "def isCompleteTree(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isCompleteTree2",
"signature": "def isCompleteTree2(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008740 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isCompleteTree(self, root): :type root: TreeNode :rtype: bool
- def isCompleteTree2(self, root): :type root: TreeNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isCompleteTree(self, root): :type root: TreeNode :rtype: bool
- def isCompleteTree2(self, root): :type root: TreeNode :rtype: bool
<|skeleton|>
class Solution:
def isCo... | 61966ef769b079024a6f72bcf608486343e033e6 | <|skeleton|>
class Solution:
def isCompleteTree(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def isCompleteTree2(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isCompleteTree(self, root):
""":type root: TreeNode :rtype: bool"""
q = [root]
while q:
pp = []
node = q.pop(0)
while node:
if not node.left and node.right:
return False
Nochild = Fals... | the_stack_v2_python_sparse | lintcode/P11.py | hanrick2000/coderunpython | train | 0 | |
b346509a2819d4df2dfcc90e6cf3a75349e614a8 | [
"if type(tor_entries) is not list:\n raise ValueError('tor_entries has to be an array')\nfor tor_entry in tor_entries:\n if 'node_id' not in tor_entry:\n raise ValueError('tor_entries instances must have node_id')\n id = str(tor_entry['node_id'])\n vtep_cmd = cls.OVS_VTEP_BIN\n vtep_cmd = vtep... | <|body_start_0|>
if type(tor_entries) is not list:
raise ValueError('tor_entries has to be an array')
for tor_entry in tor_entries:
if 'node_id' not in tor_entry:
raise ValueError('tor_entries instances must have node_id')
id = str(tor_entry['node_id']... | Ubuntu1204ServiceImpl | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Ubuntu1204ServiceImpl:
def start_service(cls, client_object, service_name=None, tor_entries=None):
"""Starts ovs vtep service on torgateway. @type client_object: BaseClient @param client_object: Used to pass commands to the host. @type service_name: string @param service_name: name of se... | stack_v2_sparse_classes_36k_train_033475 | 2,432 | no_license | [
{
"docstring": "Starts ovs vtep service on torgateway. @type client_object: BaseClient @param client_object: Used to pass commands to the host. @type service_name: string @param service_name: name of service to be started @type tor_entries: list @param tor_entries: List of torswitch ids @rtype: NonType @return:... | 2 | null | Implement the Python class `Ubuntu1204ServiceImpl` described below.
Class description:
Implement the Ubuntu1204ServiceImpl class.
Method signatures and docstrings:
- def start_service(cls, client_object, service_name=None, tor_entries=None): Starts ovs vtep service on torgateway. @type client_object: BaseClient @para... | Implement the Python class `Ubuntu1204ServiceImpl` described below.
Class description:
Implement the Ubuntu1204ServiceImpl class.
Method signatures and docstrings:
- def start_service(cls, client_object, service_name=None, tor_entries=None): Starts ovs vtep service on torgateway. @type client_object: BaseClient @para... | 5b55817c050b637e2747084290f6206d2e622938 | <|skeleton|>
class Ubuntu1204ServiceImpl:
def start_service(cls, client_object, service_name=None, tor_entries=None):
"""Starts ovs vtep service on torgateway. @type client_object: BaseClient @param client_object: Used to pass commands to the host. @type service_name: string @param service_name: name of se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Ubuntu1204ServiceImpl:
def start_service(cls, client_object, service_name=None, tor_entries=None):
"""Starts ovs vtep service on torgateway. @type client_object: BaseClient @param client_object: Used to pass commands to the host. @type service_name: string @param service_name: name of service to be st... | the_stack_v2_python_sparse | SystemTesting/pylib/vmware/torgateway/cmd/ubuntu1204_service_impl.py | Cloudxtreme/MyProject | train | 0 | |
a77178b6300f4d67a242e57599942c56d2c6956d | [
"self.num_features = num_features\nself.filter_func_list = filter_func_list\nself.word_pattern = re.compile('[a-z]{3,}')",
"tf = self._countTermFrequency(raw_instance)\nfeatures = []\nfor word in self.order:\n if word in tf:\n features += [tf[word] * self.idf[word]]\n else:\n features += [0]\n... | <|body_start_0|>
self.num_features = num_features
self.filter_func_list = filter_func_list
self.word_pattern = re.compile('[a-z]{3,}')
<|end_body_0|>
<|body_start_1|>
tf = self._countTermFrequency(raw_instance)
features = []
for word in self.order:
if word in... | Extracts a bag of words representation with TFIDF scores from raw text. | BagOfWordsFiltered | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BagOfWordsFiltered:
"""Extracts a bag of words representation with TFIDF scores from raw text."""
def __init__(self, num_features, filter_func_list):
"""Constructor. @param num_features: The number of features to extract. @param param: filter_func_list [terms filter function,..]"""
... | stack_v2_sparse_classes_36k_train_033476 | 4,111 | no_license | [
{
"docstring": "Constructor. @param num_features: The number of features to extract. @param param: filter_func_list [terms filter function,..]",
"name": "__init__",
"signature": "def __init__(self, num_features, filter_func_list)"
},
{
"docstring": "Creates a new instance in the feature-space fr... | 6 | stack_v2_sparse_classes_30k_train_019278 | Implement the Python class `BagOfWordsFiltered` described below.
Class description:
Extracts a bag of words representation with TFIDF scores from raw text.
Method signatures and docstrings:
- def __init__(self, num_features, filter_func_list): Constructor. @param num_features: The number of features to extract. @para... | Implement the Python class `BagOfWordsFiltered` described below.
Class description:
Extracts a bag of words representation with TFIDF scores from raw text.
Method signatures and docstrings:
- def __init__(self, num_features, filter_func_list): Constructor. @param num_features: The number of features to extract. @para... | fe417881ea523a64e9ab05b975b86cc3357835db | <|skeleton|>
class BagOfWordsFiltered:
"""Extracts a bag of words representation with TFIDF scores from raw text."""
def __init__(self, num_features, filter_func_list):
"""Constructor. @param num_features: The number of features to extract. @param param: filter_func_list [terms filter function,..]"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BagOfWordsFiltered:
"""Extracts a bag of words representation with TFIDF scores from raw text."""
def __init__(self, num_features, filter_func_list):
"""Constructor. @param num_features: The number of features to extract. @param param: filter_func_list [terms filter function,..]"""
self.n... | the_stack_v2_python_sparse | src/s_bag_of_words.py | gzvulon/IAI3-LM | train | 0 |
3a921b99d94ae16bcc8d48ef8b0666efe431a36d | [
"left = 1\nright = len(nums)\nwhile left < right:\n cnt = 0\n mid = (left + right) / 2\n for i in nums:\n if i <= mid:\n cnt += 1\n if cnt <= mid:\n left = mid + 1\n else:\n right = mid\nreturn right",
"dic = dict(collections.Counter(nums))\nfor i, v in dic.items():\... | <|body_start_0|>
left = 1
right = len(nums)
while left < right:
cnt = 0
mid = (left + right) / 2
for i in nums:
if i <= mid:
cnt += 1
if cnt <= mid:
left = mid + 1
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
left = 1
right = len(nums)
... | stack_v2_sparse_classes_36k_train_033477 | 1,037 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findDuplicate",
"signature": "def findDuplicate(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findDuplicate",
"signature": "def findDuplicate(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000780 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDuplicate(self, nums): :type nums: List[int] :rtype: int
- def findDuplicate(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDuplicate(self, nums): :type nums: List[int] :rtype: int
- def findDuplicate(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def findDup... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
left = 1
right = len(nums)
while left < right:
cnt = 0
mid = (left + right) / 2
for i in nums:
if i <= mid:
cnt += 1
... | the_stack_v2_python_sparse | 0287_Find_the_Duplicate_Number.py | bingli8802/leetcode | train | 0 | |
e37c7a2b403a5ea08a4c4dca7671bbb891921288 | [
"super(EnhancedEmbedding, self).__init__()\nself.word_embedding = nn.Embedding(vocab_size, hidden_size)\nself.position_embedding = nn.Embedding(max_position_size, hidden_size)\nself.LayerNorm = LayerNorm(hidden_size)\nself.dropout = nn.Dropout(dropout_ratio)",
"seq_len = input_id.size(1)\nposition_id = paddle.ara... | <|body_start_0|>
super(EnhancedEmbedding, self).__init__()
self.word_embedding = nn.Embedding(vocab_size, hidden_size)
self.position_embedding = nn.Embedding(max_position_size, hidden_size)
self.LayerNorm = LayerNorm(hidden_size)
self.dropout = nn.Dropout(dropout_ratio)
<|end_bod... | Enhanced Embeddings of drug, target | EnhancedEmbedding | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnhancedEmbedding:
"""Enhanced Embeddings of drug, target"""
def __init__(self, vocab_size, hidden_size, max_position_size, dropout_ratio):
"""Initialization"""
<|body_0|>
def forward(self, input_id):
"""Embeddings"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_033478 | 12,741 | permissive | [
{
"docstring": "Initialization",
"name": "__init__",
"signature": "def __init__(self, vocab_size, hidden_size, max_position_size, dropout_ratio)"
},
{
"docstring": "Embeddings",
"name": "forward",
"signature": "def forward(self, input_id)"
}
] | 2 | null | Implement the Python class `EnhancedEmbedding` described below.
Class description:
Enhanced Embeddings of drug, target
Method signatures and docstrings:
- def __init__(self, vocab_size, hidden_size, max_position_size, dropout_ratio): Initialization
- def forward(self, input_id): Embeddings | Implement the Python class `EnhancedEmbedding` described below.
Class description:
Enhanced Embeddings of drug, target
Method signatures and docstrings:
- def __init__(self, vocab_size, hidden_size, max_position_size, dropout_ratio): Initialization
- def forward(self, input_id): Embeddings
<|skeleton|>
class Enhance... | e6ab0261eb719c21806bbadfd94001ecfe27de45 | <|skeleton|>
class EnhancedEmbedding:
"""Enhanced Embeddings of drug, target"""
def __init__(self, vocab_size, hidden_size, max_position_size, dropout_ratio):
"""Initialization"""
<|body_0|>
def forward(self, input_id):
"""Embeddings"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EnhancedEmbedding:
"""Enhanced Embeddings of drug, target"""
def __init__(self, vocab_size, hidden_size, max_position_size, dropout_ratio):
"""Initialization"""
super(EnhancedEmbedding, self).__init__()
self.word_embedding = nn.Embedding(vocab_size, hidden_size)
self.posit... | the_stack_v2_python_sparse | apps/drug_target_interaction/moltrans_dti/double_towers.py | PaddlePaddle/PaddleHelix | train | 771 |
8a15c5893c654c2b36786bb85c4c96552f3dd296 | [
"actor = eventContext.event.actor\nif actor.HasField(type_id_field):\n if not (actor.HasField(identifier_field) and actor.HasField(uuid_field)):\n if actor.HasField(uuid_field):\n uuid = getattr(actor, uuid_field, None)\n element = evtProcessorManager.getElementByUuid(uuid)\n ... | <|body_start_0|>
actor = eventContext.event.actor
if actor.HasField(type_id_field):
if not (actor.HasField(identifier_field) and actor.HasField(uuid_field)):
if actor.HasField(uuid_field):
uuid = getattr(actor, uuid_field, None)
element... | BaseEventIdentifierPlugin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseEventIdentifierPlugin:
def _resolveElement(self, evtProcessorManager, catalog, eventContext, type_id_field, identifier_field, uuid_field):
"""Lookup an element by identifier or uuid and make sure both identifier and uuid are set."""
<|body_0|>
def resolveIdentifiers(self... | stack_v2_sparse_classes_36k_train_033479 | 35,633 | no_license | [
{
"docstring": "Lookup an element by identifier or uuid and make sure both identifier and uuid are set.",
"name": "_resolveElement",
"signature": "def _resolveElement(self, evtProcessorManager, catalog, eventContext, type_id_field, identifier_field, uuid_field)"
},
{
"docstring": "Update eventCo... | 2 | stack_v2_sparse_classes_30k_train_017930 | Implement the Python class `BaseEventIdentifierPlugin` described below.
Class description:
Implement the BaseEventIdentifierPlugin class.
Method signatures and docstrings:
- def _resolveElement(self, evtProcessorManager, catalog, eventContext, type_id_field, identifier_field, uuid_field): Lookup an element by identif... | Implement the Python class `BaseEventIdentifierPlugin` described below.
Class description:
Implement the BaseEventIdentifierPlugin class.
Method signatures and docstrings:
- def _resolveElement(self, evtProcessorManager, catalog, eventContext, type_id_field, identifier_field, uuid_field): Lookup an element by identif... | 1ea508c3d2b51742bc3b448c445cd0a3dba9e798 | <|skeleton|>
class BaseEventIdentifierPlugin:
def _resolveElement(self, evtProcessorManager, catalog, eventContext, type_id_field, identifier_field, uuid_field):
"""Lookup an element by identifier or uuid and make sure both identifier and uuid are set."""
<|body_0|>
def resolveIdentifiers(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseEventIdentifierPlugin:
def _resolveElement(self, evtProcessorManager, catalog, eventContext, type_id_field, identifier_field, uuid_field):
"""Lookup an element by identifier or uuid and make sure both identifier and uuid are set."""
actor = eventContext.event.actor
if actor.HasFiel... | the_stack_v2_python_sparse | Products/ZenEvents/events2/processing.py | zenoss/zenoss-prodbin | train | 27 | |
7da86e4b476d500aede58fa2e682b7fea9a8fd15 | [
"super().setupUI(Form)\nself.label_4 = QtWidgets.QLabel(self.verticalLayoutWidget)\nself.label_4.setToolTip('')\nself.label_4.setAlignment(QtCore.Qt.AlignCenter)\nself.label_4.setObjectName('label_4')\nself.verticalLayout_2.addWidget(self.label_4)\nself.label_8 = QtWidgets.QLabel(self.verticalLayoutWidget)\nself.la... | <|body_start_0|>
super().setupUI(Form)
self.label_4 = QtWidgets.QLabel(self.verticalLayoutWidget)
self.label_4.setToolTip('')
self.label_4.setAlignment(QtCore.Qt.AlignCenter)
self.label_4.setObjectName('label_4')
self.verticalLayout_2.addWidget(self.label_4)
self.... | MVCWindowWidget | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MVCWindowWidget:
def setupUi(self, Form):
"""Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)"""
<|body_0|>
def retranslateUi(sel... | stack_v2_sparse_classes_36k_train_033480 | 2,746 | no_license | [
{
"docstring": "Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)",
"name": "setupUi",
"signature": "def setupUi(self, Form)"
},
{
"docstring": "Método... | 2 | null | Implement the Python class `MVCWindowWidget` described below.
Class description:
Implement the MVCWindowWidget class.
Method signatures and docstrings:
- def setupUi(self, Form): Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la... | Implement the Python class `MVCWindowWidget` described below.
Class description:
Implement the MVCWindowWidget class.
Method signatures and docstrings:
- def setupUi(self, Form): Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la... | 5d1d68fc4476ed866ecfc305112854d9a49c3876 | <|skeleton|>
class MVCWindowWidget:
def setupUi(self, Form):
"""Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)"""
<|body_0|>
def retranslateUi(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MVCWindowWidget:
def setupUi(self, Form):
"""Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)"""
super().setupUI(Form)
self.label_4 = QtWidg... | the_stack_v2_python_sparse | src/main/python/vistas/MVCWindowWidget.py | ProyectoIntegrador2018/reportes-neurociencias | train | 1 | |
20fa88941344e2835fa8e175a61cf16ba8c14d00 | [
"self.flow = flow\nself.porosity = 0.92\nself.k_matrix = 0.0058\nself.PPI = 10.0\nself.k = self.k_matrix\nself.Nu_D = 4.93",
"self.G = self.flow.rho * self.flow.velocity\nself.D_pore = 0.0122 * self.PPI ** (-0.849)\nself.Re_D = self.D_pore * self.G / (self.flow.mu * self.porosity)\nself.F = 1.765 * self.Re_D ** (... | <|body_start_0|>
self.flow = flow
self.porosity = 0.92
self.k_matrix = 0.0058
self.PPI = 10.0
self.k = self.k_matrix
self.Nu_D = 4.93
<|end_body_0|>
<|body_start_1|>
self.G = self.flow.rho * self.flow.velocity
self.D_pore = 0.0122 * self.PPI ** (-0.849)
... | Class for modeling porous media according to Mancin. Mancin, S., C. Zilio, A. Cavallini, and L. Rossetto. “Pressure Drop During Air Flow in Aluminum Foams.” International Journal of Heat and Mass Transfer 53, no. 15–16 (2010): 3121–3130. Methods: __init__ solve_enh | MancinPorous | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MancinPorous:
"""Class for modeling porous media according to Mancin. Mancin, S., C. Zilio, A. Cavallini, and L. Rossetto. “Pressure Drop During Air Flow in Aluminum Foams.” International Journal of Heat and Mass Transfer 53, no. 15–16 (2010): 3121–3130. Methods: __init__ solve_enh"""
def __... | stack_v2_sparse_classes_36k_train_033481 | 15,856 | no_license | [
{
"docstring": "Sets constants.",
"name": "__init__",
"signature": "def __init__(self, flow)"
},
{
"docstring": "Solves for convection parameters with enhancement.",
"name": "solve_enh",
"signature": "def solve_enh(self)"
}
] | 2 | null | Implement the Python class `MancinPorous` described below.
Class description:
Class for modeling porous media according to Mancin. Mancin, S., C. Zilio, A. Cavallini, and L. Rossetto. “Pressure Drop During Air Flow in Aluminum Foams.” International Journal of Heat and Mass Transfer 53, no. 15–16 (2010): 3121–3130. Met... | Implement the Python class `MancinPorous` described below.
Class description:
Class for modeling porous media according to Mancin. Mancin, S., C. Zilio, A. Cavallini, and L. Rossetto. “Pressure Drop During Air Flow in Aluminum Foams.” International Journal of Heat and Mass Transfer 53, no. 15–16 (2010): 3121–3130. Met... | d619b66b1f16557e06c94eee1c16d4ee2a9e896a | <|skeleton|>
class MancinPorous:
"""Class for modeling porous media according to Mancin. Mancin, S., C. Zilio, A. Cavallini, and L. Rossetto. “Pressure Drop During Air Flow in Aluminum Foams.” International Journal of Heat and Mass Transfer 53, no. 15–16 (2010): 3121–3130. Methods: __init__ solve_enh"""
def __... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MancinPorous:
"""Class for modeling porous media according to Mancin. Mancin, S., C. Zilio, A. Cavallini, and L. Rossetto. “Pressure Drop During Air Flow in Aluminum Foams.” International Journal of Heat and Mass Transfer 53, no. 15–16 (2010): 3121–3130. Methods: __init__ solve_enh"""
def __init__(self, ... | the_stack_v2_python_sparse | Modules/enhancement.py | hfateh/TE_Model-1 | train | 0 |
18471895f6d7dd36b558eed15daf9a356edcb36c | [
"if is_datetime_type(obj):\n return convert_datetime_type(obj)\nif is_timedelta_type(obj):\n return convert_timedelta_type(obj)\nelif isinstance(obj, slice):\n return dict(start=obj.start, stop=obj.stop, step=obj.step)\nelif np.issubdtype(type(obj), np.floating):\n return float(obj)\nelif np.issubdtype(... | <|body_start_0|>
if is_datetime_type(obj):
return convert_datetime_type(obj)
if is_timedelta_type(obj):
return convert_timedelta_type(obj)
elif isinstance(obj, slice):
return dict(start=obj.start, stop=obj.stop, step=obj.step)
elif np.issubdtype(type(o... | A custom ``json.JSONEncoder`` subclass for encoding objects in accordance with the BokehJS protocol. | BokehJSONEncoder | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BokehJSONEncoder:
"""A custom ``json.JSONEncoder`` subclass for encoding objects in accordance with the BokehJS protocol."""
def transform_python_types(self, obj):
"""Handle special scalars such as (Python, NumPy, or Pandas) datetimes, or Decimal values. Args: obj (obj) : The object ... | stack_v2_sparse_classes_36k_train_033482 | 9,015 | permissive | [
{
"docstring": "Handle special scalars such as (Python, NumPy, or Pandas) datetimes, or Decimal values. Args: obj (obj) : The object to encode. Anything not specifically handled in this method is passed on to the default system JSON encoder.",
"name": "transform_python_types",
"signature": "def transfor... | 2 | null | Implement the Python class `BokehJSONEncoder` described below.
Class description:
A custom ``json.JSONEncoder`` subclass for encoding objects in accordance with the BokehJS protocol.
Method signatures and docstrings:
- def transform_python_types(self, obj): Handle special scalars such as (Python, NumPy, or Pandas) da... | Implement the Python class `BokehJSONEncoder` described below.
Class description:
A custom ``json.JSONEncoder`` subclass for encoding objects in accordance with the BokehJS protocol.
Method signatures and docstrings:
- def transform_python_types(self, obj): Handle special scalars such as (Python, NumPy, or Pandas) da... | 1ad7ec05fb1e3676ac879585296c513c3ee50ef9 | <|skeleton|>
class BokehJSONEncoder:
"""A custom ``json.JSONEncoder`` subclass for encoding objects in accordance with the BokehJS protocol."""
def transform_python_types(self, obj):
"""Handle special scalars such as (Python, NumPy, or Pandas) datetimes, or Decimal values. Args: obj (obj) : The object ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BokehJSONEncoder:
"""A custom ``json.JSONEncoder`` subclass for encoding objects in accordance with the BokehJS protocol."""
def transform_python_types(self, obj):
"""Handle special scalars such as (Python, NumPy, or Pandas) datetimes, or Decimal values. Args: obj (obj) : The object to encode. An... | the_stack_v2_python_sparse | Library/lib/python3.7/site-packages/bokeh-1.4.0-py3.7.egg/bokeh/core/json_encoder.py | holzschu/Carnets | train | 541 |
20ec1b0df243c6691540e6039a2c9ab99025b643 | [
"t_argsTuples = []\nfor t_tuple in _cls_functorClass.getFunctorArgs():\n t_widgetArgs = copy.copy(t_tuple[_cls_functorClass.U_FUNC_ARG_KWARGS_INDEX])\n s_key = t_tuple[_cls_functorClass.U_FUNC_ARG_KEY_INDEX]\n s_label = t_widgetArgs.pop('_s_label')\n x_defaultValue = t_tuple[_cls_functorClass.U_FUNC_ARG... | <|body_start_0|>
t_argsTuples = []
for t_tuple in _cls_functorClass.getFunctorArgs():
t_widgetArgs = copy.copy(t_tuple[_cls_functorClass.U_FUNC_ARG_KWARGS_INDEX])
s_key = t_tuple[_cls_functorClass.U_FUNC_ARG_KEY_INDEX]
s_label = t_widgetArgs.pop('_s_label')
... | QArkFunctorFactory | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QArkFunctorFactory:
def functorToWidget(cls, parent, _cls_functorClass, _o_vectorDataSet):
"""Construct a generic widget to enter the functor arguments @param _cls_functorClass : the functor class for which we want to generate a widget @param _x_data : the data on which we want the funct... | stack_v2_sparse_classes_36k_train_033483 | 6,171 | permissive | [
{
"docstring": "Construct a generic widget to enter the functor arguments @param _cls_functorClass : the functor class for which we want to generate a widget @param _x_data : the data on which we want the functor to be applied",
"name": "functorToWidget",
"signature": "def functorToWidget(cls, parent, _... | 2 | null | Implement the Python class `QArkFunctorFactory` described below.
Class description:
Implement the QArkFunctorFactory class.
Method signatures and docstrings:
- def functorToWidget(cls, parent, _cls_functorClass, _o_vectorDataSet): Construct a generic widget to enter the functor arguments @param _cls_functorClass : th... | Implement the Python class `QArkFunctorFactory` described below.
Class description:
Implement the QArkFunctorFactory class.
Method signatures and docstrings:
- def functorToWidget(cls, parent, _cls_functorClass, _o_vectorDataSet): Construct a generic widget to enter the functor arguments @param _cls_functorClass : th... | 46e03095028d2a2f153959d910ceab06a633223d | <|skeleton|>
class QArkFunctorFactory:
def functorToWidget(cls, parent, _cls_functorClass, _o_vectorDataSet):
"""Construct a generic widget to enter the functor arguments @param _cls_functorClass : the functor class for which we want to generate a widget @param _x_data : the data on which we want the funct... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QArkFunctorFactory:
def functorToWidget(cls, parent, _cls_functorClass, _o_vectorDataSet):
"""Construct a generic widget to enter the functor arguments @param _cls_functorClass : the functor class for which we want to generate a widget @param _x_data : the data on which we want the functor to be appli... | the_stack_v2_python_sparse | src/pyQArk/Core/QArkFunctorFactory.py | arnaudkelbert/pyQArk | train | 1 | |
bc07131ea149006a74120c4bfb2dedbaef8abd60 | [
"try:\n for field in dataclasses.fields(self):\n setattr(self, field.name, field.type(env_file))\nexcept ValidationError as err:\n config_field = None\n first_error = err.errors()[0]\n loc: str = first_error['loc'][0]\n if loc != '__root__':\n settings_model = cast(BaseSettings, err.mod... | <|body_start_0|>
try:
for field in dataclasses.fields(self):
setattr(self, field.name, field.type(env_file))
except ValidationError as err:
config_field = None
first_error = err.errors()[0]
loc: str = first_error['loc'][0]
if lo... | Globally manage environment variables configuration options. | Settings | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Settings:
"""Globally manage environment variables configuration options."""
def __init__(self, env_file: Optional[Path]=None) -> None:
"""Checks the validity of each configuration option. Args: env_file: Path to a file defining environment variables. Raises: ConfigError: A configura... | stack_v2_sparse_classes_36k_train_033484 | 6,638 | permissive | [
{
"docstring": "Checks the validity of each configuration option. Args: env_file: Path to a file defining environment variables. Raises: ConfigError: A configuration option is not valid.",
"name": "__init__",
"signature": "def __init__(self, env_file: Optional[Path]=None) -> None"
},
{
"docstrin... | 2 | stack_v2_sparse_classes_30k_train_021092 | Implement the Python class `Settings` described below.
Class description:
Globally manage environment variables configuration options.
Method signatures and docstrings:
- def __init__(self, env_file: Optional[Path]=None) -> None: Checks the validity of each configuration option. Args: env_file: Path to a file definin... | Implement the Python class `Settings` described below.
Class description:
Globally manage environment variables configuration options.
Method signatures and docstrings:
- def __init__(self, env_file: Optional[Path]=None) -> None: Checks the validity of each configuration option. Args: env_file: Path to a file definin... | 9e3370a7656b415058acf2d39a690a72f6eb343f | <|skeleton|>
class Settings:
"""Globally manage environment variables configuration options."""
def __init__(self, env_file: Optional[Path]=None) -> None:
"""Checks the validity of each configuration option. Args: env_file: Path to a file defining environment variables. Raises: ConfigError: A configura... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Settings:
"""Globally manage environment variables configuration options."""
def __init__(self, env_file: Optional[Path]=None) -> None:
"""Checks the validity of each configuration option. Args: env_file: Path to a file defining environment variables. Raises: ConfigError: A configuration option i... | the_stack_v2_python_sparse | src/opcua_webhmi_bridge/config.py | renovate-tests/opcua-webhmi-bridge | train | 0 |
19f1fd2f3ffb66de5e6afa4b04cfb09280008422 | [
"ngram2count = dict()\ntokens = re.split('\\\\s', text)\nfor order in range(startOrder, maxOrder + 1):\n for token in tokens:\n token = '_' + token + '_'\n for i in range(len(token) - order + 1):\n ngram = token[i:i + order]\n if not cls.NGRAM_PATTERN.match(ngram):\n ... | <|body_start_0|>
ngram2count = dict()
tokens = re.split('\\s', text)
for order in range(startOrder, maxOrder + 1):
for token in tokens:
token = '_' + token + '_'
for i in range(len(token) - order + 1):
ngram = token[i:i + order]
... | Some convenient string utilities. | SimilarityUtils | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimilarityUtils:
"""Some convenient string utilities."""
def computeNGrams(cls, startOrder, maxOrder, text):
"""Compute N Grams. @param startOrder @param maxOrder @param text @return a n gram to frequency map."""
<|body_0|>
def computeWord2count(text):
"""Calcula... | stack_v2_sparse_classes_36k_train_033485 | 4,522 | no_license | [
{
"docstring": "Compute N Grams. @param startOrder @param maxOrder @param text @return a n gram to frequency map.",
"name": "computeNGrams",
"signature": "def computeNGrams(cls, startOrder, maxOrder, text)"
},
{
"docstring": "Calculate word frequency. @param text a text to process @return a map ... | 6 | null | Implement the Python class `SimilarityUtils` described below.
Class description:
Some convenient string utilities.
Method signatures and docstrings:
- def computeNGrams(cls, startOrder, maxOrder, text): Compute N Grams. @param startOrder @param maxOrder @param text @return a n gram to frequency map.
- def computeWord... | Implement the Python class `SimilarityUtils` described below.
Class description:
Some convenient string utilities.
Method signatures and docstrings:
- def computeNGrams(cls, startOrder, maxOrder, text): Compute N Grams. @param startOrder @param maxOrder @param text @return a n gram to frequency map.
- def computeWord... | 58e12957dee8b4b18127df9daeb8825d8ada7923 | <|skeleton|>
class SimilarityUtils:
"""Some convenient string utilities."""
def computeNGrams(cls, startOrder, maxOrder, text):
"""Compute N Grams. @param startOrder @param maxOrder @param text @return a n gram to frequency map."""
<|body_0|>
def computeWord2count(text):
"""Calcula... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimilarityUtils:
"""Some convenient string utilities."""
def computeNGrams(cls, startOrder, maxOrder, text):
"""Compute N Grams. @param startOrder @param maxOrder @param text @return a n gram to frequency map."""
ngram2count = dict()
tokens = re.split('\\s', text)
for orde... | the_stack_v2_python_sparse | parser/util/SimilarityUtils.py | oldeucryptoboi/wiktionary-parser | train | 0 |
256ee79fc966263956ee2c6585826fefd470393a | [
"self.head = head\nself.count = 0\nslow = head\nfast = head\nwhile fast != None and fast.next != None:\n self.count += 1\n slow = slow.next\n fast = fast.next.next\nelse:\n self.count *= 2\n if fast != None:\n self.count += 1\nself.mid = slow",
"import random\nrand = random.randint(0, self.c... | <|body_start_0|>
self.head = head
self.count = 0
slow = head
fast = head
while fast != None and fast.next != None:
self.count += 1
slow = slow.next
fast = fast.next.next
else:
self.count *= 2
if fast != None:
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, head):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode"""
<|body_0|>
def getRandom(self):
"""Returns a random node's value. :rtype: int"""
... | stack_v2_sparse_classes_36k_train_033486 | 1,453 | permissive | [
{
"docstring": "@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode",
"name": "__init__",
"signature": "def __init__(self, head)"
},
{
"docstring": "Returns a random node's value. :rtype: int",
"name": "g... | 2 | stack_v2_sparse_classes_30k_train_006786 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, head): @param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode
- def getRan... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, head): @param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode
- def getRan... | 48454a8e6b5b86f80e89eca1b396480df8960cfd | <|skeleton|>
class Solution:
def __init__(self, head):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode"""
<|body_0|>
def getRandom(self):
"""Returns a random node's value. :rtype: int"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, head):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode"""
self.head = head
self.count = 0
slow = head
fast = head
while fast != None and f... | the_stack_v2_python_sparse | leetcode/medium/linked_list_random_node/py/solution.py | lilsweetcaligula/sandbox-online-judges | train | 0 | |
52199d5344bb74983cb53ee0493b9ae79490b3d4 | [
"username = request.user.get_username()\nserializer = CollaboratorSerializer(username=username, repo_base=repo_base, request=request)\ncollaborators = serializer.list_collaborators(repo_name)\nreturn Response(collaborators, status=status.HTTP_200_OK)",
"username = request.user.get_username()\nserializer = Collabo... | <|body_start_0|>
username = request.user.get_username()
serializer = CollaboratorSerializer(username=username, repo_base=repo_base, request=request)
collaborators = serializer.list_collaborators(repo_name)
return Response(collaborators, status=status.HTTP_200_OK)
<|end_body_0|>
<|body_s... | List and create collaborators. GET to list the collaborators. Accepts: None --- POST to add a collaborator. Accepts: { "user":, "permissions": []} e.g. {"user":"foo_user", "permissions": ['SELECT', 'INSERT', 'UPDATE']} | Collaborators | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Collaborators:
"""List and create collaborators. GET to list the collaborators. Accepts: None --- POST to add a collaborator. Accepts: { "user":, "permissions": []} e.g. {"user":"foo_user", "permissions": ['SELECT', 'INSERT', 'UPDATE']}"""
def get(self, request, repo_base, repo_name, format=... | stack_v2_sparse_classes_36k_train_033487 | 31,465 | permissive | [
{
"docstring": "Collaborators in a repo",
"name": "get",
"signature": "def get(self, request, repo_base, repo_name, format=None)"
},
{
"docstring": "Add a collaborator to a repo --- omit_serializer: true parameters: - name: user in: body type: string description: user to be added as a collaborat... | 2 | stack_v2_sparse_classes_30k_train_008413 | Implement the Python class `Collaborators` described below.
Class description:
List and create collaborators. GET to list the collaborators. Accepts: None --- POST to add a collaborator. Accepts: { "user":, "permissions": []} e.g. {"user":"foo_user", "permissions": ['SELECT', 'INSERT', 'UPDATE']}
Method signatures an... | Implement the Python class `Collaborators` described below.
Class description:
List and create collaborators. GET to list the collaborators. Accepts: None --- POST to add a collaborator. Accepts: { "user":, "permissions": []} e.g. {"user":"foo_user", "permissions": ['SELECT', 'INSERT', 'UPDATE']}
Method signatures an... | f066b472c2b66cc3b868bbe433aed2d4557aea32 | <|skeleton|>
class Collaborators:
"""List and create collaborators. GET to list the collaborators. Accepts: None --- POST to add a collaborator. Accepts: { "user":, "permissions": []} e.g. {"user":"foo_user", "permissions": ['SELECT', 'INSERT', 'UPDATE']}"""
def get(self, request, repo_base, repo_name, format=... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Collaborators:
"""List and create collaborators. GET to list the collaborators. Accepts: None --- POST to add a collaborator. Accepts: { "user":, "permissions": []} e.g. {"user":"foo_user", "permissions": ['SELECT', 'INSERT', 'UPDATE']}"""
def get(self, request, repo_base, repo_name, format=None):
... | the_stack_v2_python_sparse | src/api/views.py | datahuborg/datahub | train | 199 |
f95a7d3aa143bdedb97a0e62a5a21d23d8138c8d | [
"cmd = NodeController.MULTICHAIN_D_ARG + [admin_node_address]\ntry:\n output = run(cmd, capture_output=True, check=True)\n return re.findall('(?<=grant )(.*)(?= connect\\\\\\\\n)', str(output.stdout.strip()))[0]\nexcept CalledProcessError as err:\n raise MultiChainError(err.stderr)\nexcept Exception as err... | <|body_start_0|>
cmd = NodeController.MULTICHAIN_D_ARG + [admin_node_address]
try:
output = run(cmd, capture_output=True, check=True)
return re.findall('(?<=grant )(.*)(?= connect\\\\n)', str(output.stdout.strip()))[0]
except CalledProcessError as err:
raise M... | NodeController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NodeController:
def connect_to_admin_node(admin_node_address: str):
"""Initializes the connection between the current node and the admin :param admin_node_address: Node address of the admin node If successful, it returns a wallet address, which must used on the admin node to verify"""
... | stack_v2_sparse_classes_36k_train_033488 | 1,918 | permissive | [
{
"docstring": "Initializes the connection between the current node and the admin :param admin_node_address: Node address of the admin node If successful, it returns a wallet address, which must used on the admin node to verify",
"name": "connect_to_admin_node",
"signature": "def connect_to_admin_node(a... | 2 | stack_v2_sparse_classes_30k_train_011380 | Implement the Python class `NodeController` described below.
Class description:
Implement the NodeController class.
Method signatures and docstrings:
- def connect_to_admin_node(admin_node_address: str): Initializes the connection between the current node and the admin :param admin_node_address: Node address of the a... | Implement the Python class `NodeController` described below.
Class description:
Implement the NodeController class.
Method signatures and docstrings:
- def connect_to_admin_node(admin_node_address: str): Initializes the connection between the current node and the admin :param admin_node_address: Node address of the a... | 6be199fcaf836415b7d32ffb2cee911a9d600395 | <|skeleton|>
class NodeController:
def connect_to_admin_node(admin_node_address: str):
"""Initializes the connection between the current node and the admin :param admin_node_address: Node address of the admin node If successful, it returns a wallet address, which must used on the admin node to verify"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NodeController:
def connect_to_admin_node(admin_node_address: str):
"""Initializes the connection between the current node and the admin :param admin_node_address: Node address of the admin node If successful, it returns a wallet address, which must used on the admin node to verify"""
cmd = No... | the_stack_v2_python_sparse | app/models/node/node_controller.py | talos-org/server | train | 1 | |
ca0ac5df2395bd27181475bf718f2ab609a0e096 | [
"super().__init__('human_model_generation_client')\nself.bridge_cv = CvBridge()\nself.bridge_ros = ROS2Bridge()\nself.cli = self.create_client(ImgToMesh, service_name)\nwhile not self.cli.wait_for_service(timeout_sec=1.0):\n self.get_logger().info('service not available, waiting again...')\nself.req = ImgToMesh.... | <|body_start_0|>
super().__init__('human_model_generation_client')
self.bridge_cv = CvBridge()
self.bridge_ros = ROS2Bridge()
self.cli = self.create_client(ImgToMesh, service_name)
while not self.cli.wait_for_service(timeout_sec=1.0):
self.get_logger().info('service n... | HumanModelGenerationClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HumanModelGenerationClient:
def __init__(self, service_name='human_model_generation'):
"""Creates a ROS Client for human model generation :param service_name: The name of the service :type service_name: str"""
<|body_0|>
def send_request(self, img_rgb, img_msk, extract_pose)... | stack_v2_sparse_classes_36k_train_033489 | 5,361 | permissive | [
{
"docstring": "Creates a ROS Client for human model generation :param service_name: The name of the service :type service_name: str",
"name": "__init__",
"signature": "def __init__(self, service_name='human_model_generation')"
},
{
"docstring": "Send request to service assigned with the task to... | 2 | stack_v2_sparse_classes_30k_train_011613 | Implement the Python class `HumanModelGenerationClient` described below.
Class description:
Implement the HumanModelGenerationClient class.
Method signatures and docstrings:
- def __init__(self, service_name='human_model_generation'): Creates a ROS Client for human model generation :param service_name: The name of th... | Implement the Python class `HumanModelGenerationClient` described below.
Class description:
Implement the HumanModelGenerationClient class.
Method signatures and docstrings:
- def __init__(self, service_name='human_model_generation'): Creates a ROS Client for human model generation :param service_name: The name of th... | b3d6ce670cdf63469fc5766630eb295d67b3d788 | <|skeleton|>
class HumanModelGenerationClient:
def __init__(self, service_name='human_model_generation'):
"""Creates a ROS Client for human model generation :param service_name: The name of the service :type service_name: str"""
<|body_0|>
def send_request(self, img_rgb, img_msk, extract_pose)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HumanModelGenerationClient:
def __init__(self, service_name='human_model_generation'):
"""Creates a ROS Client for human model generation :param service_name: The name of the service :type service_name: str"""
super().__init__('human_model_generation_client')
self.bridge_cv = CvBridge(... | the_stack_v2_python_sparse | projects/opendr_ws_2/src/opendr_simulation/opendr_simulation/human_model_generation_client.py | opendr-eu/opendr | train | 535 | |
4c9b987a23474eafe4ebcf335aa9b4ff6cdee589 | [
"item_links = response.css(\"a[itemprop='url']::attr(href)\").extract()\nitem_links = map(lambda link: link + '.json', item_links)\nyield from response.follow_all(item_links, self.parse_details)\nnext_page = response.css(\"span[class='next'] > a::attr(href)\").get()\nif next_page is not None:\n next_page = respo... | <|body_start_0|>
item_links = response.css("a[itemprop='url']::attr(href)").extract()
item_links = map(lambda link: link + '.json', item_links)
yield from response.follow_all(item_links, self.parse_details)
next_page = response.css("span[class='next'] > a::attr(href)").get()
if n... | BazicSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BazicSpider:
def parse(self, response, **kwargs):
"""This function should extract and loop item urls. @url https://www.bazicstore.com/collections/all @returns items 0 @returns requests 48 @request https://www.bazicstore.com/collections/all/products/575.json"""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_033490 | 2,293 | no_license | [
{
"docstring": "This function should extract and loop item urls. @url https://www.bazicstore.com/collections/all @returns items 0 @returns requests 48 @request https://www.bazicstore.com/collections/all/products/575.json",
"name": "parse",
"signature": "def parse(self, response, **kwargs)"
},
{
... | 2 | null | Implement the Python class `BazicSpider` described below.
Class description:
Implement the BazicSpider class.
Method signatures and docstrings:
- def parse(self, response, **kwargs): This function should extract and loop item urls. @url https://www.bazicstore.com/collections/all @returns items 0 @returns requests 48 ... | Implement the Python class `BazicSpider` described below.
Class description:
Implement the BazicSpider class.
Method signatures and docstrings:
- def parse(self, response, **kwargs): This function should extract and loop item urls. @url https://www.bazicstore.com/collections/all @returns items 0 @returns requests 48 ... | 025babe4a03553d720806828f89929c6e773d683 | <|skeleton|>
class BazicSpider:
def parse(self, response, **kwargs):
"""This function should extract and loop item urls. @url https://www.bazicstore.com/collections/all @returns items 0 @returns requests 48 @request https://www.bazicstore.com/collections/all/products/575.json"""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BazicSpider:
def parse(self, response, **kwargs):
"""This function should extract and loop item urls. @url https://www.bazicstore.com/collections/all @returns items 0 @returns requests 48 @request https://www.bazicstore.com/collections/all/products/575.json"""
item_links = response.css("a[item... | the_stack_v2_python_sparse | data_scraping/gmd/spiders/bazic.py | panky2202/scrapy-dev | train | 1 | |
8134be26b083bea97f14a1f9d00d6aa12556a004 | [
"super(ConvolutionModule, self).__init__()\nassert (kernel_size - 1) % 2 == 0\nself.pointwise_conv1 = nn.Conv1d(channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias)\nself.depthwise_conv = nn.Conv1d(channels, channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias=bia... | <|body_start_0|>
super(ConvolutionModule, self).__init__()
assert (kernel_size - 1) % 2 == 0
self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias)
self.depthwise_conv = nn.Conv1d(channels, channels, kernel_size, stride=1, padding=(kernel_... | ConvolutionModule in Conformer model. Args: channels (int): The number of channels of conv layers. kernel_size (int): Kernerl size of conv layers. | ConvolutionModule | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConvolutionModule:
"""ConvolutionModule in Conformer model. Args: channels (int): The number of channels of conv layers. kernel_size (int): Kernerl size of conv layers."""
def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
"""Construct an ConvolutionModule ob... | stack_v2_sparse_classes_36k_train_033491 | 37,737 | permissive | [
{
"docstring": "Construct an ConvolutionModule object.",
"name": "__init__",
"signature": "def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True)"
},
{
"docstring": "Compute convolution module. Args: x (torch.Tensor): Input tensor (#batch, time, channels). Returns: torch.Tens... | 2 | null | Implement the Python class `ConvolutionModule` described below.
Class description:
ConvolutionModule in Conformer model. Args: channels (int): The number of channels of conv layers. kernel_size (int): Kernerl size of conv layers.
Method signatures and docstrings:
- def __init__(self, channels, kernel_size, activation... | Implement the Python class `ConvolutionModule` described below.
Class description:
ConvolutionModule in Conformer model. Args: channels (int): The number of channels of conv layers. kernel_size (int): Kernerl size of conv layers.
Method signatures and docstrings:
- def __init__(self, channels, kernel_size, activation... | 31d50b1ea1dea92f4182c5b2b6fe9fe4c981ae39 | <|skeleton|>
class ConvolutionModule:
"""ConvolutionModule in Conformer model. Args: channels (int): The number of channels of conv layers. kernel_size (int): Kernerl size of conv layers."""
def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
"""Construct an ConvolutionModule ob... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConvolutionModule:
"""ConvolutionModule in Conformer model. Args: channels (int): The number of channels of conv layers. kernel_size (int): Kernerl size of conv layers."""
def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
"""Construct an ConvolutionModule object."""
... | the_stack_v2_python_sparse | SVS/model/layers/conformer_related.py | SJTMusicTeam/SVS_system | train | 85 |
54a1639ec9a99a0d06f7989d11bc3a6715502a5e | [
"super().__init__()\nself.class_dim = class_dim\nself.mode = mode\nif self.mode == 'weighted':\n assert weights is not None\n self.weights = torch.Tensor(weights)\nself.eps = eps",
"n_dims = len(outputs_shape)\ndims = list(range(n_dims))\nif self.class_dim < 0:\n self.class_dim = n_dims + self.class_dim\... | <|body_start_0|>
super().__init__()
self.class_dim = class_dim
self.mode = mode
if self.mode == 'weighted':
assert weights is not None
self.weights = torch.Tensor(weights)
self.eps = eps
<|end_body_0|>
<|body_start_1|>
n_dims = len(outputs_shape)
... | The Smoothing Dice loss. ``SmoothingDiceloss = 1 - smoothing dice score`` ``smoothing dice score = 2 * intersection / (|outputs|^2 + |targets|^2)`` Criterion was inspired by https://arxiv.org/abs/1606.04797 Examples: >>> import torch >>> from catalyst.contrib.losses import SmoothingDiceLoss >>> targets = torch.abs(torc... | SmoothingDiceLoss | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmoothingDiceLoss:
"""The Smoothing Dice loss. ``SmoothingDiceloss = 1 - smoothing dice score`` ``smoothing dice score = 2 * intersection / (|outputs|^2 + |targets|^2)`` Criterion was inspired by https://arxiv.org/abs/1606.04797 Examples: >>> import torch >>> from catalyst.contrib.losses import S... | stack_v2_sparse_classes_36k_train_033492 | 3,456 | permissive | [
{
"docstring": "Args: class_dim: indicates class dimention (K) for ``outputs`` and ``targets`` tensors (default = 1) mode: class summation strategy. Must be one of ['micro', 'macro', 'weighted']. If mode='micro', classes are ignored, and metric are calculated generally. If mode='macro', metric are calculated pe... | 3 | null | Implement the Python class `SmoothingDiceLoss` described below.
Class description:
The Smoothing Dice loss. ``SmoothingDiceloss = 1 - smoothing dice score`` ``smoothing dice score = 2 * intersection / (|outputs|^2 + |targets|^2)`` Criterion was inspired by https://arxiv.org/abs/1606.04797 Examples: >>> import torch >>... | Implement the Python class `SmoothingDiceLoss` described below.
Class description:
The Smoothing Dice loss. ``SmoothingDiceloss = 1 - smoothing dice score`` ``smoothing dice score = 2 * intersection / (|outputs|^2 + |targets|^2)`` Criterion was inspired by https://arxiv.org/abs/1606.04797 Examples: >>> import torch >>... | e99f90655d0efcf22559a46e928f0f98c9807ebf | <|skeleton|>
class SmoothingDiceLoss:
"""The Smoothing Dice loss. ``SmoothingDiceloss = 1 - smoothing dice score`` ``smoothing dice score = 2 * intersection / (|outputs|^2 + |targets|^2)`` Criterion was inspired by https://arxiv.org/abs/1606.04797 Examples: >>> import torch >>> from catalyst.contrib.losses import S... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SmoothingDiceLoss:
"""The Smoothing Dice loss. ``SmoothingDiceloss = 1 - smoothing dice score`` ``smoothing dice score = 2 * intersection / (|outputs|^2 + |targets|^2)`` Criterion was inspired by https://arxiv.org/abs/1606.04797 Examples: >>> import torch >>> from catalyst.contrib.losses import SmoothingDiceL... | the_stack_v2_python_sparse | catalyst/contrib/losses/smoothing_dice.py | catalyst-team/catalyst | train | 3,038 |
81352ea0d3000a27e1b9bcc2e81f4be3679a1305 | [
"stack = []\nres = []\nfor i in range(len(s)):\n if s[i] == '(':\n stack.append(i)\n elif stack:\n res.append(stack.pop())\n res.append(i)\nres.sort()\nmax_len, i = (0, 0)\nwhile i < len(res) - 1:\n tmp = i\n while i < len(res) - 1 and res[i + 1] - res[i] == 1:\n i += 1\n ... | <|body_start_0|>
stack = []
res = []
for i in range(len(s)):
if s[i] == '(':
stack.append(i)
elif stack:
res.append(stack.pop())
res.append(i)
res.sort()
max_len, i = (0, 0)
while i < len(res) - 1:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestValidParentheses(self, s: str) -> int:
""":param s: :return: 栈+排序 索引索引索引 利用栈stack存放左括号索引，进行配对 配对成功索引的放入res列表中， 对列表进行排序后寻找最长的连续 时间复杂度：O(nlog(n))，括号匹配O(n)，排序复杂度O(nlog(n))， 寻找最长连续子序列O(n)，总体O(nlog(n)) 空间复杂度：O(n)"""
<|body_0|>
def longestValidParentheses2(sel... | stack_v2_sparse_classes_36k_train_033493 | 3,419 | no_license | [
{
"docstring": ":param s: :return: 栈+排序 索引索引索引 利用栈stack存放左括号索引，进行配对 配对成功索引的放入res列表中， 对列表进行排序后寻找最长的连续 时间复杂度：O(nlog(n))，括号匹配O(n)，排序复杂度O(nlog(n))， 寻找最长连续子序列O(n)，总体O(nlog(n)) 空间复杂度：O(n)",
"name": "longestValidParentheses",
"signature": "def longestValidParentheses(self, s: str) -> int"
},
{
"docstri... | 3 | stack_v2_sparse_classes_30k_train_003183 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestValidParentheses(self, s: str) -> int: :param s: :return: 栈+排序 索引索引索引 利用栈stack存放左括号索引，进行配对 配对成功索引的放入res列表中， 对列表进行排序后寻找最长的连续 时间复杂度：O(nlog(n))，括号匹配O(n)，排序复杂度O(nlog(n))， ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestValidParentheses(self, s: str) -> int: :param s: :return: 栈+排序 索引索引索引 利用栈stack存放左括号索引，进行配对 配对成功索引的放入res列表中， 对列表进行排序后寻找最长的连续 时间复杂度：O(nlog(n))，括号匹配O(n)，排序复杂度O(nlog(n))， ... | 65e260f0b5b396ecfc235a924c6861893c268272 | <|skeleton|>
class Solution:
def longestValidParentheses(self, s: str) -> int:
""":param s: :return: 栈+排序 索引索引索引 利用栈stack存放左括号索引，进行配对 配对成功索引的放入res列表中， 对列表进行排序后寻找最长的连续 时间复杂度：O(nlog(n))，括号匹配O(n)，排序复杂度O(nlog(n))， 寻找最长连续子序列O(n)，总体O(nlog(n)) 空间复杂度：O(n)"""
<|body_0|>
def longestValidParentheses2(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestValidParentheses(self, s: str) -> int:
""":param s: :return: 栈+排序 索引索引索引 利用栈stack存放左括号索引，进行配对 配对成功索引的放入res列表中， 对列表进行排序后寻找最长的连续 时间复杂度：O(nlog(n))，括号匹配O(n)，排序复杂度O(nlog(n))， 寻找最长连续子序列O(n)，总体O(nlog(n)) 空间复杂度：O(n)"""
stack = []
res = []
for i in range(len(s)):
... | the_stack_v2_python_sparse | leetcode/032_longestValid.py | CKZfd/LeetCode | train | 1 | |
c1a88f411645e2bf0ff79f37e8a793e670cd24df | [
"if grid[0][0] == 1 or grid[-1][-1] == 1 or (not grid):\n return -1\nqueue = [(0, 0, 1)]\ngrid[0][0] = 1\nn = len(grid)\nif n == 1:\n return 1\ndirections = [(-1, 0), (1, 0), (0, -1), (0, 1), (1, 1), (1, -1), (-1, 1), (-1, -1)]\nwhile len(queue) > 0:\n size = len(queue)\n for k in range(size):\n ... | <|body_start_0|>
if grid[0][0] == 1 or grid[-1][-1] == 1 or (not grid):
return -1
queue = [(0, 0, 1)]
grid[0][0] = 1
n = len(grid)
if n == 1:
return 1
directions = [(-1, 0), (1, 0), (0, -1), (0, 1), (1, 1), (1, -1), (-1, 1), (-1, -1)]
while... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def shortestPathBinaryMatrix_standard(self, grid):
""":type grid: List[List[int]] :rtype: int"""
<|body_0|>
def shortestPathBinaryMatrix(self, grid):
""":type grid: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_033494 | 4,024 | no_license | [
{
"docstring": ":type grid: List[List[int]] :rtype: int",
"name": "shortestPathBinaryMatrix_standard",
"signature": "def shortestPathBinaryMatrix_standard(self, grid)"
},
{
"docstring": ":type grid: List[List[int]] :rtype: int",
"name": "shortestPathBinaryMatrix",
"signature": "def short... | 2 | stack_v2_sparse_classes_30k_train_000284 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestPathBinaryMatrix_standard(self, grid): :type grid: List[List[int]] :rtype: int
- def shortestPathBinaryMatrix(self, grid): :type grid: List[List[int]] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestPathBinaryMatrix_standard(self, grid): :type grid: List[List[int]] :rtype: int
- def shortestPathBinaryMatrix(self, grid): :type grid: List[List[int]] :rtype: int
<|... | d36655924edb9e364c956f912ba4797fb962be7e | <|skeleton|>
class Solution:
def shortestPathBinaryMatrix_standard(self, grid):
""":type grid: List[List[int]] :rtype: int"""
<|body_0|>
def shortestPathBinaryMatrix(self, grid):
""":type grid: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def shortestPathBinaryMatrix_standard(self, grid):
""":type grid: List[List[int]] :rtype: int"""
if grid[0][0] == 1 or grid[-1][-1] == 1 or (not grid):
return -1
queue = [(0, 0, 1)]
grid[0][0] = 1
n = len(grid)
if n == 1:
return... | the_stack_v2_python_sparse | 1091.BfsShortestPath.py | casssie-zhang/LeetcodeNotes | train | 2 | |
c2559715a8277a67a112867422bc740d5058d60c | [
"if cache_root and (not supports_cache_root(model)):\n warnings.warn(_get_cache_root_not_supported_message(type(model)), RuntimeWarning)\n cache_root = False\nself._cache_root = cache_root",
"if isinstance(posterior.distribution, MultitaskMultivariateNormal):\n lazy_covar = extract_batch_covar(posterior.... | <|body_start_0|>
if cache_root and (not supports_cache_root(model)):
warnings.warn(_get_cache_root_not_supported_message(type(model)), RuntimeWarning)
cache_root = False
self._cache_root = cache_root
<|end_body_0|>
<|body_start_1|>
if isinstance(posterior.distribution, M... | Abstract class for acquisition functions using a cached Cholesky. Specifically, this is for acquisition functions that require sampling from the posterior P(f(X_baseline, X) | D). The Cholesky of the posterior covariance over f(X_baseline) is cached. :meta private: | CachedCholeskyMCAcquisitionFunction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CachedCholeskyMCAcquisitionFunction:
"""Abstract class for acquisition functions using a cached Cholesky. Specifically, this is for acquisition functions that require sampling from the posterior P(f(X_baseline, X) | D). The Cholesky of the posterior covariance over f(X_baseline) is cached. :meta ... | stack_v2_sparse_classes_36k_train_033495 | 7,369 | permissive | [
{
"docstring": "Set class attributes and perform compatibility checks. Args: model: A model. cache_root: A boolean indicating whether to cache the Cholesky. This might be overridden in the model is not compatible.",
"name": "_setup",
"signature": "def _setup(self, model: Model, cache_root: bool=False) -... | 4 | null | Implement the Python class `CachedCholeskyMCAcquisitionFunction` described below.
Class description:
Abstract class for acquisition functions using a cached Cholesky. Specifically, this is for acquisition functions that require sampling from the posterior P(f(X_baseline, X) | D). The Cholesky of the posterior covarian... | Implement the Python class `CachedCholeskyMCAcquisitionFunction` described below.
Class description:
Abstract class for acquisition functions using a cached Cholesky. Specifically, this is for acquisition functions that require sampling from the posterior P(f(X_baseline, X) | D). The Cholesky of the posterior covarian... | 4cc5ed59b2e8a9c780f786830c548e05cc74d53c | <|skeleton|>
class CachedCholeskyMCAcquisitionFunction:
"""Abstract class for acquisition functions using a cached Cholesky. Specifically, this is for acquisition functions that require sampling from the posterior P(f(X_baseline, X) | D). The Cholesky of the posterior covariance over f(X_baseline) is cached. :meta ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CachedCholeskyMCAcquisitionFunction:
"""Abstract class for acquisition functions using a cached Cholesky. Specifically, this is for acquisition functions that require sampling from the posterior P(f(X_baseline, X) | D). The Cholesky of the posterior covariance over f(X_baseline) is cached. :meta private:"""
... | the_stack_v2_python_sparse | botorch/acquisition/cached_cholesky.py | pytorch/botorch | train | 2,891 |
5ebefc01e80cdd571928bceeb277005ef622e265 | [
"data_dict = json.loads(request.data)\nvalidator.validate(data_dict, validator.USER)\nuser = user_controller.register(data_dict)\nuser_dto = user_schema.serialize_user(user)\nresponse = Response(response=json.dumps(user_dto), status=201, mimetype='application/json')\nreturn response",
"user = user_controller.get_... | <|body_start_0|>
data_dict = json.loads(request.data)
validator.validate(data_dict, validator.USER)
user = user_controller.register(data_dict)
user_dto = user_schema.serialize_user(user)
response = Response(response=json.dumps(user_dto), status=201, mimetype='application/json')
... | UserResource | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserResource:
def post(self):
"""Register a new user"""
<|body_0|>
def get(self, user_id):
"""Get a user"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
data_dict = json.loads(request.data)
validator.validate(data_dict, validator.USER)
... | stack_v2_sparse_classes_36k_train_033496 | 4,871 | no_license | [
{
"docstring": "Register a new user",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Get a user",
"name": "get",
"signature": "def get(self, user_id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008823 | Implement the Python class `UserResource` described below.
Class description:
Implement the UserResource class.
Method signatures and docstrings:
- def post(self): Register a new user
- def get(self, user_id): Get a user | Implement the Python class `UserResource` described below.
Class description:
Implement the UserResource class.
Method signatures and docstrings:
- def post(self): Register a new user
- def get(self, user_id): Get a user
<|skeleton|>
class UserResource:
def post(self):
"""Register a new user"""
... | e0c8ea99886f10aea14b9ca95af8a4f42f2af493 | <|skeleton|>
class UserResource:
def post(self):
"""Register a new user"""
<|body_0|>
def get(self, user_id):
"""Get a user"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserResource:
def post(self):
"""Register a new user"""
data_dict = json.loads(request.data)
validator.validate(data_dict, validator.USER)
user = user_controller.register(data_dict)
user_dto = user_schema.serialize_user(user)
response = Response(response=json.du... | the_stack_v2_python_sparse | imdb_api/resources/user_resources.py | Matiasmoratti7/imdb | train | 0 | |
e69efe883225e8ab4824ebcb0e792ce7a79da91b | [
"step_metrics = OrderedDict()\nfor key in cls:\n step_metrics[key.value] = None\nreturn step_metrics",
"for key in cls:\n if input_dict[key.value] is None:\n raise Exception(\"StepMetrics dict's key({})'s value is None\".format(key.value))"
] | <|body_start_0|>
step_metrics = OrderedDict()
for key in cls:
step_metrics[key.value] = None
return step_metrics
<|end_body_0|>
<|body_start_1|>
for key in cls:
if input_dict[key.value] is None:
raise Exception("StepMetrics dict's key({})'s value ... | The keys for the sim trace metrics | StepMetrics | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StepMetrics:
"""The keys for the sim trace metrics"""
def make_default_metric(cls):
"""Returns the default step metrics dict"""
<|body_0|>
def validate_dict(cls, input_dict):
"""Throws an exception if a key is missing"""
<|body_1|>
<|end_skeleton|>
<|bo... | stack_v2_sparse_classes_36k_train_033497 | 4,235 | permissive | [
{
"docstring": "Returns the default step metrics dict",
"name": "make_default_metric",
"signature": "def make_default_metric(cls)"
},
{
"docstring": "Throws an exception if a key is missing",
"name": "validate_dict",
"signature": "def validate_dict(cls, input_dict)"
}
] | 2 | null | Implement the Python class `StepMetrics` described below.
Class description:
The keys for the sim trace metrics
Method signatures and docstrings:
- def make_default_metric(cls): Returns the default step metrics dict
- def validate_dict(cls, input_dict): Throws an exception if a key is missing | Implement the Python class `StepMetrics` described below.
Class description:
The keys for the sim trace metrics
Method signatures and docstrings:
- def make_default_metric(cls): Returns the default step metrics dict
- def validate_dict(cls, input_dict): Throws an exception if a key is missing
<|skeleton|>
class Step... | 2ce50508dd4100eaef7f8729436549a801505705 | <|skeleton|>
class StepMetrics:
"""The keys for the sim trace metrics"""
def make_default_metric(cls):
"""Returns the default step metrics dict"""
<|body_0|>
def validate_dict(cls, input_dict):
"""Throws an exception if a key is missing"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StepMetrics:
"""The keys for the sim trace metrics"""
def make_default_metric(cls):
"""Returns the default step metrics dict"""
step_metrics = OrderedDict()
for key in cls:
step_metrics[key.value] = None
return step_metrics
def validate_dict(cls, input_dic... | the_stack_v2_python_sparse | bundle/markov/metrics/constants.py | aws-deepracer-community/deepracer-simapp | train | 83 |
7d04e6311d7bf3858c0e44ed14e060ba75169e48 | [
"result = DBFormatter.format(self, results)\nif len(result) == 0:\n return None\nreturn result[0][0]",
"if isinstance(jobID, list):\n if len(jobID) == 0:\n return {}\n binds = []\n for entry in jobID:\n binds.append({'jobid': entry})\n result = self.dbi.processData(self.bulkSQL, binds... | <|body_start_0|>
result = DBFormatter.format(self, results)
if len(result) == 0:
return None
return result[0][0]
<|end_body_0|>
<|body_start_1|>
if isinstance(jobID, list):
if len(jobID) == 0:
return {}
binds = []
for entry... | _GetCouchID_ Given a job ID retrieve the couch document ID. | GetCouchID | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetCouchID:
"""_GetCouchID_ Given a job ID retrieve the couch document ID."""
def format(self, results):
"""_format_ Return the couch document ID or None if one has not been set."""
<|body_0|>
def execute(self, jobID, conn=None, transaction=False):
"""_execute_ E... | stack_v2_sparse_classes_36k_train_033498 | 1,473 | permissive | [
{
"docstring": "_format_ Return the couch document ID or None if one has not been set.",
"name": "format",
"signature": "def format(self, results)"
},
{
"docstring": "_execute_ Execute the SQL for the given job ID and then format and return the result.",
"name": "execute",
"signature": "... | 2 | null | Implement the Python class `GetCouchID` described below.
Class description:
_GetCouchID_ Given a job ID retrieve the couch document ID.
Method signatures and docstrings:
- def format(self, results): _format_ Return the couch document ID or None if one has not been set.
- def execute(self, jobID, conn=None, transactio... | Implement the Python class `GetCouchID` described below.
Class description:
_GetCouchID_ Given a job ID retrieve the couch document ID.
Method signatures and docstrings:
- def format(self, results): _format_ Return the couch document ID or None if one has not been set.
- def execute(self, jobID, conn=None, transactio... | de110ccf6fc63ef5589b4e871ef4d51d5bce7a25 | <|skeleton|>
class GetCouchID:
"""_GetCouchID_ Given a job ID retrieve the couch document ID."""
def format(self, results):
"""_format_ Return the couch document ID or None if one has not been set."""
<|body_0|>
def execute(self, jobID, conn=None, transaction=False):
"""_execute_ E... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetCouchID:
"""_GetCouchID_ Given a job ID retrieve the couch document ID."""
def format(self, results):
"""_format_ Return the couch document ID or None if one has not been set."""
result = DBFormatter.format(self, results)
if len(result) == 0:
return None
ret... | the_stack_v2_python_sparse | src/python/WMCore/WMBS/MySQL/Jobs/GetCouchID.py | vkuznet/WMCore | train | 0 |
8a56c1b27358a50020d7739e4fef6caaf0d43e17 | [
"self.matrix = matrix\nfor row in range(len(matrix)):\n for col in range(1, len(matrix[0])):\n self.matrix[row][col] = self.matrix[row][col - 1] + self.matrix[row][col]",
"original = self.matrix[row][col]\nif col != 0:\n original -= self.matrix[row][col - 1]\ndiff = original - val\nfor y in xrange(co... | <|body_start_0|>
self.matrix = matrix
for row in range(len(matrix)):
for col in range(1, len(matrix[0])):
self.matrix[row][col] = self.matrix[row][col - 1] + self.matrix[row][col]
<|end_body_0|>
<|body_start_1|>
original = self.matrix[row][col]
if col != 0:
... | NumMatrix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. :type matrix: List[List[int]]"""
<|body_0|>
def update(self, row, col, val):
"""update the element at matrix[row,col] to val. :type row: int :type col: int :type val: int :rtype: void"""
... | stack_v2_sparse_classes_36k_train_033499 | 1,645 | no_license | [
{
"docstring": "initialize your data structure here. :type matrix: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, matrix)"
},
{
"docstring": "update the element at matrix[row,col] to val. :type row: int :type col: int :type val: int :rtype: void",
"name": "update",
... | 3 | null | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): initialize your data structure here. :type matrix: List[List[int]]
- def update(self, row, col, val): update the element at matrix[row,col] to val. ... | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): initialize your data structure here. :type matrix: List[List[int]]
- def update(self, row, col, val): update the element at matrix[row,col] to val. ... | 6de551327f96ec4d4b63d0045281b65bbb4f5d0f | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. :type matrix: List[List[int]]"""
<|body_0|>
def update(self, row, col, val):
"""update the element at matrix[row,col] to val. :type row: int :type col: int :type val: int :rtype: void"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. :type matrix: List[List[int]]"""
self.matrix = matrix
for row in range(len(matrix)):
for col in range(1, len(matrix[0])):
self.matrix[row][col] = self.matrix[row][col - 1] + self.... | the_stack_v2_python_sparse | sumRegion.py | JingweiTu/leetcode | train | 0 |
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