blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
46780f22b185fa8b881a63bb12a4c78e9e993e7b | [
"if not t1 and (not t2):\n return True\nif not t1 or not t2:\n return False\nif t1.val != t2.val:\n return False\nreturn self.is_same_tree_(t1.left, t2.left) and self.is_same_tree_(t1.right, t2.right)",
"def check(tree_1: 'TreeNode', tree_2: 'TreeNode') -> bool:\n if not tree_1 and (not tree_2):\n ... | <|body_start_0|>
if not t1 and (not t2):
return True
if not t1 or not t2:
return False
if t1.val != t2.val:
return False
return self.is_same_tree_(t1.left, t2.left) and self.is_same_tree_(t1.right, t2.right)
<|end_body_0|>
<|body_start_1|>
def... | BinaryTree | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinaryTree:
def is_same_tree_(self, t1: 'TreeNode', t2: 'TreeNode') -> bool:
"""Approach: Recursion Time Complexity: O(N) Space Complexity: O(N) :param t1: :param t2: :return:"""
<|body_0|>
def is_same_tree_(self, t1: 'TreeNode', t2: 'TreeNode') -> bool:
"""Approach:... | stack_v2_sparse_classes_36k_train_021800 | 1,537 | no_license | [
{
"docstring": "Approach: Recursion Time Complexity: O(N) Space Complexity: O(N) :param t1: :param t2: :return:",
"name": "is_same_tree_",
"signature": "def is_same_tree_(self, t1: 'TreeNode', t2: 'TreeNode') -> bool"
},
{
"docstring": "Approach: Iteration Time Complexity: O(N) Space Complexity:... | 2 | stack_v2_sparse_classes_30k_train_015641 | Implement the Python class `BinaryTree` described below.
Class description:
Implement the BinaryTree class.
Method signatures and docstrings:
- def is_same_tree_(self, t1: 'TreeNode', t2: 'TreeNode') -> bool: Approach: Recursion Time Complexity: O(N) Space Complexity: O(N) :param t1: :param t2: :return:
- def is_same... | Implement the Python class `BinaryTree` described below.
Class description:
Implement the BinaryTree class.
Method signatures and docstrings:
- def is_same_tree_(self, t1: 'TreeNode', t2: 'TreeNode') -> bool: Approach: Recursion Time Complexity: O(N) Space Complexity: O(N) :param t1: :param t2: :return:
- def is_same... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class BinaryTree:
def is_same_tree_(self, t1: 'TreeNode', t2: 'TreeNode') -> bool:
"""Approach: Recursion Time Complexity: O(N) Space Complexity: O(N) :param t1: :param t2: :return:"""
<|body_0|>
def is_same_tree_(self, t1: 'TreeNode', t2: 'TreeNode') -> bool:
"""Approach:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BinaryTree:
def is_same_tree_(self, t1: 'TreeNode', t2: 'TreeNode') -> bool:
"""Approach: Recursion Time Complexity: O(N) Space Complexity: O(N) :param t1: :param t2: :return:"""
if not t1 and (not t2):
return True
if not t1 or not t2:
return False
if t1... | the_stack_v2_python_sparse | revisited/trees/same_tree.py | Shiv2157k/leet_code | train | 1 | |
645021bac95726a95e7f8e26f805e3b68af5ede3 | [
"self.snn = snn\nself.xtrn = xtrn\nself.ytrn = ytrn",
"self.snn.coefs_[0] = weights[:1800].reshape((30, 60))\nself.snn.coefs_[1] = weights[1800:1860].reshape((60, 1))\nself.snn.intercepts_[0] = weights[1860:1920]\nself.snn.intercepts_[1] = weights[1920]\nreturn 1.0 - self.snn.score(self.xtrn, self.ytrn)"
] | <|body_start_0|>
self.snn = snn
self.xtrn = xtrn
self.ytrn = ytrn
<|end_body_0|>
<|body_start_1|>
self.snn.coefs_[0] = weights[:1800].reshape((30, 60))
self.snn.coefs_[1] = weights[1800:1860].reshape((60, 1))
self.snn.intercepts_[0] = weights[1860:1920]
self.snn.... | SwarmObjective | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SwarmObjective:
def __init__(self, snn, xtrn, ytrn):
"""Keep the NN object and test data"""
<|body_0|>
def Evaluate(self, weights):
"""Test the NN with the given weights"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.snn = snn
self.xtr... | stack_v2_sparse_classes_36k_train_021801 | 5,989 | permissive | [
{
"docstring": "Keep the NN object and test data",
"name": "__init__",
"signature": "def __init__(self, snn, xtrn, ytrn)"
},
{
"docstring": "Test the NN with the given weights",
"name": "Evaluate",
"signature": "def Evaluate(self, weights)"
}
] | 2 | null | Implement the Python class `SwarmObjective` described below.
Class description:
Implement the SwarmObjective class.
Method signatures and docstrings:
- def __init__(self, snn, xtrn, ytrn): Keep the NN object and test data
- def Evaluate(self, weights): Test the NN with the given weights | Implement the Python class `SwarmObjective` described below.
Class description:
Implement the SwarmObjective class.
Method signatures and docstrings:
- def __init__(self, snn, xtrn, ytrn): Keep the NN object and test data
- def Evaluate(self, weights): Test the NN with the given weights
<|skeleton|>
class SwarmObjec... | 5445b6f90ab49339ca0fdb71e98d44e6827c95a8 | <|skeleton|>
class SwarmObjective:
def __init__(self, snn, xtrn, ytrn):
"""Keep the NN object and test data"""
<|body_0|>
def Evaluate(self, weights):
"""Test the NN with the given weights"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SwarmObjective:
def __init__(self, snn, xtrn, ytrn):
"""Keep the NN object and test data"""
self.snn = snn
self.xtrn = xtrn
self.ytrn = ytrn
def Evaluate(self, weights):
"""Test the NN with the given weights"""
self.snn.coefs_[0] = weights[:1800].reshape((3... | the_stack_v2_python_sparse | nn/nn.py | dayoladejo/SwarmOptimization | train | 0 | |
1b7575a64366b7da437ac0ffd9fddd6860b639ac | [
"self.cutoff = cutoff\nself.box_width = box_width\nself.voxel_width = voxel_width\nself.reduce_to_contacts = reduce_to_contacts",
"if 'complex' in kwargs:\n datapoint = kwargs.get('complex')\n raise DeprecationWarning('Complex is being phased out as a parameter, please pass \"datapoint\" instead.')\ntry:\n ... | <|body_start_0|>
self.cutoff = cutoff
self.box_width = box_width
self.voxel_width = voxel_width
self.reduce_to_contacts = reduce_to_contacts
<|end_body_0|>
<|body_start_1|>
if 'complex' in kwargs:
datapoint = kwargs.get('complex')
raise DeprecationWarning... | Localize salt bridges between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute salt bridges between atoms in the macromolecular complex. For each atom, localize this salt bridge in the voxel in which it originated to create a local salt bridge array. ... | SaltBridgeVoxelizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SaltBridgeVoxelizer:
"""Localize salt bridges between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute salt bridges between atoms in the macromolecular complex. For each atom, localize this salt bridge in the voxel in which it o... | stack_v2_sparse_classes_36k_train_021802 | 27,676 | permissive | [
{
"docstring": "Parameters ---------- cutoff: float, optional (default 5.0) The distance in angstroms within which atoms must be to be considered for a salt bridge between them. box_width: float, optional (default 16.0) Size of a box in which voxel features are calculated. Box is centered on a ligand centroid. ... | 2 | null | Implement the Python class `SaltBridgeVoxelizer` described below.
Class description:
Localize salt bridges between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute salt bridges between atoms in the macromolecular complex. For each atom, localize this... | Implement the Python class `SaltBridgeVoxelizer` described below.
Class description:
Localize salt bridges between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute salt bridges between atoms in the macromolecular complex. For each atom, localize this... | ee6e67ebcf7bf04259cf13aff6388e2b791fea3d | <|skeleton|>
class SaltBridgeVoxelizer:
"""Localize salt bridges between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute salt bridges between atoms in the macromolecular complex. For each atom, localize this salt bridge in the voxel in which it o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SaltBridgeVoxelizer:
"""Localize salt bridges between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute salt bridges between atoms in the macromolecular complex. For each atom, localize this salt bridge in the voxel in which it originated to ... | the_stack_v2_python_sparse | deepchem/feat/complex_featurizers/grid_featurizers.py | deepchem/deepchem | train | 4,876 |
d14f8beeb9e8e03cbbb7f62f5d364e9578d23aa4 | [
"ReconstFit.__init__(self, fiber_model, vox_data)\nself.life_matrix = life_matrix\nself.vox_coords = vox_coords\nself.fit_data = to_fit\nself.beta = beta\nself.weighted_signal = weighted_signal\nself.b0_signal = b0_signal\nself.relative_signal = relative_signal\nself.mean_signal = mean_sig\nself.streamline = stream... | <|body_start_0|>
ReconstFit.__init__(self, fiber_model, vox_data)
self.life_matrix = life_matrix
self.vox_coords = vox_coords
self.fit_data = to_fit
self.beta = beta
self.weighted_signal = weighted_signal
self.b0_signal = b0_signal
self.relative_signal = r... | A fit of the LiFE model to diffusion data | FiberFit | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FiberFit:
"""A fit of the LiFE model to diffusion data"""
def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals):
"""Parameters ---------- fiber_model : A FiberModel class inst... | stack_v2_sparse_classes_36k_train_021803 | 20,065 | permissive | [
{
"docstring": "Parameters ---------- fiber_model : A FiberModel class instance params : the parameters derived from a fit of the model to the data.",
"name": "__init__",
"signature": "def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mea... | 2 | stack_v2_sparse_classes_30k_train_018197 | Implement the Python class `FiberFit` described below.
Class description:
A fit of the LiFE model to diffusion data
Method signatures and docstrings:
- def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals): Pa... | Implement the Python class `FiberFit` described below.
Class description:
A fit of the LiFE model to diffusion data
Method signatures and docstrings:
- def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals): Pa... | 3c3acc55de8ba741e673063378e6cbaf10b64c7a | <|skeleton|>
class FiberFit:
"""A fit of the LiFE model to diffusion data"""
def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals):
"""Parameters ---------- fiber_model : A FiberModel class inst... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FiberFit:
"""A fit of the LiFE model to diffusion data"""
def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals):
"""Parameters ---------- fiber_model : A FiberModel class instance params :... | the_stack_v2_python_sparse | env/lib/python3.6/site-packages/dipy/tracking/life.py | Raniac/NEURO-LEARN | train | 9 |
3088d426fbd8143cc42f87b0b294569f20b89150 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Proto file describing the Ad Group Criterion Label service. Service to manage labels on ad group criteria. | AdGroupCriterionLabelServiceServicer | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdGroupCriterionLabelServiceServicer:
"""Proto file describing the Ad Group Criterion Label service. Service to manage labels on ad group criteria."""
def GetAdGroupCriterionLabel(self, request, context):
"""Returns the requested ad group criterion label in full detail."""
<|... | stack_v2_sparse_classes_36k_train_021804 | 3,841 | permissive | [
{
"docstring": "Returns the requested ad group criterion label in full detail.",
"name": "GetAdGroupCriterionLabel",
"signature": "def GetAdGroupCriterionLabel(self, request, context)"
},
{
"docstring": "Creates and removes ad group criterion labels. Operation statuses are returned.",
"name"... | 2 | null | Implement the Python class `AdGroupCriterionLabelServiceServicer` described below.
Class description:
Proto file describing the Ad Group Criterion Label service. Service to manage labels on ad group criteria.
Method signatures and docstrings:
- def GetAdGroupCriterionLabel(self, request, context): Returns the request... | Implement the Python class `AdGroupCriterionLabelServiceServicer` described below.
Class description:
Proto file describing the Ad Group Criterion Label service. Service to manage labels on ad group criteria.
Method signatures and docstrings:
- def GetAdGroupCriterionLabel(self, request, context): Returns the request... | 0fc8a7dbf31d9e8e2a4364df93bec5f6b7edd50a | <|skeleton|>
class AdGroupCriterionLabelServiceServicer:
"""Proto file describing the Ad Group Criterion Label service. Service to manage labels on ad group criteria."""
def GetAdGroupCriterionLabel(self, request, context):
"""Returns the requested ad group criterion label in full detail."""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdGroupCriterionLabelServiceServicer:
"""Proto file describing the Ad Group Criterion Label service. Service to manage labels on ad group criteria."""
def GetAdGroupCriterionLabel(self, request, context):
"""Returns the requested ad group criterion label in full detail."""
context.set_cod... | the_stack_v2_python_sparse | google/ads/google_ads/v2/proto/services/ad_group_criterion_label_service_pb2_grpc.py | juanmacugat/google-ads-python | train | 1 |
44462eb6d42cea46ad622054eac699a07e37c013 | [
"from m4.devices.i4d import I4D\nself._i4d = I4D(Interferometer.i4d_IP, Interferometer.i4d_port)\nself._ic = InterferometerConverter()\nself._logger = logging.getLogger('4D')",
"if nframes == 1:\n width, height, pixel_size_in_microns, data_array = self._i4d.takeSingleMeasurement()\nelse:\n data_array = np.z... | <|body_start_0|>
from m4.devices.i4d import I4D
self._i4d = I4D(Interferometer.i4d_IP, Interferometer.i4d_port)
self._ic = InterferometerConverter()
self._logger = logging.getLogger('4D')
<|end_body_0|>
<|body_start_1|>
if nframes == 1:
width, height, pixel_size_in_m... | Class for i4d 6110 interferometer | I4d6110 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class I4d6110:
"""Class for i4d 6110 interferometer"""
def __init__(self):
"""The constructor"""
<|body_0|>
def acquire_phasemap(self, nframes=1, show=0):
"""Parameters ---------- nframes: int number of frames show: int 0 to not show the image Returns ------- masked_im... | stack_v2_sparse_classes_36k_train_021805 | 4,915 | no_license | [
{
"docstring": "The constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Parameters ---------- nframes: int number of frames show: int 0 to not show the image Returns ------- masked_ima: numpy masked array interferometer image",
"name": "acquire_phasemap",
... | 3 | null | Implement the Python class `I4d6110` described below.
Class description:
Class for i4d 6110 interferometer
Method signatures and docstrings:
- def __init__(self): The constructor
- def acquire_phasemap(self, nframes=1, show=0): Parameters ---------- nframes: int number of frames show: int 0 to not show the image Retu... | Implement the Python class `I4d6110` described below.
Class description:
Class for i4d 6110 interferometer
Method signatures and docstrings:
- def __init__(self): The constructor
- def acquire_phasemap(self, nframes=1, show=0): Parameters ---------- nframes: int number of frames show: int 0 to not show the image Retu... | cfb3757cc491199248dba767ddf47dce9b191261 | <|skeleton|>
class I4d6110:
"""Class for i4d 6110 interferometer"""
def __init__(self):
"""The constructor"""
<|body_0|>
def acquire_phasemap(self, nframes=1, show=0):
"""Parameters ---------- nframes: int number of frames show: int 0 to not show the image Returns ------- masked_im... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class I4d6110:
"""Class for i4d 6110 interferometer"""
def __init__(self):
"""The constructor"""
from m4.devices.i4d import I4D
self._i4d = I4D(Interferometer.i4d_IP, Interferometer.i4d_port)
self._ic = InterferometerConverter()
self._logger = logging.getLogger('4D')
... | the_stack_v2_python_sparse | m4/devices/interferometer.py | alfiopuglisi/M4 | train | 0 |
e8fe9e7e0ad0442326e31da4469c1a303711a6f1 | [
"max_sub_sum = max(nums)\nfor i in range(len(nums)):\n tmp = nums[i]\n for j in range(i + 1, len(nums)):\n if tmp + nums[j] > 0:\n tmp += nums[j]\n if max_sub_sum < tmp:\n max_sub_sum = tmp\n else:\n break\nreturn max_sub_sum",
"if not nums:\n ... | <|body_start_0|>
max_sub_sum = max(nums)
for i in range(len(nums)):
tmp = nums[i]
for j in range(i + 1, len(nums)):
if tmp + nums[j] > 0:
tmp += nums[j]
if max_sub_sum < tmp:
max_sub_sum = tmp
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxSubArray(self, nums: list):
"""时间复杂度为O(n^2) :param nums: :return:"""
<|body_0|>
def maxSubArray1(self, nums: list):
"""动态规划，时间复杂度为O(n) :param nums: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
max_sub_sum = max(nums)
... | stack_v2_sparse_classes_36k_train_021806 | 1,219 | no_license | [
{
"docstring": "时间复杂度为O(n^2) :param nums: :return:",
"name": "maxSubArray",
"signature": "def maxSubArray(self, nums: list)"
},
{
"docstring": "动态规划，时间复杂度为O(n) :param nums: :return:",
"name": "maxSubArray1",
"signature": "def maxSubArray1(self, nums: list)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002954 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSubArray(self, nums: list): 时间复杂度为O(n^2) :param nums: :return:
- def maxSubArray1(self, nums: list): 动态规划，时间复杂度为O(n) :param nums: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSubArray(self, nums: list): 时间复杂度为O(n^2) :param nums: :return:
- def maxSubArray1(self, nums: list): 动态规划，时间复杂度为O(n) :param nums: :return:
<|skeleton|>
class Solution:
... | 5f67368e72c376c1299b849e7a92e6d0cbd9ae55 | <|skeleton|>
class Solution:
def maxSubArray(self, nums: list):
"""时间复杂度为O(n^2) :param nums: :return:"""
<|body_0|>
def maxSubArray1(self, nums: list):
"""动态规划，时间复杂度为O(n) :param nums: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxSubArray(self, nums: list):
"""时间复杂度为O(n^2) :param nums: :return:"""
max_sub_sum = max(nums)
for i in range(len(nums)):
tmp = nums[i]
for j in range(i + 1, len(nums)):
if tmp + nums[j] > 0:
tmp += nums[j]
... | the_stack_v2_python_sparse | 53-最大子序和/solution.py | BillyChao/leetcode | train | 5 | |
fe13420446b89d6d86399e9f1086d0ac6bcfa080 | [
"packagestr = request.data\nif packagestr['path'][-1] != '/':\n packagestr['path'] = packagestr['path'] + '/'\npackage_dir = settings.PACKAGE_DIR\nif package_dir[-1] == '/':\n package_dir = package_dir[:-1]\npackagestr['path'] = package_dir + packagestr['path']\ncmd = 'rpm -ivh ' + packagestr['path'] + packag... | <|body_start_0|>
packagestr = request.data
if packagestr['path'][-1] != '/':
packagestr['path'] = packagestr['path'] + '/'
package_dir = settings.PACKAGE_DIR
if package_dir[-1] == '/':
package_dir = package_dir[:-1]
packagestr['path'] = package_dir + packa... | install packages,uninstall packages ['post', 'delete'] | packages | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class packages:
"""install packages,uninstall packages ['post', 'delete']"""
def post(self, request, format=None):
"""install packages"""
<|body_0|>
def delete(self, request, format=None):
"""uninstall packages"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_021807 | 7,576 | no_license | [
{
"docstring": "install packages",
"name": "post",
"signature": "def post(self, request, format=None)"
},
{
"docstring": "uninstall packages",
"name": "delete",
"signature": "def delete(self, request, format=None)"
}
] | 2 | null | Implement the Python class `packages` described below.
Class description:
install packages,uninstall packages ['post', 'delete']
Method signatures and docstrings:
- def post(self, request, format=None): install packages
- def delete(self, request, format=None): uninstall packages | Implement the Python class `packages` described below.
Class description:
install packages,uninstall packages ['post', 'delete']
Method signatures and docstrings:
- def post(self, request, format=None): install packages
- def delete(self, request, format=None): uninstall packages
<|skeleton|>
class packages:
"""... | 7f801a569a396a27371d0831752595877c224a6b | <|skeleton|>
class packages:
"""install packages,uninstall packages ['post', 'delete']"""
def post(self, request, format=None):
"""install packages"""
<|body_0|>
def delete(self, request, format=None):
"""uninstall packages"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class packages:
"""install packages,uninstall packages ['post', 'delete']"""
def post(self, request, format=None):
"""install packages"""
packagestr = request.data
if packagestr['path'][-1] != '/':
packagestr['path'] = packagestr['path'] + '/'
package_dir = settings.... | the_stack_v2_python_sparse | Python_projects/flask_projects/unicorn_project/packages/views.py | sdtimothy8/Coding | train | 0 |
bbd805c106c412cf5e124b5ba0b85fa21a782357 | [
"pointer_a, pointer_b = (headA, headB)\nwhile pointer_a is not pointer_b:\n pointer_a = headB if pointer_a is None else pointer_a.next\n pointer_b = headA if pointer_b is None else pointer_b.next\nreturn pointer_a",
"if headA is None or headB is None:\n return None\na, b = (headA, headB)\ncntA, cntB = (1... | <|body_start_0|>
pointer_a, pointer_b = (headA, headB)
while pointer_a is not pointer_b:
pointer_a = headB if pointer_a is None else pointer_a.next
pointer_b = headA if pointer_b is None else pointer_b.next
return pointer_a
<|end_body_0|>
<|body_start_1|>
if head... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_0|>
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_36k_train_021808 | 2,275 | no_license | [
{
"docstring": ":type head1, head1: ListNode :rtype: ListNode",
"name": "getIntersectionNode",
"signature": "def getIntersectionNode(self, headA, headB)"
},
{
"docstring": ":type head1, head1: ListNode :rtype: ListNode",
"name": "getIntersectionNode",
"signature": "def getIntersectionNod... | 2 | stack_v2_sparse_classes_30k_train_006662 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getIntersectionNode(self, headA, headB): :type head1, head1: ListNode :rtype: ListNode
- def getIntersectionNode(self, headA, headB): :type head1, head1: ListNode :rtype: Lis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getIntersectionNode(self, headA, headB): :type head1, head1: ListNode :rtype: ListNode
- def getIntersectionNode(self, headA, headB): :type head1, head1: ListNode :rtype: Lis... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_0|>
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
pointer_a, pointer_b = (headA, headB)
while pointer_a is not pointer_b:
pointer_a = headB if pointer_a is None else pointer_a.next
pointer_b = headA if poi... | the_stack_v2_python_sparse | code160IntersectionOfTwoLinkedLists.py | cybelewang/leetcode-python | train | 0 | |
98159f369cfe107735b777c0541331cf421af48a | [
"if len(temp) == k:\n result.append(temp[:])\n return\ni = num\nwhile i <= n:\n temp.append(i)\n self.search_num(n, k, i + 1, result, temp)\n temp.pop()\n i += 1",
"result = []\ntemp = []\nself.search_num(n, k, 1, result, temp)\nreturn result",
"result = []\ntemp = []\nself.search_helper(n, k,... | <|body_start_0|>
if len(temp) == k:
result.append(temp[:])
return
i = num
while i <= n:
temp.append(i)
self.search_num(n, k, i + 1, result, temp)
temp.pop()
i += 1
<|end_body_0|>
<|body_start_1|>
result = []
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def search_num(self, n: int, k: int, num: int, result: List[List[int]], temp: List[int]) -> None:
"""深度优先遍历 Args: n: 1-n之间的数 k: 选择k个数 num:当前数字 result:结果链表 temp:当前链表 Returns: 结果链表"""
<|body_0|>
def combine(self, n: int, k: int) -> List[List[int]]:
"""数组排列组合 ... | stack_v2_sparse_classes_36k_train_021809 | 3,290 | permissive | [
{
"docstring": "深度优先遍历 Args: n: 1-n之间的数 k: 选择k个数 num:当前数字 result:结果链表 temp:当前链表 Returns: 结果链表",
"name": "search_num",
"signature": "def search_num(self, n: int, k: int, num: int, result: List[List[int]], temp: List[int]) -> None"
},
{
"docstring": "数组排列组合 Args: n: 1-n之间的数 k: 选择k个数 Returns: 排列组合之... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search_num(self, n: int, k: int, num: int, result: List[List[int]], temp: List[int]) -> None: 深度优先遍历 Args: n: 1-n之间的数 k: 选择k个数 num:当前数字 result:结果链表 temp:当前链表 Returns: 结果链表
- ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search_num(self, n: int, k: int, num: int, result: List[List[int]], temp: List[int]) -> None: 深度优先遍历 Args: n: 1-n之间的数 k: 选择k个数 num:当前数字 result:结果链表 temp:当前链表 Returns: 结果链表
- ... | 50f35eef6a0ad63173efed10df3c835b1dceaa3f | <|skeleton|>
class Solution:
def search_num(self, n: int, k: int, num: int, result: List[List[int]], temp: List[int]) -> None:
"""深度优先遍历 Args: n: 1-n之间的数 k: 选择k个数 num:当前数字 result:结果链表 temp:当前链表 Returns: 结果链表"""
<|body_0|>
def combine(self, n: int, k: int) -> List[List[int]]:
"""数组排列组合 ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def search_num(self, n: int, k: int, num: int, result: List[List[int]], temp: List[int]) -> None:
"""深度优先遍历 Args: n: 1-n之间的数 k: 选择k个数 num:当前数字 result:结果链表 temp:当前链表 Returns: 结果链表"""
if len(temp) == k:
result.append(temp[:])
return
i = num
while... | the_stack_v2_python_sparse | src/leetcodepython/array/combinations_77.py | zhangyu345293721/leetcode | train | 101 | |
e204ca8a0b9f42541fa54ca452229e35b41a5c13 | [
"return_url = post.pop('return_url', '')\nif not return_url:\n return_url = '/shop/payment/validate/'\nreturn return_url",
"res = False\nreference = post['orderid']\nif reference:\n _logger.info('bambora: validated data')\n res = request.env['payment.transaction'].sudo().form_feedback(post, 'bambora_vkda... | <|body_start_0|>
return_url = post.pop('return_url', '')
if not return_url:
return_url = '/shop/payment/validate/'
return return_url
<|end_body_0|>
<|body_start_1|>
res = False
reference = post['orderid']
if reference:
_logger.info('bambora: valid... | Handles the redirection back from payment gateway to merchant site | BamboraController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BamboraController:
"""Handles the redirection back from payment gateway to merchant site"""
def _get_return_url(self, **post):
"""Extract the return URL from the data coming from bambora."""
<|body_0|>
def bambora_validate_data(self, **post):
"""Validate the data... | stack_v2_sparse_classes_36k_train_021810 | 2,006 | no_license | [
{
"docstring": "Extract the return URL from the data coming from bambora.",
"name": "_get_return_url",
"signature": "def _get_return_url(self, **post)"
},
{
"docstring": "Validate the data coming from bambora.",
"name": "bambora_validate_data",
"signature": "def bambora_validate_data(sel... | 4 | null | Implement the Python class `BamboraController` described below.
Class description:
Handles the redirection back from payment gateway to merchant site
Method signatures and docstrings:
- def _get_return_url(self, **post): Extract the return URL from the data coming from bambora.
- def bambora_validate_data(self, **pos... | Implement the Python class `BamboraController` described below.
Class description:
Handles the redirection back from payment gateway to merchant site
Method signatures and docstrings:
- def _get_return_url(self, **post): Extract the return URL from the data coming from bambora.
- def bambora_validate_data(self, **pos... | 0ed19a2d40a5a9de44e3247cca56211c65e6c63a | <|skeleton|>
class BamboraController:
"""Handles the redirection back from payment gateway to merchant site"""
def _get_return_url(self, **post):
"""Extract the return URL from the data coming from bambora."""
<|body_0|>
def bambora_validate_data(self, **post):
"""Validate the data... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BamboraController:
"""Handles the redirection back from payment gateway to merchant site"""
def _get_return_url(self, **post):
"""Extract the return URL from the data coming from bambora."""
return_url = post.pop('return_url', '')
if not return_url:
return_url = '/shop... | the_stack_v2_python_sparse | payment_bambora_vkdata/controllers/main.py | tate11/vkd-odoo-sh | train | 0 |
337dc67ed4620a488f66b001d77dd9753dde6486 | [
"try:\n activity = request.json\n (services.log_service().upsert_activity(activity), 201)\nexcept Exception as e:\n nsp.abort(500, 'An internal error has occurred: {}'.format(e))",
"try:\n activity = request.json\n (services.log_service().upsert_activity(activity), 204)\nexcept Exception as e:\n ... | <|body_start_0|>
try:
activity = request.json
(services.log_service().upsert_activity(activity), 201)
except Exception as e:
nsp.abort(500, 'An internal error has occurred: {}'.format(e))
<|end_body_0|>
<|body_start_1|>
try:
activity = request.jso... | Activity | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Activity:
def post(self):
"""Insert a new activity log"""
<|body_0|>
def put(self):
"""Update an activity object by it's id."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
activity = request.json
(services.log_service()... | stack_v2_sparse_classes_36k_train_021811 | 4,427 | no_license | [
{
"docstring": "Insert a new activity log",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Update an activity object by it's id.",
"name": "put",
"signature": "def put(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002633 | Implement the Python class `Activity` described below.
Class description:
Implement the Activity class.
Method signatures and docstrings:
- def post(self): Insert a new activity log
- def put(self): Update an activity object by it's id. | Implement the Python class `Activity` described below.
Class description:
Implement the Activity class.
Method signatures and docstrings:
- def post(self): Insert a new activity log
- def put(self): Update an activity object by it's id.
<|skeleton|>
class Activity:
def post(self):
"""Insert a new activi... | df826cf7098aee59e0a1ced6f465c2e8bb3df9a5 | <|skeleton|>
class Activity:
def post(self):
"""Insert a new activity log"""
<|body_0|>
def put(self):
"""Update an activity object by it's id."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Activity:
def post(self):
"""Insert a new activity log"""
try:
activity = request.json
(services.log_service().upsert_activity(activity), 201)
except Exception as e:
nsp.abort(500, 'An internal error has occurred: {}'.format(e))
def put(self):
... | the_stack_v2_python_sparse | patient_portal/patient_portal/api/logs.py | bkh148/patient-cloud | train | 0 | |
6d7dc80a330fe276c6b2d5583c475d55b72b2bb4 | [
"digits = []\ndec = 10\nwhile n > 0:\n d = n % dec\n digits.append(d)\n n = (n - d) // dec\nm = len(digits)\ni = 1\nwhile i < m:\n if digits[i] < digits[i - 1]:\n break\n i += 1\nif i == m:\n return -1\nj = 0\nwhile j <= i:\n if digits[j] > digits[i]:\n break\n j += 1\ndigits[i... | <|body_start_0|>
digits = []
dec = 10
while n > 0:
d = n % dec
digits.append(d)
n = (n - d) // dec
m = len(digits)
i = 1
while i < m:
if digits[i] < digits[i - 1]:
break
i += 1
if i == m:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def nextGreaterElement(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def nextGreaterElementStr(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
digits = []
dec = 10
while n > 0:
... | stack_v2_sparse_classes_36k_train_021812 | 2,345 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "nextGreaterElement",
"signature": "def nextGreaterElement(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "nextGreaterElementStr",
"signature": "def nextGreaterElementStr(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010077 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextGreaterElement(self, n): :type n: int :rtype: int
- def nextGreaterElementStr(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextGreaterElement(self, n): :type n: int :rtype: int
- def nextGreaterElementStr(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def nextGreaterElement... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def nextGreaterElement(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def nextGreaterElementStr(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def nextGreaterElement(self, n):
""":type n: int :rtype: int"""
digits = []
dec = 10
while n > 0:
d = n % dec
digits.append(d)
n = (n - d) // dec
m = len(digits)
i = 1
while i < m:
if digits[i] < ... | the_stack_v2_python_sparse | N/NextGreaterElementIII.py | bssrdf/pyleet | train | 2 | |
61c4a7646c8cc0c477a84dc3446ee4e6df7d488e | [
"try:\n response = super(APIBaseView, self).dispatch_request(request, *args, **kwargs)\n if isawaitable(response):\n response = await response\nexcept Exception as exception:\n response = await self.handle_exception(exception)\nreturn response",
"if isinstance(exception, ValidationError):\n res... | <|body_start_0|>
try:
response = super(APIBaseView, self).dispatch_request(request, *args, **kwargs)
if isawaitable(response):
response = await response
except Exception as exception:
response = await self.handle_exception(exception)
return res... | 扩展 class based view, 增加异常处理 | APIBaseView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class APIBaseView:
"""扩展 class based view, 增加异常处理"""
async def dispatch_request(self, request, *args, **kwargs):
"""扩展 http 请求的分发, 添加错误处理"""
<|body_0|>
async def handle_exception(self, exception):
"""处理异常 ValidationError, APIException: 返回适当的错误信息 else: 重新抛出异常"""
... | stack_v2_sparse_classes_36k_train_021813 | 3,695 | no_license | [
{
"docstring": "扩展 http 请求的分发, 添加错误处理",
"name": "dispatch_request",
"signature": "async def dispatch_request(self, request, *args, **kwargs)"
},
{
"docstring": "处理异常 ValidationError, APIException: 返回适当的错误信息 else: 重新抛出异常",
"name": "handle_exception",
"signature": "async def handle_excepti... | 2 | stack_v2_sparse_classes_30k_train_009872 | Implement the Python class `APIBaseView` described below.
Class description:
扩展 class based view, 增加异常处理
Method signatures and docstrings:
- async def dispatch_request(self, request, *args, **kwargs): 扩展 http 请求的分发, 添加错误处理
- async def handle_exception(self, exception): 处理异常 ValidationError, APIException: 返回适当的错误信息 el... | Implement the Python class `APIBaseView` described below.
Class description:
扩展 class based view, 增加异常处理
Method signatures and docstrings:
- async def dispatch_request(self, request, *args, **kwargs): 扩展 http 请求的分发, 添加错误处理
- async def handle_exception(self, exception): 处理异常 ValidationError, APIException: 返回适当的错误信息 el... | 8b8a0684de8e7fcdf0c229b05816cf7cbd5909f2 | <|skeleton|>
class APIBaseView:
"""扩展 class based view, 增加异常处理"""
async def dispatch_request(self, request, *args, **kwargs):
"""扩展 http 请求的分发, 添加错误处理"""
<|body_0|>
async def handle_exception(self, exception):
"""处理异常 ValidationError, APIException: 返回适当的错误信息 else: 重新抛出异常"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class APIBaseView:
"""扩展 class based view, 增加异常处理"""
async def dispatch_request(self, request, *args, **kwargs):
"""扩展 http 请求的分发, 添加错误处理"""
try:
response = super(APIBaseView, self).dispatch_request(request, *args, **kwargs)
if isawaitable(response):
resp... | the_stack_v2_python_sparse | src/views/utils.py | Kingvast/qrcode_web | train | 1 |
94738d0dd4bd733307056b3e8f31ca059734579b | [
"nr_rows, _ = data_frame.shape\nif not nr_rows:\n return '*<empty table>*'\nif nr_rows <= self._DATAFRAM_HEADER_ROWS + self._DATAFRAM_TAIL_ROWS:\n return tabulate.tabulate(data_frame, tablefmt='pipe', headers='keys')\nreturn_lines = []\nreturn_lines.append(tabulate.tabulate(data_frame[:self._DATAFRAM_HEADER_R... | <|body_start_0|>
nr_rows, _ = data_frame.shape
if not nr_rows:
return '*<empty table>*'
if nr_rows <= self._DATAFRAM_HEADER_ROWS + self._DATAFRAM_TAIL_ROWS:
return tabulate.tabulate(data_frame, tablefmt='pipe', headers='keys')
return_lines = []
return_line... | Markdown story exporter. | MarkdownStoryExporter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MarkdownStoryExporter:
"""Markdown story exporter."""
def _dataframe_to_markdown(self, data_frame):
"""Returns a markdown formatted string from a pandas DataFrame."""
<|body_0|>
def export_story(self):
"""Export the story as a markdown."""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k_train_021814 | 3,234 | permissive | [
{
"docstring": "Returns a markdown formatted string from a pandas DataFrame.",
"name": "_dataframe_to_markdown",
"signature": "def _dataframe_to_markdown(self, data_frame)"
},
{
"docstring": "Export the story as a markdown.",
"name": "export_story",
"signature": "def export_story(self)"
... | 2 | stack_v2_sparse_classes_30k_train_005638 | Implement the Python class `MarkdownStoryExporter` described below.
Class description:
Markdown story exporter.
Method signatures and docstrings:
- def _dataframe_to_markdown(self, data_frame): Returns a markdown formatted string from a pandas DataFrame.
- def export_story(self): Export the story as a markdown. | Implement the Python class `MarkdownStoryExporter` described below.
Class description:
Markdown story exporter.
Method signatures and docstrings:
- def _dataframe_to_markdown(self, data_frame): Returns a markdown formatted string from a pandas DataFrame.
- def export_story(self): Export the story as a markdown.
<|sk... | 24f471b58ca4a87cb053961b5f05c07a544ca7b8 | <|skeleton|>
class MarkdownStoryExporter:
"""Markdown story exporter."""
def _dataframe_to_markdown(self, data_frame):
"""Returns a markdown formatted string from a pandas DataFrame."""
<|body_0|>
def export_story(self):
"""Export the story as a markdown."""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MarkdownStoryExporter:
"""Markdown story exporter."""
def _dataframe_to_markdown(self, data_frame):
"""Returns a markdown formatted string from a pandas DataFrame."""
nr_rows, _ = data_frame.shape
if not nr_rows:
return '*<empty table>*'
if nr_rows <= self._DAT... | the_stack_v2_python_sparse | timesketch/lib/stories/markdown.py | google/timesketch | train | 2,263 |
6d3c7468846a6671d70aaa25304680bec4242f78 | [
"super(EncoderBlock, self).__init__()\nself.mha = MultiHeadAttention(dm, h)\nself.dense_hidden = tf.keras.layers.Dense(hidden, activation='relu')\nself.dense_output = tf.keras.layers.Dense(dm)\nself.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)\nself.layernorm2 = tf.keras.layers.LayerNormalization(... | <|body_start_0|>
super(EncoderBlock, self).__init__()
self.mha = MultiHeadAttention(dm, h)
self.dense_hidden = tf.keras.layers.Dense(hidden, activation='relu')
self.dense_output = tf.keras.layers.Dense(dm)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
... | create an encoder block for a transformer | EncoderBlock | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncoderBlock:
"""create an encoder block for a transformer"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""Class constructor"""
<|body_0|>
def call(self, x, training, mask=None):
"""Public instance method"""
<|body_1|>
<|end_skeleton|>
<|body_st... | stack_v2_sparse_classes_36k_train_021815 | 1,459 | no_license | [
{
"docstring": "Class constructor",
"name": "__init__",
"signature": "def __init__(self, dm, h, hidden, drop_rate=0.1)"
},
{
"docstring": "Public instance method",
"name": "call",
"signature": "def call(self, x, training, mask=None)"
}
] | 2 | null | Implement the Python class `EncoderBlock` described below.
Class description:
create an encoder block for a transformer
Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): Class constructor
- def call(self, x, training, mask=None): Public instance method | Implement the Python class `EncoderBlock` described below.
Class description:
create an encoder block for a transformer
Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): Class constructor
- def call(self, x, training, mask=None): Public instance method
<|skeleton|>
class EncoderBl... | c23deee331a71a089197547fcae4c1eefb8d24ef | <|skeleton|>
class EncoderBlock:
"""create an encoder block for a transformer"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""Class constructor"""
<|body_0|>
def call(self, x, training, mask=None):
"""Public instance method"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncoderBlock:
"""create an encoder block for a transformer"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""Class constructor"""
super(EncoderBlock, self).__init__()
self.mha = MultiHeadAttention(dm, h)
self.dense_hidden = tf.keras.layers.Dense(hidden, activation='r... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/7-transformer_encoder_block.py | YosriGFX/holbertonschool-machine_learning | train | 0 |
ebca861fe181c2e8074734923c1a73988444313f | [
"if config is None:\n config = KubernetesDagRunnerConfig()\nsuper().__init__(config)",
"if not pipeline.pipeline_info.run_id:\n pipeline.pipeline_info.run_id = datetime.datetime.now().isoformat()\nif not kube_utils.is_inside_cluster():\n kubernetes_remote_runner.run_as_kubernetes_job(pipeline=pipeline, t... | <|body_start_0|>
if config is None:
config = KubernetesDagRunnerConfig()
super().__init__(config)
<|end_body_0|>
<|body_start_1|>
if not pipeline.pipeline_info.run_id:
pipeline.pipeline_info.run_id = datetime.datetime.now().isoformat()
if not kube_utils.is_inside... | TFX runner on Kubernetes. | KubernetesDagRunner | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KubernetesDagRunner:
"""TFX runner on Kubernetes."""
def __init__(self, config: Optional[KubernetesDagRunnerConfig]=None):
"""Initializes KubernetesDagRunner as a TFX orchestrator. Args: config: Optional pipeline config for customizing the launching of each component. Defaults to pip... | stack_v2_sparse_classes_36k_train_021816 | 10,406 | permissive | [
{
"docstring": "Initializes KubernetesDagRunner as a TFX orchestrator. Args: config: Optional pipeline config for customizing the launching of each component. Defaults to pipeline config that supports InProcessComponentLauncher and KubernetesComponentLauncher.",
"name": "__init__",
"signature": "def __i... | 3 | null | Implement the Python class `KubernetesDagRunner` described below.
Class description:
TFX runner on Kubernetes.
Method signatures and docstrings:
- def __init__(self, config: Optional[KubernetesDagRunnerConfig]=None): Initializes KubernetesDagRunner as a TFX orchestrator. Args: config: Optional pipeline config for cus... | Implement the Python class `KubernetesDagRunner` described below.
Class description:
TFX runner on Kubernetes.
Method signatures and docstrings:
- def __init__(self, config: Optional[KubernetesDagRunnerConfig]=None): Initializes KubernetesDagRunner as a TFX orchestrator. Args: config: Optional pipeline config for cus... | 1b328504fa08a70388691e4072df76f143631325 | <|skeleton|>
class KubernetesDagRunner:
"""TFX runner on Kubernetes."""
def __init__(self, config: Optional[KubernetesDagRunnerConfig]=None):
"""Initializes KubernetesDagRunner as a TFX orchestrator. Args: config: Optional pipeline config for customizing the launching of each component. Defaults to pip... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KubernetesDagRunner:
"""TFX runner on Kubernetes."""
def __init__(self, config: Optional[KubernetesDagRunnerConfig]=None):
"""Initializes KubernetesDagRunner as a TFX orchestrator. Args: config: Optional pipeline config for customizing the launching of each component. Defaults to pipeline config ... | the_stack_v2_python_sparse | tfx/orchestration/experimental/kubernetes/kubernetes_dag_runner.py | tensorflow/tfx | train | 2,116 |
571151cd5cd6fd19018954fa1d525e5b8ed3e358 | [
"self.data_type = data[:4]\nself.data_type = struct.unpack('<I', str(self.data_type))[0]\nself.unknown = data[4:8]\nself.length = data[8:12]\nself.length = struct.unpack('<I', str(self.length))[0]\nself.length2 = data[12:16]\nself.length2 = struct.unpack('<I', str(self.length2))[0]\nself.segment_length = 16 + self.... | <|body_start_0|>
self.data_type = data[:4]
self.data_type = struct.unpack('<I', str(self.data_type))[0]
self.unknown = data[4:8]
self.length = data[8:12]
self.length = struct.unpack('<I', str(self.length))[0]
self.length2 = data[12:16]
self.length2 = struct.unpack... | Represents the header and content of a zeus message segment. | ZeusSegment | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZeusSegment:
"""Represents the header and content of a zeus message segment."""
def __init__(self, data):
"""The initializer. :type data: bytearray :param data:"""
<|body_0|>
def extract_command(self):
"""Extracts command data from segment. :raise RuntimeError: i... | stack_v2_sparse_classes_36k_train_021817 | 30,437 | no_license | [
{
"docstring": "The initializer. :type data: bytearray :param data:",
"name": "__init__",
"signature": "def __init__(self, data)"
},
{
"docstring": "Extracts command data from segment. :raise RuntimeError: if segment is not of type 1 (command) :return: string containing the command :rtype: str",... | 2 | stack_v2_sparse_classes_30k_train_021581 | Implement the Python class `ZeusSegment` described below.
Class description:
Represents the header and content of a zeus message segment.
Method signatures and docstrings:
- def __init__(self, data): The initializer. :type data: bytearray :param data:
- def extract_command(self): Extracts command data from segment. :... | Implement the Python class `ZeusSegment` described below.
Class description:
Represents the header and content of a zeus message segment.
Method signatures and docstrings:
- def __init__(self, data): The initializer. :type data: bytearray :param data:
- def extract_command(self): Extracts command data from segment. :... | 925ff53eb0c7a750ae784e3a2c059ed5e8b140e3 | <|skeleton|>
class ZeusSegment:
"""Represents the header and content of a zeus message segment."""
def __init__(self, data):
"""The initializer. :type data: bytearray :param data:"""
<|body_0|>
def extract_command(self):
"""Extracts command data from segment. :raise RuntimeError: i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ZeusSegment:
"""Represents the header and content of a zeus message segment."""
def __init__(self, data):
"""The initializer. :type data: bytearray :param data:"""
self.data_type = data[:4]
self.data_type = struct.unpack('<I', str(self.data_type))[0]
self.unknown = data[4:... | the_stack_v2_python_sparse | src/hystck/botnet/bots/zeus/zeus_generators.py | dasec/hystck | train | 5 |
82f69f582f649481e31305520662e70d778e4107 | [
"if not head:\n return head\ndummyHead = Node(None, None, head, None)\nself.flattenDFS(dummyHead, head)\ndummyHead.next.prev = None\nreturn dummyHead.next",
"if not current:\n return previous\ncurrent.prev = previous\nprevious.next = current\ntempNext = current.next\ncurrentTail = self.flattenDFS(current, c... | <|body_start_0|>
if not head:
return head
dummyHead = Node(None, None, head, None)
self.flattenDFS(dummyHead, head)
dummyHead.next.prev = None
return dummyHead.next
<|end_body_0|>
<|body_start_1|>
if not current:
return previous
current.pr... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def flatten(self, head):
""":type head: Node :rtype: Node"""
<|body_0|>
def flattenDFS(self, previous, current):
""":type head: Node :rtype: Node"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not head:
return head
... | stack_v2_sparse_classes_36k_train_021818 | 1,645 | permissive | [
{
"docstring": ":type head: Node :rtype: Node",
"name": "flatten",
"signature": "def flatten(self, head)"
},
{
"docstring": ":type head: Node :rtype: Node",
"name": "flattenDFS",
"signature": "def flattenDFS(self, previous, current)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flatten(self, head): :type head: Node :rtype: Node
- def flattenDFS(self, previous, current): :type head: Node :rtype: Node | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flatten(self, head): :type head: Node :rtype: Node
- def flattenDFS(self, previous, current): :type head: Node :rtype: Node
<|skeleton|>
class Solution:
def flatten(sel... | 20ae1a048eddbc9a32c819cf61258e2b57572f05 | <|skeleton|>
class Solution:
def flatten(self, head):
""":type head: Node :rtype: Node"""
<|body_0|>
def flattenDFS(self, previous, current):
""":type head: Node :rtype: Node"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def flatten(self, head):
""":type head: Node :rtype: Node"""
if not head:
return head
dummyHead = Node(None, None, head, None)
self.flattenDFS(dummyHead, head)
dummyHead.next.prev = None
return dummyHead.next
def flattenDFS(self, previ... | the_stack_v2_python_sparse | leetcode.com/python/430_Flatten_a_Multilevel_Doubly_Linked_List.py | partho-maple/coding-interview-gym | train | 862 | |
c75186ad1a92476048421c4b15c6f9ad0292734f | [
"if not load_data:\n return\ndata_paths = ['app_data', '..app_data']\nadp = None\nfor data_path in data_paths:\n if os.path.exists(os.path.join(os.curdir, data_path)):\n adp = os.path.join(os.curdir, data_path)\nif not adp:\n _logger.error('app data path not found.')\n return\nfiles = glob(os.pat... | <|body_start_0|>
if not load_data:
return
data_paths = ['app_data', '..app_data']
adp = None
for data_path in data_paths:
if os.path.exists(os.path.join(os.curdir, data_path)):
adp = os.path.join(os.curdir, data_path)
if not adp:
... | base data generator object. | BaseGen | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseGen:
"""base data generator object."""
def __init__(self, load_data=True):
"""Initialize the QuestionnaireGen object. Try to re-use to save on loading files. :param load_data: load data from app_data directory"""
<|body_0|>
def update(self, resp):
"""Update t... | stack_v2_sparse_classes_36k_train_021819 | 2,060 | permissive | [
{
"docstring": "Initialize the QuestionnaireGen object. Try to re-use to save on loading files. :param load_data: load data from app_data directory",
"name": "__init__",
"signature": "def __init__(self, load_data=True)"
},
{
"docstring": "Update this object with response data :param resp: reques... | 2 | stack_v2_sparse_classes_30k_train_015092 | Implement the Python class `BaseGen` described below.
Class description:
base data generator object.
Method signatures and docstrings:
- def __init__(self, load_data=True): Initialize the QuestionnaireGen object. Try to re-use to save on loading files. :param load_data: load data from app_data directory
- def update(... | Implement the Python class `BaseGen` described below.
Class description:
base data generator object.
Method signatures and docstrings:
- def __init__(self, load_data=True): Initialize the QuestionnaireGen object. Try to re-use to save on loading files. :param load_data: load data from app_data directory
- def update(... | 461ae46aeda21d54de8a91aa5ef677676d5db541 | <|skeleton|>
class BaseGen:
"""base data generator object."""
def __init__(self, load_data=True):
"""Initialize the QuestionnaireGen object. Try to re-use to save on loading files. :param load_data: load data from app_data directory"""
<|body_0|>
def update(self, resp):
"""Update t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseGen:
"""base data generator object."""
def __init__(self, load_data=True):
"""Initialize the QuestionnaireGen object. Try to re-use to save on loading files. :param load_data: load data from app_data directory"""
if not load_data:
return
data_paths = ['app_data', '... | the_stack_v2_python_sparse | rdr_service/data_gen/generators/base_gen.py | all-of-us/raw-data-repository | train | 46 |
07d89e0c7e4ecdc6c9b8f613ab3d4abccd19f745 | [
"m = self.CMD_RE.match(cmd_key)\nif m:\n return int(m.group(1))\nelse:\n return 0",
"self.uninstall(connector_comment)\ndb = registry.get_service(registry.SERVICE_DB_INTERFACE)\nconn = db.get_connection()\ncursor = conn.cursor()\ndb.select(cursor, ['*'], 'monsetting', where=\"$name = 'snmpmon' AND $key LIKE... | <|body_start_0|>
m = self.CMD_RE.match(cmd_key)
if m:
return int(m.group(1))
else:
return 0
<|end_body_0|>
<|body_start_1|>
self.uninstall(connector_comment)
db = registry.get_service(registry.SERVICE_DB_INTERFACE)
conn = db.get_connection()
... | This class is responsible for installing and uninstalling the TEAL SNMP connector support | SNMPConnectorInstaller | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SNMPConnectorInstaller:
"""This class is responsible for installing and uninstalling the TEAL SNMP connector support"""
def _get_cmd_num(self, cmd_key):
"""Get the command number from the cmds monsetting entry If the cmds field is incorrectly formatted, 0 is returned"""
<|bod... | stack_v2_sparse_classes_36k_train_021820 | 3,969 | no_license | [
{
"docstring": "Get the command number from the cmds monsetting entry If the cmds field is incorrectly formatted, 0 is returned",
"name": "_get_cmd_num",
"signature": "def _get_cmd_num(self, cmd_key)"
},
{
"docstring": "Install the SNMP connectors into the xCAT monsetting table",
"name": "in... | 4 | null | Implement the Python class `SNMPConnectorInstaller` described below.
Class description:
This class is responsible for installing and uninstalling the TEAL SNMP connector support
Method signatures and docstrings:
- def _get_cmd_num(self, cmd_key): Get the command number from the cmds monsetting entry If the cmds field... | Implement the Python class `SNMPConnectorInstaller` described below.
Class description:
This class is responsible for installing and uninstalling the TEAL SNMP connector support
Method signatures and docstrings:
- def _get_cmd_num(self, cmd_key): Get the command number from the cmds monsetting entry If the cmds field... | eba6c1489b503fdcf040a126942643b355867bcd | <|skeleton|>
class SNMPConnectorInstaller:
"""This class is responsible for installing and uninstalling the TEAL SNMP connector support"""
def _get_cmd_num(self, cmd_key):
"""Get the command number from the cmds monsetting entry If the cmds field is incorrectly formatted, 0 is returned"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SNMPConnectorInstaller:
"""This class is responsible for installing and uninstalling the TEAL SNMP connector support"""
def _get_cmd_num(self, cmd_key):
"""Get the command number from the cmds monsetting entry If the cmds field is incorrectly formatted, 0 is returned"""
m = self.CMD_RE.ma... | the_stack_v2_python_sparse | src/ibm/teal/util/snmp_config.py | ppjsand/pyteal | train | 1 |
f43e37db49d57b403a976c971ee66b25680e557e | [
"try:\n cluster = Cluster([host], port=port)\n self.session = cluster.connect(keyspace)\nexcept Exception as e:\n print('The connection was unsuccessful.\\n' + str(e))",
"try:\n df = pd.DataFrame(list(self.session.execute(query)))\n return df\nexcept Exception as e:\n print('An error occurred du... | <|body_start_0|>
try:
cluster = Cluster([host], port=port)
self.session = cluster.connect(keyspace)
except Exception as e:
print('The connection was unsuccessful.\n' + str(e))
<|end_body_0|>
<|body_start_1|>
try:
df = pd.DataFrame(list(self.sessio... | CassandraHelper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CassandraHelper:
def __init__(self, host, port, keyspace):
"""creating connection with cassandra :param host: The host name. :param port: The port in use. :param keyspace: The name of the keyspace"""
<|body_0|>
def execute_query_cassandra(self, query):
"""For executi... | stack_v2_sparse_classes_36k_train_021821 | 861 | no_license | [
{
"docstring": "creating connection with cassandra :param host: The host name. :param port: The port in use. :param keyspace: The name of the keyspace",
"name": "__init__",
"signature": "def __init__(self, host, port, keyspace)"
},
{
"docstring": "For executing cassandra query :param query: The ... | 2 | stack_v2_sparse_classes_30k_train_012975 | Implement the Python class `CassandraHelper` described below.
Class description:
Implement the CassandraHelper class.
Method signatures and docstrings:
- def __init__(self, host, port, keyspace): creating connection with cassandra :param host: The host name. :param port: The port in use. :param keyspace: The name of ... | Implement the Python class `CassandraHelper` described below.
Class description:
Implement the CassandraHelper class.
Method signatures and docstrings:
- def __init__(self, host, port, keyspace): creating connection with cassandra :param host: The host name. :param port: The port in use. :param keyspace: The name of ... | 0ee797be88095388c41bc5074df926760a0e3f8f | <|skeleton|>
class CassandraHelper:
def __init__(self, host, port, keyspace):
"""creating connection with cassandra :param host: The host name. :param port: The port in use. :param keyspace: The name of the keyspace"""
<|body_0|>
def execute_query_cassandra(self, query):
"""For executi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CassandraHelper:
def __init__(self, host, port, keyspace):
"""creating connection with cassandra :param host: The host name. :param port: The port in use. :param keyspace: The name of the keyspace"""
try:
cluster = Cluster([host], port=port)
self.session = cluster.conne... | the_stack_v2_python_sparse | helpers/cassandra_helper.py | taimoorpashanbs17/DataLake_Automation | train | 0 | |
5bdbe1be9354c5424de8de13a2317aad2cbc47c8 | [
"osm_user_details_url = f\"{current_app.config['OSM_SERVER_URL']}/api/0.6/user/{user_id}.json\"\nresponse = requests.get(osm_user_details_url)\nif response.status_code != 200:\n raise OSMServiceError('Bad response from OSM')\nreturn OSMService._parse_osm_user_details_response(response.json())",
"osm_user = osm... | <|body_start_0|>
osm_user_details_url = f"{current_app.config['OSM_SERVER_URL']}/api/0.6/user/{user_id}.json"
response = requests.get(osm_user_details_url)
if response.status_code != 200:
raise OSMServiceError('Bad response from OSM')
return OSMService._parse_osm_user_details... | OSMService | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OSMService:
def get_osm_details_for_user(user_id: int) -> UserOSMDTO:
"""Gets OSM details for the user from OSM API :param user_id: user_id in scope :raises OSMServiceError"""
<|body_0|>
def _parse_osm_user_details_response(osm_response: dict, user_element='user') -> UserOSM... | stack_v2_sparse_classes_36k_train_021822 | 1,488 | permissive | [
{
"docstring": "Gets OSM details for the user from OSM API :param user_id: user_id in scope :raises OSMServiceError",
"name": "get_osm_details_for_user",
"signature": "def get_osm_details_for_user(user_id: int) -> UserOSMDTO"
},
{
"docstring": "Parses the OSM user details response and extracts u... | 2 | null | Implement the Python class `OSMService` described below.
Class description:
Implement the OSMService class.
Method signatures and docstrings:
- def get_osm_details_for_user(user_id: int) -> UserOSMDTO: Gets OSM details for the user from OSM API :param user_id: user_id in scope :raises OSMServiceError
- def _parse_osm... | Implement the Python class `OSMService` described below.
Class description:
Implement the OSMService class.
Method signatures and docstrings:
- def get_osm_details_for_user(user_id: int) -> UserOSMDTO: Gets OSM details for the user from OSM API :param user_id: user_id in scope :raises OSMServiceError
- def _parse_osm... | 45bf3937c74902226096aee5b49e7abea62df524 | <|skeleton|>
class OSMService:
def get_osm_details_for_user(user_id: int) -> UserOSMDTO:
"""Gets OSM details for the user from OSM API :param user_id: user_id in scope :raises OSMServiceError"""
<|body_0|>
def _parse_osm_user_details_response(osm_response: dict, user_element='user') -> UserOSM... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OSMService:
def get_osm_details_for_user(user_id: int) -> UserOSMDTO:
"""Gets OSM details for the user from OSM API :param user_id: user_id in scope :raises OSMServiceError"""
osm_user_details_url = f"{current_app.config['OSM_SERVER_URL']}/api/0.6/user/{user_id}.json"
response = reques... | the_stack_v2_python_sparse | backend/services/users/osm_service.py | hotosm/tasking-manager | train | 526 | |
1448e8715b7f0dd20ceed73ec4e74d2632c57585 | [
"self.directory = directory\nself.function_table = {}\nself.status = DataPackStatus.INACTIVE\nself.name = directory.split('/')[-1].split('\\\\')[-1]\nself.access = None\nself.description = ''",
"if self.status == DataPackStatus.SYSTEM_ERROR:\n return\ntry:\n if self.status in (DataPackStatus.ACTIVATED, Data... | <|body_start_0|>
self.directory = directory
self.function_table = {}
self.status = DataPackStatus.INACTIVE
self.name = directory.split('/')[-1].split('\\')[-1]
self.access = None
self.description = ''
<|end_body_0|>
<|body_start_1|>
if self.status == DataPackStat... | Class for a single data pack | DataPack | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataPack:
"""Class for a single data pack"""
def __init__(self, directory: str):
"""Will create a new DataPack-object :param directory: where the datapack is located"""
<|body_0|>
async def load(self):
"""Will load the data pack"""
<|body_1|>
def unl... | stack_v2_sparse_classes_36k_train_021823 | 10,029 | permissive | [
{
"docstring": "Will create a new DataPack-object :param directory: where the datapack is located",
"name": "__init__",
"signature": "def __init__(self, directory: str)"
},
{
"docstring": "Will load the data pack",
"name": "load",
"signature": "async def load(self)"
},
{
"docstri... | 4 | stack_v2_sparse_classes_30k_train_011341 | Implement the Python class `DataPack` described below.
Class description:
Class for a single data pack
Method signatures and docstrings:
- def __init__(self, directory: str): Will create a new DataPack-object :param directory: where the datapack is located
- async def load(self): Will load the data pack
- def unload(... | Implement the Python class `DataPack` described below.
Class description:
Class for a single data pack
Method signatures and docstrings:
- def __init__(self, directory: str): Will create a new DataPack-object :param directory: where the datapack is located
- async def load(self): Will load the data pack
- def unload(... | 644ef36a70c45a70820f6f6069b2f36545a187e5 | <|skeleton|>
class DataPack:
"""Class for a single data pack"""
def __init__(self, directory: str):
"""Will create a new DataPack-object :param directory: where the datapack is located"""
<|body_0|>
async def load(self):
"""Will load the data pack"""
<|body_1|>
def unl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataPack:
"""Class for a single data pack"""
def __init__(self, directory: str):
"""Will create a new DataPack-object :param directory: where the datapack is located"""
self.directory = directory
self.function_table = {}
self.status = DataPackStatus.INACTIVE
self.n... | the_stack_v2_python_sparse | mcpython/common/data/DataPacks.py | mcpython4-coding/core | train | 4 |
0c7c692536dc5e58d65661314e16b826ad56779f | [
"self.object = self.get_object()\neditor = self.object\ncontractor = self.request.user.contractorprofile\nactive_assignments = editor.assignment_set.all()\nassignments_for_viewer = active_assignments.filter(contractor=contractor)\nreturn active_assignments",
"self.object = self.get_object()\neditor = self.object\... | <|body_start_0|>
self.object = self.get_object()
editor = self.object
contractor = self.request.user.contractorprofile
active_assignments = editor.assignment_set.all()
assignments_for_viewer = active_assignments.filter(contractor=contractor)
return active_assignments
<|en... | A public profile page for talent editors. Displays details about an editor that works with contractors. Contractors see assignments and calls from this editor and pitches they've submitted. | PublicTalentEditorDetailView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PublicTalentEditorDetailView:
"""A public profile page for talent editors. Displays details about an editor that works with contractors. Contractors see assignments and calls from this editor and pitches they've submitted."""
def assignments(self):
"""Get assignments from this editor... | stack_v2_sparse_classes_36k_train_021824 | 28,644 | permissive | [
{
"docstring": "Get assignments from this editor that are relevant to requesting user.",
"name": "assignments",
"signature": "def assignments(self)"
},
{
"docstring": "Get pitches to this editor that are relevant to contractor viewing this profile.",
"name": "pitches",
"signature": "def ... | 3 | stack_v2_sparse_classes_30k_train_004635 | Implement the Python class `PublicTalentEditorDetailView` described below.
Class description:
A public profile page for talent editors. Displays details about an editor that works with contractors. Contractors see assignments and calls from this editor and pitches they've submitted.
Method signatures and docstrings:
... | Implement the Python class `PublicTalentEditorDetailView` described below.
Class description:
A public profile page for talent editors. Displays details about an editor that works with contractors. Contractors see assignments and calls from this editor and pitches they've submitted.
Method signatures and docstrings:
... | dc6bc79d450f7e2bdf59cfbcd306d05a736e4db9 | <|skeleton|>
class PublicTalentEditorDetailView:
"""A public profile page for talent editors. Displays details about an editor that works with contractors. Contractors see assignments and calls from this editor and pitches they've submitted."""
def assignments(self):
"""Get assignments from this editor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PublicTalentEditorDetailView:
"""A public profile page for talent editors. Displays details about an editor that works with contractors. Contractors see assignments and calls from this editor and pitches they've submitted."""
def assignments(self):
"""Get assignments from this editor that are rel... | the_stack_v2_python_sparse | project/editorial/views/contractors.py | ProjectFacet/facet | train | 25 |
65300aef5964778af611c2b8c9cd7565489067cb | [
"self.config = {}\nfor key, value in configs:\n self.setConfig(key, value)",
"gist_md_pattern = GistPattern(GIST_MD_RE, self.getConfigs())\ngist_md_pattern.md = md\nmd.inlinePatterns.register(gist_md_pattern, 'gist', 175)\ngist_rst_pattern = GistPattern(GIST_RST_RE, self.getConfigs())\ngist_rst_pattern.md = md... | <|body_start_0|>
self.config = {}
for key, value in configs:
self.setConfig(key, value)
<|end_body_0|>
<|body_start_1|>
gist_md_pattern = GistPattern(GIST_MD_RE, self.getConfigs())
gist_md_pattern.md = md
md.inlinePatterns.register(gist_md_pattern, 'gist', 175)
... | Gist extension for Markdown. | GistExtension | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GistExtension:
"""Gist extension for Markdown."""
def __init__(self, configs={}):
"""Initialize the extension."""
<|body_0|>
def extendMarkdown(self, md, md_globals=None):
"""Extend Markdown."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.... | stack_v2_sparse_classes_36k_train_021825 | 6,764 | permissive | [
{
"docstring": "Initialize the extension.",
"name": "__init__",
"signature": "def __init__(self, configs={})"
},
{
"docstring": "Extend Markdown.",
"name": "extendMarkdown",
"signature": "def extendMarkdown(self, md, md_globals=None)"
}
] | 2 | null | Implement the Python class `GistExtension` described below.
Class description:
Gist extension for Markdown.
Method signatures and docstrings:
- def __init__(self, configs={}): Initialize the extension.
- def extendMarkdown(self, md, md_globals=None): Extend Markdown. | Implement the Python class `GistExtension` described below.
Class description:
Gist extension for Markdown.
Method signatures and docstrings:
- def __init__(self, configs={}): Initialize the extension.
- def extendMarkdown(self, md, md_globals=None): Extend Markdown.
<|skeleton|>
class GistExtension:
"""Gist ext... | 2b10e9952bac5a1119e6845c7a2c28273aca9775 | <|skeleton|>
class GistExtension:
"""Gist extension for Markdown."""
def __init__(self, configs={}):
"""Initialize the extension."""
<|body_0|>
def extendMarkdown(self, md, md_globals=None):
"""Extend Markdown."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GistExtension:
"""Gist extension for Markdown."""
def __init__(self, configs={}):
"""Initialize the extension."""
self.config = {}
for key, value in configs:
self.setConfig(key, value)
def extendMarkdown(self, md, md_globals=None):
"""Extend Markdown."""
... | the_stack_v2_python_sparse | nikola/plugins/compile/markdown/mdx_gist.py | getnikola/nikola | train | 2,142 |
ef272aed6c74791199beb3e61af87d7ff7582757 | [
"self.output_attribute = output_attribute\nself.separator = separator\nself.distance = distance",
"try:\n for graph in graphs:\n for n, d in graph.nodes_iter(data=True):\n edge_labels = []\n if self.distance == 1:\n edge_labels += [graph.edge[u][v].get('label', '-') ... | <|body_start_0|>
self.output_attribute = output_attribute
self.separator = separator
self.distance = distance
<|end_body_0|>
<|body_start_1|>
try:
for graph in graphs:
for n, d in graph.nodes_iter(data=True):
edge_labels = []
... | RelabelWithLabelOfIncidentEdges. Delete an edge if its dictionary has a key equal to 'attribute' and the 'condition' is true between 'value' and the value associated to key=attribute. | RelabelWithLabelOfIncidentEdges | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelabelWithLabelOfIncidentEdges:
"""RelabelWithLabelOfIncidentEdges. Delete an edge if its dictionary has a key equal to 'attribute' and the 'condition' is true between 'value' and the value associated to key=attribute."""
def __init__(self, output_attribute='type', separator='', distance=1)... | stack_v2_sparse_classes_36k_train_021826 | 23,871 | permissive | [
{
"docstring": "\"Construct. Parameters ---------- graphs : iterator over path graphs of RNA sequences output_attribute : string The key of the node dictionary where to write the result. separator : string The string used to separate the sorted concatenation of labels. distance : integer (default 1) The neighbo... | 2 | stack_v2_sparse_classes_30k_train_002962 | Implement the Python class `RelabelWithLabelOfIncidentEdges` described below.
Class description:
RelabelWithLabelOfIncidentEdges. Delete an edge if its dictionary has a key equal to 'attribute' and the 'condition' is true between 'value' and the value associated to key=attribute.
Method signatures and docstrings:
- d... | Implement the Python class `RelabelWithLabelOfIncidentEdges` described below.
Class description:
RelabelWithLabelOfIncidentEdges. Delete an edge if its dictionary has a key equal to 'attribute' and the 'condition' is true between 'value' and the value associated to key=attribute.
Method signatures and docstrings:
- d... | 227ad49d0b3d4611866011bd1648cd8946ede05a | <|skeleton|>
class RelabelWithLabelOfIncidentEdges:
"""RelabelWithLabelOfIncidentEdges. Delete an edge if its dictionary has a key equal to 'attribute' and the 'condition' is true between 'value' and the value associated to key=attribute."""
def __init__(self, output_attribute='type', separator='', distance=1)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RelabelWithLabelOfIncidentEdges:
"""RelabelWithLabelOfIncidentEdges. Delete an edge if its dictionary has a key equal to 'attribute' and the 'condition' is true between 'value' and the value associated to key=attribute."""
def __init__(self, output_attribute='type', separator='', distance=1):
"""... | the_stack_v2_python_sparse | GArDen/transform/node.py | rgerkin/EDeN | train | 0 |
3c168de818983787368e162ef2c1988976f3206b | [
"index = 0\ncount = 0\nlength = len(intervals)\nwhile count < length:\n if intervals[index][1] < newInterval[0]:\n index += 1\n count += 1\n else:\n if newInterval[1] < intervals[index][0]:\n intervals.insert(index, newInterval)\n return intervals\n intervals[... | <|body_start_0|>
index = 0
count = 0
length = len(intervals)
while count < length:
if intervals[index][1] < newInterval[0]:
index += 1
count += 1
else:
if newInterval[1] < intervals[index][0]:
int... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def _insert(self, intervals, newInterval):
""":type intervals: List[List[int]] :type newInterval: List[int] :rtype: List[List[int]]"""
<|body_0|>
def insert(self, intervals, newInterval):
""":type intervals: List[List[int]] :type newInterval: List[int] :rty... | stack_v2_sparse_classes_36k_train_021827 | 3,143 | permissive | [
{
"docstring": ":type intervals: List[List[int]] :type newInterval: List[int] :rtype: List[List[int]]",
"name": "_insert",
"signature": "def _insert(self, intervals, newInterval)"
},
{
"docstring": ":type intervals: List[List[int]] :type newInterval: List[int] :rtype: List[List[int]]",
"name... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _insert(self, intervals, newInterval): :type intervals: List[List[int]] :type newInterval: List[int] :rtype: List[List[int]]
- def insert(self, intervals, newInterval): :type... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _insert(self, intervals, newInterval): :type intervals: List[List[int]] :type newInterval: List[int] :rtype: List[List[int]]
- def insert(self, intervals, newInterval): :type... | 0dd67edca4e0b0323cb5a7239f02ea46383cd15a | <|skeleton|>
class Solution:
def _insert(self, intervals, newInterval):
""":type intervals: List[List[int]] :type newInterval: List[int] :rtype: List[List[int]]"""
<|body_0|>
def insert(self, intervals, newInterval):
""":type intervals: List[List[int]] :type newInterval: List[int] :rty... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def _insert(self, intervals, newInterval):
""":type intervals: List[List[int]] :type newInterval: List[int] :rtype: List[List[int]]"""
index = 0
count = 0
length = len(intervals)
while count < length:
if intervals[index][1] < newInterval[0]:
... | the_stack_v2_python_sparse | 57.insert-interval.py | windard/leeeeee | train | 0 | |
413bdd815447f7247ba6487955eaed7192a265c9 | [
"super(particles_output, self).__init__()\nfig = plt.figure()\nself.ax = fig.add_subplot(111, projection='3d')\nself.ax.set_xlim3d([-20, 20])\nself.ax.set_ylim3d([-20, 20])\nself.ax.set_zlim3d([-20, 20])\nplt.ion()\nself.sframe = None\nself.bar_run = None",
"super(particles_output, self).pre_run(step, level_numbe... | <|body_start_0|>
super(particles_output, self).__init__()
fig = plt.figure()
self.ax = fig.add_subplot(111, projection='3d')
self.ax.set_xlim3d([-20, 20])
self.ax.set_ylim3d([-20, 20])
self.ax.set_zlim3d([-20, 20])
plt.ion()
self.sframe = None
self... | particles_output | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class particles_output:
def __init__(self):
"""Initialization of particles output"""
<|body_0|>
def pre_run(self, step, level_number):
"""Overwrite default routine called before time-loop starts Args: step: the current step level_number: the current level number"""
... | stack_v2_sparse_classes_36k_train_021828 | 4,455 | permissive | [
{
"docstring": "Initialization of particles output",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Overwrite default routine called before time-loop starts Args: step: the current step level_number: the current level number",
"name": "pre_run",
"signature": "de... | 3 | null | Implement the Python class `particles_output` described below.
Class description:
Implement the particles_output class.
Method signatures and docstrings:
- def __init__(self): Initialization of particles output
- def pre_run(self, step, level_number): Overwrite default routine called before time-loop starts Args: ste... | Implement the Python class `particles_output` described below.
Class description:
Implement the particles_output class.
Method signatures and docstrings:
- def __init__(self): Initialization of particles output
- def pre_run(self, step, level_number): Overwrite default routine called before time-loop starts Args: ste... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class particles_output:
def __init__(self):
"""Initialization of particles output"""
<|body_0|>
def pre_run(self, step, level_number):
"""Overwrite default routine called before time-loop starts Args: step: the current step level_number: the current level number"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class particles_output:
def __init__(self):
"""Initialization of particles output"""
super(particles_output, self).__init__()
fig = plt.figure()
self.ax = fig.add_subplot(111, projection='3d')
self.ax.set_xlim3d([-20, 20])
self.ax.set_ylim3d([-20, 20])
self.ax... | the_stack_v2_python_sparse | pySDC/playgrounds/Boris/penningtrap_HookClass.py | Parallel-in-Time/pySDC | train | 30 | |
1ea06a26bdb4a8f304a810865fa8605fb9fbd67f | [
"self.radio = RF24(RPI_BPLUS_GPIO_J8_15, RPI_BPLUS_GPIO_J8_24, BCM2835_SPI_SPEED_8MHZ)\nself.radio.begin()\nself.radio.setPALevel(RF24_PA_MAX)\nself.radio.enableDynamicPayloads()\nself.radio.setDataRate(RF24_250KBPS)\nself.radio.setRetries(5, options['repetitions'])\nself.radio.openWritingPipe(nrf24l01pConn.PIPES[1... | <|body_start_0|>
self.radio = RF24(RPI_BPLUS_GPIO_J8_15, RPI_BPLUS_GPIO_J8_24, BCM2835_SPI_SPEED_8MHZ)
self.radio.begin()
self.radio.setPALevel(RF24_PA_MAX)
self.radio.enableDynamicPayloads()
self.radio.setDataRate(RF24_250KBPS)
self.radio.setRetries(5, options['repetitio... | Class in charge of the reading the data using nrf24l01p receiver. It uses the library https://github.com/TMRh20/RF24 to do the transmission of the data. | nrf24l01pConn | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class nrf24l01pConn:
"""Class in charge of the reading the data using nrf24l01p receiver. It uses the library https://github.com/TMRh20/RF24 to do the transmission of the data."""
def __init__(self, options):
"""Initialization of the nrf24l01p connexion. Args: options: Is a dictionary, it ... | stack_v2_sparse_classes_36k_train_021829 | 1,637 | permissive | [
{
"docstring": "Initialization of the nrf24l01p connexion. Args: options: Is a dictionary, it must have a 'repetitions' key. It is the maximum number of repetitions to do when you are sending a value and the receiver do not get it, the value must be between [1, 15].",
"name": "__init__",
"signature": "d... | 2 | stack_v2_sparse_classes_30k_test_000765 | Implement the Python class `nrf24l01pConn` described below.
Class description:
Class in charge of the reading the data using nrf24l01p receiver. It uses the library https://github.com/TMRh20/RF24 to do the transmission of the data.
Method signatures and docstrings:
- def __init__(self, options): Initialization of the... | Implement the Python class `nrf24l01pConn` described below.
Class description:
Class in charge of the reading the data using nrf24l01p receiver. It uses the library https://github.com/TMRh20/RF24 to do the transmission of the data.
Method signatures and docstrings:
- def __init__(self, options): Initialization of the... | 9c8a6bd5708241b30ee8c2b37b0c2d15977e84cd | <|skeleton|>
class nrf24l01pConn:
"""Class in charge of the reading the data using nrf24l01p receiver. It uses the library https://github.com/TMRh20/RF24 to do the transmission of the data."""
def __init__(self, options):
"""Initialization of the nrf24l01p connexion. Args: options: Is a dictionary, it ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class nrf24l01pConn:
"""Class in charge of the reading the data using nrf24l01p receiver. It uses the library https://github.com/TMRh20/RF24 to do the transmission of the data."""
def __init__(self, options):
"""Initialization of the nrf24l01p connexion. Args: options: Is a dictionary, it must have a '... | the_stack_v2_python_sparse | RaspberryPi/ardupi_weather/arduino/nrf24l01pConn.py | jordivilaseca/ardupi-weather | train | 2 |
08cfdcc97016edc56b3a099c5f01cd65edbe7b3d | [
"Part = self.old_state.apps.get_model('part', 'part')\nunits = ['mm', 'INCH', '', '%']\nfor idx, unit in enumerate(units):\n Part.objects.create(name=f'Part {idx + 1}', description=f'My part at index {idx}', units=unit, level=0, lft=0, rght=0, tree_id=0)",
"Part = self.new_state.apps.get_model('part', 'part')\... | <|body_start_0|>
Part = self.old_state.apps.get_model('part', 'part')
units = ['mm', 'INCH', '', '%']
for idx, unit in enumerate(units):
Part.objects.create(name=f'Part {idx + 1}', description=f'My part at index {idx}', units=unit, level=0, lft=0, rght=0, tree_id=0)
<|end_body_0|>
<... | Test for data migration of Part.units field | PartUnitsMigrationTest | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PartUnitsMigrationTest:
"""Test for data migration of Part.units field"""
def prepare(self):
"""Prepare some parts with units"""
<|body_0|>
def test_units_migration(self):
"""Test that the units have migrated OK"""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_021830 | 8,200 | permissive | [
{
"docstring": "Prepare some parts with units",
"name": "prepare",
"signature": "def prepare(self)"
},
{
"docstring": "Test that the units have migrated OK",
"name": "test_units_migration",
"signature": "def test_units_migration(self)"
}
] | 2 | null | Implement the Python class `PartUnitsMigrationTest` described below.
Class description:
Test for data migration of Part.units field
Method signatures and docstrings:
- def prepare(self): Prepare some parts with units
- def test_units_migration(self): Test that the units have migrated OK | Implement the Python class `PartUnitsMigrationTest` described below.
Class description:
Test for data migration of Part.units field
Method signatures and docstrings:
- def prepare(self): Prepare some parts with units
- def test_units_migration(self): Test that the units have migrated OK
<|skeleton|>
class PartUnitsM... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class PartUnitsMigrationTest:
"""Test for data migration of Part.units field"""
def prepare(self):
"""Prepare some parts with units"""
<|body_0|>
def test_units_migration(self):
"""Test that the units have migrated OK"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PartUnitsMigrationTest:
"""Test for data migration of Part.units field"""
def prepare(self):
"""Prepare some parts with units"""
Part = self.old_state.apps.get_model('part', 'part')
units = ['mm', 'INCH', '', '%']
for idx, unit in enumerate(units):
Part.objects... | the_stack_v2_python_sparse | InvenTree/part/test_migrations.py | inventree/InvenTree | train | 3,077 |
6100f1a09996674b67a958a7026ada368ae699fb | [
"nn.Module.__init__(self)\nself.tau = tau\nself.y_list = y_list\nself.batch_size = batch_size\nself.device = device",
"p = torch.cat((z_i, z_j), dim=0)\nsim = nn.CosineSimilarity(dim=2)(p.unsqueeze(1), p.unsqueeze(0)) / self.tau\ny2 = torch.cat([y, y], dim=0).view(-1, 1)\nif self.y_list == 'all':\n mask = torc... | <|body_start_0|>
nn.Module.__init__(self)
self.tau = tau
self.y_list = y_list
self.batch_size = batch_size
self.device = device
<|end_body_0|>
<|body_start_1|>
p = torch.cat((z_i, z_j), dim=0)
sim = nn.CosineSimilarity(dim=2)(p.unsqueeze(1), p.unsqueeze(0)) / sel... | Define the Supervised Contrastive Loss as a Pytorch Module. | SupervisedContrastiveLoss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SupervisedContrastiveLoss:
"""Define the Supervised Contrastive Loss as a Pytorch Module."""
def __init__(self, tau, batch_size, y_list='all', device='cuda'):
"""Initialize a Supervised Contrastive Loss Module. ---------- INPUT |---- tau (float) the temperature hyperparameter. |---- ... | stack_v2_sparse_classes_36k_train_021831 | 18,386 | permissive | [
{
"docstring": "Initialize a Supervised Contrastive Loss Module. ---------- INPUT |---- tau (float) the temperature hyperparameter. |---- y_list (list of int) the list of class to conisder for positive. | Default is using all classes. |---- batch_size (int) the batch_size used. |---- device (str) the device to ... | 2 | stack_v2_sparse_classes_30k_train_004572 | Implement the Python class `SupervisedContrastiveLoss` described below.
Class description:
Define the Supervised Contrastive Loss as a Pytorch Module.
Method signatures and docstrings:
- def __init__(self, tau, batch_size, y_list='all', device='cuda'): Initialize a Supervised Contrastive Loss Module. ---------- INPUT... | Implement the Python class `SupervisedContrastiveLoss` described below.
Class description:
Define the Supervised Contrastive Loss as a Pytorch Module.
Method signatures and docstrings:
- def __init__(self, tau, batch_size, y_list='all', device='cuda'): Initialize a Supervised Contrastive Loss Module. ---------- INPUT... | 850b6195d6290a50eee865b4d5a66f5db5260e8f | <|skeleton|>
class SupervisedContrastiveLoss:
"""Define the Supervised Contrastive Loss as a Pytorch Module."""
def __init__(self, tau, batch_size, y_list='all', device='cuda'):
"""Initialize a Supervised Contrastive Loss Module. ---------- INPUT |---- tau (float) the temperature hyperparameter. |---- ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SupervisedContrastiveLoss:
"""Define the Supervised Contrastive Loss as a Pytorch Module."""
def __init__(self, tau, batch_size, y_list='all', device='cuda'):
"""Initialize a Supervised Contrastive Loss Module. ---------- INPUT |---- tau (float) the temperature hyperparameter. |---- y_list (list ... | the_stack_v2_python_sparse | Code/src/models/optim/CustomLosses.py | antoine-spahr/X-ray-Anomaly-Detection | train | 3 |
0385c7f0deeb55f6c38847633693a9160f816003 | [
"super(RnnEncoder, self).__init__()\ncells = {'GRU': nn.GRU, 'LSTM': nn.LSTM}\nself.rnn = cells[args.cell_type](input_size=args.embedding_dim, hidden_size=args.hidden_dim // 2, num_layers=args.layer_num, bidirectional=True)",
"e_T = e.permute(1, 0, 2)\nif m is not None:\n seq_lens = list(map(int, torch.sum(m, ... | <|body_start_0|>
super(RnnEncoder, self).__init__()
cells = {'GRU': nn.GRU, 'LSTM': nn.LSTM}
self.rnn = cells[args.cell_type](input_size=args.embedding_dim, hidden_size=args.hidden_dim // 2, num_layers=args.layer_num, bidirectional=True)
<|end_body_0|>
<|body_start_1|>
e_T = e.permute(1... | Basic RNN encoder module, input embeddings and output hidden states. | RnnEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RnnEncoder:
"""Basic RNN encoder module, input embeddings and output hidden states."""
def __init__(self, args):
"""Inputs: args.hidden_dim -- dimension of hidden states. args.layer_num -- number of RNN layers. args.cell_type -- type of RNN cells, "GRU" or "LSTM". args.embedding_dim ... | stack_v2_sparse_classes_36k_train_021832 | 6,319 | no_license | [
{
"docstring": "Inputs: args.hidden_dim -- dimension of hidden states. args.layer_num -- number of RNN layers. args.cell_type -- type of RNN cells, \"GRU\" or \"LSTM\". args.embedding_dim -- dimension of word embeddings.",
"name": "__init__",
"signature": "def __init__(self, args)"
},
{
"docstri... | 2 | stack_v2_sparse_classes_30k_train_018611 | Implement the Python class `RnnEncoder` described below.
Class description:
Basic RNN encoder module, input embeddings and output hidden states.
Method signatures and docstrings:
- def __init__(self, args): Inputs: args.hidden_dim -- dimension of hidden states. args.layer_num -- number of RNN layers. args.cell_type -... | Implement the Python class `RnnEncoder` described below.
Class description:
Basic RNN encoder module, input embeddings and output hidden states.
Method signatures and docstrings:
- def __init__(self, args): Inputs: args.hidden_dim -- dimension of hidden states. args.layer_num -- number of RNN layers. args.cell_type -... | e79606e24ecc6fd713b481afb527c34eec7d5d66 | <|skeleton|>
class RnnEncoder:
"""Basic RNN encoder module, input embeddings and output hidden states."""
def __init__(self, args):
"""Inputs: args.hidden_dim -- dimension of hidden states. args.layer_num -- number of RNN layers. args.cell_type -- type of RNN cells, "GRU" or "LSTM". args.embedding_dim ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RnnEncoder:
"""Basic RNN encoder module, input embeddings and output hidden states."""
def __init__(self, args):
"""Inputs: args.hidden_dim -- dimension of hidden states. args.layer_num -- number of RNN layers. args.cell_type -- type of RNN cells, "GRU" or "LSTM". args.embedding_dim -- dimension ... | the_stack_v2_python_sparse | rationalize/models/encoder.py | anshiquanshu66/factcheck-acl2021 | train | 0 |
c5624780b5dac24ae3ab0dac321794bfb387a0e5 | [
"super().__init__('human_model_generation_service')\nself.bridge = ROS2Bridge()\nself.service_name = service_name\nself.model_generator = PIFuGeneratorLearner(device=device, checkpoint_dir=checkpoint_dir)\nmy_callback_group = MutuallyExclusiveCallbackGroup()\nself.srv = self.create_service(ImgToMesh, 'human_model_g... | <|body_start_0|>
super().__init__('human_model_generation_service')
self.bridge = ROS2Bridge()
self.service_name = service_name
self.model_generator = PIFuGeneratorLearner(device=device, checkpoint_dir=checkpoint_dir)
my_callback_group = MutuallyExclusiveCallbackGroup()
s... | PifuService | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PifuService:
def __init__(self, service_name='human_model_generation', device='cuda', checkpoint_dir='.'):
"""Creates a ROS Service for human model generation :param service_name: The name of the service :type service_name: str :param device: device on which we are running inference ('cp... | stack_v2_sparse_classes_36k_train_021833 | 4,356 | permissive | [
{
"docstring": "Creates a ROS Service for human model generation :param service_name: The name of the service :type service_name: str :param device: device on which we are running inference ('cpu' or 'cuda') :type device: str :param checkpoint_dir: the directory where the PIFu weights will be downloaded/loaded ... | 2 | null | Implement the Python class `PifuService` described below.
Class description:
Implement the PifuService class.
Method signatures and docstrings:
- def __init__(self, service_name='human_model_generation', device='cuda', checkpoint_dir='.'): Creates a ROS Service for human model generation :param service_name: The name... | Implement the Python class `PifuService` described below.
Class description:
Implement the PifuService class.
Method signatures and docstrings:
- def __init__(self, service_name='human_model_generation', device='cuda', checkpoint_dir='.'): Creates a ROS Service for human model generation :param service_name: The name... | b3d6ce670cdf63469fc5766630eb295d67b3d788 | <|skeleton|>
class PifuService:
def __init__(self, service_name='human_model_generation', device='cuda', checkpoint_dir='.'):
"""Creates a ROS Service for human model generation :param service_name: The name of the service :type service_name: str :param device: device on which we are running inference ('cp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PifuService:
def __init__(self, service_name='human_model_generation', device='cuda', checkpoint_dir='.'):
"""Creates a ROS Service for human model generation :param service_name: The name of the service :type service_name: str :param device: device on which we are running inference ('cpu' or 'cuda') ... | the_stack_v2_python_sparse | projects/opendr_ws_2/src/opendr_simulation/opendr_simulation/human_model_generation_service.py | opendr-eu/opendr | train | 535 | |
4d2cd9a3615bae19cb46a835b6601bfc607f525d | [
"self._query = '{!join}' + query\nself._from = from_field\nself._to = to_field",
"params = []\nparams.append(('from', self._from))\nparams.append(('to', self._to))\nreturn params"
] | <|body_start_0|>
self._query = '{!join}' + query
self._from = from_field
self._to = to_field
<|end_body_0|>
<|body_start_1|>
params = []
params.append(('from', self._from))
params.append(('to', self._to))
return params
<|end_body_1|>
| A base query for all join operations. | JoinBaseQuery | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JoinBaseQuery:
"""A base query for all join operations."""
def __init__(self, query, from_field, to_field):
"""Join base query takes care of joining syntax"""
<|body_0|>
def get_params(self):
"""Return the list of query params for the `JoinBaseQuery`."""
... | stack_v2_sparse_classes_36k_train_021834 | 2,190 | permissive | [
{
"docstring": "Join base query takes care of joining syntax",
"name": "__init__",
"signature": "def __init__(self, query, from_field, to_field)"
},
{
"docstring": "Return the list of query params for the `JoinBaseQuery`.",
"name": "get_params",
"signature": "def get_params(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017173 | Implement the Python class `JoinBaseQuery` described below.
Class description:
A base query for all join operations.
Method signatures and docstrings:
- def __init__(self, query, from_field, to_field): Join base query takes care of joining syntax
- def get_params(self): Return the list of query params for the `JoinBa... | Implement the Python class `JoinBaseQuery` described below.
Class description:
A base query for all join operations.
Method signatures and docstrings:
- def __init__(self, query, from_field, to_field): Join base query takes care of joining syntax
- def get_params(self): Return the list of query params for the `JoinBa... | 2810f3202166b045a7f5f9a21b964c681bfd8136 | <|skeleton|>
class JoinBaseQuery:
"""A base query for all join operations."""
def __init__(self, query, from_field, to_field):
"""Join base query takes care of joining syntax"""
<|body_0|>
def get_params(self):
"""Return the list of query params for the `JoinBaseQuery`."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JoinBaseQuery:
"""A base query for all join operations."""
def __init__(self, query, from_field, to_field):
"""Join base query takes care of joining syntax"""
self._query = '{!join}' + query
self._from = from_field
self._to = to_field
def get_params(self):
"""... | the_stack_v2_python_sparse | dopplr/solr/query/join.py | renatoaquino/dopplr | train | 1 |
bd39b9c0fc423a30003a5ff0ff5555f01e9e8a6d | [
"self.length = len(nums)\nself.Map = dict()\nfor i in range(self.length):\n if nums[i] != 0:\n self.Map[i] = nums[i]",
"res = 0\nfor i in range(self.length):\n if i in self.Map and i in vec.Map:\n res += self.Map[i] * vec.Map[i]\nreturn res"
] | <|body_start_0|>
self.length = len(nums)
self.Map = dict()
for i in range(self.length):
if nums[i] != 0:
self.Map[i] = nums[i]
<|end_body_0|>
<|body_start_1|>
res = 0
for i in range(self.length):
if i in self.Map and i in vec.Map:
... | SparseVector | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SparseVector:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def dotProduct(self, vec):
""":type vec: 'SparseVector' :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.length = len(nums)
self.Map = dict()
... | stack_v2_sparse_classes_36k_train_021835 | 741 | no_license | [
{
"docstring": ":type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": ":type vec: 'SparseVector' :rtype: int",
"name": "dotProduct",
"signature": "def dotProduct(self, vec)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019557 | Implement the Python class `SparseVector` described below.
Class description:
Implement the SparseVector class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def dotProduct(self, vec): :type vec: 'SparseVector' :rtype: int | Implement the Python class `SparseVector` described below.
Class description:
Implement the SparseVector class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def dotProduct(self, vec): :type vec: 'SparseVector' :rtype: int
<|skeleton|>
class SparseVector:
def __init__(sel... | 8a82905d40b882b20a9b6f862942f8f3e4bebcf0 | <|skeleton|>
class SparseVector:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def dotProduct(self, vec):
""":type vec: 'SparseVector' :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SparseVector:
def __init__(self, nums):
""":type nums: List[int]"""
self.length = len(nums)
self.Map = dict()
for i in range(self.length):
if nums[i] != 0:
self.Map[i] = nums[i]
def dotProduct(self, vec):
""":type vec: 'SparseVector' :rt... | the_stack_v2_python_sparse | ByTags/Others/1570. Dot Product of Two Sparse Vectors.py | lynkeib/LeetCode | train | 0 | |
33dba0c9cc5193a336cd06e95c3352ab78557a52 | [
"for i in range(len(nums)):\n for j in range(i + 1, i + k + 1):\n if j >= len(nums):\n break\n if abs(nums[i] - nums[j]) <= t:\n return True\nreturn False",
"if t < 0:\n return False\nbucket = {}\nw = t + 1\nfor i in range(len(nums)):\n m = nums[i] // w\n if m in bu... | <|body_start_0|>
for i in range(len(nums)):
for j in range(i + 1, i + k + 1):
if j >= len(nums):
break
if abs(nums[i] - nums[j]) <= t:
return True
return False
<|end_body_0|>
<|body_start_1|>
if t < 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
"""brute force :type nums: List[int] :type k: int :type t: int :rtype: bool"""
<|body_0|>
def containsNearbyAlmostDuplicate2(self, nums, k, t):
"""bucket :type nums: List[int] :type k: int :type t: int :r... | stack_v2_sparse_classes_36k_train_021836 | 1,195 | no_license | [
{
"docstring": "brute force :type nums: List[int] :type k: int :type t: int :rtype: bool",
"name": "containsNearbyAlmostDuplicate",
"signature": "def containsNearbyAlmostDuplicate(self, nums, k, t)"
},
{
"docstring": "bucket :type nums: List[int] :type k: int :type t: int :rtype: bool",
"nam... | 2 | stack_v2_sparse_classes_30k_train_009849 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyAlmostDuplicate(self, nums, k, t): brute force :type nums: List[int] :type k: int :type t: int :rtype: bool
- def containsNearbyAlmostDuplicate2(self, nums, k, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyAlmostDuplicate(self, nums, k, t): brute force :type nums: List[int] :type k: int :type t: int :rtype: bool
- def containsNearbyAlmostDuplicate2(self, nums, k, ... | e16702d2b3ec4e5054baad56f4320bc3b31676ad | <|skeleton|>
class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
"""brute force :type nums: List[int] :type k: int :type t: int :rtype: bool"""
<|body_0|>
def containsNearbyAlmostDuplicate2(self, nums, k, t):
"""bucket :type nums: List[int] :type k: int :type t: int :r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
"""brute force :type nums: List[int] :type k: int :type t: int :rtype: bool"""
for i in range(len(nums)):
for j in range(i + 1, i + k + 1):
if j >= len(nums):
break
if... | the_stack_v2_python_sparse | leetcode/hard/contains_duplicate3.py | SuperMartinYang/learning_algorithm | train | 0 | |
e62c5178d1022d4b5763661e4cbcd8602f9c716c | [
"super(ROIAlign, self).__init__(**kwargs)\nself.pool_shape = pool_shape\nself.max_pool = tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=2, name='roi_max_pool')",
"rois, feature_map_list, img_metas = inputs\nroi_indices = tf.cast(rois[:, 0], tf.int32)\nrois = rois[:, 1:]\nrois = tf.stop_gradient(rois)\npooled... | <|body_start_0|>
super(ROIAlign, self).__init__(**kwargs)
self.pool_shape = pool_shape
self.max_pool = tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=2, name='roi_max_pool')
<|end_body_0|>
<|body_start_1|>
rois, feature_map_list, img_metas = inputs
roi_indices = tf.cast(roi... | ROIAlign | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ROIAlign:
def __init__(self, pool_shape, **kwargs):
"""Implements ROI Pooling. Attributes --- pool_shape: (height, width) of the output pooled regions. Example: (14, 14)"""
<|body_0|>
def __call__(self, inputs):
"""Args --- rois: [batch_size * num_rois, (batch_ind, y... | stack_v2_sparse_classes_36k_train_021837 | 2,353 | no_license | [
{
"docstring": "Implements ROI Pooling. Attributes --- pool_shape: (height, width) of the output pooled regions. Example: (14, 14)",
"name": "__init__",
"signature": "def __init__(self, pool_shape, **kwargs)"
},
{
"docstring": "Args --- rois: [batch_size * num_rois, (batch_ind, y1, x1, y2, x2)] ... | 2 | stack_v2_sparse_classes_30k_train_005452 | Implement the Python class `ROIAlign` described below.
Class description:
Implement the ROIAlign class.
Method signatures and docstrings:
- def __init__(self, pool_shape, **kwargs): Implements ROI Pooling. Attributes --- pool_shape: (height, width) of the output pooled regions. Example: (14, 14)
- def __call__(self, ... | Implement the Python class `ROIAlign` described below.
Class description:
Implement the ROIAlign class.
Method signatures and docstrings:
- def __init__(self, pool_shape, **kwargs): Implements ROI Pooling. Attributes --- pool_shape: (height, width) of the output pooled regions. Example: (14, 14)
- def __call__(self, ... | ff1ecb407f33697b02f2f2061912841e168fd33f | <|skeleton|>
class ROIAlign:
def __init__(self, pool_shape, **kwargs):
"""Implements ROI Pooling. Attributes --- pool_shape: (height, width) of the output pooled regions. Example: (14, 14)"""
<|body_0|>
def __call__(self, inputs):
"""Args --- rois: [batch_size * num_rois, (batch_ind, y... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ROIAlign:
def __init__(self, pool_shape, **kwargs):
"""Implements ROI Pooling. Attributes --- pool_shape: (height, width) of the output pooled regions. Example: (14, 14)"""
super(ROIAlign, self).__init__(**kwargs)
self.pool_shape = pool_shape
self.max_pool = tf.keras.layers.Max... | the_stack_v2_python_sparse | venv/Lib/site-packages/detecting/models/roi_extractors/roi_align.py | RavinduAye/pythonProject | train | 0 | |
92858f4c6c8b2f078572f95837dccfd0bf4b6cfa | [
"self.component_name = component_name\nself.component_type = component_type\nself.indent = None if ndjson else 4\nself.separators = (',', ':') if ndjson else (', ', ': ')\nsuper(StructuredFormatter, self).__init__()",
"data: Dict[str, Union[str, List[str]]] = {'timestamp': datetime.utcnow().isoformat(), 'message'... | <|body_start_0|>
self.component_name = component_name
self.component_type = component_type
self.indent = None if ndjson else 4
self.separators = (',', ':') if ndjson else (', ', ': ')
super(StructuredFormatter, self).__init__()
<|end_body_0|>
<|body_start_1|>
data: Dict[... | StructuredFormatter is a Formatter for structured logging. LogRecord objects are formatted as JSON. Under the default configuration, a StructuredFormatter will render these as NDJSON (http://ndjson.org/) | StructuredFormatter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StructuredFormatter:
"""StructuredFormatter is a Formatter for structured logging. LogRecord objects are formatted as JSON. Under the default configuration, a StructuredFormatter will render these as NDJSON (http://ndjson.org/)"""
def __init__(self, component_name: Optional[str]=None, compon... | stack_v2_sparse_classes_36k_train_021838 | 3,202 | permissive | [
{
"docstring": "Create a StructuredFormatter object. component_name: Optional[str] - The name of the software component component_type: Optional[str] - The type of the software component ndjson: bool - Output as NDJSON; defaults to True.",
"name": "__init__",
"signature": "def __init__(self, component_n... | 2 | stack_v2_sparse_classes_30k_test_000578 | Implement the Python class `StructuredFormatter` described below.
Class description:
StructuredFormatter is a Formatter for structured logging. LogRecord objects are formatted as JSON. Under the default configuration, a StructuredFormatter will render these as NDJSON (http://ndjson.org/)
Method signatures and docstri... | Implement the Python class `StructuredFormatter` described below.
Class description:
StructuredFormatter is a Formatter for structured logging. LogRecord objects are formatted as JSON. Under the default configuration, a StructuredFormatter will render these as NDJSON (http://ndjson.org/)
Method signatures and docstri... | 12719efa84be2281debe98a18c69bbe7a6d0f399 | <|skeleton|>
class StructuredFormatter:
"""StructuredFormatter is a Formatter for structured logging. LogRecord objects are formatted as JSON. Under the default configuration, a StructuredFormatter will render these as NDJSON (http://ndjson.org/)"""
def __init__(self, component_name: Optional[str]=None, compon... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StructuredFormatter:
"""StructuredFormatter is a Formatter for structured logging. LogRecord objects are formatted as JSON. Under the default configuration, a StructuredFormatter will render these as NDJSON (http://ndjson.org/)"""
def __init__(self, component_name: Optional[str]=None, component_type: Opt... | the_stack_v2_python_sparse | lta/log_format.py | blinkdog/lta | train | 0 |
7381c5c01276b5e75019fb720543a79ae36f34b7 | [
"root = BinaryTree.Node(3)\nroot.left = BinaryTree.Node(1)\nroot.left.right = BinaryTree.Node(2)\ntree = BinaryTree(root)\ntree.double_rotate_left_right(tree.root)\nassert tree.root.key == 2\nassert tree.root.left.key == 1\nassert tree.root.right.key == 3",
"root = BinaryTree.Node(1)\nroot.right = BinaryTree.Node... | <|body_start_0|>
root = BinaryTree.Node(3)
root.left = BinaryTree.Node(1)
root.left.right = BinaryTree.Node(2)
tree = BinaryTree(root)
tree.double_rotate_left_right(tree.root)
assert tree.root.key == 2
assert tree.root.left.key == 1
assert tree.root.right.... | A double rotation of a BinaryTree is used in the special case where a node has a child to the left which in turn has a child to the right or the inverse case where a node has a child to the right which in turn has a child to the left. The double rotation will balance this structure. | TestBinaryTreeDoubleRotation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestBinaryTreeDoubleRotation:
"""A double rotation of a BinaryTree is used in the special case where a node has a child to the left which in turn has a child to the right or the inverse case where a node has a child to the right which in turn has a child to the left. The double rotation will bala... | stack_v2_sparse_classes_36k_train_021839 | 4,225 | no_license | [
{
"docstring": "Test a double rotation with a left-rotate followed by a right-rotate. The starting tree is on the left, the result is on the right: 3 3 2 / / / 1 ===> 2 ===> 1 3 \\\\ / 2 1",
"name": "test_double_rotate_left_right",
"signature": "def test_double_rotate_left_right(self)"
},
{
"doc... | 2 | stack_v2_sparse_classes_30k_train_005374 | Implement the Python class `TestBinaryTreeDoubleRotation` described below.
Class description:
A double rotation of a BinaryTree is used in the special case where a node has a child to the left which in turn has a child to the right or the inverse case where a node has a child to the right which in turn has a child to ... | Implement the Python class `TestBinaryTreeDoubleRotation` described below.
Class description:
A double rotation of a BinaryTree is used in the special case where a node has a child to the left which in turn has a child to the right or the inverse case where a node has a child to the right which in turn has a child to ... | f086ca1dba5f4ca329b5650b3f7b01dc9f89299d | <|skeleton|>
class TestBinaryTreeDoubleRotation:
"""A double rotation of a BinaryTree is used in the special case where a node has a child to the left which in turn has a child to the right or the inverse case where a node has a child to the right which in turn has a child to the left. The double rotation will bala... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestBinaryTreeDoubleRotation:
"""A double rotation of a BinaryTree is used in the special case where a node has a child to the left which in turn has a child to the right or the inverse case where a node has a child to the right which in turn has a child to the left. The double rotation will balance this stru... | the_stack_v2_python_sparse | data_structures/binary_tree/test_binary_tree_rotation.py | hans25041/python_practice | train | 0 |
b10502290abd996fcefeaca9860475321a342501 | [
"self.id = id\nself.name = name\nself.last_edited = APIHelper.RFC3339DateTime(last_edited) if last_edited else None\nself.additional_properties = additional_properties",
"if dictionary is None:\n return None\nid = dictionary.get('Id')\nname = dictionary.get('Name')\nlast_edited = APIHelper.RFC3339DateTime.from... | <|body_start_0|>
self.id = id
self.name = name
self.last_edited = APIHelper.RFC3339DateTime(last_edited) if last_edited else None
self.additional_properties = additional_properties
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
id = dictio... | Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the template is last edited | PdfTemplateListItem | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PdfTemplateListItem:
"""Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the template is last edited"""
def __ini... | stack_v2_sparse_classes_36k_train_021840 | 2,450 | permissive | [
{
"docstring": "Constructor for the PdfTemplateListItem class",
"name": "__init__",
"signature": "def __init__(self, id=None, name=None, last_edited=None, additional_properties={})"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary... | 2 | stack_v2_sparse_classes_30k_train_000935 | Implement the Python class `PdfTemplateListItem` described below.
Class description:
Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the te... | Implement the Python class `PdfTemplateListItem` described below.
Class description:
Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the te... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class PdfTemplateListItem:
"""Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the template is last edited"""
def __ini... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PdfTemplateListItem:
"""Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the template is last edited"""
def __init__(self, id=... | the_stack_v2_python_sparse | idfy_rest_client/models/pdf_template_list_item.py | dealflowteam/Idfy | train | 0 |
3b79f1da407ce35df0781001c524bac20927a1d4 | [
"best_side = 'index_left'\nindex_left = 0\nindex_right = len(subHeight) - 1\nfor i in range(1, int(len(subHeight) / 2 - 1)):\n index_left += 1\n index_right -= 1\n if subHeight[index_left] < subHeight[index_right]:\n best_side = 'index_left'\n elif subHeight[index_left] > subHeight[index_right]:\... | <|body_start_0|>
best_side = 'index_left'
index_left = 0
index_right = len(subHeight) - 1
for i in range(1, int(len(subHeight) / 2 - 1)):
index_left += 1
index_right -= 1
if subHeight[index_left] < subHeight[index_right]:
best_side = 'i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def best_side(self, subHeight):
"""两边相同时，往内搜索，如果单边随机选有可能选不到最好的 :param subHeight: :return:"""
<|body_0|>
def maxArea(self, height):
""":type height: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
best_side = 'index_le... | stack_v2_sparse_classes_36k_train_021841 | 1,949 | no_license | [
{
"docstring": "两边相同时，往内搜索，如果单边随机选有可能选不到最好的 :param subHeight: :return:",
"name": "best_side",
"signature": "def best_side(self, subHeight)"
},
{
"docstring": ":type height: List[int] :rtype: int",
"name": "maxArea",
"signature": "def maxArea(self, height)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000982 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def best_side(self, subHeight): 两边相同时，往内搜索，如果单边随机选有可能选不到最好的 :param subHeight: :return:
- def maxArea(self, height): :type height: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def best_side(self, subHeight): 两边相同时，往内搜索，如果单边随机选有可能选不到最好的 :param subHeight: :return:
- def maxArea(self, height): :type height: List[int] :rtype: int
<|skeleton|>
class Soluti... | 17b22a7201de65cf9ac8807efee225f475d72ef3 | <|skeleton|>
class Solution:
def best_side(self, subHeight):
"""两边相同时，往内搜索，如果单边随机选有可能选不到最好的 :param subHeight: :return:"""
<|body_0|>
def maxArea(self, height):
""":type height: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def best_side(self, subHeight):
"""两边相同时，往内搜索，如果单边随机选有可能选不到最好的 :param subHeight: :return:"""
best_side = 'index_left'
index_left = 0
index_right = len(subHeight) - 1
for i in range(1, int(len(subHeight) / 2 - 1)):
index_left += 1
index_... | the_stack_v2_python_sparse | day5/code11.py | ohquai/LeetCode | train | 0 | |
bfe842722dd7a3de77080ef6d8b16f18ddd28507 | [
"new_process_ip = request.remote_addr\nif not request.is_json:\n parser = reqparse.RequestParser()\n parser.add_argument(constants.PID_KEY, type=str, help='Process id of process attempting to join the group')\n data = parser.parse_args()\nelse:\n data = request.json\nresult = client._coordinate_process_... | <|body_start_0|>
new_process_ip = request.remote_addr
if not request.is_json:
parser = reqparse.RequestParser()
parser.add_argument(constants.PID_KEY, type=str, help='Process id of process attempting to join the group')
data = parser.parse_args()
else:
... | Message handler for coordinator operations particular to a specific process and a specific group: join group, and leave group | CoordinatorRes | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CoordinatorRes:
"""Message handler for coordinator operations particular to a specific process and a specific group: join group, and leave group"""
def post(self, process_id=None, group_id=None):
"""Handler that listens for join group messages. :param process_id: process id of coordi... | stack_v2_sparse_classes_36k_train_021842 | 8,983 | no_license | [
{
"docstring": "Handler that listens for join group messages. :param process_id: process id of coordinator (self in this case) :param group_id: group id of the group the process is trying to join body of request: { contants.PID_KEY: process id of the process making the request to join } :return:",
"name": "... | 2 | stack_v2_sparse_classes_30k_val_000693 | Implement the Python class `CoordinatorRes` described below.
Class description:
Message handler for coordinator operations particular to a specific process and a specific group: join group, and leave group
Method signatures and docstrings:
- def post(self, process_id=None, group_id=None): Handler that listens for joi... | Implement the Python class `CoordinatorRes` described below.
Class description:
Message handler for coordinator operations particular to a specific process and a specific group: join group, and leave group
Method signatures and docstrings:
- def post(self, process_id=None, group_id=None): Handler that listens for joi... | 45df130f30bcf106d863efe800ab22a5ef56cbea | <|skeleton|>
class CoordinatorRes:
"""Message handler for coordinator operations particular to a specific process and a specific group: join group, and leave group"""
def post(self, process_id=None, group_id=None):
"""Handler that listens for join group messages. :param process_id: process id of coordi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CoordinatorRes:
"""Message handler for coordinator operations particular to a specific process and a specific group: join group, and leave group"""
def post(self, process_id=None, group_id=None):
"""Handler that listens for join group messages. :param process_id: process id of coordinator (self i... | the_stack_v2_python_sparse | src/client_comms_rx.py | lavelle96/group-management-system | train | 0 |
5a3e77ed905eb5336b7117a3c08e681b279bf8ed | [
"logger.warning(f'\\tWARNING: {self.__class__.__name__} are experimental for quantile regression. They may change or be removed without warning in future releases.')\nif sample_weight is not None:\n logger.warning(f'\\tWARNING: {self.__class__.__name__} ignores sample_weight.')\nX, y = check_X_y(X, y, accept_spa... | <|body_start_0|>
logger.warning(f'\tWARNING: {self.__class__.__name__} are experimental for quantile regression. They may change or be removed without warning in future releases.')
if sample_weight is not None:
logger.warning(f'\tWARNING: {self.__class__.__name__} ignores sample_weight.')
... | BaseForestQuantileRegressor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseForestQuantileRegressor:
def fit(self, X, y, sample_weight=None):
"""Build a forest from the training set (X, y). Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`... | stack_v2_sparse_classes_36k_train_021843 | 36,172 | permissive | [
{
"docstring": "Build a forest from the training set (X, y). Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like, ... | 2 | null | Implement the Python class `BaseForestQuantileRegressor` described below.
Class description:
Implement the BaseForestQuantileRegressor class.
Method signatures and docstrings:
- def fit(self, X, y, sample_weight=None): Build a forest from the training set (X, y). Parameters ---------- X : array-like or sparse matrix,... | Implement the Python class `BaseForestQuantileRegressor` described below.
Class description:
Implement the BaseForestQuantileRegressor class.
Method signatures and docstrings:
- def fit(self, X, y, sample_weight=None): Build a forest from the training set (X, y). Parameters ---------- X : array-like or sparse matrix,... | 6af92e149491f6e5062495d87306b3625d12d992 | <|skeleton|>
class BaseForestQuantileRegressor:
def fit(self, X, y, sample_weight=None):
"""Build a forest from the training set (X, y). Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseForestQuantileRegressor:
def fit(self, X, y, sample_weight=None):
"""Build a forest from the training set (X, y). Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a spa... | the_stack_v2_python_sparse | tabular/src/autogluon/tabular/models/rf/rf_quantile.py | stjordanis/autogluon | train | 0 | |
5f668d66c245ee150a4b78534839dc3a55f76cfa | [
"try:\n args = parser.parse_args()\n action = args['action']\n if action == 'rescan':\n DBSubdomainResult.delete_by_tid(tid)\n DBSubdomainTask.update_by_id(tid, {'date': int(time.time()), 'status': 'waiting', 'end_date': 0})\n cid = t_subdomain_task.delay(tid)\n DBSubdomainTask.... | <|body_start_0|>
try:
args = parser.parse_args()
action = args['action']
if action == 'rescan':
DBSubdomainResult.delete_by_tid(tid)
DBSubdomainTask.update_by_id(tid, {'date': int(time.time()), 'status': 'waiting', 'end_date': 0})
... | SubdomainTaskManageV1 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubdomainTaskManageV1:
def put(self, tid):
"""task rescan"""
<|body_0|>
def delete(self, tid):
"""delete task by task_id DELETE /api/v1/discovery/subdomain/task/<tid> :param tid: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
... | stack_v2_sparse_classes_36k_train_021844 | 8,221 | permissive | [
{
"docstring": "task rescan",
"name": "put",
"signature": "def put(self, tid)"
},
{
"docstring": "delete task by task_id DELETE /api/v1/discovery/subdomain/task/<tid> :param tid: :return:",
"name": "delete",
"signature": "def delete(self, tid)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008826 | Implement the Python class `SubdomainTaskManageV1` described below.
Class description:
Implement the SubdomainTaskManageV1 class.
Method signatures and docstrings:
- def put(self, tid): task rescan
- def delete(self, tid): delete task by task_id DELETE /api/v1/discovery/subdomain/task/<tid> :param tid: :return: | Implement the Python class `SubdomainTaskManageV1` described below.
Class description:
Implement the SubdomainTaskManageV1 class.
Method signatures and docstrings:
- def put(self, tid): task rescan
- def delete(self, tid): delete task by task_id DELETE /api/v1/discovery/subdomain/task/<tid> :param tid: :return:
<|sk... | fadb1136b8896fe2a0f7783627bda867d5e6fd98 | <|skeleton|>
class SubdomainTaskManageV1:
def put(self, tid):
"""task rescan"""
<|body_0|>
def delete(self, tid):
"""delete task by task_id DELETE /api/v1/discovery/subdomain/task/<tid> :param tid: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubdomainTaskManageV1:
def put(self, tid):
"""task rescan"""
try:
args = parser.parse_args()
action = args['action']
if action == 'rescan':
DBSubdomainResult.delete_by_tid(tid)
DBSubdomainTask.update_by_id(tid, {'date': int(ti... | the_stack_v2_python_sparse | fuxi/web/api/discovery/subdomain_api.py | Solotov/fuxi | train | 0 | |
c3297933c2e3c35d59ed609019077c04ec1cd5cf | [
"if n < 1:\n return 0\ncount = 0\nfor i in range(1, n + 1):\n num = i\n while num != 0:\n if num % 10 == 1:\n count += 1\n num /= 10\nreturn count",
"if n < 1:\n return 0\ncount = 0\nfactot = 1\nwhile n / factot != 0:\n digit = n / factot % 10\n high = n / (10 * factot)\... | <|body_start_0|>
if n < 1:
return 0
count = 0
for i in range(1, n + 1):
num = i
while num != 0:
if num % 10 == 1:
count += 1
num /= 10
return count
<|end_body_0|>
<|body_start_1|>
if n < 1:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def countDigitOneBruteForce(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def countDigitOneMath(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if n < 1:
return 0
count = 0
... | stack_v2_sparse_classes_36k_train_021845 | 2,642 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "countDigitOneBruteForce",
"signature": "def countDigitOneBruteForce(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "countDigitOneMath",
"signature": "def countDigitOneMath(self, n)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countDigitOneBruteForce(self, n): :type n: int :rtype: int
- def countDigitOneMath(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countDigitOneBruteForce(self, n): :type n: int :rtype: int
- def countDigitOneMath(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def countDigitOneBrut... | 819fbc523f3b33742333b6b39b72337a24a26f7a | <|skeleton|>
class Solution:
def countDigitOneBruteForce(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def countDigitOneMath(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def countDigitOneBruteForce(self, n):
""":type n: int :rtype: int"""
if n < 1:
return 0
count = 0
for i in range(1, n + 1):
num = i
while num != 0:
if num % 10 == 1:
count += 1
num... | the_stack_v2_python_sparse | leetcode/Math/233. Number of Digit One整数中1的个数.py | Andrewlearning/Leetcoding | train | 1 | |
99f061aa214cc4993a3a1e02c0805a6f6c3bf848 | [
"session = Session()\ntry:\n item = find_it_asset_instance(it_asset_instance_id, organization_code, session)\n if item is None:\n raise falcon.HTTPNotFound()\n resp.media = {'data': custom_asdict(item)}\nfinally:\n session.close()",
"session = Session()\ntry:\n it_asset_instance = find_it_as... | <|body_start_0|>
session = Session()
try:
item = find_it_asset_instance(it_asset_instance_id, organization_code, session)
if item is None:
raise falcon.HTTPNotFound()
resp.media = {'data': custom_asdict(item)}
finally:
session.close... | GET and DELETE an organization's IT asset instance. | Item | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Item:
"""GET and DELETE an organization's IT asset instance."""
def on_get(self, req, resp, organization_code, it_asset_instance_id):
"""GETs a single instance of IT asset of an organization. :param req: See Falcon Request documentation. :param resp: See Falcon Response documentation... | stack_v2_sparse_classes_36k_train_021846 | 9,295 | no_license | [
{
"docstring": "GETs a single instance of IT asset of an organization. :param req: See Falcon Request documentation. :param resp: See Falcon Response documentation. :param organization_code: The code of the organization. :param it_asset_instance_id: The id of the IT asset instance to retrieve.",
"name": "on... | 3 | stack_v2_sparse_classes_30k_train_013794 | Implement the Python class `Item` described below.
Class description:
GET and DELETE an organization's IT asset instance.
Method signatures and docstrings:
- def on_get(self, req, resp, organization_code, it_asset_instance_id): GETs a single instance of IT asset of an organization. :param req: See Falcon Request docu... | Implement the Python class `Item` described below.
Class description:
GET and DELETE an organization's IT asset instance.
Method signatures and docstrings:
- def on_get(self, req, resp, organization_code, it_asset_instance_id): GETs a single instance of IT asset of an organization. :param req: See Falcon Request docu... | 62723133595829230e5b589431a32cda3b092460 | <|skeleton|>
class Item:
"""GET and DELETE an organization's IT asset instance."""
def on_get(self, req, resp, organization_code, it_asset_instance_id):
"""GETs a single instance of IT asset of an organization. :param req: See Falcon Request documentation. :param resp: See Falcon Response documentation... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Item:
"""GET and DELETE an organization's IT asset instance."""
def on_get(self, req, resp, organization_code, it_asset_instance_id):
"""GETs a single instance of IT asset of an organization. :param req: See Falcon Request documentation. :param resp: See Falcon Response documentation. :param orga... | the_stack_v2_python_sparse | knoweak/api/resources/organization_it_asset.py | psvaiter/knoweak-api | train | 0 |
2fec99e2909f81b2a0869b9545a453d08954052c | [
"self.edc_id: str = edc_id\nself.location: tuple[float, ...] = location\nself.r_mngr_config: RManagerConfig = RManagerConfig() if r_mngr_config is None else r_mngr_config\nself.pu_configs: dict[str, ProcessingUnitConfig] = dict()\nself.edc_temp: float = edc_temp\nself.cooler_config: CoolerConfig = CoolerConfig('def... | <|body_start_0|>
self.edc_id: str = edc_id
self.location: tuple[float, ...] = location
self.r_mngr_config: RManagerConfig = RManagerConfig() if r_mngr_config is None else r_mngr_config
self.pu_configs: dict[str, ProcessingUnitConfig] = dict()
self.edc_temp: float = edc_temp
... | EdgeDataCenterConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EdgeDataCenterConfig:
def __init__(self, edc_id: str, location: tuple[float, ...], r_mngr_config: RManagerConfig=None, cooler_config: CoolerConfig | None=None, edc_temp: float=298, edc_trx: TransceiverConfig=None):
"""Edge data center configuration. :param edc_id: ID of the Edge Data Cen... | stack_v2_sparse_classes_36k_train_021847 | 18,403 | permissive | [
{
"docstring": "Edge data center configuration. :param edc_id: ID of the Edge Data Center. :param location: Location of the EDC (coordinates in meters). :param r_mngr_config: Resource manager configuration. :param cooler_config: configuration of cooling infrastructure of EDC. :param edc_temp: temperature (in Ke... | 4 | null | Implement the Python class `EdgeDataCenterConfig` described below.
Class description:
Implement the EdgeDataCenterConfig class.
Method signatures and docstrings:
- def __init__(self, edc_id: str, location: tuple[float, ...], r_mngr_config: RManagerConfig=None, cooler_config: CoolerConfig | None=None, edc_temp: float=... | Implement the Python class `EdgeDataCenterConfig` described below.
Class description:
Implement the EdgeDataCenterConfig class.
Method signatures and docstrings:
- def __init__(self, edc_id: str, location: tuple[float, ...], r_mngr_config: RManagerConfig=None, cooler_config: CoolerConfig | None=None, edc_temp: float=... | cb425605de3341d27ce43fb326b300cb8ac781f6 | <|skeleton|>
class EdgeDataCenterConfig:
def __init__(self, edc_id: str, location: tuple[float, ...], r_mngr_config: RManagerConfig=None, cooler_config: CoolerConfig | None=None, edc_temp: float=298, edc_trx: TransceiverConfig=None):
"""Edge data center configuration. :param edc_id: ID of the Edge Data Cen... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EdgeDataCenterConfig:
def __init__(self, edc_id: str, location: tuple[float, ...], r_mngr_config: RManagerConfig=None, cooler_config: CoolerConfig | None=None, edc_temp: float=298, edc_trx: TransceiverConfig=None):
"""Edge data center configuration. :param edc_id: ID of the Edge Data Center. :param lo... | the_stack_v2_python_sparse | mercury/config/edcs.py | greenlsi/mercury_mso_framework | train | 2 | |
9b8f70ecb5efc70b9fbb550bf94a8d9c9ddcdb8b | [
"self.m = m\nself.group = group\nself.fullscreen = fullscreen\nbtn = MapBtn(content=icon_content, v_on='menu.on')\nslot = {'name': 'activator', 'variable': 'menu', 'children': btn}\nchildren = [card_content]\nif isinstance(card_content, sw.Tile):\n card_title = card_content.get_title()\n card_content.nest()\n... | <|body_start_0|>
self.m = m
self.group = group
self.fullscreen = fullscreen
btn = MapBtn(content=icon_content, v_on='menu.on')
slot = {'name': 'activator', 'variable': 'menu', 'children': btn}
children = [card_content]
if isinstance(card_content, sw.Tile):
... | MenuControl | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MenuControl:
def __init__(self, icon_content: str, card_content: Union[v.VuetifyWidget, str], card_title: str='', m: Optional[Map]=None, group: int=0, fullscreen: bool=False, **kwargs) -> None:
"""Widget control displaying a btn on the map. When clicked the menu expand to show the conten... | stack_v2_sparse_classes_36k_train_021848 | 6,732 | permissive | [
{
"docstring": "Widget control displaying a btn on the map. When clicked the menu expand to show the content set by the user and all the others are closed. It's used to display interactive tiles directly in the map. If the card_content is a Tile it will be automatically nested. Args: icon_content: the icon cont... | 5 | stack_v2_sparse_classes_30k_test_000404 | Implement the Python class `MenuControl` described below.
Class description:
Implement the MenuControl class.
Method signatures and docstrings:
- def __init__(self, icon_content: str, card_content: Union[v.VuetifyWidget, str], card_title: str='', m: Optional[Map]=None, group: int=0, fullscreen: bool=False, **kwargs) ... | Implement the Python class `MenuControl` described below.
Class description:
Implement the MenuControl class.
Method signatures and docstrings:
- def __init__(self, icon_content: str, card_content: Union[v.VuetifyWidget, str], card_title: str='', m: Optional[Map]=None, group: int=0, fullscreen: bool=False, **kwargs) ... | b26c7d698659d5c5a2029d02fc94dcd9daf0df98 | <|skeleton|>
class MenuControl:
def __init__(self, icon_content: str, card_content: Union[v.VuetifyWidget, str], card_title: str='', m: Optional[Map]=None, group: int=0, fullscreen: bool=False, **kwargs) -> None:
"""Widget control displaying a btn on the map. When clicked the menu expand to show the conten... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MenuControl:
def __init__(self, icon_content: str, card_content: Union[v.VuetifyWidget, str], card_title: str='', m: Optional[Map]=None, group: int=0, fullscreen: bool=False, **kwargs) -> None:
"""Widget control displaying a btn on the map. When clicked the menu expand to show the content set by the u... | the_stack_v2_python_sparse | sepal_ui/mapping/menu_control.py | 12rambau/sepal_ui | train | 17 | |
13bc22c36cac712e09d5ec992cd3225eb04fd438 | [
"super().setUp()\nself.request = self.request_context['request']\nself.request.path = '/api/v1/status/'\nself.request.META['QUERY_STRING'] = ''",
"middleware = RequestTimingMiddleware()\nmiddleware.process_request(self.request)\nself.assertTrue(hasattr(self.request, 'start_time'))",
"mock_get_tenant.return_valu... | <|body_start_0|>
super().setUp()
self.request = self.request_context['request']
self.request.path = '/api/v1/status/'
self.request.META['QUERY_STRING'] = ''
<|end_body_0|>
<|body_start_1|>
middleware = RequestTimingMiddleware()
middleware.process_request(self.request)
... | Tests against the koku tenant middleware. | RequestTimingMiddlewareTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RequestTimingMiddlewareTest:
"""Tests against the koku tenant middleware."""
def setUp(self):
"""Set up middleware tests."""
<|body_0|>
def test_process_request(self):
"""Test that the request gets a user."""
<|body_1|>
def test_process_response(self... | stack_v2_sparse_classes_36k_train_021849 | 27,733 | permissive | [
{
"docstring": "Set up middleware tests.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test that the request gets a user.",
"name": "test_process_request",
"signature": "def test_process_request(self)"
},
{
"docstring": "Test that the request gets a user.",... | 3 | stack_v2_sparse_classes_30k_train_007677 | Implement the Python class `RequestTimingMiddlewareTest` described below.
Class description:
Tests against the koku tenant middleware.
Method signatures and docstrings:
- def setUp(self): Set up middleware tests.
- def test_process_request(self): Test that the request gets a user.
- def test_process_response(self, mo... | Implement the Python class `RequestTimingMiddlewareTest` described below.
Class description:
Tests against the koku tenant middleware.
Method signatures and docstrings:
- def setUp(self): Set up middleware tests.
- def test_process_request(self): Test that the request gets a user.
- def test_process_response(self, mo... | 0416e5216eb1ec4b41c8dd4999adde218b1ab2e1 | <|skeleton|>
class RequestTimingMiddlewareTest:
"""Tests against the koku tenant middleware."""
def setUp(self):
"""Set up middleware tests."""
<|body_0|>
def test_process_request(self):
"""Test that the request gets a user."""
<|body_1|>
def test_process_response(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RequestTimingMiddlewareTest:
"""Tests against the koku tenant middleware."""
def setUp(self):
"""Set up middleware tests."""
super().setUp()
self.request = self.request_context['request']
self.request.path = '/api/v1/status/'
self.request.META['QUERY_STRING'] = ''
... | the_stack_v2_python_sparse | koku/koku/test_middleware.py | project-koku/koku | train | 225 |
c694d733d0b5b55de39e8dc3846e8e7dd92b9717 | [
"self.logger = logger.SecureTeaLogger(__name__, debug=debug)\nself.system_log_map = {'debian': '/var/log/auth.log'}\nos_name = utils.categorize_os()\nself.log_file = None\nif os_name:\n try:\n self.log_file = self.system_log_map[os_name]\n except KeyError:\n self.logger.log('Could not find path ... | <|body_start_0|>
self.logger = logger.SecureTeaLogger(__name__, debug=debug)
self.system_log_map = {'debian': '/var/log/auth.log'}
os_name = utils.categorize_os()
self.log_file = None
if os_name:
try:
self.log_file = self.system_log_map[os_name]
... | PortScan Class. | PortScan | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PortScan:
"""PortScan Class."""
def __init__(self, debug=False):
"""Initialize PortScan. Args: debug (bool): Log on terminal or not Raises: None Returns: None"""
<|body_0|>
def parse_log_file(self):
"""Parse the log file to extract IP address showing quick Reciev... | stack_v2_sparse_classes_36k_train_021850 | 5,683 | permissive | [
{
"docstring": "Initialize PortScan. Args: debug (bool): Log on terminal or not Raises: None Returns: None",
"name": "__init__",
"signature": "def __init__(self, debug=False)"
},
{
"docstring": "Parse the log file to extract IP address showing quick Recieved Disconnect. Args: None Raises: None R... | 5 | null | Implement the Python class `PortScan` described below.
Class description:
PortScan Class.
Method signatures and docstrings:
- def __init__(self, debug=False): Initialize PortScan. Args: debug (bool): Log on terminal or not Raises: None Returns: None
- def parse_log_file(self): Parse the log file to extract IP address... | Implement the Python class `PortScan` described below.
Class description:
PortScan Class.
Method signatures and docstrings:
- def __init__(self, debug=False): Initialize PortScan. Args: debug (bool): Log on terminal or not Raises: None Returns: None
- def parse_log_file(self): Parse the log file to extract IP address... | 43dec187e5848b9ced8a6b4957b6e9028d4d43cd | <|skeleton|>
class PortScan:
"""PortScan Class."""
def __init__(self, debug=False):
"""Initialize PortScan. Args: debug (bool): Log on terminal or not Raises: None Returns: None"""
<|body_0|>
def parse_log_file(self):
"""Parse the log file to extract IP address showing quick Reciev... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PortScan:
"""PortScan Class."""
def __init__(self, debug=False):
"""Initialize PortScan. Args: debug (bool): Log on terminal or not Raises: None Returns: None"""
self.logger = logger.SecureTeaLogger(__name__, debug=debug)
self.system_log_map = {'debian': '/var/log/auth.log'}
... | the_stack_v2_python_sparse | securetea/lib/log_monitor/system_log/port_scan.py | rejahrehim/SecureTea-Project | train | 1 |
4f1db3fa7960c270ec7f8a2cd615593a9cab1439 | [
"edge_strings = []\nfor u in range(0, self.num_nodes):\n for v, cost in self.adjancency_lists[u]:\n edge_strings.append('%d->%d|%d' % (u, v, cost))\nreturn '[' + ', '.join(edge_strings) + ']'",
"self.num_nodes = 0\nself.num_edges = 0\nself.adjancency_lists = []",
"with open(file_name) as f:\n self.... | <|body_start_0|>
edge_strings = []
for u in range(0, self.num_nodes):
for v, cost in self.adjancency_lists[u]:
edge_strings.append('%d->%d|%d' % (u, v, cost))
return '[' + ', '.join(edge_strings) + ']'
<|end_body_0|>
<|body_start_1|>
self.num_nodes = 0
... | Graph | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Graph:
def __repr__(self):
"""The graph as human-readable and testable string."""
<|body_0|>
def __init__(self):
"""Create an empty graph."""
<|body_1|>
def read(self, file_name):
"""Read graph from given file. >>> graph = Graph() >>> graph.read(... | stack_v2_sparse_classes_36k_train_021851 | 1,143 | no_license | [
{
"docstring": "The graph as human-readable and testable string.",
"name": "__repr__",
"signature": "def __repr__(self)"
},
{
"docstring": "Create an empty graph.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Read graph from given file. >>> graph = Gr... | 3 | stack_v2_sparse_classes_30k_train_009836 | Implement the Python class `Graph` described below.
Class description:
Implement the Graph class.
Method signatures and docstrings:
- def __repr__(self): The graph as human-readable and testable string.
- def __init__(self): Create an empty graph.
- def read(self, file_name): Read graph from given file. >>> graph = G... | Implement the Python class `Graph` described below.
Class description:
Implement the Graph class.
Method signatures and docstrings:
- def __repr__(self): The graph as human-readable and testable string.
- def __init__(self): Create an empty graph.
- def read(self, file_name): Read graph from given file. >>> graph = G... | 5e51c57c17ee8c233a0fe63f32942e80549040fd | <|skeleton|>
class Graph:
def __repr__(self):
"""The graph as human-readable and testable string."""
<|body_0|>
def __init__(self):
"""Create an empty graph."""
<|body_1|>
def read(self, file_name):
"""Read graph from given file. >>> graph = Graph() >>> graph.read(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Graph:
def __repr__(self):
"""The graph as human-readable and testable string."""
edge_strings = []
for u in range(0, self.num_nodes):
for v, cost in self.adjancency_lists[u]:
edge_strings.append('%d->%d|%d' % (u, v, cost))
return '[' + ', '.join(edg... | the_stack_v2_python_sparse | semester_two/algoDat/public/code/vorlesung-10/graph.py | fkarg/uni-stuff | train | 0 | |
652a1e2e8b6505c8509e78e302d308bdd1f23802 | [
"max_texture_size = pyglet.image.get_max_texture_size()\nself.texture_width = min(texture_width, max_texture_size)\nself.texture_height = min(texture_height, max_texture_size)\nself.atlases = []",
"for atlas in list(self.atlases):\n try:\n return atlas.add(img, border)\n except AllocatorException:\n ... | <|body_start_0|>
max_texture_size = pyglet.image.get_max_texture_size()
self.texture_width = min(texture_width, max_texture_size)
self.texture_height = min(texture_height, max_texture_size)
self.atlases = []
<|end_body_0|>
<|body_start_1|>
for atlas in list(self.atlases):
... | Collection of texture atlases. :py:class:`~pyglet.image.atlas.TextureBin` maintains a collection of texture atlases, and creates new ones as necessary to accommodate images added to the bin. | TextureBin | [
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextureBin:
"""Collection of texture atlases. :py:class:`~pyglet.image.atlas.TextureBin` maintains a collection of texture atlases, and creates new ones as necessary to accommodate images added to the bin."""
def __init__(self, texture_width: int=2048, texture_height: int=2048) -> None:
... | stack_v2_sparse_classes_36k_train_021852 | 10,284 | permissive | [
{
"docstring": "Create a texture bin for holding atlases of the given size. :Parameters: `texture_width` : int Width of texture atlases to create. `texture_height` : int Height of texture atlases to create. `border` : int Leaves specified pixels of blank space around each image added to the Atlases.",
"name... | 2 | stack_v2_sparse_classes_30k_train_013144 | Implement the Python class `TextureBin` described below.
Class description:
Collection of texture atlases. :py:class:`~pyglet.image.atlas.TextureBin` maintains a collection of texture atlases, and creates new ones as necessary to accommodate images added to the bin.
Method signatures and docstrings:
- def __init__(se... | Implement the Python class `TextureBin` described below.
Class description:
Collection of texture atlases. :py:class:`~pyglet.image.atlas.TextureBin` maintains a collection of texture atlases, and creates new ones as necessary to accommodate images added to the bin.
Method signatures and docstrings:
- def __init__(se... | 094c638f0529fecab4e74556487b92453a78753c | <|skeleton|>
class TextureBin:
"""Collection of texture atlases. :py:class:`~pyglet.image.atlas.TextureBin` maintains a collection of texture atlases, and creates new ones as necessary to accommodate images added to the bin."""
def __init__(self, texture_width: int=2048, texture_height: int=2048) -> None:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextureBin:
"""Collection of texture atlases. :py:class:`~pyglet.image.atlas.TextureBin` maintains a collection of texture atlases, and creates new ones as necessary to accommodate images added to the bin."""
def __init__(self, texture_width: int=2048, texture_height: int=2048) -> None:
"""Create... | the_stack_v2_python_sparse | pyglet/image/atlas.py | pyglet/pyglet | train | 1,687 |
6374fb173bfd37053c07ba970ea3fe46d5fee626 | [
"self.name = name\nself.data_path = paths.dn + '/data/' + name\nself.train_path = self.data_path + '/train'\nself.test_path = self.data_path + '/test'\nself.populate()\nself.cfg_path = paths.dn + '/cfg/' + name + '.cfg'\nself.dat_path = paths.dn + '/cfg/' + name + '.dat'\nself.backup_path = paths.dn + '/backup'\nse... | <|body_start_0|>
self.name = name
self.data_path = paths.dn + '/data/' + name
self.train_path = self.data_path + '/train'
self.test_path = self.data_path + '/test'
self.populate()
self.cfg_path = paths.dn + '/cfg/' + name + '.cfg'
self.dat_path = paths.dn + '/cfg/... | Wrapper for an on-disk Darknet network configuration. | Darknetwork | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Darknetwork:
"""Wrapper for an on-disk Darknet network configuration."""
def __init__(self, name):
"""Creates a new network and populates it with metadata found on disk if it could be found. Parameters ---------- name : str unique identifying name"""
<|body_0|>
def make_... | stack_v2_sparse_classes_36k_train_021853 | 5,215 | no_license | [
{
"docstring": "Creates a new network and populates it with metadata found on disk if it could be found. Parameters ---------- name : str unique identifying name",
"name": "__init__",
"signature": "def __init__(self, name)"
},
{
"docstring": "Generates the .dat file used to convey network metada... | 6 | stack_v2_sparse_classes_30k_train_015812 | Implement the Python class `Darknetwork` described below.
Class description:
Wrapper for an on-disk Darknet network configuration.
Method signatures and docstrings:
- def __init__(self, name): Creates a new network and populates it with metadata found on disk if it could be found. Parameters ---------- name : str uni... | Implement the Python class `Darknetwork` described below.
Class description:
Wrapper for an on-disk Darknet network configuration.
Method signatures and docstrings:
- def __init__(self, name): Creates a new network and populates it with metadata found on disk if it could be found. Parameters ---------- name : str uni... | bebffb2e886ba990f6b5fe6d51aa3ec4571c8d8c | <|skeleton|>
class Darknetwork:
"""Wrapper for an on-disk Darknet network configuration."""
def __init__(self, name):
"""Creates a new network and populates it with metadata found on disk if it could be found. Parameters ---------- name : str unique identifying name"""
<|body_0|>
def make_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Darknetwork:
"""Wrapper for an on-disk Darknet network configuration."""
def __init__(self, name):
"""Creates a new network and populates it with metadata found on disk if it could be found. Parameters ---------- name : str unique identifying name"""
self.name = name
self.data_pat... | the_stack_v2_python_sparse | bwi_scavenger/scripts/darknet_structure.py | utexas-bwi/scavenger_hunt | train | 2 |
e1c06e9a51f3f32d182d916d96cd7d5c9328e2bd | [
"if self.events is None:\n self.events = {}\nevents = self.events\nall_events = all_events or ALL_EVENTS\nexclude = set(exclude or ())\nfor name in all_events:\n if name in exclude:\n continue\n if name not in events:\n events[name] = []\n handlers = events[name]\n if hasattr(extension,... | <|body_start_0|>
if self.events is None:
self.events = {}
events = self.events
all_events = all_events or ALL_EVENTS
exclude = set(exclude or ())
for name in all_events:
if name in exclude:
continue
if name not in events:
... | EventMixin | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventMixin:
def bind_events(self, extension, all_events=None, exclude=None):
"""Bind ``all_events`` to an ``extension``. :param extension: an class:`.Extension` :param all_events: optional list of event names. If not supplied, the default lux events are used. :param exclude: optional lis... | stack_v2_sparse_classes_36k_train_021854 | 8,545 | permissive | [
{
"docstring": "Bind ``all_events`` to an ``extension``. :param extension: an class:`.Extension` :param all_events: optional list of event names. If not supplied, the default lux events are used. :param exclude: optional list of event to exclude",
"name": "bind_events",
"signature": "def bind_events(sel... | 2 | null | Implement the Python class `EventMixin` described below.
Class description:
Implement the EventMixin class.
Method signatures and docstrings:
- def bind_events(self, extension, all_events=None, exclude=None): Bind ``all_events`` to an ``extension``. :param extension: an class:`.Extension` :param all_events: optional ... | Implement the Python class `EventMixin` described below.
Class description:
Implement the EventMixin class.
Method signatures and docstrings:
- def bind_events(self, extension, all_events=None, exclude=None): Bind ``all_events`` to an ``extension``. :param extension: an class:`.Extension` :param all_events: optional ... | d647c34d11d1172d40e16b6afaba4ee67950fb5a | <|skeleton|>
class EventMixin:
def bind_events(self, extension, all_events=None, exclude=None):
"""Bind ``all_events`` to an ``extension``. :param extension: an class:`.Extension` :param all_events: optional list of event names. If not supplied, the default lux events are used. :param exclude: optional lis... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventMixin:
def bind_events(self, extension, all_events=None, exclude=None):
"""Bind ``all_events`` to an ``extension``. :param extension: an class:`.Extension` :param all_events: optional list of event names. If not supplied, the default lux events are used. :param exclude: optional list of event to ... | the_stack_v2_python_sparse | lux/core/extension.py | SirZazu/lux | train | 0 | |
8f1cb251047bc0f41c858c9af34575fb5203e192 | [
"instanceA = Enum()\ninstanceB = Enum()\nself.assertEqual(instanceA, instanceB)",
"instance = Enum()\nHORIZONTAL = instance.getNextId('orientation')\nVERTICAL = instance.getNextId('orientation')\nself.assertTrue(HORIZONTAL < VERTICAL)",
"class Direction:\n TOP = enum('direction')\n LEFT = enum('direction'... | <|body_start_0|>
instanceA = Enum()
instanceB = Enum()
self.assertEqual(instanceA, instanceB)
<|end_body_0|>
<|body_start_1|>
instance = Enum()
HORIZONTAL = instance.getNextId('orientation')
VERTICAL = instance.getNextId('orientation')
self.assertTrue(HORIZONTAL ... | testing of class Enum | EnumTestCase | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnumTestCase:
"""testing of class Enum"""
def testSingleton(self):
"""testing the singleton mechanism"""
<|body_0|>
def testGetNextId(self):
"""example of creating two constants"""
<|body_1|>
def testEnumFunctionWithStringContext(self):
"""ex... | stack_v2_sparse_classes_36k_train_021855 | 4,176 | permissive | [
{
"docstring": "testing the singleton mechanism",
"name": "testSingleton",
"signature": "def testSingleton(self)"
},
{
"docstring": "example of creating two constants",
"name": "testGetNextId",
"signature": "def testGetNextId(self)"
},
{
"docstring": "example of creating four con... | 4 | null | Implement the Python class `EnumTestCase` described below.
Class description:
testing of class Enum
Method signatures and docstrings:
- def testSingleton(self): testing the singleton mechanism
- def testGetNextId(self): example of creating two constants
- def testEnumFunctionWithStringContext(self): example of creati... | Implement the Python class `EnumTestCase` described below.
Class description:
testing of class Enum
Method signatures and docstrings:
- def testSingleton(self): testing the singleton mechanism
- def testGetNextId(self): example of creating two constants
- def testEnumFunctionWithStringContext(self): example of creati... | d097ca0ad6a6aee2180d32dce6a3322621f655fd | <|skeleton|>
class EnumTestCase:
"""testing of class Enum"""
def testSingleton(self):
"""testing the singleton mechanism"""
<|body_0|>
def testGetNextId(self):
"""example of creating two constants"""
<|body_1|>
def testEnumFunctionWithStringContext(self):
"""ex... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EnumTestCase:
"""testing of class Enum"""
def testSingleton(self):
"""testing the singleton mechanism"""
instanceA = Enum()
instanceB = Enum()
self.assertEqual(instanceA, instanceB)
def testGetNextId(self):
"""example of creating two constants"""
insta... | the_stack_v2_python_sparse | recipes/Python/578015_Simple_enum_mechanism/recipe-578015.py | betty29/code-1 | train | 0 |
b9c3e8297140917b9ab7f685fa74f38b3fed1ea4 | [
"from dials.util.options import ArgumentParser\nimport libtbx.load_env\nusage = 'usage: %s combined.expt combined.refl' % libtbx.env.dispatcher_name\nself.parser = ArgumentParser(usage=usage, sort_options=True, phil=phil_scope, read_experiments=True, read_reflections=True, check_format=False, epilog=help_message)",... | <|body_start_0|>
from dials.util.options import ArgumentParser
import libtbx.load_env
usage = 'usage: %s combined.expt combined.refl' % libtbx.env.dispatcher_name
self.parser = ArgumentParser(usage=usage, sort_options=True, phil=phil_scope, read_experiments=True, read_reflections=True, c... | Class to parse the command line options. | Script | [
"BSD-3-Clause-LBNL"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Script:
"""Class to parse the command line options."""
def __init__(self):
"""Set the expected options."""
<|body_0|>
def run(self):
"""Parse the options."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
from dials.util.options import ArgumentPar... | stack_v2_sparse_classes_36k_train_021856 | 7,820 | permissive | [
{
"docstring": "Set the expected options.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Parse the options.",
"name": "run",
"signature": "def run(self)"
}
] | 2 | null | Implement the Python class `Script` described below.
Class description:
Class to parse the command line options.
Method signatures and docstrings:
- def __init__(self): Set the expected options.
- def run(self): Parse the options. | Implement the Python class `Script` described below.
Class description:
Class to parse the command line options.
Method signatures and docstrings:
- def __init__(self): Set the expected options.
- def run(self): Parse the options.
<|skeleton|>
class Script:
"""Class to parse the command line options."""
def... | 7f4dfb6c873fd560920f697cbfd8a5ff6eed82fa | <|skeleton|>
class Script:
"""Class to parse the command line options."""
def __init__(self):
"""Set the expected options."""
<|body_0|>
def run(self):
"""Parse the options."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Script:
"""Class to parse the command line options."""
def __init__(self):
"""Set the expected options."""
from dials.util.options import ArgumentParser
import libtbx.load_env
usage = 'usage: %s combined.expt combined.refl' % libtbx.env.dispatcher_name
self.parser ... | the_stack_v2_python_sparse | xfel/command_line/filter_experiments_by_rmsd.py | cctbx/cctbx_project | train | 206 |
aeb8e21c411e031d8cda978ce85e924ca1145d22 | [
"super(HypervisorsClient, self).__init__(serialize_format, deserialize_format)\nself.auth_token = auth_token\nself.default_headers['X-Auth-Token'] = auth_token\nct = ''.join(['application/', self.serialize_format])\naccept = ''.join(['application/', self.deserialize_format])\nself.default_headers['Content-Type'] = ... | <|body_start_0|>
super(HypervisorsClient, self).__init__(serialize_format, deserialize_format)
self.auth_token = auth_token
self.default_headers['X-Auth-Token'] = auth_token
ct = ''.join(['application/', self.serialize_format])
accept = ''.join(['application/', self.deserialize_f... | HypervisorsClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HypervisorsClient:
def __init__(self, url, auth_token, serialize_format=None, deserialize_format=None):
"""@param url: Base URL for the compute service @type url: String @param auth_token: Auth token to be used for all requests @type auth_token: String @param serialize_format: Format for... | stack_v2_sparse_classes_36k_train_021857 | 3,393 | permissive | [
{
"docstring": "@param url: Base URL for the compute service @type url: String @param auth_token: Auth token to be used for all requests @type auth_token: String @param serialize_format: Format for serializing requests @type serialize_format: String @param deserialize_format: Format for de-serializing responses... | 4 | stack_v2_sparse_classes_30k_train_008276 | Implement the Python class `HypervisorsClient` described below.
Class description:
Implement the HypervisorsClient class.
Method signatures and docstrings:
- def __init__(self, url, auth_token, serialize_format=None, deserialize_format=None): @param url: Base URL for the compute service @type url: String @param auth_... | Implement the Python class `HypervisorsClient` described below.
Class description:
Implement the HypervisorsClient class.
Method signatures and docstrings:
- def __init__(self, url, auth_token, serialize_format=None, deserialize_format=None): @param url: Base URL for the compute service @type url: String @param auth_... | 7d49cf6bfd7e1a6e5b739e7de52f2e18e5ccf924 | <|skeleton|>
class HypervisorsClient:
def __init__(self, url, auth_token, serialize_format=None, deserialize_format=None):
"""@param url: Base URL for the compute service @type url: String @param auth_token: Auth token to be used for all requests @type auth_token: String @param serialize_format: Format for... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HypervisorsClient:
def __init__(self, url, auth_token, serialize_format=None, deserialize_format=None):
"""@param url: Base URL for the compute service @type url: String @param auth_token: Auth token to be used for all requests @type auth_token: String @param serialize_format: Format for serializing r... | the_stack_v2_python_sparse | cloudcafe/compute/hypervisors_api/client.py | kurhula/cloudcafe | train | 0 | |
5aac1328faf366fa51f598d9699c2d818bd5471a | [
"self.driver.get('http://www.baidu.com')\nself.driver.find_element_by_id('kw').send_keys('selenium测试')\nelement = self.driver.execute_script(\"return document.getElementById('su')\")\nelement.click()\nself.driver.execute_script('document.documentElement.scrollTop=0')\nsleep(1)\nself.driver.execute_script('document.... | <|body_start_0|>
self.driver.get('http://www.baidu.com')
self.driver.find_element_by_id('kw').send_keys('selenium测试')
element = self.driver.execute_script("return document.getElementById('su')")
element.click()
self.driver.execute_script('document.documentElement.scrollTop=0')
... | jS测试类 | TestJs | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestJs:
"""jS测试类"""
def test_js_scroll(self):
"""js滑动处理"""
<|body_0|>
def test_datetime(self):
"""更改12306出发日期的时间控件"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.driver.get('http://www.baidu.com')
self.driver.find_element_by_id('kw... | stack_v2_sparse_classes_36k_train_021858 | 1,741 | no_license | [
{
"docstring": "js滑动处理",
"name": "test_js_scroll",
"signature": "def test_js_scroll(self)"
},
{
"docstring": "更改12306出发日期的时间控件",
"name": "test_datetime",
"signature": "def test_datetime(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007981 | Implement the Python class `TestJs` described below.
Class description:
jS测试类
Method signatures and docstrings:
- def test_js_scroll(self): js滑动处理
- def test_datetime(self): 更改12306出发日期的时间控件 | Implement the Python class `TestJs` described below.
Class description:
jS测试类
Method signatures and docstrings:
- def test_js_scroll(self): js滑动处理
- def test_datetime(self): 更改12306出发日期的时间控件
<|skeleton|>
class TestJs:
"""jS测试类"""
def test_js_scroll(self):
"""js滑动处理"""
<|body_0|>
def tes... | 41651054386069fb3da5ec80d4acd922561f6de5 | <|skeleton|>
class TestJs:
"""jS测试类"""
def test_js_scroll(self):
"""js滑动处理"""
<|body_0|>
def test_datetime(self):
"""更改12306出发日期的时间控件"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestJs:
"""jS测试类"""
def test_js_scroll(self):
"""js滑动处理"""
self.driver.get('http://www.baidu.com')
self.driver.find_element_by_id('kw').send_keys('selenium测试')
element = self.driver.execute_script("return document.getElementById('su')")
element.click()
self... | the_stack_v2_python_sparse | com/python/pytest_test1/selenium_js/test_js.py | fengzige1993/PythonData | train | 0 |
55f6da9f4aa1cf2d35fbfae4560bc4dcd0697d1c | [
"letters = [1] * n\nrunning_total = n\nfor i in range(len(letters) - 1, -1, -1):\n if running_total == k:\n break\n elif running_total + 25 <= k:\n letters[i] += 25\n running_total += 25\n else:\n letters[i] += k - running_total\n running_total += k - running_total\nretur... | <|body_start_0|>
letters = [1] * n
running_total = n
for i in range(len(letters) - 1, -1, -1):
if running_total == k:
break
elif running_total + 25 <= k:
letters[i] += 25
running_total += 25
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getSmallestString(self, n, k):
""":type n: int :type k: int :rtype: str"""
<|body_0|>
def getSmallestString(self, n, k):
""":type n: int :type k: int :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
letters = [1] * n
... | stack_v2_sparse_classes_36k_train_021859 | 2,141 | no_license | [
{
"docstring": ":type n: int :type k: int :rtype: str",
"name": "getSmallestString",
"signature": "def getSmallestString(self, n, k)"
},
{
"docstring": ":type n: int :type k: int :rtype: str",
"name": "getSmallestString",
"signature": "def getSmallestString(self, n, k)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getSmallestString(self, n, k): :type n: int :type k: int :rtype: str
- def getSmallestString(self, n, k): :type n: int :type k: int :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getSmallestString(self, n, k): :type n: int :type k: int :rtype: str
- def getSmallestString(self, n, k): :type n: int :type k: int :rtype: str
<|skeleton|>
class Solution:
... | 844f502da4d6fb9cd69cf0a1ef71da3385a4d2b4 | <|skeleton|>
class Solution:
def getSmallestString(self, n, k):
""":type n: int :type k: int :rtype: str"""
<|body_0|>
def getSmallestString(self, n, k):
""":type n: int :type k: int :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def getSmallestString(self, n, k):
""":type n: int :type k: int :rtype: str"""
letters = [1] * n
running_total = n
for i in range(len(letters) - 1, -1, -1):
if running_total == k:
break
elif running_total + 25 <= k:
... | the_stack_v2_python_sparse | 1663-smallest_string_with_given_numeric_value.py | stevestar888/leetcode-problems | train | 2 | |
c2f2115a4594c0cf5a2c1627d82f006cad890554 | [
"assert isinstance(response, scrapy.http.response.html.HtmlResponse)\nurls = [response.url]\nposts_per_page = 25\nlast_page = response.selector.xpath('//a[contains(@title, \"Click to jump to page\")]/strong[2]/text()').extract_first()\nif last_page:\n last_page = read_number(last_page)\nelse:\n last_page = 0\... | <|body_start_0|>
assert isinstance(response, scrapy.http.response.html.HtmlResponse)
urls = [response.url]
posts_per_page = 25
last_page = response.selector.xpath('//a[contains(@title, "Click to jump to page")]/strong[2]/text()').extract_first()
if last_page:
last_pag... | scrape images from angling addicts forum | AnglingAddictsSpeciesHuntImageSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnglingAddictsSpeciesHuntImageSpider:
"""scrape images from angling addicts forum"""
def parse(self, response):
"""get links to post pages in board YIELDING...: http://www.anglingaddicts.co.uk/forum/saltwater-species-hunt-f97.html http://www.anglingaddicts.co.uk/forum/saltwater-speci... | stack_v2_sparse_classes_36k_train_021860 | 4,571 | no_license | [
{
"docstring": "get links to post pages in board YIELDING...: http://www.anglingaddicts.co.uk/forum/saltwater-species-hunt-f97.html http://www.anglingaddicts.co.uk/forum/saltwater-species-hunt-f97-25.html",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "get all thre... | 3 | null | Implement the Python class `AnglingAddictsSpeciesHuntImageSpider` described below.
Class description:
scrape images from angling addicts forum
Method signatures and docstrings:
- def parse(self, response): get links to post pages in board YIELDING...: http://www.anglingaddicts.co.uk/forum/saltwater-species-hunt-f97.h... | Implement the Python class `AnglingAddictsSpeciesHuntImageSpider` described below.
Class description:
scrape images from angling addicts forum
Method signatures and docstrings:
- def parse(self, response): get links to post pages in board YIELDING...: http://www.anglingaddicts.co.uk/forum/saltwater-species-hunt-f97.h... | 9123aa6baf538b662143b9098d963d55165e8409 | <|skeleton|>
class AnglingAddictsSpeciesHuntImageSpider:
"""scrape images from angling addicts forum"""
def parse(self, response):
"""get links to post pages in board YIELDING...: http://www.anglingaddicts.co.uk/forum/saltwater-species-hunt-f97.html http://www.anglingaddicts.co.uk/forum/saltwater-speci... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnglingAddictsSpeciesHuntImageSpider:
"""scrape images from angling addicts forum"""
def parse(self, response):
"""get links to post pages in board YIELDING...: http://www.anglingaddicts.co.uk/forum/saltwater-species-hunt-f97.html http://www.anglingaddicts.co.uk/forum/saltwater-species-hunt-f97-2... | the_stack_v2_python_sparse | imgscrape/spiders/angling_addicts.py | gmonkman/python | train | 0 |
f53bb2124ce5b4d5d7c7dcdd43be118a412e5ba0 | [
"for line in matrix:\n if target in line:\n return True\nreturn False",
"h = len(matrix)\nif h == 0:\n return False\nw = len(matrix[0])\nif w == 0:\n return False\nleft = 0\nright = h * w - 1\nwhile left <= right:\n mid = (left + right) // 2\n i = mid // w\n j = mid % w\n if matrix[i][... | <|body_start_0|>
for line in matrix:
if target in line:
return True
return False
<|end_body_0|>
<|body_start_1|>
h = len(matrix)
if h == 0:
return False
w = len(matrix[0])
if w == 0:
return False
left = 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def searchMatrix(self, matrix: List[List[int]], target: int) -> bool:
"""可以直接搜索，也就是暴力搜索法，但是这种效率太低 注意题目说的是高效的算法 :param matrix:list :param target: int :return: bool"""
<|body_0|>
def searchMatrix1(self, matrix: List[List[int]], target: int) -> bool:
"""利用二分法查... | stack_v2_sparse_classes_36k_train_021861 | 2,816 | no_license | [
{
"docstring": "可以直接搜索，也就是暴力搜索法，但是这种效率太低 注意题目说的是高效的算法 :param matrix:list :param target: int :return: bool",
"name": "searchMatrix",
"signature": "def searchMatrix(self, matrix: List[List[int]], target: int) -> bool"
},
{
"docstring": "利用二分法查找，其时间复杂度为O(mn) 注意： h = len(matrix) if h == 0: return Fa... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchMatrix(self, matrix: List[List[int]], target: int) -> bool: 可以直接搜索，也就是暴力搜索法，但是这种效率太低 注意题目说的是高效的算法 :param matrix:list :param target: int :return: bool
- def searchMatrix... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchMatrix(self, matrix: List[List[int]], target: int) -> bool: 可以直接搜索，也就是暴力搜索法，但是这种效率太低 注意题目说的是高效的算法 :param matrix:list :param target: int :return: bool
- def searchMatrix... | 51943e2c2c4ec70c7c1d5b53c9fdf0a719428d7a | <|skeleton|>
class Solution:
def searchMatrix(self, matrix: List[List[int]], target: int) -> bool:
"""可以直接搜索，也就是暴力搜索法，但是这种效率太低 注意题目说的是高效的算法 :param matrix:list :param target: int :return: bool"""
<|body_0|>
def searchMatrix1(self, matrix: List[List[int]], target: int) -> bool:
"""利用二分法查... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def searchMatrix(self, matrix: List[List[int]], target: int) -> bool:
"""可以直接搜索，也就是暴力搜索法，但是这种效率太低 注意题目说的是高效的算法 :param matrix:list :param target: int :return: bool"""
for line in matrix:
if target in line:
return True
return False
def searchMat... | the_stack_v2_python_sparse | LeetCode_practice/0074_SearchMatrix.py | LeBron-Jian/BasicAlgorithmPractice | train | 13 | |
7356b3fd65d28b754e4a6e1ea3de68f95059bcfe | [
"if not rid:\n return\nlog = t_log_onoffline()\nlog.rid = rid\nlog.sock = sock\nDBEngine.Add(log)",
"if not rid:\n return\nlog = t_log_onoffline()\nlog.rid = rid\nlog.opt = 'off'\nlog.sock = sock\nDBEngine.Add(log)"
] | <|body_start_0|>
if not rid:
return
log = t_log_onoffline()
log.rid = rid
log.sock = sock
DBEngine.Add(log)
<|end_body_0|>
<|body_start_1|>
if not rid:
return
log = t_log_onoffline()
log.rid = rid
log.opt = 'off'
lo... | 角色在线|离线日志 | t_log_onoffline | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class t_log_onoffline:
"""角色在线|离线日志"""
def On(rid, sock):
"""在线 :param rid: 角色rid :param sock: sockfileno :return:"""
<|body_0|>
def Off(rid, sock):
"""离线 :param rid: 角色rid :param sock: sockfileno :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_021862 | 1,373 | no_license | [
{
"docstring": "在线 :param rid: 角色rid :param sock: sockfileno :return:",
"name": "On",
"signature": "def On(rid, sock)"
},
{
"docstring": "离线 :param rid: 角色rid :param sock: sockfileno :return:",
"name": "Off",
"signature": "def Off(rid, sock)"
}
] | 2 | null | Implement the Python class `t_log_onoffline` described below.
Class description:
角色在线|离线日志
Method signatures and docstrings:
- def On(rid, sock): 在线 :param rid: 角色rid :param sock: sockfileno :return:
- def Off(rid, sock): 离线 :param rid: 角色rid :param sock: sockfileno :return: | Implement the Python class `t_log_onoffline` described below.
Class description:
角色在线|离线日志
Method signatures and docstrings:
- def On(rid, sock): 在线 :param rid: 角色rid :param sock: sockfileno :return:
- def Off(rid, sock): 离线 :param rid: 角色rid :param sock: sockfileno :return:
<|skeleton|>
class t_log_onoffline:
"... | fa1591863985a418fd361eb6dac36d1301bc1231 | <|skeleton|>
class t_log_onoffline:
"""角色在线|离线日志"""
def On(rid, sock):
"""在线 :param rid: 角色rid :param sock: sockfileno :return:"""
<|body_0|>
def Off(rid, sock):
"""离线 :param rid: 角色rid :param sock: sockfileno :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class t_log_onoffline:
"""角色在线|离线日志"""
def On(rid, sock):
"""在线 :param rid: 角色rid :param sock: sockfileno :return:"""
if not rid:
return
log = t_log_onoffline()
log.rid = rid
log.sock = sock
DBEngine.Add(log)
def Off(rid, sock):
"""离线 :pa... | the_stack_v2_python_sparse | learn_hb_game/game/source/DataBase/Table/Log/t_log_onoffline.py | isoundy000/learn_python | train | 0 |
9b5d5373e907fc76cabfe781745d27d75de2dec5 | [
"self.screen_width = 1200\nself.screen_height = 800\nself.menu_bg_color = (0, 0, 0)\nself.game_bg_color = (0, 0, 255)\nself.font_color = (255, 255, 255)\nself.title_font_size = 100\nself.sub_title_font_size = 80\nself.menu_font_size = 48\nself.show_menu = True\nself.show_high_scores = False\nself.show_game = False\... | <|body_start_0|>
self.screen_width = 1200
self.screen_height = 800
self.menu_bg_color = (0, 0, 0)
self.game_bg_color = (0, 0, 255)
self.font_color = (255, 255, 255)
self.title_font_size = 100
self.sub_title_font_size = 80
self.menu_font_size = 48
s... | A class to store all settings for Alien Invasion | Settings | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Settings:
"""A class to store all settings for Alien Invasion"""
def __init__(self):
"""Initialize the game's static settings"""
<|body_0|>
def initialize_dynamic_settings(self):
"""Init settings that change throughout the game"""
<|body_1|>
def incr... | stack_v2_sparse_classes_36k_train_021863 | 2,509 | no_license | [
{
"docstring": "Initialize the game's static settings",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Init settings that change throughout the game",
"name": "initialize_dynamic_settings",
"signature": "def initialize_dynamic_settings(self)"
},
{
"docst... | 3 | stack_v2_sparse_classes_30k_train_019450 | Implement the Python class `Settings` described below.
Class description:
A class to store all settings for Alien Invasion
Method signatures and docstrings:
- def __init__(self): Initialize the game's static settings
- def initialize_dynamic_settings(self): Init settings that change throughout the game
- def increase... | Implement the Python class `Settings` described below.
Class description:
A class to store all settings for Alien Invasion
Method signatures and docstrings:
- def __init__(self): Initialize the game's static settings
- def initialize_dynamic_settings(self): Init settings that change throughout the game
- def increase... | eed86936f440e2881d3671fead051f30bc680e43 | <|skeleton|>
class Settings:
"""A class to store all settings for Alien Invasion"""
def __init__(self):
"""Initialize the game's static settings"""
<|body_0|>
def initialize_dynamic_settings(self):
"""Init settings that change throughout the game"""
<|body_1|>
def incr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Settings:
"""A class to store all settings for Alien Invasion"""
def __init__(self):
"""Initialize the game's static settings"""
self.screen_width = 1200
self.screen_height = 800
self.menu_bg_color = (0, 0, 0)
self.game_bg_color = (0, 0, 255)
self.font_colo... | the_stack_v2_python_sparse | Alien Invasion/settings.py | dpham147/Python | train | 0 |
05113f3c399cfafc2a6bf09083db5747c81a0c2d | [
"self.prefix = args[0]\nself.suffix = args[1]\nself.order = order",
"for entry in results:\n entry['link'] = self.prefix + entry['link'] + self.suffix\nreturn results"
] | <|body_start_0|>
self.prefix = args[0]
self.suffix = args[1]
self.order = order
<|end_body_0|>
<|body_start_1|>
for entry in results:
entry['link'] = self.prefix + entry['link'] + self.suffix
return results
<|end_body_1|>
| Decorates links to search results with given pre- and suffixes, returning [prefix]+url+[suffix]. | URLDecorator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class URLDecorator:
"""Decorates links to search results with given pre- and suffixes, returning [prefix]+url+[suffix]."""
def __init__(self, args, order=0):
"""Constructor for URLDecorator. Parameters: * prefix: String to preceed the original url. * suffix: String to tail the original url... | stack_v2_sparse_classes_36k_train_021864 | 726 | permissive | [
{
"docstring": "Constructor for URLDecorator. Parameters: * prefix: String to preceed the original url. * suffix: String to tail the original url.",
"name": "__init__",
"signature": "def __init__(self, args, order=0)"
},
{
"docstring": "Returns a result set with modified urls.",
"name": "mod... | 2 | null | Implement the Python class `URLDecorator` described below.
Class description:
Decorates links to search results with given pre- and suffixes, returning [prefix]+url+[suffix].
Method signatures and docstrings:
- def __init__(self, args, order=0): Constructor for URLDecorator. Parameters: * prefix: String to preceed th... | Implement the Python class `URLDecorator` described below.
Class description:
Decorates links to search results with given pre- and suffixes, returning [prefix]+url+[suffix].
Method signatures and docstrings:
- def __init__(self, args, order=0): Constructor for URLDecorator. Parameters: * prefix: String to preceed th... | ed72aee466649bd834d5b4459eb6e0173df6e2ec | <|skeleton|>
class URLDecorator:
"""Decorates links to search results with given pre- and suffixes, returning [prefix]+url+[suffix]."""
def __init__(self, args, order=0):
"""Constructor for URLDecorator. Parameters: * prefix: String to preceed the original url. * suffix: String to tail the original url... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class URLDecorator:
"""Decorates links to search results with given pre- and suffixes, returning [prefix]+url+[suffix]."""
def __init__(self, args, order=0):
"""Constructor for URLDecorator. Parameters: * prefix: String to preceed the original url. * suffix: String to tail the original url."""
... | the_stack_v2_python_sparse | reference-code/puppy/result/modifier/urldecorator.py | Granvanoeli/ifind | train | 0 |
dac925a2145fa34e8bd7ef71b271c2d78bc05f2c | [
"samples = len(y_pred)\ny_pred_clipped = np.clip(y_pred, 1e-07, 1 - 1e-07)\nif len(y_true.shape) == 1:\n correct_confidences = y_pred_clipped[range(samples), y_true]\nelif len(y_true.shape) == 2:\n correct_confidences = np.sum(y_pred_clipped * y_true, axis=1)\nnegative_log_likelihoods = -np.log(correct_confid... | <|body_start_0|>
samples = len(y_pred)
y_pred_clipped = np.clip(y_pred, 1e-07, 1 - 1e-07)
if len(y_true.shape) == 1:
correct_confidences = y_pred_clipped[range(samples), y_true]
elif len(y_true.shape) == 2:
correct_confidences = np.sum(y_pred_clipped * y_true, axi... | The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116] | CategoricalCrossentropy | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CategoricalCrossentropy:
"""The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]"""
def forward(self, y_pred, y_true):
"""Performs the forward p... | stack_v2_sparse_classes_36k_train_021865 | 2,192 | no_license | [
{
"docstring": "Performs the forward pass. Args : y_pred(np.array): Model predictions y_true(np.array): Actual values Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]",
"name": "forward",
"signature": "def forward(self, y_pred, y_true)"
},
{
"docstring": "... | 2 | stack_v2_sparse_classes_30k_val_001002 | Implement the Python class `CategoricalCrossentropy` described below.
Class description:
The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]
Method signatures and docstrings:
- ... | Implement the Python class `CategoricalCrossentropy` described below.
Class description:
The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]
Method signatures and docstrings:
- ... | 8ffd24971d8808e7c9caa722a7ff4df306b75b90 | <|skeleton|>
class CategoricalCrossentropy:
"""The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]"""
def forward(self, y_pred, y_true):
"""Performs the forward p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CategoricalCrossentropy:
"""The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]"""
def forward(self, y_pred, y_true):
"""Performs the forward pass. Args : y... | the_stack_v2_python_sparse | Music Recognizer/Metrics/CategoricalCrossentropy.py | andutzu7/Lucrare-Licenta-MusicRecognizer | train | 0 |
d1f09bf7a77d0bca021cc42435855bd12f5e7557 | [
"model_card_dict = self.to_dict()\nmodel_card_dict[json_utils.SCHEMA_VERSION_STRING] = json_utils.get_latest_schema_version()\nreturn json_lib.dumps(model_card_dict, indent=2)",
"if isinstance(json, str):\n json = json_lib.loads(json)\njson_utils.validate_json_schema(json)\nself._from_json(json, self)\nreturn ... | <|body_start_0|>
model_card_dict = self.to_dict()
model_card_dict[json_utils.SCHEMA_VERSION_STRING] = json_utils.get_latest_schema_version()
return json_lib.dumps(model_card_dict, indent=2)
<|end_body_0|>
<|body_start_1|>
if isinstance(json, str):
json = json_lib.loads(json)... | Fields used to generate the Model Card. Attributes: model_details: Descriptive metadata for the model. model_parameters: Technical metadata for the model. quantitative_analysis: Quantitative analysis of model performance. considerations: Any considerations related to model construction, training, and application. | ModelCard | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelCard:
"""Fields used to generate the Model Card. Attributes: model_details: Descriptive metadata for the model. model_parameters: Technical metadata for the model. quantitative_analysis: Quantitative analysis of model performance. considerations: Any considerations related to model construct... | stack_v2_sparse_classes_36k_train_021866 | 22,161 | permissive | [
{
"docstring": "Write ModelCard to JSON.",
"name": "to_json",
"signature": "def to_json(self) -> str"
},
{
"docstring": "Reads ModelCard from JSON. This function will only overwrite ModelCard fields specified in the JSON. Args: json: A JSON object from which to populate fields in the model card.... | 5 | stack_v2_sparse_classes_30k_train_014385 | Implement the Python class `ModelCard` described below.
Class description:
Fields used to generate the Model Card. Attributes: model_details: Descriptive metadata for the model. model_parameters: Technical metadata for the model. quantitative_analysis: Quantitative analysis of model performance. considerations: Any co... | Implement the Python class `ModelCard` described below.
Class description:
Fields used to generate the Model Card. Attributes: model_details: Descriptive metadata for the model. model_parameters: Technical metadata for the model. quantitative_analysis: Quantitative analysis of model performance. considerations: Any co... | 74d7e6d8d3163b830711b226491ccd976a2d7018 | <|skeleton|>
class ModelCard:
"""Fields used to generate the Model Card. Attributes: model_details: Descriptive metadata for the model. model_parameters: Technical metadata for the model. quantitative_analysis: Quantitative analysis of model performance. considerations: Any considerations related to model construct... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModelCard:
"""Fields used to generate the Model Card. Attributes: model_details: Descriptive metadata for the model. model_parameters: Technical metadata for the model. quantitative_analysis: Quantitative analysis of model performance. considerations: Any considerations related to model construction, training... | the_stack_v2_python_sparse | model_card_toolkit/model_card.py | tensorflow/model-card-toolkit | train | 389 |
34ce94b4488517cf866c95e2e2b2fd21f00fe6b8 | [
"while True:\n result = 0\n while num != 0:\n ind = num % 10\n num = num // 10\n result += ind\n if result < 10:\n break\n else:\n num = result\nreturn result",
"while num >= 10:\n num = sum(map(int, str(num)))\nreturn num",
"if num == 0:\n return 0\nelse:\n ... | <|body_start_0|>
while True:
result = 0
while num != 0:
ind = num % 10
num = num // 10
result += ind
if result < 10:
break
else:
num = result
return result
<|end_body_0|>
<|bo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addDigits(self, num):
""":type num: int :rtype: int"""
<|body_0|>
def addDigits(self, num):
""":type num: int :rtype: int"""
<|body_1|>
def addDigits(self, num):
""":type num: int :rtype: int"""
<|body_2|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_021867 | 969 | no_license | [
{
"docstring": ":type num: int :rtype: int",
"name": "addDigits",
"signature": "def addDigits(self, num)"
},
{
"docstring": ":type num: int :rtype: int",
"name": "addDigits",
"signature": "def addDigits(self, num)"
},
{
"docstring": ":type num: int :rtype: int",
"name": "addD... | 3 | stack_v2_sparse_classes_30k_train_000712 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addDigits(self, num): :type num: int :rtype: int
- def addDigits(self, num): :type num: int :rtype: int
- def addDigits(self, num): :type num: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addDigits(self, num): :type num: int :rtype: int
- def addDigits(self, num): :type num: int :rtype: int
- def addDigits(self, num): :type num: int :rtype: int
<|skeleton|>
c... | c92a5ddcc56e3f69be1e6fb25e9c8ed277e57ee0 | <|skeleton|>
class Solution:
def addDigits(self, num):
""":type num: int :rtype: int"""
<|body_0|>
def addDigits(self, num):
""":type num: int :rtype: int"""
<|body_1|>
def addDigits(self, num):
""":type num: int :rtype: int"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def addDigits(self, num):
""":type num: int :rtype: int"""
while True:
result = 0
while num != 0:
ind = num % 10
num = num // 10
result += ind
if result < 10:
break
else:
... | the_stack_v2_python_sparse | code/258#Add Digits.py | EachenKuang/LeetCode | train | 28 | |
10544f4c60b88773dd53ae4e8f51be8feb718ac5 | [
"if not root:\n return ''\nresult = []\nstack = [root]\nwhile stack:\n node = stack.pop()\n result.append(str(node.val))\n if node.right:\n stack.append(node.right)\n if node.left:\n stack.append(node.left)\nreturn '#'.join(result)",
"if not data:\n return None\nelements = collecti... | <|body_start_0|>
if not root:
return ''
result = []
stack = [root]
while stack:
node = stack.pop()
result.append(str(node.val))
if node.right:
stack.append(node.right)
if node.left:
stack.append(n... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
def deserialize2(self, data: str) -> TreeNode:
... | stack_v2_sparse_classes_36k_train_021868 | 2,157 | no_license | [
{
"docstring": "Encodes a tree to a single string.",
"name": "serialize",
"signature": "def serialize(self, root: TreeNode) -> str"
},
{
"docstring": "Decodes your encoded data to tree.",
"name": "deserialize",
"signature": "def deserialize(self, data: str) -> TreeNode"
},
{
"doc... | 3 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree.
- def deserialize2(sel... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree.
- def deserialize2(sel... | d953abe2c9680f636563e76287d2f907e90ced63 | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
def deserialize2(self, data: str) -> TreeNode:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
if not root:
return ''
result = []
stack = [root]
while stack:
node = stack.pop()
result.append(str(node.val))
if node.right:
... | the_stack_v2_python_sparse | python_leetcode_2020/Python_Leetcode_2020/serialization/449_serialize_and_deserialize_BST.py | xiangcao/Leetcode | train | 0 | |
a30319e7f31c3c93f0b092ab286e46ce7e313c66 | [
"if not isinstance(a_list, list) or not all((isinstance(sub, list) for sub in a_list)):\n return None\ntry:\n arr = np.array(a_list, dtype=dtype)\n if arr.dtype == object:\n return None\n return arr\nexcept ValueError:\n return None",
"if isinstance(a_tuple, tuple):\n try:\n arr = ... | <|body_start_0|>
if not isinstance(a_list, list) or not all((isinstance(sub, list) for sub in a_list)):
return None
try:
arr = np.array(a_list, dtype=dtype)
if arr.dtype == object:
return None
return arr
except ValueError:
... | NumPyCreator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumPyCreator:
def from_list(self, a_list, dtype=None):
"""Convert a list or nested list into a NumPy array"""
<|body_0|>
def from_tuple(self, a_tuple, dtype=None):
"""Convert a tuple or nested tuples into a NumPy array"""
<|body_1|>
def from_iterable(sel... | stack_v2_sparse_classes_36k_train_021869 | 2,322 | no_license | [
{
"docstring": "Convert a list or nested list into a NumPy array",
"name": "from_list",
"signature": "def from_list(self, a_list, dtype=None)"
},
{
"docstring": "Convert a tuple or nested tuples into a NumPy array",
"name": "from_tuple",
"signature": "def from_tuple(self, a_tuple, dtype=... | 6 | stack_v2_sparse_classes_30k_train_017916 | Implement the Python class `NumPyCreator` described below.
Class description:
Implement the NumPyCreator class.
Method signatures and docstrings:
- def from_list(self, a_list, dtype=None): Convert a list or nested list into a NumPy array
- def from_tuple(self, a_tuple, dtype=None): Convert a tuple or nested tuples in... | Implement the Python class `NumPyCreator` described below.
Class description:
Implement the NumPyCreator class.
Method signatures and docstrings:
- def from_list(self, a_list, dtype=None): Convert a list or nested list into a NumPy array
- def from_tuple(self, a_tuple, dtype=None): Convert a tuple or nested tuples in... | 24358cc6807d86fe5da766bb4505eef29f1e371f | <|skeleton|>
class NumPyCreator:
def from_list(self, a_list, dtype=None):
"""Convert a list or nested list into a NumPy array"""
<|body_0|>
def from_tuple(self, a_tuple, dtype=None):
"""Convert a tuple or nested tuples into a NumPy array"""
<|body_1|>
def from_iterable(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumPyCreator:
def from_list(self, a_list, dtype=None):
"""Convert a list or nested list into a NumPy array"""
if not isinstance(a_list, list) or not all((isinstance(sub, list) for sub in a_list)):
return None
try:
arr = np.array(a_list, dtype=dtype)
... | the_stack_v2_python_sparse | day03/ex00/NumPyCreator.py | Ghilphar/bootcamp_python | train | 0 | |
1a5a58c30c91f56408a12f8d91c918ab697aa1dd | [
"A = Assignment.objects.getAssignmentByCode(request)\nS = Student.objects.getStudentByRegIdOrRollNo(request)\nAR = AssignmentResponse(assignment=A, student=S, responseLink=request['responseLink'], status=1)\nAR.save()\nreturn AR",
"A = Assignment.objects.getAssignmentByCode(request)\nS = Student.objects.getStuden... | <|body_start_0|>
A = Assignment.objects.getAssignmentByCode(request)
S = Student.objects.getStudentByRegIdOrRollNo(request)
AR = AssignmentResponse(assignment=A, student=S, responseLink=request['responseLink'], status=1)
AR.save()
return AR
<|end_body_0|>
<|body_start_1|>
... | AssignmentResponseManager | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AssignmentResponseManager:
def addAssignmentResponse(self, request):
"""add assignment response"""
<|body_0|>
def evaluateAssignmentResponse(self, request):
"""saves evaluation grade of an assignment response"""
<|body_1|>
def deleteAssignmentResponse(se... | stack_v2_sparse_classes_36k_train_021870 | 2,948 | permissive | [
{
"docstring": "add assignment response",
"name": "addAssignmentResponse",
"signature": "def addAssignmentResponse(self, request)"
},
{
"docstring": "saves evaluation grade of an assignment response",
"name": "evaluateAssignmentResponse",
"signature": "def evaluateAssignmentResponse(self... | 5 | null | Implement the Python class `AssignmentResponseManager` described below.
Class description:
Implement the AssignmentResponseManager class.
Method signatures and docstrings:
- def addAssignmentResponse(self, request): add assignment response
- def evaluateAssignmentResponse(self, request): saves evaluation grade of an ... | Implement the Python class `AssignmentResponseManager` described below.
Class description:
Implement the AssignmentResponseManager class.
Method signatures and docstrings:
- def addAssignmentResponse(self, request): add assignment response
- def evaluateAssignmentResponse(self, request): saves evaluation grade of an ... | 9673bf8b6094560f0e5cb60efb536139deaa965e | <|skeleton|>
class AssignmentResponseManager:
def addAssignmentResponse(self, request):
"""add assignment response"""
<|body_0|>
def evaluateAssignmentResponse(self, request):
"""saves evaluation grade of an assignment response"""
<|body_1|>
def deleteAssignmentResponse(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AssignmentResponseManager:
def addAssignmentResponse(self, request):
"""add assignment response"""
A = Assignment.objects.getAssignmentByCode(request)
S = Student.objects.getStudentByRegIdOrRollNo(request)
AR = AssignmentResponse(assignment=A, student=S, responseLink=request['r... | the_stack_v2_python_sparse | Assessment/models/AssignmentResponse.py | IEEEDTU/CMS | train | 5 | |
bd714210b95959697327cefd9831661239f78468 | [
"self.root = root\nself.dictAdj = dict()\nfor u, v, w in weightedEdges:\n self._addEdgeAndWeight(u, v, w)\n self._addEdgeAndWeight(v, u, w)\nself.heapHandler = DHeapHandler()\nself.heap = self.heapHandler.makeheap([])\nself.dictHeapItems = dict()\nfor x in self.dictAdj.keys():\n if x != self.root:\n ... | <|body_start_0|>
self.root = root
self.dictAdj = dict()
for u, v, w in weightedEdges:
self._addEdgeAndWeight(u, v, w)
self._addEdgeAndWeight(v, u, w)
self.heapHandler = DHeapHandler()
self.heap = self.heapHandler.makeheap([])
self.dictHeapItems = d... | Prim algorithm to find the minimum spanning tree | Prim | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Prim:
"""Prim algorithm to find the minimum spanning tree"""
def __init__(self, weightedEdges, root):
"""set of 3-uples (u,v,weight) and root of the search"""
<|body_0|>
def _addEdgeAndWeight(self, u, v, w):
"""add the edge (u,v) with weight w to adjacency list o... | stack_v2_sparse_classes_36k_train_021871 | 2,779 | no_license | [
{
"docstring": "set of 3-uples (u,v,weight) and root of the search",
"name": "__init__",
"signature": "def __init__(self, weightedEdges, root)"
},
{
"docstring": "add the edge (u,v) with weight w to adjacency list of u + the parent of u",
"name": "_addEdgeAndWeight",
"signature": "def _a... | 3 | stack_v2_sparse_classes_30k_train_005332 | Implement the Python class `Prim` described below.
Class description:
Prim algorithm to find the minimum spanning tree
Method signatures and docstrings:
- def __init__(self, weightedEdges, root): set of 3-uples (u,v,weight) and root of the search
- def _addEdgeAndWeight(self, u, v, w): add the edge (u,v) with weight ... | Implement the Python class `Prim` described below.
Class description:
Prim algorithm to find the minimum spanning tree
Method signatures and docstrings:
- def __init__(self, weightedEdges, root): set of 3-uples (u,v,weight) and root of the search
- def _addEdgeAndWeight(self, u, v, w): add the edge (u,v) with weight ... | 775071c849b582ae3fc5ea2fc647a4438e3d1a32 | <|skeleton|>
class Prim:
"""Prim algorithm to find the minimum spanning tree"""
def __init__(self, weightedEdges, root):
"""set of 3-uples (u,v,weight) and root of the search"""
<|body_0|>
def _addEdgeAndWeight(self, u, v, w):
"""add the edge (u,v) with weight w to adjacency list o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Prim:
"""Prim algorithm to find the minimum spanning tree"""
def __init__(self, weightedEdges, root):
"""set of 3-uples (u,v,weight) and root of the search"""
self.root = root
self.dictAdj = dict()
for u, v, w in weightedEdges:
self._addEdgeAndWeight(u, v, w)
... | the_stack_v2_python_sparse | prim.py | acrodeon/pythonInteractive-algos | train | 0 |
68c72573f9e7af2f6f1e171f8dae01518197289f | [
"self.Scenario = Scenario\nself.Observer = Observer\nself.popSize = self.Scenario.Parameter('PopulationSize')\nself.SimulationEnd = 400 * self.popSize\nself.Pop = []\nfor ID in range(self.popSize):\n Node = Nodes[ID % len(Nodes)]\n self.Pop.append(Ant('A%d' % ID, InitialNode=Node))",
"self.Observer.season()... | <|body_start_0|>
self.Scenario = Scenario
self.Observer = Observer
self.popSize = self.Scenario.Parameter('PopulationSize')
self.SimulationEnd = 400 * self.popSize
self.Pop = []
for ID in range(self.popSize):
Node = Nodes[ID % len(Nodes)]
self.Pop.... | defines the population of agents | AntPopulation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AntPopulation:
"""defines the population of agents"""
def __init__(self, Scenario, Observer, Nodes):
"""creates a population of ant agents"""
<|body_0|>
def one_decision(self):
"""This function is repeatedly called by the simulation thread. One ant is randomly ch... | stack_v2_sparse_classes_36k_train_021872 | 14,320 | no_license | [
{
"docstring": "creates a population of ant agents",
"name": "__init__",
"signature": "def __init__(self, Scenario, Observer, Nodes)"
},
{
"docstring": "This function is repeatedly called by the simulation thread. One ant is randomly chosen and decides what it does",
"name": "one_decision",
... | 2 | stack_v2_sparse_classes_30k_train_010036 | Implement the Python class `AntPopulation` described below.
Class description:
defines the population of agents
Method signatures and docstrings:
- def __init__(self, Scenario, Observer, Nodes): creates a population of ant agents
- def one_decision(self): This function is repeatedly called by the simulation thread. O... | Implement the Python class `AntPopulation` described below.
Class description:
defines the population of agents
Method signatures and docstrings:
- def __init__(self, Scenario, Observer, Nodes): creates a population of ant agents
- def one_decision(self): This function is repeatedly called by the simulation thread. O... | 58d45d5b7df4379da955dbe58f4445b3bf9c8283 | <|skeleton|>
class AntPopulation:
"""defines the population of agents"""
def __init__(self, Scenario, Observer, Nodes):
"""creates a population of ant agents"""
<|body_0|>
def one_decision(self):
"""This function is repeatedly called by the simulation thread. One ant is randomly ch... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AntPopulation:
"""defines the population of agents"""
def __init__(self, Scenario, Observer, Nodes):
"""creates a population of ant agents"""
self.Scenario = Scenario
self.Observer = Observer
self.popSize = self.Scenario.Parameter('PopulationSize')
self.SimulationE... | the_stack_v2_python_sparse | Evolife/Other/Antnet/Antnet.py | piochelepiotr/jump | train | 0 |
061f2259706d1d83f505c6bf28fa276351e1fc0a | [
"v = APIValidator()\ndraft_id = draft_id or deposition.get_default_draft_id()\nmetadata_schema = deposition.type.api_metadata_schema(draft_id)\nif metadata_schema:\n schema = self.input_schema.copy()\n schema['metadata'] = metadata_schema\nelse:\n schema = self.input_schema\nif not v.validate(request.json,... | <|body_start_0|>
v = APIValidator()
draft_id = draft_id or deposition.get_default_draft_id()
metadata_schema = deposition.type.api_metadata_schema(draft_id)
if metadata_schema:
schema = self.input_schema.copy()
schema['metadata'] = metadata_schema
else:
... | Mix-in class for validating and processing deposition input data. | InputProcessorMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InputProcessorMixin:
"""Mix-in class for validating and processing deposition input data."""
def validate_input(self, deposition, draft_id=None):
"""Validate input data for creating and update a deposition."""
<|body_0|>
def process_input(self, deposition, draft_id=None)... | stack_v2_sparse_classes_36k_train_021873 | 19,789 | no_license | [
{
"docstring": "Validate input data for creating and update a deposition.",
"name": "validate_input",
"signature": "def validate_input(self, deposition, draft_id=None)"
},
{
"docstring": "Process input data.",
"name": "process_input",
"signature": "def process_input(self, deposition, dra... | 2 | stack_v2_sparse_classes_30k_train_011731 | Implement the Python class `InputProcessorMixin` described below.
Class description:
Mix-in class for validating and processing deposition input data.
Method signatures and docstrings:
- def validate_input(self, deposition, draft_id=None): Validate input data for creating and update a deposition.
- def process_input(... | Implement the Python class `InputProcessorMixin` described below.
Class description:
Mix-in class for validating and processing deposition input data.
Method signatures and docstrings:
- def validate_input(self, deposition, draft_id=None): Validate input data for creating and update a deposition.
- def process_input(... | e84cb33310506fcdab1dcdb1e8bd425d44435fbe | <|skeleton|>
class InputProcessorMixin:
"""Mix-in class for validating and processing deposition input data."""
def validate_input(self, deposition, draft_id=None):
"""Validate input data for creating and update a deposition."""
<|body_0|>
def process_input(self, deposition, draft_id=None)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InputProcessorMixin:
"""Mix-in class for validating and processing deposition input data."""
def validate_input(self, deposition, draft_id=None):
"""Validate input data for creating and update a deposition."""
v = APIValidator()
draft_id = draft_id or deposition.get_default_draft_... | the_stack_v2_python_sparse | lw_daap/modules/invenio_deposit/restful.py | groundnuty/lw-daap | train | 0 |
fa7c14d16e88ca37b378614013319cfe61b5fa58 | [
"super(Decoder, self).__init__()\nself.opt = opt\nself.fc1 = nn.Linear(16, 512)\nself.fc2 = nn.Linear(512, 1024)\nself.fc3 = nn.Linear(1024, 784)",
"batch_size = target.size(0)\ntarget = target.type(torch.FloatTensor)\nmask = torch.stack([target for i in range(16)], dim=2)\nassert mask.size() == torch.Size([batch... | <|body_start_0|>
super(Decoder, self).__init__()
self.opt = opt
self.fc1 = nn.Linear(16, 512)
self.fc2 = nn.Linear(512, 1024)
self.fc3 = nn.Linear(1024, 784)
<|end_body_0|>
<|body_start_1|>
batch_size = target.size(0)
target = target.type(torch.FloatTensor)
... | The decoder network consists of 3 fully connected layers. For each [10, 16] output, we mask out the incorrect predictions, and send the [16,] vector to the decoder network to reconstruct a [784,] size image. Reference: Section 4.1, Fig. 2 | Decoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
"""The decoder network consists of 3 fully connected layers. For each [10, 16] output, we mask out the incorrect predictions, and send the [16,] vector to the decoder network to reconstruct a [784,] size image. Reference: Section 4.1, Fig. 2"""
def __init__(self, opt):
"""Th... | stack_v2_sparse_classes_36k_train_021874 | 14,893 | no_license | [
{
"docstring": "The decoder network consists of 3 fully connected layers, with 512, 1024, 784 neurons each.",
"name": "__init__",
"signature": "def __init__(self, opt)"
},
{
"docstring": "Args: `v`: [batch_size, 10, 16] `target`: [batch_size, 10] Return: `reconstruction`: [batch_size, 784] We se... | 2 | null | Implement the Python class `Decoder` described below.
Class description:
The decoder network consists of 3 fully connected layers. For each [10, 16] output, we mask out the incorrect predictions, and send the [16,] vector to the decoder network to reconstruct a [784,] size image. Reference: Section 4.1, Fig. 2
Method... | Implement the Python class `Decoder` described below.
Class description:
The decoder network consists of 3 fully connected layers. For each [10, 16] output, we mask out the incorrect predictions, and send the [16,] vector to the decoder network to reconstruct a [784,] size image. Reference: Section 4.1, Fig. 2
Method... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class Decoder:
"""The decoder network consists of 3 fully connected layers. For each [10, 16] output, we mask out the incorrect predictions, and send the [16,] vector to the decoder network to reconstruct a [784,] size image. Reference: Section 4.1, Fig. 2"""
def __init__(self, opt):
"""Th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Decoder:
"""The decoder network consists of 3 fully connected layers. For each [10, 16] output, we mask out the incorrect predictions, and send the [16,] vector to the decoder network to reconstruct a [784,] size image. Reference: Section 4.1, Fig. 2"""
def __init__(self, opt):
"""The decoder net... | the_stack_v2_python_sparse | generated/test_laubonghaudoi_CapsNet_guide_PyTorch.py | jansel/pytorch-jit-paritybench | train | 35 |
437da607e5e27f70750f22b4e1fbb1a8614e60f0 | [
"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... | Manages knowledge bases. Allows users to setup and maintain knowledge bases with their knowledge data. | KnowledgeBasesServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KnowledgeBasesServicer:
"""Manages knowledge bases. Allows users to setup and maintain knowledge bases with their knowledge data."""
def ListKnowledgeBases(self, request, context):
"""Returns the list of all knowledge bases of the specified agent."""
<|body_0|>
def GetKn... | stack_v2_sparse_classes_36k_train_021875 | 5,251 | permissive | [
{
"docstring": "Returns the list of all knowledge bases of the specified agent.",
"name": "ListKnowledgeBases",
"signature": "def ListKnowledgeBases(self, request, context)"
},
{
"docstring": "Retrieves the specified knowledge base.",
"name": "GetKnowledgeBase",
"signature": "def GetKnow... | 4 | stack_v2_sparse_classes_30k_train_003283 | Implement the Python class `KnowledgeBasesServicer` described below.
Class description:
Manages knowledge bases. Allows users to setup and maintain knowledge bases with their knowledge data.
Method signatures and docstrings:
- def ListKnowledgeBases(self, request, context): Returns the list of all knowledge bases of ... | Implement the Python class `KnowledgeBasesServicer` described below.
Class description:
Manages knowledge bases. Allows users to setup and maintain knowledge bases with their knowledge data.
Method signatures and docstrings:
- def ListKnowledgeBases(self, request, context): Returns the list of all knowledge bases of ... | c9c830feb6b66c2e362f8fb5d147ef0c4f4a08cf | <|skeleton|>
class KnowledgeBasesServicer:
"""Manages knowledge bases. Allows users to setup and maintain knowledge bases with their knowledge data."""
def ListKnowledgeBases(self, request, context):
"""Returns the list of all knowledge bases of the specified agent."""
<|body_0|>
def GetKn... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KnowledgeBasesServicer:
"""Manages knowledge bases. Allows users to setup and maintain knowledge bases with their knowledge data."""
def ListKnowledgeBases(self, request, context):
"""Returns the list of all knowledge bases of the specified agent."""
context.set_code(grpc.StatusCode.UNIMP... | the_stack_v2_python_sparse | pyenv/lib/python3.6/site-packages/dialogflow_v2beta1/proto/knowledge_base_pb2_grpc.py | ronald-rgr/ai-chatbot-smartguide | train | 0 |
598da755a8e74ac6d70895132c01e6e5d63f02c6 | [
"user = self.context.get('user')\npassword = value\nuser_auth = authenticate(username=user.username, password=password)\nif not user_auth:\n raise serializers.ValidationError('Contraseña actual incorrecta.')\nreturn value",
"old_passwd = data['password']\npasswd = data['new_password']\npasswd_conf = data['new_... | <|body_start_0|>
user = self.context.get('user')
password = value
user_auth = authenticate(username=user.username, password=password)
if not user_auth:
raise serializers.ValidationError('Contraseña actual incorrecta.')
return value
<|end_body_0|>
<|body_start_1|>
... | Change password serializer. | PasswordChangeSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PasswordChangeSerializer:
"""Change password serializer."""
def validate_password(self, value):
"""Check that the password is valid for current user."""
<|body_0|>
def validate(self, data):
"""Verify passwords match."""
<|body_1|>
def create(self, da... | stack_v2_sparse_classes_36k_train_021876 | 14,720 | permissive | [
{
"docstring": "Check that the password is valid for current user.",
"name": "validate_password",
"signature": "def validate_password(self, value)"
},
{
"docstring": "Verify passwords match.",
"name": "validate",
"signature": "def validate(self, data)"
},
{
"docstring": "Handle u... | 3 | stack_v2_sparse_classes_30k_train_021229 | Implement the Python class `PasswordChangeSerializer` described below.
Class description:
Change password serializer.
Method signatures and docstrings:
- def validate_password(self, value): Check that the password is valid for current user.
- def validate(self, data): Verify passwords match.
- def create(self, data):... | Implement the Python class `PasswordChangeSerializer` described below.
Class description:
Change password serializer.
Method signatures and docstrings:
- def validate_password(self, value): Check that the password is valid for current user.
- def validate(self, data): Verify passwords match.
- def create(self, data):... | ba013f8a06d4b68464a599a1bfaad917e801eeef | <|skeleton|>
class PasswordChangeSerializer:
"""Change password serializer."""
def validate_password(self, value):
"""Check that the password is valid for current user."""
<|body_0|>
def validate(self, data):
"""Verify passwords match."""
<|body_1|>
def create(self, da... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PasswordChangeSerializer:
"""Change password serializer."""
def validate_password(self, value):
"""Check that the password is valid for current user."""
user = self.context.get('user')
password = value
user_auth = authenticate(username=user.username, password=password)
... | the_stack_v2_python_sparse | id/modules/api/accounts/serializers.py | argob/id-mi-argentina-distro | train | 5 |
3db12e07ee9a624450497ace9b2da985892b0fe5 | [
"self.url = url\nself.cluster = cluster\nself.app_id = app_id\nself.secret = secret if secret is not None else ''\nself.read_apollo = read_apollo\nif read_apollo:\n from apollo.apollo_client import ApolloClient\n self.client = ApolloClient(self.url, self.app_id, cluster=self.cluster, secret=self.secret)\nelse... | <|body_start_0|>
self.url = url
self.cluster = cluster
self.app_id = app_id
self.secret = secret if secret is not None else ''
self.read_apollo = read_apollo
if read_apollo:
from apollo.apollo_client import ApolloClient
self.client = ApolloClient(s... | python 连接 apollo配置中心工具类 | ApolloHelper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApolloHelper:
"""python 连接 apollo配置中心工具类"""
def __init__(self, app_id, url='http://localhost:8080', secret='', cluster='default', read_apollo=False):
"""初始化apollo连接, 同时将apollo配置同步到本地, 以防止apollo服务挂了导致整个系统瘫痪 :param app_id: :param secret: :param cluster:"""
<|body_0|>
def g... | stack_v2_sparse_classes_36k_train_021877 | 2,702 | no_license | [
{
"docstring": "初始化apollo连接, 同时将apollo配置同步到本地, 以防止apollo服务挂了导致整个系统瘫痪 :param app_id: :param secret: :param cluster:",
"name": "__init__",
"signature": "def __init__(self, app_id, url='http://localhost:8080', secret='', cluster='default', read_apollo=False)"
},
{
"docstring": "通过配置文件实例化apollo连接 :p... | 4 | stack_v2_sparse_classes_30k_train_011032 | Implement the Python class `ApolloHelper` described below.
Class description:
python 连接 apollo配置中心工具类
Method signatures and docstrings:
- def __init__(self, app_id, url='http://localhost:8080', secret='', cluster='default', read_apollo=False): 初始化apollo连接, 同时将apollo配置同步到本地, 以防止apollo服务挂了导致整个系统瘫痪 :param app_id: :param... | Implement the Python class `ApolloHelper` described below.
Class description:
python 连接 apollo配置中心工具类
Method signatures and docstrings:
- def __init__(self, app_id, url='http://localhost:8080', secret='', cluster='default', read_apollo=False): 初始化apollo连接, 同时将apollo配置同步到本地, 以防止apollo服务挂了导致整个系统瘫痪 :param app_id: :param... | f096a7168cb45a7e3e2b677ec650bfd01c7b59cd | <|skeleton|>
class ApolloHelper:
"""python 连接 apollo配置中心工具类"""
def __init__(self, app_id, url='http://localhost:8080', secret='', cluster='default', read_apollo=False):
"""初始化apollo连接, 同时将apollo配置同步到本地, 以防止apollo服务挂了导致整个系统瘫痪 :param app_id: :param secret: :param cluster:"""
<|body_0|>
def g... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApolloHelper:
"""python 连接 apollo配置中心工具类"""
def __init__(self, app_id, url='http://localhost:8080', secret='', cluster='default', read_apollo=False):
"""初始化apollo连接, 同时将apollo配置同步到本地, 以防止apollo服务挂了导致整个系统瘫痪 :param app_id: :param secret: :param cluster:"""
self.url = url
self.cluste... | the_stack_v2_python_sparse | helper/apollo_helper.py | weidongcao/huanLing | train | 47 |
3c6fae92e75ffb2027be2c702311f4af52fd363f | [
"AssessmentResults.__init__(self, controller, **kwargs)\nself._lst_labels.append(u'π<sub>A</sub>:')\nself._lst_labels.append(u'π<sub>F</sub>:')\nself._lst_labels.append(u'π<sub>T</sub>:')\nself._lblModel.set_tooltip_markup(_(u'The assessment model used to calculate the meter failure rate.'))\nself.txtPiA = ramstk.R... | <|body_start_0|>
AssessmentResults.__init__(self, controller, **kwargs)
self._lst_labels.append(u'π<sub>A</sub>:')
self._lst_labels.append(u'π<sub>F</sub>:')
self._lst_labels.append(u'π<sub>T</sub>:')
self._lblModel.set_tooltip_markup(_(u'The assessment model used to calculate th... | Display Meter assessment results attribute data in the RAMSTK Work Book. The Meter assessment result view displays all the assessment results for the selected meter. This includes, currently, results for MIL-HDBK-217FN2 parts count and MIL-HDBK-217FN2 part stress methods. The attributes of a meter assessment result vie... | MeterAssessmentResults | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MeterAssessmentResults:
"""Display Meter assessment results attribute data in the RAMSTK Work Book. The Meter assessment result view displays all the assessment results for the selected meter. This includes, currently, results for MIL-HDBK-217FN2 parts count and MIL-HDBK-217FN2 part stress method... | stack_v2_sparse_classes_36k_train_021878 | 14,683 | permissive | [
{
"docstring": "Initialize an instance of the Meter assessment result view. :param controller: the meter data controller instance. :type controller: :class:`ramstk.meter.Controller.MeterBoMDataController`",
"name": "__init__",
"signature": "def __init__(self, controller, **kwargs)"
},
{
"docstri... | 5 | null | Implement the Python class `MeterAssessmentResults` described below.
Class description:
Display Meter assessment results attribute data in the RAMSTK Work Book. The Meter assessment result view displays all the assessment results for the selected meter. This includes, currently, results for MIL-HDBK-217FN2 parts count... | Implement the Python class `MeterAssessmentResults` described below.
Class description:
Display Meter assessment results attribute data in the RAMSTK Work Book. The Meter assessment result view displays all the assessment results for the selected meter. This includes, currently, results for MIL-HDBK-217FN2 parts count... | 488ffed8b842399ddcae93007de6c6f1dda23d05 | <|skeleton|>
class MeterAssessmentResults:
"""Display Meter assessment results attribute data in the RAMSTK Work Book. The Meter assessment result view displays all the assessment results for the selected meter. This includes, currently, results for MIL-HDBK-217FN2 parts count and MIL-HDBK-217FN2 part stress method... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MeterAssessmentResults:
"""Display Meter assessment results attribute data in the RAMSTK Work Book. The Meter assessment result view displays all the assessment results for the selected meter. This includes, currently, results for MIL-HDBK-217FN2 parts count and MIL-HDBK-217FN2 part stress methods. The attrib... | the_stack_v2_python_sparse | src/ramstk/gui/gtk/workviews/components/Meter.py | JmiXIII/ramstk | train | 0 |
df22a1e2abf1dd222d939296e3fc2c6d9b88e1ee | [
"for donor_name in donors:\n newFile = open(donor_name[0] + '.txt', mode='w')\n newFile.write(thankyou_dispacth[int_option](donor_name[0], lifetime_donations(tup_donor_names)))\n newFile.close()\n print(donor_name[0] + 'saved')",
"str_salutation = 'Dear {},'.format(donor)\nstr_body = '\\n\\n' + 'Thank... | <|body_start_0|>
for donor_name in donors:
newFile = open(donor_name[0] + '.txt', mode='w')
newFile.write(thankyou_dispacth[int_option](donor_name[0], lifetime_donations(tup_donor_names)))
newFile.close()
print(donor_name[0] + 'saved')
<|end_body_0|>
<|body_start... | files | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class files:
def write_email(donors):
"""Writes emails to the donors and saves them to text files. :param donors: Tuple table that stores all the donor's donation information. :return:"""
<|body_0|>
def donor_email(donor, dictionary):
"""Formats the email with donor's name... | stack_v2_sparse_classes_36k_train_021879 | 14,291 | no_license | [
{
"docstring": "Writes emails to the donors and saves them to text files. :param donors: Tuple table that stores all the donor's donation information. :return:",
"name": "write_email",
"signature": "def write_email(donors)"
},
{
"docstring": "Formats the email with donor's name and total lifetim... | 2 | null | Implement the Python class `files` described below.
Class description:
Implement the files class.
Method signatures and docstrings:
- def write_email(donors): Writes emails to the donors and saves them to text files. :param donors: Tuple table that stores all the donor's donation information. :return:
- def donor_ema... | Implement the Python class `files` described below.
Class description:
Implement the files class.
Method signatures and docstrings:
- def write_email(donors): Writes emails to the donors and saves them to text files. :param donors: Tuple table that stores all the donor's donation information. :return:
- def donor_ema... | e298b1151dab639659d8dfa56f47bcb43dd3438f | <|skeleton|>
class files:
def write_email(donors):
"""Writes emails to the donors and saves them to text files. :param donors: Tuple table that stores all the donor's donation information. :return:"""
<|body_0|>
def donor_email(donor, dictionary):
"""Formats the email with donor's name... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class files:
def write_email(donors):
"""Writes emails to the donors and saves them to text files. :param donors: Tuple table that stores all the donor's donation information. :return:"""
for donor_name in donors:
newFile = open(donor_name[0] + '.txt', mode='w')
newFile.write... | the_stack_v2_python_sparse | students/rhamersky/Lesson_5/mailroom_part3.py | UWPCE-PythonCert-ClassRepos/Self_Paced-Online | train | 13 | |
1f8ae88e20d73f0dd4235ea4d4931a1a29e11083 | [
"LDC_Info.__init__(self)\nself.setTitle(self.name)\nif info_res:\n self.status = compat_res[0]\n self.ui.setupUi(self.frame)\n self.__fill_frame(info_res, compat_res, diag_res)\nelse:\n self.status = False\n self.__labelError(compat_res)",
"vendor = self._check_invalid_values(info_res.vendor[1])\nm... | <|body_start_0|>
LDC_Info.__init__(self)
self.setTitle(self.name)
if info_res:
self.status = compat_res[0]
self.ui.setupUi(self.frame)
self.__fill_frame(info_res, compat_res, diag_res)
else:
self.status = False
self.__labelError... | Estende a classe 'LDC_Info'. Classe que define a interface gráfica de exibição dos resultados de mouse | GUIMouse | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GUIMouse:
"""Estende a classe 'LDC_Info'. Classe que define a interface gráfica de exibição dos resultados de mouse"""
def __init__(self, info_res, compat_res, diag_res):
"""Construtor Parâmetros: info_res -- lista com os resultados informativos (lista de 'InfoResMouse') compat_res -... | stack_v2_sparse_classes_36k_train_021880 | 3,283 | no_license | [
{
"docstring": "Construtor Parâmetros: info_res -- lista com os resultados informativos (lista de 'InfoResMouse') compat_res -- Lista com as tuples de resultado de compatibilidade [(True, msg)] diag_res -- Lista com os resultados do diagnóstico (nesse caso não existe teste de diagnóstico, recebe-se uma lista va... | 3 | null | Implement the Python class `GUIMouse` described below.
Class description:
Estende a classe 'LDC_Info'. Classe que define a interface gráfica de exibição dos resultados de mouse
Method signatures and docstrings:
- def __init__(self, info_res, compat_res, diag_res): Construtor Parâmetros: info_res -- lista com os resul... | Implement the Python class `GUIMouse` described below.
Class description:
Estende a classe 'LDC_Info'. Classe que define a interface gráfica de exibição dos resultados de mouse
Method signatures and docstrings:
- def __init__(self, info_res, compat_res, diag_res): Construtor Parâmetros: info_res -- lista com os resul... | bda0c2c8977dd1246339f1f0f4718d29e8795f21 | <|skeleton|>
class GUIMouse:
"""Estende a classe 'LDC_Info'. Classe que define a interface gráfica de exibição dos resultados de mouse"""
def __init__(self, info_res, compat_res, diag_res):
"""Construtor Parâmetros: info_res -- lista com os resultados informativos (lista de 'InfoResMouse') compat_res -... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GUIMouse:
"""Estende a classe 'LDC_Info'. Classe que define a interface gráfica de exibição dos resultados de mouse"""
def __init__(self, info_res, compat_res, diag_res):
"""Construtor Parâmetros: info_res -- lista com os resultados informativos (lista de 'InfoResMouse') compat_res -- Lista com a... | the_stack_v2_python_sparse | src/libs/mouse/gui_mouse.py | adrianomelo/ldc-desktop | train | 1 |
69a5d7cb9e4791d5730c7dac0b76819ab291faeb | [
"self.root = None\nfor item in container:\n self.insert(item)",
"def _str(indent: str, root: _BSTNode) -> str:\n \"\"\"\n Return a 'sideways' representation of the values in the BST rooted\n at root, with right subtree indented above root, and left indented\n below root, eac... | <|body_start_0|>
self.root = None
for item in container:
self.insert(item)
<|end_body_0|>
<|body_start_1|>
def _str(indent: str, root: _BSTNode) -> str:
"""
Return a 'sideways' representation of the values in the BST rooted
at root... | A Binary Search Tree. | BST | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BST:
"""A Binary Search Tree."""
def __init__(self, container=[]):
"""(BST, list) -> NoneType Initialize this BST by inserting the items from container (default []) one by one, in the order given."""
<|body_0|>
def __str__(self):
"""(BST) -> str Return a "sideway... | stack_v2_sparse_classes_36k_train_021881 | 3,829 | no_license | [
{
"docstring": "(BST, list) -> NoneType Initialize this BST by inserting the items from container (default []) one by one, in the order given.",
"name": "__init__",
"signature": "def __init__(self, container=[])"
},
{
"docstring": "(BST) -> str Return a \"sideways\" representation of the values ... | 5 | stack_v2_sparse_classes_30k_train_005201 | Implement the Python class `BST` described below.
Class description:
A Binary Search Tree.
Method signatures and docstrings:
- def __init__(self, container=[]): (BST, list) -> NoneType Initialize this BST by inserting the items from container (default []) one by one, in the order given.
- def __str__(self): (BST) -> ... | Implement the Python class `BST` described below.
Class description:
A Binary Search Tree.
Method signatures and docstrings:
- def __init__(self, container=[]): (BST, list) -> NoneType Initialize this BST by inserting the items from container (default []) one by one, in the order given.
- def __str__(self): (BST) -> ... | c7437d387dc2b9a8039c60d8786373899c2e28bd | <|skeleton|>
class BST:
"""A Binary Search Tree."""
def __init__(self, container=[]):
"""(BST, list) -> NoneType Initialize this BST by inserting the items from container (default []) one by one, in the order given."""
<|body_0|>
def __str__(self):
"""(BST) -> str Return a "sideway... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BST:
"""A Binary Search Tree."""
def __init__(self, container=[]):
"""(BST, list) -> NoneType Initialize this BST by inserting the items from container (default []) one by one, in the order given."""
self.root = None
for item in container:
self.insert(item)
def __... | the_stack_v2_python_sparse | CSC148/06 Tree(BST)/lab9/BST_rec1.py | xxcocoymlxx/Study-Notes | train | 2 |
1c6315bf1ee497701ab03a0319aa9cf1024b13f0 | [
"url = '/admin/'\nresponse = self.client.get(url, HTTP_HOST='website.domain')\nself.assertEqual(response.status_code, 302)",
"url = '/admin/'\nself.client.login(username=self.adminUN, password='pass')\nresponse = self.client.get(url, HTTP_HOST='website.domain')\nself.assertEqual(response.status_code, 200)",
"ur... | <|body_start_0|>
url = '/admin/'
response = self.client.get(url, HTTP_HOST='website.domain')
self.assertEqual(response.status_code, 302)
<|end_body_0|>
<|body_start_1|>
url = '/admin/'
self.client.login(username=self.adminUN, password='pass')
response = self.client.get(u... | AdminTestCase | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdminTestCase:
def test_not_logged_in(self):
"""Test that the admin view will redirect to login page whilst not logged in."""
<|body_0|>
def test_logged_in_admin(self):
"""Test that the admin view will load whilst logged in as admin."""
<|body_1|>
def te... | stack_v2_sparse_classes_36k_train_021882 | 26,818 | permissive | [
{
"docstring": "Test that the admin view will redirect to login page whilst not logged in.",
"name": "test_not_logged_in",
"signature": "def test_not_logged_in(self)"
},
{
"docstring": "Test that the admin view will load whilst logged in as admin.",
"name": "test_logged_in_admin",
"signa... | 3 | null | Implement the Python class `AdminTestCase` described below.
Class description:
Implement the AdminTestCase class.
Method signatures and docstrings:
- def test_not_logged_in(self): Test that the admin view will redirect to login page whilst not logged in.
- def test_logged_in_admin(self): Test that the admin view will... | Implement the Python class `AdminTestCase` described below.
Class description:
Implement the AdminTestCase class.
Method signatures and docstrings:
- def test_not_logged_in(self): Test that the admin view will redirect to login page whilst not logged in.
- def test_logged_in_admin(self): Test that the admin view will... | 37d2942efcbdaad072f7a06ac876a40e0f69f702 | <|skeleton|>
class AdminTestCase:
def test_not_logged_in(self):
"""Test that the admin view will redirect to login page whilst not logged in."""
<|body_0|>
def test_logged_in_admin(self):
"""Test that the admin view will load whilst logged in as admin."""
<|body_1|>
def te... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdminTestCase:
def test_not_logged_in(self):
"""Test that the admin view will redirect to login page whilst not logged in."""
url = '/admin/'
response = self.client.get(url, HTTP_HOST='website.domain')
self.assertEqual(response.status_code, 302)
def test_logged_in_admin(se... | the_stack_v2_python_sparse | mooring/test_views.py | dbca-wa/moorings | train | 0 | |
cadf5e67cec2abcab1fd47d10e613ebd9ac7e09a | [
"self.Whf = np.random.normal(size=(i + h, h))\nself.Whb = np.random.normal(size=(i + h, h))\nself.Wy = np.random.normal(size=(2 * h, o))\nself.bhf = np.zeros((1, h))\nself.bhb = np.zeros((1, h))\nself.by = np.zeros((1, o))",
"x_concat = np.concatenate((h_prev, x_t), axis=1)\nh_next = np.matmul(x_concat, self.Whf)... | <|body_start_0|>
self.Whf = np.random.normal(size=(i + h, h))
self.Whb = np.random.normal(size=(i + h, h))
self.Wy = np.random.normal(size=(2 * h, o))
self.bhf = np.zeros((1, h))
self.bhb = np.zeros((1, h))
self.by = np.zeros((1, o))
<|end_body_0|>
<|body_start_1|>
... | Class that represents a bidirectional cell of an RNN | BidirectionalCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BidirectionalCell:
"""Class that represents a bidirectional cell of an RNN"""
def __init__(self, i, h, o):
"""i is the dimensionality of the data h is the dimensionality of the hidden states o is the dimensionality of the outputs"""
<|body_0|>
def forward(self, h_prev, x... | stack_v2_sparse_classes_36k_train_021883 | 2,621 | no_license | [
{
"docstring": "i is the dimensionality of the data h is the dimensionality of the hidden states o is the dimensionality of the outputs",
"name": "__init__",
"signature": "def __init__(self, i, h, o)"
},
{
"docstring": "x_t is a numpy.ndarray of shape (m, i) that contains the data input for the ... | 4 | null | Implement the Python class `BidirectionalCell` described below.
Class description:
Class that represents a bidirectional cell of an RNN
Method signatures and docstrings:
- def __init__(self, i, h, o): i is the dimensionality of the data h is the dimensionality of the hidden states o is the dimensionality of the outpu... | Implement the Python class `BidirectionalCell` described below.
Class description:
Class that represents a bidirectional cell of an RNN
Method signatures and docstrings:
- def __init__(self, i, h, o): i is the dimensionality of the data h is the dimensionality of the hidden states o is the dimensionality of the outpu... | d3802fc2e552447cd5b17d1ed593aee46a8ae929 | <|skeleton|>
class BidirectionalCell:
"""Class that represents a bidirectional cell of an RNN"""
def __init__(self, i, h, o):
"""i is the dimensionality of the data h is the dimensionality of the hidden states o is the dimensionality of the outputs"""
<|body_0|>
def forward(self, h_prev, x... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BidirectionalCell:
"""Class that represents a bidirectional cell of an RNN"""
def __init__(self, i, h, o):
"""i is the dimensionality of the data h is the dimensionality of the hidden states o is the dimensionality of the outputs"""
self.Whf = np.random.normal(size=(i + h, h))
sel... | the_stack_v2_python_sparse | supervised_learning/0x0D-RNNs/7-bi_output.py | RodrigoSierraV/holbertonschool-machine_learning | train | 0 |
dacc7f965586859011a47d372bd4c9410f936e4c | [
"circuit = Circuit(2)\ncircuit.append_gate(HGate(), 1)\ncircuit.append_gate(CZGate(), (0, 1))\ncircuit.append_gate(HGate(), 1)\nself.cg = CircuitGate(circuit)",
"cnot_points = []\nfor cycle, op in circuit.operations_with_cycles():\n if isinstance(op.gate, CNOTGate):\n cnot_points.append((cycle, op.locat... | <|body_start_0|>
circuit = Circuit(2)
circuit.append_gate(HGate(), 1)
circuit.append_gate(CZGate(), (0, 1))
circuit.append_gate(HGate(), 1)
self.cg = CircuitGate(circuit)
<|end_body_0|>
<|body_start_1|>
cnot_points = []
for cycle, op in circuit.operations_with_cy... | The CNOTToCZPass class. This uses a rule to convert CNOTs to CZs. | CNOTToCZPass | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CNOTToCZPass:
"""The CNOTToCZPass class. This uses a rule to convert CNOTs to CZs."""
def __init__(self) -> None:
"""Construct a CNOTToCZPass."""
<|body_0|>
async def run(self, circuit: Circuit, data: PassData) -> None:
"""Perform the pass's operation, see :class... | stack_v2_sparse_classes_36k_train_021884 | 1,580 | permissive | [
{
"docstring": "Construct a CNOTToCZPass.",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Perform the pass's operation, see :class:`BasePass` for more.",
"name": "run",
"signature": "async def run(self, circuit: Circuit, data: PassData) -> None"
}
] | 2 | null | Implement the Python class `CNOTToCZPass` described below.
Class description:
The CNOTToCZPass class. This uses a rule to convert CNOTs to CZs.
Method signatures and docstrings:
- def __init__(self) -> None: Construct a CNOTToCZPass.
- async def run(self, circuit: Circuit, data: PassData) -> None: Perform the pass's ... | Implement the Python class `CNOTToCZPass` described below.
Class description:
The CNOTToCZPass class. This uses a rule to convert CNOTs to CZs.
Method signatures and docstrings:
- def __init__(self) -> None: Construct a CNOTToCZPass.
- async def run(self, circuit: Circuit, data: PassData) -> None: Perform the pass's ... | c89112d15072e8ffffb68cf1757b184e2aeb3dc8 | <|skeleton|>
class CNOTToCZPass:
"""The CNOTToCZPass class. This uses a rule to convert CNOTs to CZs."""
def __init__(self) -> None:
"""Construct a CNOTToCZPass."""
<|body_0|>
async def run(self, circuit: Circuit, data: PassData) -> None:
"""Perform the pass's operation, see :class... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CNOTToCZPass:
"""The CNOTToCZPass class. This uses a rule to convert CNOTs to CZs."""
def __init__(self) -> None:
"""Construct a CNOTToCZPass."""
circuit = Circuit(2)
circuit.append_gate(HGate(), 1)
circuit.append_gate(CZGate(), (0, 1))
circuit.append_gate(HGate(),... | the_stack_v2_python_sparse | bqskit/passes/rules/cnot2cz.py | BQSKit/bqskit | train | 54 |
d813a4f9013c9770cf1b0828877f4ed64c0c29e9 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn SimulationAutomationRun()",
"from .entity import Entity\nfrom .simulation_automation_run_status import SimulationAutomationRunStatus\nfrom .entity import Entity\nfrom .simulation_automation_run_status import SimulationAutomationRunStat... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return SimulationAutomationRun()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .simulation_automation_run_status import SimulationAutomationRunStatus
from .entity impo... | SimulationAutomationRun | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimulationAutomationRun:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SimulationAutomationRun:
"""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 creat... | stack_v2_sparse_classes_36k_train_021885 | 3,312 | 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: SimulationAutomationRun",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrimin... | 3 | stack_v2_sparse_classes_30k_train_003427 | Implement the Python class `SimulationAutomationRun` described below.
Class description:
Implement the SimulationAutomationRun class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SimulationAutomationRun: Creates a new instance of the appropriate clas... | Implement the Python class `SimulationAutomationRun` described below.
Class description:
Implement the SimulationAutomationRun class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SimulationAutomationRun: Creates a new instance of the appropriate clas... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class SimulationAutomationRun:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SimulationAutomationRun:
"""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 creat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimulationAutomationRun:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SimulationAutomationRun:
"""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 R... | the_stack_v2_python_sparse | msgraph/generated/models/simulation_automation_run.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
a639412340c7c976da22e0ae2f1cb875ddf94df8 | [
"r = Round.query.get(round_id)\nif r is not None:\n if r.has_dance(dance_id):\n return r.adjudication_data(dance_id, dancing_round=True)\n abort(404, 'Round does not have dance with given dance_id')\nabort(404, 'Unknown round_id')",
"r = Round.query.get(round_id)\nif r is not None:\n if r.has_danc... | <|body_start_0|>
r = Round.query.get(round_id)
if r is not None:
if r.has_dance(dance_id):
return r.adjudication_data(dance_id, dancing_round=True)
abort(404, 'Round does not have dance with given dance_id')
abort(404, 'Unknown round_id')
<|end_body_0|>
<... | RoundAPIAdjudicationDance | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RoundAPIAdjudicationDance:
def get(self, round_id, dance_id):
"""Get adjudication data for a specific dance"""
<|body_0|>
def patch(self, round_id, dance_id):
"""Gives a mark to a list of couples for a specific dance or placing for couples in the final"""
<|b... | stack_v2_sparse_classes_36k_train_021886 | 25,303 | no_license | [
{
"docstring": "Get adjudication data for a specific dance",
"name": "get",
"signature": "def get(self, round_id, dance_id)"
},
{
"docstring": "Gives a mark to a list of couples for a specific dance or placing for couples in the final",
"name": "patch",
"signature": "def patch(self, roun... | 2 | null | Implement the Python class `RoundAPIAdjudicationDance` described below.
Class description:
Implement the RoundAPIAdjudicationDance class.
Method signatures and docstrings:
- def get(self, round_id, dance_id): Get adjudication data for a specific dance
- def patch(self, round_id, dance_id): Gives a mark to a list of c... | Implement the Python class `RoundAPIAdjudicationDance` described below.
Class description:
Implement the RoundAPIAdjudicationDance class.
Method signatures and docstrings:
- def get(self, round_id, dance_id): Get adjudication data for a specific dance
- def patch(self, round_id, dance_id): Gives a mark to a list of c... | 079b109fd13683a31d1d632faa5ab72cf0e78ddf | <|skeleton|>
class RoundAPIAdjudicationDance:
def get(self, round_id, dance_id):
"""Get adjudication data for a specific dance"""
<|body_0|>
def patch(self, round_id, dance_id):
"""Gives a mark to a list of couples for a specific dance or placing for couples in the final"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RoundAPIAdjudicationDance:
def get(self, round_id, dance_id):
"""Get adjudication data for a specific dance"""
r = Round.query.get(round_id)
if r is not None:
if r.has_dance(dance_id):
return r.adjudication_data(dance_id, dancing_round=True)
abor... | the_stack_v2_python_sparse | backend/apis/round/apis.py | AlenAlic/DANCE | train | 0 | |
5719de02c8b56e9c1a4c5b8efa338146b0461852 | [
"super(Downsample, self).__init__()\nself.apply_batchnorm = apply_batchnorm\ninitializer = tf.random_normal_initializer(0, 0.02)\nself.conv1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=(size, size), strides=(2, 2), padding='same', kernel_initializer=initializer, use_bias=False)\nif self.apply_batchnorm:\n... | <|body_start_0|>
super(Downsample, self).__init__()
self.apply_batchnorm = apply_batchnorm
initializer = tf.random_normal_initializer(0, 0.02)
self.conv1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=(size, size), strides=(2, 2), padding='same', kernel_initializer=initializer, us... | Use convolution layer to downsample | Downsample | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Downsample:
"""Use convolution layer to downsample"""
def __init__(self, filters, size, apply_batchnorm=True):
"""The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_batchnorm If use batch normalization:"""
<|body_0|... | stack_v2_sparse_classes_36k_train_021887 | 20,044 | no_license | [
{
"docstring": "The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_batchnorm If use batch normalization:",
"name": "__init__",
"signature": "def __init__(self, filters, size, apply_batchnorm=True)"
},
{
"docstring": "Calls the model on... | 2 | stack_v2_sparse_classes_30k_train_001109 | Implement the Python class `Downsample` described below.
Class description:
Use convolution layer to downsample
Method signatures and docstrings:
- def __init__(self, filters, size, apply_batchnorm=True): The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_b... | Implement the Python class `Downsample` described below.
Class description:
Use convolution layer to downsample
Method signatures and docstrings:
- def __init__(self, filters, size, apply_batchnorm=True): The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_b... | d1b70b2a954f4665b628ba252b03c1a74b95559f | <|skeleton|>
class Downsample:
"""Use convolution layer to downsample"""
def __init__(self, filters, size, apply_batchnorm=True):
"""The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_batchnorm If use batch normalization:"""
<|body_0|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Downsample:
"""Use convolution layer to downsample"""
def __init__(self, filters, size, apply_batchnorm=True):
"""The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_batchnorm If use batch normalization:"""
super(Downsample, self... | the_stack_v2_python_sparse | NeuralNetworks-tensorflow/generation_network_model/GAN/pix2pix.py | zhaocc1106/machine_learn | train | 15 |
494497b8d6531af5349291667ca1a80f4f7a1721 | [
"self._frame_queue = framequeue\nself._listening_host = '127.0.0.1'\nself._listening_port = int(ait.config.get('dsn.sle.frame_output_port', kwargs.get('frame_output_port', ait.DEFAULT_FRAME_PORT)))\nself._downlink_frame_type = ait.config.get('dsn.sle.downlink_frame_type', kwargs.get('downlink_frame_type', ait.DEFAU... | <|body_start_0|>
self._frame_queue = framequeue
self._listening_host = '127.0.0.1'
self._listening_port = int(ait.config.get('dsn.sle.frame_output_port', kwargs.get('frame_output_port', ait.DEFAULT_FRAME_PORT)))
self._downlink_frame_type = ait.config.get('dsn.sle.downlink_frame_type', kw... | UDP Datagram server that handles incoming messages TMFrames/AOSFrames from frame output port of the upstream service (AIT DSN RAF/RCF server?) | Frame_Service | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Frame_Service:
"""UDP Datagram server that handles incoming messages TMFrames/AOSFrames from frame output port of the upstream service (AIT DSN RAF/RCF server?)"""
def __init__(self, framequeue, *args, **kwargs):
"""Constructor :param framequeue: The frame queue to which frames from ... | stack_v2_sparse_classes_36k_train_021888 | 43,287 | permissive | [
{
"docstring": "Constructor :param framequeue: The frame queue to which frames from ports will be added :param args: Arguments :param kwargs: Keyword arguments",
"name": "__init__",
"signature": "def __init__(self, framequeue, *args, **kwargs)"
},
{
"docstring": "This handler is called whenever ... | 4 | stack_v2_sparse_classes_30k_train_019280 | Implement the Python class `Frame_Service` described below.
Class description:
UDP Datagram server that handles incoming messages TMFrames/AOSFrames from frame output port of the upstream service (AIT DSN RAF/RCF server?)
Method signatures and docstrings:
- def __init__(self, framequeue, *args, **kwargs): Constructor... | Implement the Python class `Frame_Service` described below.
Class description:
UDP Datagram server that handles incoming messages TMFrames/AOSFrames from frame output port of the upstream service (AIT DSN RAF/RCF server?)
Method signatures and docstrings:
- def __init__(self, framequeue, *args, **kwargs): Constructor... | 654bc5575aa2c9792052a220854bea2d30841f8d | <|skeleton|>
class Frame_Service:
"""UDP Datagram server that handles incoming messages TMFrames/AOSFrames from frame output port of the upstream service (AIT DSN RAF/RCF server?)"""
def __init__(self, framequeue, *args, **kwargs):
"""Constructor :param framequeue: The frame queue to which frames from ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Frame_Service:
"""UDP Datagram server that handles incoming messages TMFrames/AOSFrames from frame output port of the upstream service (AIT DSN RAF/RCF server?)"""
def __init__(self, framequeue, *args, **kwargs):
"""Constructor :param framequeue: The frame queue to which frames from ports will be... | the_stack_v2_python_sparse | ait/dsn/proc/deframe_packet_processor.py | NASA-AMMOS/AIT-DSN | train | 22 |
9e4eae77ec00fe1fcb0cf779812c6172876b0031 | [
"self.script_type = script_type\nself.default_shell = default_shell\nname = '%s-script' % self.script_type\nfacility = logging.handlers.SysLogHandler.LOG_DAEMON\nself.logger = logger.Logger(name=name, debug=debug, facility=facility)\nself.retriever = script_retriever.ScriptRetriever(self.logger, script_type)\nself.... | <|body_start_0|>
self.script_type = script_type
self.default_shell = default_shell
name = '%s-script' % self.script_type
facility = logging.handlers.SysLogHandler.LOG_DAEMON
self.logger = logger.Logger(name=name, debug=debug, facility=facility)
self.retriever = script_ret... | A class for retrieving and executing metadata scripts. | ScriptManager | [
"LicenseRef-scancode-generic-cla",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScriptManager:
"""A class for retrieving and executing metadata scripts."""
def __init__(self, script_type, default_shell=None, run_dir=None, debug=False):
"""Constructor. Args: script_type: string, the metadata script type to run. default_shell: string, the default shell to execute ... | stack_v2_sparse_classes_36k_train_021889 | 4,004 | permissive | [
{
"docstring": "Constructor. Args: script_type: string, the metadata script type to run. default_shell: string, the default shell to execute the script. run_dir: string, the base directory location of the temporary directory. debug: bool, True if debug output should write to the console.",
"name": "__init__... | 2 | stack_v2_sparse_classes_30k_train_021571 | Implement the Python class `ScriptManager` described below.
Class description:
A class for retrieving and executing metadata scripts.
Method signatures and docstrings:
- def __init__(self, script_type, default_shell=None, run_dir=None, debug=False): Constructor. Args: script_type: string, the metadata script type to ... | Implement the Python class `ScriptManager` described below.
Class description:
A class for retrieving and executing metadata scripts.
Method signatures and docstrings:
- def __init__(self, script_type, default_shell=None, run_dir=None, debug=False): Constructor. Args: script_type: string, the metadata script type to ... | cf4b33214f770da2299923a5fa73d3d95f66ec35 | <|skeleton|>
class ScriptManager:
"""A class for retrieving and executing metadata scripts."""
def __init__(self, script_type, default_shell=None, run_dir=None, debug=False):
"""Constructor. Args: script_type: string, the metadata script type to run. default_shell: string, the default shell to execute ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScriptManager:
"""A class for retrieving and executing metadata scripts."""
def __init__(self, script_type, default_shell=None, run_dir=None, debug=False):
"""Constructor. Args: script_type: string, the metadata script type to run. default_shell: string, the default shell to execute the script. r... | the_stack_v2_python_sparse | packages/python-google-compute-engine/google_compute_engine/metadata_scripts/script_manager.py | GoogleCloudPlatform/compute-image-packages | train | 329 |
a63d3728fdaa2cbdf96e6a4fbb7197237db32968 | [
"dataset_type = models.DatasetType.objects.using('agdc').get(id=request.GET.get('dataset_type_ref'))\nmeasurements = dataset_type.definition['measurements']\nfor measurement in measurements:\n measurement['src_varname'] = measurement['name']\n measurement['dtype'] = 'int32' if measurement['dtype'] in ['uint16... | <|body_start_0|>
dataset_type = models.DatasetType.objects.using('agdc').get(id=request.GET.get('dataset_type_ref'))
measurements = dataset_type.definition['measurements']
for measurement in measurements:
measurement['src_varname'] = measurement['name']
measurement['dtype... | Gets a list of existing measurements and validates user added measurements | IngestionMeasurement | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IngestionMeasurement:
"""Gets a list of existing measurements and validates user added measurements"""
def get(self, request):
"""Get existing measurement forms for a dataset type Args: dataset type id, used to fetch the dataset type and its definition. Returns: Rendered HTML string ... | stack_v2_sparse_classes_36k_train_021890 | 19,958 | permissive | [
{
"docstring": "Get existing measurement forms for a dataset type Args: dataset type id, used to fetch the dataset type and its definition. Returns: Rendered HTML string containing a form for each measurement and a panel that enumerates all measurements. Essentially just the right side panel of the ingestion/da... | 2 | stack_v2_sparse_classes_30k_train_012383 | Implement the Python class `IngestionMeasurement` described below.
Class description:
Gets a list of existing measurements and validates user added measurements
Method signatures and docstrings:
- def get(self, request): Get existing measurement forms for a dataset type Args: dataset type id, used to fetch the datase... | Implement the Python class `IngestionMeasurement` described below.
Class description:
Gets a list of existing measurements and validates user added measurements
Method signatures and docstrings:
- def get(self, request): Get existing measurement forms for a dataset type Args: dataset type id, used to fetch the datase... | ef50e918df89313f130d735e7cb7c0a069da410e | <|skeleton|>
class IngestionMeasurement:
"""Gets a list of existing measurements and validates user added measurements"""
def get(self, request):
"""Get existing measurement forms for a dataset type Args: dataset type id, used to fetch the dataset type and its definition. Returns: Rendered HTML string ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IngestionMeasurement:
"""Gets a list of existing measurements and validates user added measurements"""
def get(self, request):
"""Get existing measurement forms for a dataset type Args: dataset type id, used to fetch the dataset type and its definition. Returns: Rendered HTML string containing a ... | the_stack_v2_python_sparse | apps/data_cube_manager/views/ingestion.py | ceos-seo/data_cube_ui | train | 47 |
20a35bf2a4f2b34def1ad7af7c3936569f624dbd | [
"self.verbose = verbose\nif slot_nums is None:\n self.slot_nums = set()\nelse:\n self.slot_nums = {int(slot_num) for slot_num in slot_nums}\nself.cfg_dict = {}\nself.S_dict = {}\nself.logs_dict = {}\nfor slot_num in sorted(self.slot_nums):\n self.load_single_slot(slot_num=slot_num)",
"slot_num = int(slot... | <|body_start_0|>
self.verbose = verbose
if slot_nums is None:
self.slot_nums = set()
else:
self.slot_nums = {int(slot_num) for slot_num in slot_nums}
self.cfg_dict = {}
self.S_dict = {}
self.logs_dict = {}
for slot_num in sorted(self.slot_n... | For obtaining and initializing and abstract number of SMuRF controllers (identified by slot number) for testing, commonly this is denoted as python objected denoted as an uppercase 'S'. For a single S, and cfg use: slot_num = 1 load_s = LoadS(slot_nums={slot_num}, verbose=verbose) cfg = load_s.S_dict[slot_num] S = load... | LoadS | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LoadS:
"""For obtaining and initializing and abstract number of SMuRF controllers (identified by slot number) for testing, commonly this is denoted as python objected denoted as an uppercase 'S'. For a single S, and cfg use: slot_num = 1 load_s = LoadS(slot_nums={slot_num}, verbose=verbose) cfg =... | stack_v2_sparse_classes_36k_train_021891 | 3,884 | no_license | [
{
"docstring": "Proved with a slot numbers, an instance of this class will automatically load and configure each SMuRF slot. The smurf slots are accessible through the instance variables self.cfg_dict, self.S_dict, and self.logs_dict. As the names of these variables suggest, each variable is a dictionary with t... | 3 | stack_v2_sparse_classes_30k_train_018473 | Implement the Python class `LoadS` described below.
Class description:
For obtaining and initializing and abstract number of SMuRF controllers (identified by slot number) for testing, commonly this is denoted as python objected denoted as an uppercase 'S'. For a single S, and cfg use: slot_num = 1 load_s = LoadS(slot_... | Implement the Python class `LoadS` described below.
Class description:
For obtaining and initializing and abstract number of SMuRF controllers (identified by slot number) for testing, commonly this is denoted as python objected denoted as an uppercase 'S'. For a single S, and cfg use: slot_num = 1 load_s = LoadS(slot_... | 0b002f1477efb6b5fcaddc4a282c35883165a42a | <|skeleton|>
class LoadS:
"""For obtaining and initializing and abstract number of SMuRF controllers (identified by slot number) for testing, commonly this is denoted as python objected denoted as an uppercase 'S'. For a single S, and cfg use: slot_num = 1 load_s = LoadS(slot_nums={slot_num}, verbose=verbose) cfg =... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LoadS:
"""For obtaining and initializing and abstract number of SMuRF controllers (identified by slot number) for testing, commonly this is denoted as python objected denoted as an uppercase 'S'. For a single S, and cfg use: slot_num = 1 load_s = LoadS(slot_nums={slot_num}, verbose=verbose) cfg = load_s.S_dic... | the_stack_v2_python_sparse | chw3k5/ufm_optimize/operators/controler.py | simonsobs/readout-script-dev | train | 1 |
6686b9ffc6573f146a957b5eb063297d49c57622 | [
"result = {'result': 'NG'}\ncontent = CtrlDSSection().get_usecase_by_doc_id(doc_id, 'USERCASE')\nif content:\n result = {'result': 'OK', 'content': content}\nreturn result",
"result = {'result': 'NG', 'error': ''}\ndata_json = request.get_json()\nsec_obj = CtrlDSSection()\nflag, error = sec_obj.usecase_add(dat... | <|body_start_0|>
result = {'result': 'NG'}
content = CtrlDSSection().get_usecase_by_doc_id(doc_id, 'USERCASE')
if content:
result = {'result': 'OK', 'content': content}
return result
<|end_body_0|>
<|body_start_1|>
result = {'result': 'NG', 'error': ''}
data_... | ApiDSDocUsecase | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApiDSDocUsecase:
def get(self, doc_id):
"""获取文档下所有usecase的说明 :param doc_id: :return:"""
<|body_0|>
def post(self):
"""保存和修改文档下usecase的说明 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = {'result': 'NG'}
content = CtrlDSSecti... | stack_v2_sparse_classes_36k_train_021892 | 31,026 | no_license | [
{
"docstring": "获取文档下所有usecase的说明 :param doc_id: :return:",
"name": "get",
"signature": "def get(self, doc_id)"
},
{
"docstring": "保存和修改文档下usecase的说明 :return:",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012198 | Implement the Python class `ApiDSDocUsecase` described below.
Class description:
Implement the ApiDSDocUsecase class.
Method signatures and docstrings:
- def get(self, doc_id): 获取文档下所有usecase的说明 :param doc_id: :return:
- def post(self): 保存和修改文档下usecase的说明 :return: | Implement the Python class `ApiDSDocUsecase` described below.
Class description:
Implement the ApiDSDocUsecase class.
Method signatures and docstrings:
- def get(self, doc_id): 获取文档下所有usecase的说明 :param doc_id: :return:
- def post(self): 保存和修改文档下usecase的说明 :return:
<|skeleton|>
class ApiDSDocUsecase:
def get(sel... | 64b31e7bdfcb8a4c95f0a8a607f0bcff576cec11 | <|skeleton|>
class ApiDSDocUsecase:
def get(self, doc_id):
"""获取文档下所有usecase的说明 :param doc_id: :return:"""
<|body_0|>
def post(self):
"""保存和修改文档下usecase的说明 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApiDSDocUsecase:
def get(self, doc_id):
"""获取文档下所有usecase的说明 :param doc_id: :return:"""
result = {'result': 'NG'}
content = CtrlDSSection().get_usecase_by_doc_id(doc_id, 'USERCASE')
if content:
result = {'result': 'OK', 'content': content}
return result
... | the_stack_v2_python_sparse | Source/collaboration_2/app/api_1_0/api_ds_doc.py | lsn1183/web_project | train | 0 | |
213a90e54d97c7db15b4c9972c1dcb083ec0a937 | [
"self.data = data\nself.period_begin = period_beg\nself.period_end = period_end\nself.observations = []",
"increase = Increase(self.data.copy(deep=True), self.period_begin, self.period_end)\nincrease.analyse()\nself.observations.extend(increase.observations)\ndecrease = Decrease(self.data.copy(deep=True), self.pe... | <|body_start_0|>
self.data = data
self.period_begin = period_beg
self.period_end = period_end
self.observations = []
<|end_body_0|>
<|body_start_1|>
increase = Increase(self.data.copy(deep=True), self.period_begin, self.period_end)
increase.analyse()
self.observa... | A class for running the analysis over the given data. | Analyse | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Analyse:
"""A class for running the analysis over the given data."""
def __init__(self, data, period_beg, period_end):
"""The init function. Args: data (pd.dataframe): The dataframe to be used for running the analysis period_beg (datetime.datetime): The datetime of the beginning of t... | stack_v2_sparse_classes_36k_train_021893 | 2,087 | no_license | [
{
"docstring": "The init function. Args: data (pd.dataframe): The dataframe to be used for running the analysis period_beg (datetime.datetime): The datetime of the beginning of the period period_end (datetime.datetime): The datetime of the end of the period",
"name": "__init__",
"signature": "def __init... | 4 | stack_v2_sparse_classes_30k_train_010583 | Implement the Python class `Analyse` described below.
Class description:
A class for running the analysis over the given data.
Method signatures and docstrings:
- def __init__(self, data, period_beg, period_end): The init function. Args: data (pd.dataframe): The dataframe to be used for running the analysis period_be... | Implement the Python class `Analyse` described below.
Class description:
A class for running the analysis over the given data.
Method signatures and docstrings:
- def __init__(self, data, period_beg, period_end): The init function. Args: data (pd.dataframe): The dataframe to be used for running the analysis period_be... | 5e62f96e541118ae924303b730f18d248022cac0 | <|skeleton|>
class Analyse:
"""A class for running the analysis over the given data."""
def __init__(self, data, period_beg, period_end):
"""The init function. Args: data (pd.dataframe): The dataframe to be used for running the analysis period_beg (datetime.datetime): The datetime of the beginning of t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Analyse:
"""A class for running the analysis over the given data."""
def __init__(self, data, period_beg, period_end):
"""The init function. Args: data (pd.dataframe): The dataframe to be used for running the analysis period_beg (datetime.datetime): The datetime of the beginning of the period per... | the_stack_v2_python_sparse | NLGengine/analyse.py | StanMey/Robotreporter | train | 3 |
aa2c75bb2e83a0bfb4e2e58f3dc2a96af5e910a6 | [
"if len(s) <= 1:\n return s\n\ndef isPalindrome(sub):\n if sub == sub[::-1]:\n return True\nfor l in range(len(s), -1, -1):\n for head in range(len(s) - l):\n tail = head + l + 1\n if isPalindrome(s[head:tail]):\n return s[head:tail]",
"max_palindrome = ''\nfor i in range(... | <|body_start_0|>
if len(s) <= 1:
return s
def isPalindrome(sub):
if sub == sub[::-1]:
return True
for l in range(len(s), -1, -1):
for head in range(len(s) - l):
tail = head + l + 1
if isPalindrome(s[head:tail]):... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def longestPalindrome(self, s: str) -> str:
"""This solution is similar to O(n^3) bruteforce, but uses approach that we doesn't need to check substrings shorter than current max_palin... | stack_v2_sparse_classes_36k_train_021894 | 2,296 | no_license | [
{
"docstring": ":type s: str :rtype: str",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s)"
},
{
"docstring": "This solution is similar to O(n^3) bruteforce, but uses approach that we doesn't need to check substrings shorter than current max_palindrom. On each iteration... | 2 | stack_v2_sparse_classes_30k_train_019228 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: str
- def longestPalindrome(self, s: str) -> str: This solution is similar to O(n^3) bruteforce, but uses approach that we do... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: str
- def longestPalindrome(self, s: str) -> str: This solution is similar to O(n^3) bruteforce, but uses approach that we do... | 92b4b7c6b69d39bf79a9e20a9fc947304c2a1de5 | <|skeleton|>
class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def longestPalindrome(self, s: str) -> str:
"""This solution is similar to O(n^3) bruteforce, but uses approach that we doesn't need to check substrings shorter than current max_palin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
if len(s) <= 1:
return s
def isPalindrome(sub):
if sub == sub[::-1]:
return True
for l in range(len(s), -1, -1):
for head in range(len(s) - l):
... | the_stack_v2_python_sparse | leet_0005_longest_palindromic_substring.py | kkaixiao/pythonalgo2 | train | 2 | |
5516452a231a947d42ced06ffa7d69be4605bd97 | [
"if len(nums) == 0:\n return 0\nmax_length = 1\nfor i in range(len(nums)):\n count = 1\n cur = nums[i]\n pre = None\n for j in range(i + 1, len(nums)):\n if cur < nums[j]:\n count += 1\n pre = cur\n cur = nums[j]\n elif cur > nums[j]:\n if pre... | <|body_start_0|>
if len(nums) == 0:
return 0
max_length = 1
for i in range(len(nums)):
count = 1
cur = nums[i]
pre = None
for j in range(i + 1, len(nums)):
if cur < nums[j]:
count += 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(nums) == 0:
return 0
... | stack_v2_sparse_classes_36k_train_021895 | 1,182 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "lengthOfLIS",
"signature": "def lengthOfLIS(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "lengthOfLIS",
"signature": "def lengthOfLIS(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015310 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
- def lengthOfLIS(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 lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
- def lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def lengthOfLIS... | d8ed762d1005975f0de4f07760c9671195621c88 | <|skeleton|>
class Solution:
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def lengthOfLIS(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 lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
if len(nums) == 0:
return 0
max_length = 1
for i in range(len(nums)):
count = 1
cur = nums[i]
pre = None
for j in range(i + 1, len(nums)):
... | the_stack_v2_python_sparse | longest-increasing-subsequence/solution.py | uxlsl/leetcode_practice | train | 0 | |
1fc8e5c695007c61734c84b725d8c559e698fee2 | [
"super().__init__()\nif path:\n use_pretrained = False\nelse:\n use_pretrained = True\nresnet = models.resnet50(pretrained=use_pretrained)\nself.pretrained = nn.Module()\nself.scratch = nn.Module()\nself.pretrained.layer1 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1)\ns... | <|body_start_0|>
super().__init__()
if path:
use_pretrained = False
else:
use_pretrained = True
resnet = models.resnet50(pretrained=use_pretrained)
self.pretrained = nn.Module()
self.scratch = nn.Module()
self.pretrained.layer1 = nn.Sequent... | Network for monocular depth estimation. | MidasNetOld | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MidasNetOld:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256."""
<|body_0|>
def forward(se... | stack_v2_sparse_classes_36k_train_021896 | 5,777 | permissive | [
{
"docstring": "Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256.",
"name": "__init__",
"signature": "def __init__(self, path=None, features=256)"
},
{
"docstring": "Forward pass. Args: x (tensor): input data (... | 3 | stack_v2_sparse_classes_30k_train_004581 | Implement the Python class `MidasNetOld` described below.
Class description:
Network for monocular depth estimation.
Method signatures and docstrings:
- def __init__(self, path=None, features=256): Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. D... | Implement the Python class `MidasNetOld` described below.
Class description:
Network for monocular depth estimation.
Method signatures and docstrings:
- def __init__(self, path=None, features=256): Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. D... | a00c3619bf4042e446e1919087f0b09fe9fa3a65 | <|skeleton|>
class MidasNetOld:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256."""
<|body_0|>
def forward(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MidasNetOld:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256."""
super().__init__()
if path:
... | the_stack_v2_python_sparse | nasws/cnn/search_space/monodepth/models/midas_net_old.py | kcyu2014/nas-landmarkreg | train | 10 |
be142f7992b7d27046142538c8f7a70a6e073f6a | [
"if theJSON.has('matchHostPK') or theJSON.has('matchHostSignature'):\n raise RuntimeException('Already signed JSON! Cannot sign again.')\ntheJSON.put('matchHostPK', thePK)\ntheSignature = BaseCryptography.signData(theSK, CanonicalJSON.getCanonicalForm(theJSON, CanonicalizationStrategy.SIMPLE))\ntheJSON.put('matc... | <|body_start_0|>
if theJSON.has('matchHostPK') or theJSON.has('matchHostSignature'):
raise RuntimeException('Already signed JSON! Cannot sign again.')
theJSON.put('matchHostPK', thePK)
theSignature = BaseCryptography.signData(theSK, CanonicalJSON.getCanonicalForm(theJSON, Canonicaliz... | generated source for class SignableJSON | SignableJSON | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignableJSON:
"""generated source for class SignableJSON"""
def signJSON(cls, theJSON, thePK, theSK):
"""generated source for method signJSON"""
<|body_0|>
def isSignedJSON(cls, theJSON):
"""generated source for method isSignedJSON"""
<|body_1|>
def ... | stack_v2_sparse_classes_36k_train_021897 | 2,578 | permissive | [
{
"docstring": "generated source for method signJSON",
"name": "signJSON",
"signature": "def signJSON(cls, theJSON, thePK, theSK)"
},
{
"docstring": "generated source for method isSignedJSON",
"name": "isSignedJSON",
"signature": "def isSignedJSON(cls, theJSON)"
},
{
"docstring":... | 3 | null | Implement the Python class `SignableJSON` described below.
Class description:
generated source for class SignableJSON
Method signatures and docstrings:
- def signJSON(cls, theJSON, thePK, theSK): generated source for method signJSON
- def isSignedJSON(cls, theJSON): generated source for method isSignedJSON
- def veri... | Implement the Python class `SignableJSON` described below.
Class description:
generated source for class SignableJSON
Method signatures and docstrings:
- def signJSON(cls, theJSON, thePK, theSK): generated source for method signJSON
- def isSignedJSON(cls, theJSON): generated source for method isSignedJSON
- def veri... | 4e6e6e876c3a4294cd711647051da2d9c1836b60 | <|skeleton|>
class SignableJSON:
"""generated source for class SignableJSON"""
def signJSON(cls, theJSON, thePK, theSK):
"""generated source for method signJSON"""
<|body_0|>
def isSignedJSON(cls, theJSON):
"""generated source for method isSignedJSON"""
<|body_1|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SignableJSON:
"""generated source for class SignableJSON"""
def signJSON(cls, theJSON, thePK, theSK):
"""generated source for method signJSON"""
if theJSON.has('matchHostPK') or theJSON.has('matchHostSignature'):
raise RuntimeException('Already signed JSON! Cannot sign again.'... | the_stack_v2_python_sparse | ggpy/cruft/autocode/SignableJSON.py | hobson/ggpy | train | 1 |
51a7679431c5092f992c7ed3853965bf8dedac72 | [
"self.capacity = capacity\nself.dicti = {}\nself.head = Node(0, 0)\nself.tail = Node(0, 0)\nself.head.next = self.tail\nself.tail.prev = self.head",
"if key not in self.dicti:\n return -1\nnode = self.dicti[key]\nself.remove(node)\nself.add(node)\nreturn node.val",
"if key in self.dicti:\n self.remove(sel... | <|body_start_0|>
self.capacity = capacity
self.dicti = {}
self.head = Node(0, 0)
self.tail = Node(0, 0)
self.head.next = self.tail
self.tail.prev = self.head
<|end_body_0|>
<|body_start_1|>
if key not in self.dicti:
return -1
node = self.dicti... | LRUCache | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
def... | stack_v2_sparse_classes_36k_train_021898 | 1,745 | permissive | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: void",
"name": "pu... | 5 | null | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void
- def... | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void
- def... | 147d99e273bc398c107f2aef73aba0d6bb88dea0 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.dicti = {}
self.head = Node(0, 0)
self.tail = Node(0, 0)
self.head.next = self.tail
self.tail.prev = self.head
def get(self, key):
""":type key: ... | the_stack_v2_python_sparse | 146_LRU_Cache.py | rpm1995/LeetCode | train | 0 | |
33c4866b8c3a17c5b3f0553a1eabbb4f4c6c9f25 | [
"print('Inside __init__()')\nself.arg1 = arg1\nself.arg2 = arg2\nself.arg3 = arg3",
"print('Inside __call__()')\n\ndef wrapped_f(*args):\n print('Inside wrapped_f()')\n print('Decorator arguments:', self.arg1, self.arg2, self.arg3)\n return f(*args)\nreturn wrapped_f"
] | <|body_start_0|>
print('Inside __init__()')
self.arg1 = arg1
self.arg2 = arg2
self.arg3 = arg3
<|end_body_0|>
<|body_start_1|>
print('Inside __call__()')
def wrapped_f(*args):
print('Inside wrapped_f()')
print('Decorator arguments:', self.arg1, s... | decoratorWithArguments | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class decoratorWithArguments:
def __init__(self, arg1, arg2, arg3):
"""If there are decorator arguments, the function to be decorated is not passed to the constructor!"""
<|body_0|>
def __call__(self, f):
"""If there are decorator arguments, __call__() is only called once,... | stack_v2_sparse_classes_36k_train_021899 | 2,314 | no_license | [
{
"docstring": "If there are decorator arguments, the function to be decorated is not passed to the constructor!",
"name": "__init__",
"signature": "def __init__(self, arg1, arg2, arg3)"
},
{
"docstring": "If there are decorator arguments, __call__() is only called once, as part of the decoratio... | 2 | stack_v2_sparse_classes_30k_train_020680 | Implement the Python class `decoratorWithArguments` described below.
Class description:
Implement the decoratorWithArguments class.
Method signatures and docstrings:
- def __init__(self, arg1, arg2, arg3): If there are decorator arguments, the function to be decorated is not passed to the constructor!
- def __call__(... | Implement the Python class `decoratorWithArguments` described below.
Class description:
Implement the decoratorWithArguments class.
Method signatures and docstrings:
- def __init__(self, arg1, arg2, arg3): If there are decorator arguments, the function to be decorated is not passed to the constructor!
- def __call__(... | 3fdee9a4fc87ce191643f59a3e33d03dac602156 | <|skeleton|>
class decoratorWithArguments:
def __init__(self, arg1, arg2, arg3):
"""If there are decorator arguments, the function to be decorated is not passed to the constructor!"""
<|body_0|>
def __call__(self, f):
"""If there are decorator arguments, __call__() is only called once,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class decoratorWithArguments:
def __init__(self, arg1, arg2, arg3):
"""If there are decorator arguments, the function to be decorated is not passed to the constructor!"""
print('Inside __init__()')
self.arg1 = arg1
self.arg2 = arg2
self.arg3 = arg3
def __call__(self, f):... | the_stack_v2_python_sparse | Code/dec.py | ThomasTwiton/CS330-1 | train | 0 |
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