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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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c307b1a541a8541710c5cfa34e4a57b40733e25c | [
"self.params = {}\n'\\n 我们用标准差为weight_scale的高斯分布初始化参数W,\\n 偏置B的初始化都为0：\\n (其中randn函数是基于零均值和标准差的一个高斯分布)\\n '\nself.params['W1'] = weight_scale * np.random.randn(input_dims, hidden_dims)\nself.params['b1'] = np.zeros((hidden_dims,))\nself.params['W2'] = weight_scale * np.random.randn(hidde... | <|body_start_0|>
self.params = {}
'\n 我们用标准差为weight_scale的高斯分布初始化参数W,\n 偏置B的初始化都为0：\n (其中randn函数是基于零均值和标准差的一个高斯分布)\n '
self.params['W1'] = weight_scale * np.random.randn(input_dims, hidden_dims)
self.params['b1'] = np.zeros((hidden_dims,))
self.params[... | 首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。 | TwoLayerNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TwoLayerNet:
"""首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。"""
def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100, num_classes=10, weight_scale=0.001):
"""我们把需要学习的参数(W,B)都存在s... | stack_v2_sparse_classes_36k_train_015600 | 36,287 | no_license | [
{
"docstring": "我们把需要学习的参数(W,B)都存在self.params字典中， 其中每个元素都是numpy array:",
"name": "__init__",
"signature": "def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100, num_classes=10, weight_scale=0.001)"
},
{
"docstring": "首先，输入的数据X是一个多维的array,shape为(样本图片的个数N * 32*32*3), y是与输入数据X对应的正确标签，shape为(N... | 2 | stack_v2_sparse_classes_30k_train_015691 | Implement the Python class `TwoLayerNet` described below.
Class description:
首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。
Method signatures and docstrings:
- def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100,... | Implement the Python class `TwoLayerNet` described below.
Class description:
首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。
Method signatures and docstrings:
- def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100,... | e82d9577d8a7f4ce9950bc7e5a950592dff34bbd | <|skeleton|>
class TwoLayerNet:
"""首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。"""
def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100, num_classes=10, weight_scale=0.001):
"""我们把需要学习的参数(W,B)都存在s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TwoLayerNet:
"""首先，先初始化我们的神经网络。 毕竟，数据从输入层第一次流入到神经网络里，参数(W,B)不能为空，(w,b)是 一层的参数；(W,B)是所有层参数的统一。参数初始化也不能太大或太小，因此 (W,B)的初始化时很重要的，对整个神经网络的训练影响巨大，但如何proper 的初始化参数还没定论。"""
def __init__(self, input_dims=32 * 32 * 3, hidden_dims=100, num_classes=10, weight_scale=0.001):
"""我们把需要学习的参数(W,B)都存在self.params字典中... | the_stack_v2_python_sparse | assigment2/full_connect.py | hduyuanfu/SHUQI_SHIYAN | train | 0 |
ea5eac98686f4395729fa3158cebdda0e467c4d0 | [
"self.cmd_base = cmd_base\nself.env_vars = env_vars.copy() if env_vars is not None else dict()\nself.ros_args = ros_args",
"cmd = self.cmd_base\nfor name, val in self.ros_args.items():\n cmd += f' {name}:={val}'\nreturn cmd",
"for var, val in self.env_vars.items():\n os.environ[var] = str(val)\nprocess = ... | <|body_start_0|>
self.cmd_base = cmd_base
self.env_vars = env_vars.copy() if env_vars is not None else dict()
self.ros_args = ros_args
<|end_body_0|>
<|body_start_1|>
cmd = self.cmd_base
for name, val in self.ros_args.items():
cmd += f' {name}:={val}'
return ... | Holds data for a ROS command and makes it executable. | ROSCmd | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ROSCmd:
"""Holds data for a ROS command and makes it executable."""
def __init__(self, cmd_base: str, *, env_vars: Dict[str, Any], **ros_args):
"""Initialize ROSCmd. Args: cmd_base: Command string without arguments. env_vars: Environment variables set prior to running the command. ro... | stack_v2_sparse_classes_36k_train_015601 | 1,808 | permissive | [
{
"docstring": "Initialize ROSCmd. Args: cmd_base: Command string without arguments. env_vars: Environment variables set prior to running the command. ros_args: ROS arguments passed when running the command.",
"name": "__init__",
"signature": "def __init__(self, cmd_base: str, *, env_vars: Dict[str, Any... | 3 | null | Implement the Python class `ROSCmd` described below.
Class description:
Holds data for a ROS command and makes it executable.
Method signatures and docstrings:
- def __init__(self, cmd_base: str, *, env_vars: Dict[str, Any], **ros_args): Initialize ROSCmd. Args: cmd_base: Command string without arguments. env_vars: E... | Implement the Python class `ROSCmd` described below.
Class description:
Holds data for a ROS command and makes it executable.
Method signatures and docstrings:
- def __init__(self, cmd_base: str, *, env_vars: Dict[str, Any], **ros_args): Initialize ROSCmd. Args: cmd_base: Command string without arguments. env_vars: E... | 8a9438b5a24c288721ae0302889fe55e26046310 | <|skeleton|>
class ROSCmd:
"""Holds data for a ROS command and makes it executable."""
def __init__(self, cmd_base: str, *, env_vars: Dict[str, Any], **ros_args):
"""Initialize ROSCmd. Args: cmd_base: Command string without arguments. env_vars: Environment variables set prior to running the command. ro... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ROSCmd:
"""Holds data for a ROS command and makes it executable."""
def __init__(self, cmd_base: str, *, env_vars: Dict[str, Any], **ros_args):
"""Initialize ROSCmd. Args: cmd_base: Command string without arguments. env_vars: Environment variables set prior to running the command. ros_args: ROS a... | the_stack_v2_python_sparse | simulation/utils/basics/ros_cmd.py | KITcar-Team/kitcar-gazebo-simulation | train | 19 |
d3d1b10a2cf47e6226b8b6226cb53fe0879bfcfc | [
"super(MultiHeadedAttention, self).__init__()\nassert d_model % h == 0\nself.d_k = d_model // h\nself.h = h\nself.attenuation_lambda = torch.nn.Parameter(torch.tensor(attenuation_lambda, requires_grad=True))\nself.linears = clones(nn.Linear(d_model, d_model), 5)\nself.message = None\nself.leaky_relu_slope = leaky_r... | <|body_start_0|>
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
self.d_k = d_model // h
self.h = h
self.attenuation_lambda = torch.nn.Parameter(torch.tensor(attenuation_lambda, requires_grad=True))
self.linears = clones(nn.Linear(d_model, d_model), 5... | MultiHeadedAttention | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadedAttention:
def __init__(self, h, d_model, leaky_relu_slope=0.1, dropout=0.1, attenuation_lambda=0.1, distance_matrix_kernel='softmax'):
"""Take in model size and number of heads."""
<|body_0|>
def forward(self, query_node, value_node, key_edge, adj_matrix, mask=No... | stack_v2_sparse_classes_36k_train_015602 | 18,947 | permissive | [
{
"docstring": "Take in model size and number of heads.",
"name": "__init__",
"signature": "def __init__(self, h, d_model, leaky_relu_slope=0.1, dropout=0.1, attenuation_lambda=0.1, distance_matrix_kernel='softmax')"
},
{
"docstring": "Implements Figure 2",
"name": "forward",
"signature"... | 2 | stack_v2_sparse_classes_30k_val_000460 | Implement the Python class `MultiHeadedAttention` described below.
Class description:
Implement the MultiHeadedAttention class.
Method signatures and docstrings:
- def __init__(self, h, d_model, leaky_relu_slope=0.1, dropout=0.1, attenuation_lambda=0.1, distance_matrix_kernel='softmax'): Take in model size and number... | Implement the Python class `MultiHeadedAttention` described below.
Class description:
Implement the MultiHeadedAttention class.
Method signatures and docstrings:
- def __init__(self, h, d_model, leaky_relu_slope=0.1, dropout=0.1, attenuation_lambda=0.1, distance_matrix_kernel='softmax'): Take in model size and number... | 11a36843a83ddc93748c5437f5a21f2507b66c77 | <|skeleton|>
class MultiHeadedAttention:
def __init__(self, h, d_model, leaky_relu_slope=0.1, dropout=0.1, attenuation_lambda=0.1, distance_matrix_kernel='softmax'):
"""Take in model size and number of heads."""
<|body_0|>
def forward(self, query_node, value_node, key_edge, adj_matrix, mask=No... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiHeadedAttention:
def __init__(self, h, d_model, leaky_relu_slope=0.1, dropout=0.1, attenuation_lambda=0.1, distance_matrix_kernel='softmax'):
"""Take in model size and number of heads."""
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
self.d_k = d_mod... | the_stack_v2_python_sparse | MolRep/Models/sequence_based/CoMPT.py | biomed-AI/MolRep | train | 104 | |
4d542d06223e08a24c96a31d4d834483f1bf64da | [
"def preorder(root):\n if not root:\n return '#,'\n return str(root.val) + ',' + self.serialize(root.left) + self.serialize(root.right)\nreturn preorder(root)",
"if not data or data == '#':\n return\nnodes = data.split(',')\n\ndef preorder(i):\n if i >= len(nodes) or nodes[i] == '#':\n r... | <|body_start_0|>
def preorder(root):
if not root:
return '#,'
return str(root.val) + ',' + self.serialize(root.left) + self.serialize(root.right)
return preorder(root)
<|end_body_0|>
<|body_start_1|>
if not data or data == '#':
return
... | CodecDFS | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CodecDFS:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|bo... | stack_v2_sparse_classes_36k_train_015603 | 2,652 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `CodecDFS` described below.
Class description:
Implement the CodecDFS class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :... | Implement the Python class `CodecDFS` described below.
Class description:
Implement the CodecDFS class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :... | 3a5649357e0f21cbbc5e238351300cd706d533b3 | <|skeleton|>
class CodecDFS:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CodecDFS:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def preorder(root):
if not root:
return '#,'
return str(root.val) + ',' + self.serialize(root.left) + self.serialize(root.right)
return... | the_stack_v2_python_sparse | leetcode-py/leetcode297.py | cicihou/LearningProject | train | 0 | |
a2858548c3a0a28448c13609aa7fb19d37a00e7c | [
"self.title = 'Convert Miles to Kilometres'\nself.root = Builder.load_file('convert_miles_km.kv')\nreturn self.root",
"value = self.get_valid_miles()\nresult = value * FACTOR_MILES_TO_KM\nself.root.ids.output_label.text = str(result)",
"value = self.get_valid_miles() + change\nself.root.ids.input_miles.text = s... | <|body_start_0|>
self.title = 'Convert Miles to Kilometres'
self.root = Builder.load_file('convert_miles_km.kv')
return self.root
<|end_body_0|>
<|body_start_1|>
value = self.get_valid_miles()
result = value * FACTOR_MILES_TO_KM
self.root.ids.output_label.text = str(resu... | MilesConverterApp is a Kivy App for the conversion of miles into kilometres. | MilesConverterApp | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MilesConverterApp:
"""MilesConverterApp is a Kivy App for the conversion of miles into kilometres."""
def build(self):
"""Build the Kivy App from the Kv file."""
<|body_0|>
def handle_calculate(self):
"""Handle the conversion from miles to kilometres."""
... | stack_v2_sparse_classes_36k_train_015604 | 1,548 | no_license | [
{
"docstring": "Build the Kivy App from the Kv file.",
"name": "build",
"signature": "def build(self)"
},
{
"docstring": "Handle the conversion from miles to kilometres.",
"name": "handle_calculate",
"signature": "def handle_calculate(self)"
},
{
"docstring": "Handle pressing \"U... | 4 | null | Implement the Python class `MilesConverterApp` described below.
Class description:
MilesConverterApp is a Kivy App for the conversion of miles into kilometres.
Method signatures and docstrings:
- def build(self): Build the Kivy App from the Kv file.
- def handle_calculate(self): Handle the conversion from miles to ki... | Implement the Python class `MilesConverterApp` described below.
Class description:
MilesConverterApp is a Kivy App for the conversion of miles into kilometres.
Method signatures and docstrings:
- def build(self): Build the Kivy App from the Kv file.
- def handle_calculate(self): Handle the conversion from miles to ki... | 25ce5696ee15aee95521541441a01eb18820bc93 | <|skeleton|>
class MilesConverterApp:
"""MilesConverterApp is a Kivy App for the conversion of miles into kilometres."""
def build(self):
"""Build the Kivy App from the Kv file."""
<|body_0|>
def handle_calculate(self):
"""Handle the conversion from miles to kilometres."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MilesConverterApp:
"""MilesConverterApp is a Kivy App for the conversion of miles into kilometres."""
def build(self):
"""Build the Kivy App from the Kv file."""
self.title = 'Convert Miles to Kilometres'
self.root = Builder.load_file('convert_miles_km.kv')
return self.roo... | the_stack_v2_python_sparse | prac_07/convert_miles_km.py | MomoeYoshida/cp1404practicals | train | 0 |
c4f8bc50fab035004d08b163828024a791e52c1d | [
"dummy = ListNode()\np = dummy\nwhile l1 and l2:\n if l1.val < l2.val:\n p.next, l1 = (l1, l1.next)\n else:\n p.next, l2 = (l2, l2.next)\n p = p.next\np.next = l1 or l2\nreturn dummy.next",
"if not l1:\n return l2\nif not l2:\n return l1\nif l1.val < l2.val:\n l1.next = self.mergeT... | <|body_start_0|>
dummy = ListNode()
p = dummy
while l1 and l2:
if l1.val < l2.val:
p.next, l1 = (l1, l1.next)
else:
p.next, l2 = (l2, l2.next)
p = p.next
p.next = l1 or l2
return dummy.next
<|end_body_0|>
<|body... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""Solution #1: iteratively Time: O(n) Space: O(1)"""
<|body_0|>
def mergeTwoLists2(self, l1: ListNode, l2: ListNode) -> ListNode:
"""Solution #2: recursively Time: O(n) Space: O(1)"""
... | stack_v2_sparse_classes_36k_train_015605 | 1,325 | no_license | [
{
"docstring": "Solution #1: iteratively Time: O(n) Space: O(1)",
"name": "mergeTwoLists1",
"signature": "def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListNode"
},
{
"docstring": "Solution #2: recursively Time: O(n) Space: O(1)",
"name": "mergeTwoLists2",
"signature": "def mer... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListNode: Solution #1: iteratively Time: O(n) Space: O(1)
- def mergeTwoLists2(self, l1: ListNode, l2: ListNode) -> ListNo... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListNode: Solution #1: iteratively Time: O(n) Space: O(1)
- def mergeTwoLists2(self, l1: ListNode, l2: ListNode) -> ListNo... | 59c8b144f4245ed4a8b06a458954ca05c0c73aea | <|skeleton|>
class Solution:
def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""Solution #1: iteratively Time: O(n) Space: O(1)"""
<|body_0|>
def mergeTwoLists2(self, l1: ListNode, l2: ListNode) -> ListNode:
"""Solution #2: recursively Time: O(n) Space: O(1)"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""Solution #1: iteratively Time: O(n) Space: O(1)"""
dummy = ListNode()
p = dummy
while l1 and l2:
if l1.val < l2.val:
p.next, l1 = (l1, l1.next)
else:
... | the_stack_v2_python_sparse | 01/merge-two-sorted-lists.py | TrisDing/algorithm010 | train | 1 | |
6a5a722a81a13f364138040e05080a46d6b8c506 | [
"self.theta = theta_0\nself.step_size = step_size\nself.max_iter = max_iter\nself.eps = eps\nself.verbose = verbose",
"phi = y.mean()\nmu = np.array([X[y == k].mean(axis=0) for k in [0, 1]])\nX_u = X.copy()\nfor k in [0, 1]:\n X_u[y == k] -= mu[k]\nEpsilon = X_u.T.dot(X_u) / len(y)\ninvEpsilon = np.linalg.pinv... | <|body_start_0|>
self.theta = theta_0
self.step_size = step_size
self.max_iter = max_iter
self.eps = eps
self.verbose = verbose
<|end_body_0|>
<|body_start_1|>
phi = y.mean()
mu = np.array([X[y == k].mean(axis=0) for k in [0, 1]])
X_u = X.copy()
f... | Gaussian Discriminant Analysis. Example usage: > clf = GDA() > clf.fit(x_train, y_train) > clf.predict(x_eval) | GDA | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GDA:
"""Gaussian Discriminant Analysis. Example usage: > clf = GDA() > clf.fit(x_train, y_train) > clf.predict(x_eval)"""
def __init__(self, step_size=0.01, max_iter=10000, eps=1e-05, theta_0=None, verbose=True):
"""Args: step_size: Step size for iterative solvers only. max_iter: Max... | stack_v2_sparse_classes_36k_train_015606 | 14,754 | no_license | [
{
"docstring": "Args: step_size: Step size for iterative solvers only. max_iter: Maximum number of iterations for the solver. eps: Threshold for determining convergence. theta_0: Initial guess for theta. If None, use the zero vector. verbose: Print loss values during training.",
"name": "__init__",
"sig... | 3 | stack_v2_sparse_classes_30k_val_000122 | Implement the Python class `GDA` described below.
Class description:
Gaussian Discriminant Analysis. Example usage: > clf = GDA() > clf.fit(x_train, y_train) > clf.predict(x_eval)
Method signatures and docstrings:
- def __init__(self, step_size=0.01, max_iter=10000, eps=1e-05, theta_0=None, verbose=True): Args: step_... | Implement the Python class `GDA` described below.
Class description:
Gaussian Discriminant Analysis. Example usage: > clf = GDA() > clf.fit(x_train, y_train) > clf.predict(x_eval)
Method signatures and docstrings:
- def __init__(self, step_size=0.01, max_iter=10000, eps=1e-05, theta_0=None, verbose=True): Args: step_... | 2cd881b33103435a45068fe7d5d1ac26b2d944c3 | <|skeleton|>
class GDA:
"""Gaussian Discriminant Analysis. Example usage: > clf = GDA() > clf.fit(x_train, y_train) > clf.predict(x_eval)"""
def __init__(self, step_size=0.01, max_iter=10000, eps=1e-05, theta_0=None, verbose=True):
"""Args: step_size: Step size for iterative solvers only. max_iter: Max... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GDA:
"""Gaussian Discriminant Analysis. Example usage: > clf = GDA() > clf.fit(x_train, y_train) > clf.predict(x_eval)"""
def __init__(self, step_size=0.01, max_iter=10000, eps=1e-05, theta_0=None, verbose=True):
"""Args: step_size: Step size for iterative solvers only. max_iter: Maximum number o... | the_stack_v2_python_sparse | Course Assignments/CP8318 - Machine Learning Assignments/CP8318 Assignment 2 - Rohaan Ahmed/linearclass/gda_rohaan.py | rohaan-ahmed/Master-Repository | train | 0 |
0683b59638a98126eda2b66c920a77bfb0965ede | [
"obj = context.object\nif obj is None:\n return False\nreturn all([bool(obj), obj.type == 'MESH', obj.mode == 'EDIT'])",
"pg = context.scene.pdt_pg\npg.command = f'etf'\nreturn {'FINISHED'}"
] | <|body_start_0|>
obj = context.object
if obj is None:
return False
return all([bool(obj), obj.type == 'MESH', obj.mode == 'EDIT'])
<|end_body_0|>
<|body_start_1|>
pg = context.scene.pdt_pg
pg.command = f'etf'
return {'FINISHED'}
<|end_body_1|>
| Extend Selected Edge to Projected Intersection with Selected Face | PDT_OT_EdgeToFace | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PDT_OT_EdgeToFace:
"""Extend Selected Edge to Projected Intersection with Selected Face"""
def poll(cls, context):
"""Only allow this to work if a mesh is selected in EDIT mode. Args: context: Blender bpy.context instance. Returns: Boolean."""
<|body_0|>
def execute(self... | stack_v2_sparse_classes_36k_train_015607 | 3,809 | permissive | [
{
"docstring": "Only allow this to work if a mesh is selected in EDIT mode. Args: context: Blender bpy.context instance. Returns: Boolean.",
"name": "poll",
"signature": "def poll(cls, context)"
},
{
"docstring": "Extends Disconnected Edge to Intersect with Face. Args: context: Blender bpy.conte... | 2 | null | Implement the Python class `PDT_OT_EdgeToFace` described below.
Class description:
Extend Selected Edge to Projected Intersection with Selected Face
Method signatures and docstrings:
- def poll(cls, context): Only allow this to work if a mesh is selected in EDIT mode. Args: context: Blender bpy.context instance. Retu... | Implement the Python class `PDT_OT_EdgeToFace` described below.
Class description:
Extend Selected Edge to Projected Intersection with Selected Face
Method signatures and docstrings:
- def poll(cls, context): Only allow this to work if a mesh is selected in EDIT mode. Args: context: Blender bpy.context instance. Retu... | 4d5c304878c1e0018d97c1b07bcaa3981632265a | <|skeleton|>
class PDT_OT_EdgeToFace:
"""Extend Selected Edge to Projected Intersection with Selected Face"""
def poll(cls, context):
"""Only allow this to work if a mesh is selected in EDIT mode. Args: context: Blender bpy.context instance. Returns: Boolean."""
<|body_0|>
def execute(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PDT_OT_EdgeToFace:
"""Extend Selected Edge to Projected Intersection with Selected Face"""
def poll(cls, context):
"""Only allow this to work if a mesh is selected in EDIT mode. Args: context: Blender bpy.context instance. Returns: Boolean."""
obj = context.object
if obj is None:
... | the_stack_v2_python_sparse | src/bpy/3.6/scripts/addons/precision_drawing_tools/pdt_etof.py | RnoB/3DVisualSwarm | train | 0 |
ddd8518621ddf74300d9e546c366545c2324b4a1 | [
"self.id = process.id\nself.pid = process.pid\nself.name = process.name\nself.cpu = None\nself.mem = None\nself.etime = None",
"repr_string = '{}('.format(self.__class__.__name__)\nrepr_string += 'id={}, '.format(self.id)\nrepr_string += 'pid={}, '.format(self.pid)\nrepr_string += 'name={}, '.format(self.name)\nr... | <|body_start_0|>
self.id = process.id
self.pid = process.pid
self.name = process.name
self.cpu = None
self.mem = None
self.etime = None
<|end_body_0|>
<|body_start_1|>
repr_string = '{}('.format(self.__class__.__name__)
repr_string += 'id={}, '.format(sel... | MonitorData is a container for processes that are monitored by StorageMonitor. It is designed to be pickled and send as a Message payload. | MonitorData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MonitorData:
"""MonitorData is a container for processes that are monitored by StorageMonitor. It is designed to be pickled and send as a Message payload."""
def __init__(self, process):
"""Initializes a MonitorData with: Args: process: A multiprocessing.Process instance"""
<... | stack_v2_sparse_classes_36k_train_015608 | 6,615 | no_license | [
{
"docstring": "Initializes a MonitorData with: Args: process: A multiprocessing.Process instance",
"name": "__init__",
"signature": "def __init__(self, process)"
},
{
"docstring": "Provides a repr() implementation for MonitorData. Returns: A repr string for MonitorData.",
"name": "__repr__"... | 2 | stack_v2_sparse_classes_30k_train_008767 | Implement the Python class `MonitorData` described below.
Class description:
MonitorData is a container for processes that are monitored by StorageMonitor. It is designed to be pickled and send as a Message payload.
Method signatures and docstrings:
- def __init__(self, process): Initializes a MonitorData with: Args:... | Implement the Python class `MonitorData` described below.
Class description:
MonitorData is a container for processes that are monitored by StorageMonitor. It is designed to be pickled and send as a Message payload.
Method signatures and docstrings:
- def __init__(self, process): Initializes a MonitorData with: Args:... | e2b7136c7feda4deb667bd1e6cba3c1ef7eeff9d | <|skeleton|>
class MonitorData:
"""MonitorData is a container for processes that are monitored by StorageMonitor. It is designed to be pickled and send as a Message payload."""
def __init__(self, process):
"""Initializes a MonitorData with: Args: process: A multiprocessing.Process instance"""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MonitorData:
"""MonitorData is a container for processes that are monitored by StorageMonitor. It is designed to be pickled and send as a Message payload."""
def __init__(self, process):
"""Initializes a MonitorData with: Args: process: A multiprocessing.Process instance"""
self.id = proc... | the_stack_v2_python_sparse | client/monitor.py | NickBayard/sf | train | 0 |
36e30795b5bbff7d51d39795f421f2d0cb57429a | [
"super().__init__(**kwargs)\nself._vocab_size = vocab_size\nself._num_mixtures = num_mixtures\nself._use_input_context_gate = use_input_context_gate\nself._use_output_context_gate = use_output_context_gate\nself._vocab_as_last_dim = vocab_as_last_dim\nself._normalizer_params = normalizer_params\nself._l2_regularize... | <|body_start_0|>
super().__init__(**kwargs)
self._vocab_size = vocab_size
self._num_mixtures = num_mixtures
self._use_input_context_gate = use_input_context_gate
self._use_output_context_gate = use_output_context_gate
self._vocab_as_last_dim = vocab_as_last_dim
se... | A softmax over a mixture of logistic models (with L2 regularization). | MoeModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MoeModel:
"""A softmax over a mixture of logistic models (with L2 regularization)."""
def __init__(self, vocab_size: int=3862, num_mixtures: int=2, use_input_context_gate: bool=False, use_output_context_gate: bool=False, normalizer_params: Optional[dict[str, Any]]=None, vocab_as_last_dim: bo... | stack_v2_sparse_classes_36k_train_015609 | 5,262 | permissive | [
{
"docstring": "Creates a Mixture of (Logistic) Experts model. The model consists of a per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0. Args: vocab_size: The number of classes in the dataset. num_mixtu... | 2 | stack_v2_sparse_classes_30k_train_020615 | Implement the Python class `MoeModel` described below.
Class description:
A softmax over a mixture of logistic models (with L2 regularization).
Method signatures and docstrings:
- def __init__(self, vocab_size: int=3862, num_mixtures: int=2, use_input_context_gate: bool=False, use_output_context_gate: bool=False, nor... | Implement the Python class `MoeModel` described below.
Class description:
A softmax over a mixture of logistic models (with L2 regularization).
Method signatures and docstrings:
- def __init__(self, vocab_size: int=3862, num_mixtures: int=2, use_input_context_gate: bool=False, use_output_context_gate: bool=False, nor... | 9c78e7f643231c0fc9cc64c991d2b8d5628b9668 | <|skeleton|>
class MoeModel:
"""A softmax over a mixture of logistic models (with L2 regularization)."""
def __init__(self, vocab_size: int=3862, num_mixtures: int=2, use_input_context_gate: bool=False, use_output_context_gate: bool=False, normalizer_params: Optional[dict[str, Any]]=None, vocab_as_last_dim: bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MoeModel:
"""A softmax over a mixture of logistic models (with L2 regularization)."""
def __init__(self, vocab_size: int=3862, num_mixtures: int=2, use_input_context_gate: bool=False, use_output_context_gate: bool=False, normalizer_params: Optional[dict[str, Any]]=None, vocab_as_last_dim: bool=False, l2_... | the_stack_v2_python_sparse | official/projects/yt8m/modeling/heads/moe.py | tensorflow/models | train | 86,763 |
0d7435c9c3f78fea8212d02288beb662458c31ff | [
"category = get_object_or_404(Category, pk=category_id)\nserializer = CategorySerializer(category)\nreturn Response(serializer.data)",
"category = get_object_or_404(Category, pk=category_id)\nserializer = CategorySerializer(category, data=request.data)\nif serializer.is_valid():\n serializer.save()\n return... | <|body_start_0|>
category = get_object_or_404(Category, pk=category_id)
serializer = CategorySerializer(category)
return Response(serializer.data)
<|end_body_0|>
<|body_start_1|>
category = get_object_or_404(Category, pk=category_id)
serializer = CategorySerializer(category, dat... | CategoryDetail | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CategoryDetail:
def get(self, request, category_id, format=None):
"""Get category details"""
<|body_0|>
def put(self, request, category_id, format=None):
"""Edit category --- serializer: administrator.serializers.CategorySerializer"""
<|body_1|>
def dele... | stack_v2_sparse_classes_36k_train_015610 | 30,608 | permissive | [
{
"docstring": "Get category details",
"name": "get",
"signature": "def get(self, request, category_id, format=None)"
},
{
"docstring": "Edit category --- serializer: administrator.serializers.CategorySerializer",
"name": "put",
"signature": "def put(self, request, category_id, format=No... | 3 | stack_v2_sparse_classes_30k_train_016953 | Implement the Python class `CategoryDetail` described below.
Class description:
Implement the CategoryDetail class.
Method signatures and docstrings:
- def get(self, request, category_id, format=None): Get category details
- def put(self, request, category_id, format=None): Edit category --- serializer: administrator... | Implement the Python class `CategoryDetail` described below.
Class description:
Implement the CategoryDetail class.
Method signatures and docstrings:
- def get(self, request, category_id, format=None): Get category details
- def put(self, request, category_id, format=None): Edit category --- serializer: administrator... | 73728463badb3bfd4413aa0f7aeb44a9606fdfea | <|skeleton|>
class CategoryDetail:
def get(self, request, category_id, format=None):
"""Get category details"""
<|body_0|>
def put(self, request, category_id, format=None):
"""Edit category --- serializer: administrator.serializers.CategorySerializer"""
<|body_1|>
def dele... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CategoryDetail:
def get(self, request, category_id, format=None):
"""Get category details"""
category = get_object_or_404(Category, pk=category_id)
serializer = CategorySerializer(category)
return Response(serializer.data)
def put(self, request, category_id, format=None):
... | the_stack_v2_python_sparse | administrator/views.py | belatrix/BackendAllStars | train | 5 | |
14aa790df38d91f32b18fde02c3e38fab109e0c9 | [
"if operation == 'update' and self.request.authenticated_role != self.context.author:\n self.request.errors.add('url', 'role', 'Can update document only author')\n self.request.errors.status = 403\n raise error_handler(self.request.errors)\nif self.request.validated['tender_status'] not in ['active.qualifi... | <|body_start_0|>
if operation == 'update' and self.request.authenticated_role != self.context.author:
self.request.errors.add('url', 'role', 'Can update document only author')
self.request.errors.status = 403
raise error_handler(self.request.errors)
if self.request.va... | TenderUaAwardComplaintDocumentResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TenderUaAwardComplaintDocumentResource:
def validate_complaint_document(self, operation):
"""TODO move validators This class is inherited in limited and openeu (qualification complaint) package, but validate_complaint_document function has different validators. For now, we have no way to... | stack_v2_sparse_classes_36k_train_015611 | 5,315 | permissive | [
{
"docstring": "TODO move validators This class is inherited in limited and openeu (qualification complaint) package, but validate_complaint_document function has different validators. For now, we have no way to use different validators on methods according to procedure type.",
"name": "validate_complaint_d... | 4 | stack_v2_sparse_classes_30k_train_017779 | Implement the Python class `TenderUaAwardComplaintDocumentResource` described below.
Class description:
Implement the TenderUaAwardComplaintDocumentResource class.
Method signatures and docstrings:
- def validate_complaint_document(self, operation): TODO move validators This class is inherited in limited and openeu (... | Implement the Python class `TenderUaAwardComplaintDocumentResource` described below.
Class description:
Implement the TenderUaAwardComplaintDocumentResource class.
Method signatures and docstrings:
- def validate_complaint_document(self, operation): TODO move validators This class is inherited in limited and openeu (... | 5afdd3a62a8e562cf77e2d963d88f1a26613d16a | <|skeleton|>
class TenderUaAwardComplaintDocumentResource:
def validate_complaint_document(self, operation):
"""TODO move validators This class is inherited in limited and openeu (qualification complaint) package, but validate_complaint_document function has different validators. For now, we have no way to... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TenderUaAwardComplaintDocumentResource:
def validate_complaint_document(self, operation):
"""TODO move validators This class is inherited in limited and openeu (qualification complaint) package, but validate_complaint_document function has different validators. For now, we have no way to use different... | the_stack_v2_python_sparse | src/openprocurement/tender/openua/views/award_complaint_document.py | pontostroy/api | train | 0 | |
282b489010f78169458d2ff26ea85b45ed529cd9 | [
"super(TSKVolume, self).__init__(file_entry.name)\nself._file_entry = file_entry\nself._bytes_per_sector = bytes_per_sector",
"tsk_vs_part = self._file_entry.GetTSKVsPart()\ntsk_addr = getattr(tsk_vs_part, 'addr', None)\nif tsk_addr is not None:\n address = volume_system.VolumeAttribute('address', tsk_addr)\n ... | <|body_start_0|>
super(TSKVolume, self).__init__(file_entry.name)
self._file_entry = file_entry
self._bytes_per_sector = bytes_per_sector
<|end_body_0|>
<|body_start_1|>
tsk_vs_part = self._file_entry.GetTSKVsPart()
tsk_addr = getattr(tsk_vs_part, 'addr', None)
if tsk_ad... | Volume that uses pytsk3. | TSKVolume | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TSKVolume:
"""Volume that uses pytsk3."""
def __init__(self, file_entry, bytes_per_sector):
"""Initializes a volume. Args: file_entry (TSKPartitionFileEntry): a TSK partition file entry. bytes_per_sector (int): number of bytes per sector."""
<|body_0|>
def _Parse(self):
... | stack_v2_sparse_classes_36k_train_015612 | 3,054 | permissive | [
{
"docstring": "Initializes a volume. Args: file_entry (TSKPartitionFileEntry): a TSK partition file entry. bytes_per_sector (int): number of bytes per sector.",
"name": "__init__",
"signature": "def __init__(self, file_entry, bytes_per_sector)"
},
{
"docstring": "Extracts attributes and extents... | 2 | null | Implement the Python class `TSKVolume` described below.
Class description:
Volume that uses pytsk3.
Method signatures and docstrings:
- def __init__(self, file_entry, bytes_per_sector): Initializes a volume. Args: file_entry (TSKPartitionFileEntry): a TSK partition file entry. bytes_per_sector (int): number of bytes ... | Implement the Python class `TSKVolume` described below.
Class description:
Volume that uses pytsk3.
Method signatures and docstrings:
- def __init__(self, file_entry, bytes_per_sector): Initializes a volume. Args: file_entry (TSKPartitionFileEntry): a TSK partition file entry. bytes_per_sector (int): number of bytes ... | 28756d910e951a22c5f0b2bcf5184f055a19d544 | <|skeleton|>
class TSKVolume:
"""Volume that uses pytsk3."""
def __init__(self, file_entry, bytes_per_sector):
"""Initializes a volume. Args: file_entry (TSKPartitionFileEntry): a TSK partition file entry. bytes_per_sector (int): number of bytes per sector."""
<|body_0|>
def _Parse(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TSKVolume:
"""Volume that uses pytsk3."""
def __init__(self, file_entry, bytes_per_sector):
"""Initializes a volume. Args: file_entry (TSKPartitionFileEntry): a TSK partition file entry. bytes_per_sector (int): number of bytes per sector."""
super(TSKVolume, self).__init__(file_entry.name... | the_stack_v2_python_sparse | dfvfs/volume/tsk_volume_system.py | log2timeline/dfvfs | train | 197 |
119e30c29a9dfa932f3f47b5e3676c8b8152e84c | [
"if not root:\n return []\nqueue = deque()\nqueue.append(root)\nres = []\nx = 0\nwhile queue:\n size = len(queue)\n i = 0\n level = []\n while i < size:\n if x & 1 == 0:\n tmp = queue.popleft()\n if tmp.left:\n queue.append(tmp.left)\n if tmp.rig... | <|body_start_0|>
if not root:
return []
queue = deque()
queue.append(root)
res = []
x = 0
while queue:
size = len(queue)
i = 0
level = []
while i < size:
if x & 1 == 0:
tmp = q... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]:
"""直接使用双端队列deque，模拟遍历过程。 :param root: :return:"""
<|body_0|>
def zigzagLevelOrder2(self, root: TreeNode) -> List[List[int]]:
"""大佬的思路。 使用一个标记位，标记当前在那一层，True为偶数层，False为奇数层 :param root: :return:""... | stack_v2_sparse_classes_36k_train_015613 | 2,821 | no_license | [
{
"docstring": "直接使用双端队列deque，模拟遍历过程。 :param root: :return:",
"name": "zigzagLevelOrder",
"signature": "def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]"
},
{
"docstring": "大佬的思路。 使用一个标记位，标记当前在那一层，True为偶数层，False为奇数层 :param root: :return:",
"name": "zigzagLevelOrder2",
"signa... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]: 直接使用双端队列deque，模拟遍历过程。 :param root: :return:
- def zigzagLevelOrder2(self, root: TreeNode) -> List[List[int]]: 大佬的思路... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]: 直接使用双端队列deque，模拟遍历过程。 :param root: :return:
- def zigzagLevelOrder2(self, root: TreeNode) -> List[List[int]]: 大佬的思路... | 578cacff5851c5c2522981693c34e3c318002d30 | <|skeleton|>
class Solution:
def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]:
"""直接使用双端队列deque，模拟遍历过程。 :param root: :return:"""
<|body_0|>
def zigzagLevelOrder2(self, root: TreeNode) -> List[List[int]]:
"""大佬的思路。 使用一个标记位，标记当前在那一层，True为偶数层，False为奇数层 :param root: :return:""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]:
"""直接使用双端队列deque，模拟遍历过程。 :param root: :return:"""
if not root:
return []
queue = deque()
queue.append(root)
res = []
x = 0
while queue:
size = len(queue)
... | the_stack_v2_python_sparse | 二叉树的锯齿形层序遍历.py | cjrzs/MyLeetCode | train | 8 | |
caeaf1d808ac9917645b985392db23e63befdf42 | [
"user = User.objects.create_user(username='hede', password='hede')\nurl = reverse('user-reports', args=[user.username])\nself.token_login()\ndata = simplejson.dumps({'text': 'Test'})\nrequest = self.c.post(path=url, content_type='application/json', data=data, **self.client_header)\nself.assertEqual(request.status_c... | <|body_start_0|>
user = User.objects.create_user(username='hede', password='hede')
url = reverse('user-reports', args=[user.username])
self.token_login()
data = simplejson.dumps({'text': 'Test'})
request = self.c.post(path=url, content_type='application/json', data=data, **self.c... | UserReportTestCase | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserReportTestCase:
def test_report_create(self):
"""Create Report"""
<|body_0|>
def test_invalid_user_create_report(self):
"""invalid user Create Report"""
<|body_1|>
def test_report_list(self):
"""Report List"""
<|body_2|>
def test... | stack_v2_sparse_classes_36k_train_015614 | 10,007 | no_license | [
{
"docstring": "Create Report",
"name": "test_report_create",
"signature": "def test_report_create(self)"
},
{
"docstring": "invalid user Create Report",
"name": "test_invalid_user_create_report",
"signature": "def test_invalid_user_create_report(self)"
},
{
"docstring": "Report ... | 5 | stack_v2_sparse_classes_30k_train_017829 | Implement the Python class `UserReportTestCase` described below.
Class description:
Implement the UserReportTestCase class.
Method signatures and docstrings:
- def test_report_create(self): Create Report
- def test_invalid_user_create_report(self): invalid user Create Report
- def test_report_list(self): Report List
... | Implement the Python class `UserReportTestCase` described below.
Class description:
Implement the UserReportTestCase class.
Method signatures and docstrings:
- def test_report_create(self): Create Report
- def test_invalid_user_create_report(self): invalid user Create Report
- def test_report_list(self): Report List
... | b8ba25fdde5d4ee92a3f73cb42ff892ed436d3f2 | <|skeleton|>
class UserReportTestCase:
def test_report_create(self):
"""Create Report"""
<|body_0|>
def test_invalid_user_create_report(self):
"""invalid user Create Report"""
<|body_1|>
def test_report_list(self):
"""Report List"""
<|body_2|>
def test... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserReportTestCase:
def test_report_create(self):
"""Create Report"""
user = User.objects.create_user(username='hede', password='hede')
url = reverse('user-reports', args=[user.username])
self.token_login()
data = simplejson.dumps({'text': 'Test'})
request = sel... | the_stack_v2_python_sparse | chatproject/apps/account/tests.py | QilinGu/chat-project | train | 0 | |
c84fb7d2617417eae3ace92bef568570a923d8f0 | [
"try:\n business = Business.objects.filter(users__in=[request.user])\nexcept Business.DoesNotExist:\n return Response({'message': _('Negocio no encontrado')}, status=status.HTTP_404_NOT_FOUND)\nif len(business) > 0:\n serializer = self.serializer_class(business[0])\n return Response(serializer.data, sta... | <|body_start_0|>
try:
business = Business.objects.filter(users__in=[request.user])
except Business.DoesNotExist:
return Response({'message': _('Negocio no encontrado')}, status=status.HTTP_404_NOT_FOUND)
if len(business) > 0:
serializer = self.serializer_class... | View for handle business actions | BusinessView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BusinessView:
"""View for handle business actions"""
def get(self, request, business_pk):
"""Get the business data using Token authorization :param business_pk: :param request: :return:"""
<|body_0|>
def put(self, request, business_pk):
"""Handle the business dat... | stack_v2_sparse_classes_36k_train_015615 | 5,744 | no_license | [
{
"docstring": "Get the business data using Token authorization :param business_pk: :param request: :return:",
"name": "get",
"signature": "def get(self, request, business_pk)"
},
{
"docstring": "Handle the business data update :param business_pk: :param request: :return:",
"name": "put",
... | 2 | null | Implement the Python class `BusinessView` described below.
Class description:
View for handle business actions
Method signatures and docstrings:
- def get(self, request, business_pk): Get the business data using Token authorization :param business_pk: :param request: :return:
- def put(self, request, business_pk): Ha... | Implement the Python class `BusinessView` described below.
Class description:
View for handle business actions
Method signatures and docstrings:
- def get(self, request, business_pk): Get the business data using Token authorization :param business_pk: :param request: :return:
- def put(self, request, business_pk): Ha... | 5c6f6443c7aaffbd0414c25f9adcdba72aeb7638 | <|skeleton|>
class BusinessView:
"""View for handle business actions"""
def get(self, request, business_pk):
"""Get the business data using Token authorization :param business_pk: :param request: :return:"""
<|body_0|>
def put(self, request, business_pk):
"""Handle the business dat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BusinessView:
"""View for handle business actions"""
def get(self, request, business_pk):
"""Get the business data using Token authorization :param business_pk: :param request: :return:"""
try:
business = Business.objects.filter(users__in=[request.user])
except Busines... | the_stack_v2_python_sparse | shop/applications/api_v1/views/business.py | RobertoFZ/shop-api | train | 0 |
0c8008f35fa32204097792d63c776296d4cccd6d | [
"self.optimizer = self.OptimCls(agent.parameters(), lr=self.learning_rate, **self.optim_kwargs)\nif self.initial_optim_state_dict is not None:\n self.optimizer.load_state_dict(self.initial_optim_state_dict)\nself.agent = agent\nself.n_itr = n_itr\nself.batch_spec = batch_spec\nself.mid_batch_reset = mid_batch_re... | <|body_start_0|>
self.optimizer = self.OptimCls(agent.parameters(), lr=self.learning_rate, **self.optim_kwargs)
if self.initial_optim_state_dict is not None:
self.optimizer.load_state_dict(self.initial_optim_state_dict)
self.agent = agent
self.n_itr = n_itr
self.batch... | Base policy gradient / actor-critic algorithm, which includes initialization procedure and processing of data samples to compute advantages. | PolicyGradientAlgo | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PolicyGradientAlgo:
"""Base policy gradient / actor-critic algorithm, which includes initialization procedure and processing of data samples to compute advantages."""
def initialize(self, agent, n_itr, batch_spec, mid_batch_reset=False, examples=None, world_size=1, rank=0):
"""Build ... | stack_v2_sparse_classes_36k_train_015616 | 3,139 | permissive | [
{
"docstring": "Build the torch optimizer and store other input attributes. Params ``batch_spec`` and ``examples`` are unused.",
"name": "initialize",
"signature": "def initialize(self, agent, n_itr, batch_spec, mid_batch_reset=False, examples=None, world_size=1, rank=0)"
},
{
"docstring": "Comp... | 2 | stack_v2_sparse_classes_30k_train_021669 | Implement the Python class `PolicyGradientAlgo` described below.
Class description:
Base policy gradient / actor-critic algorithm, which includes initialization procedure and processing of data samples to compute advantages.
Method signatures and docstrings:
- def initialize(self, agent, n_itr, batch_spec, mid_batch_... | Implement the Python class `PolicyGradientAlgo` described below.
Class description:
Base policy gradient / actor-critic algorithm, which includes initialization procedure and processing of data samples to compute advantages.
Method signatures and docstrings:
- def initialize(self, agent, n_itr, batch_spec, mid_batch_... | 98681a23bae9e8e5e9fbf68a0316ca2a22a27593 | <|skeleton|>
class PolicyGradientAlgo:
"""Base policy gradient / actor-critic algorithm, which includes initialization procedure and processing of data samples to compute advantages."""
def initialize(self, agent, n_itr, batch_spec, mid_batch_reset=False, examples=None, world_size=1, rank=0):
"""Build ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PolicyGradientAlgo:
"""Base policy gradient / actor-critic algorithm, which includes initialization procedure and processing of data samples to compute advantages."""
def initialize(self, agent, n_itr, batch_spec, mid_batch_reset=False, examples=None, world_size=1, rank=0):
"""Build the torch opt... | the_stack_v2_python_sparse | dependencies/rlpyt/rlpyt/algos/pg/base.py | keirp/glamor | train | 5 |
c581754286e5329a33b15c42b59075d3b5d56812 | [
"self.score_components = constants.score_components\nself.score_type = constants.score_type\nself.qsar_models = constants.qsar_models\nself.device = constants.device\nself.max_n_nodes = constants.max_n_nodes\nself.score_thresholds = constants.score_thresholds\nself.n_graphs = None\nassert len(self.score_components)... | <|body_start_0|>
self.score_components = constants.score_components
self.score_type = constants.score_type
self.qsar_models = constants.qsar_models
self.device = constants.device
self.max_n_nodes = constants.max_n_nodes
self.score_thresholds = constants.score_thresholds
... | A class for defining the scoring function components. | ScoringFunction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScoringFunction:
"""A class for defining the scoring function components."""
def __init__(self, constants: namedtuple) -> None:
"""Args: ---- constants (namedtuple) : Contains job parameters as well as global constants."""
<|body_0|>
def compute_score(self, graphs: list,... | stack_v2_sparse_classes_36k_train_015617 | 7,314 | permissive | [
{
"docstring": "Args: ---- constants (namedtuple) : Contains job parameters as well as global constants.",
"name": "__init__",
"signature": "def __init__(self, constants: namedtuple) -> None"
},
{
"docstring": "Computes the overall score for the input molecular graphs. Args: ---- graphs (list) :... | 4 | stack_v2_sparse_classes_30k_train_018555 | Implement the Python class `ScoringFunction` described below.
Class description:
A class for defining the scoring function components.
Method signatures and docstrings:
- def __init__(self, constants: namedtuple) -> None: Args: ---- constants (namedtuple) : Contains job parameters as well as global constants.
- def c... | Implement the Python class `ScoringFunction` described below.
Class description:
A class for defining the scoring function components.
Method signatures and docstrings:
- def __init__(self, constants: namedtuple) -> None: Args: ---- constants (namedtuple) : Contains job parameters as well as global constants.
- def c... | bdd69ffd11816f8781be9fc8f807750375f61809 | <|skeleton|>
class ScoringFunction:
"""A class for defining the scoring function components."""
def __init__(self, constants: namedtuple) -> None:
"""Args: ---- constants (namedtuple) : Contains job parameters as well as global constants."""
<|body_0|>
def compute_score(self, graphs: list,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScoringFunction:
"""A class for defining the scoring function components."""
def __init__(self, constants: namedtuple) -> None:
"""Args: ---- constants (namedtuple) : Contains job parameters as well as global constants."""
self.score_components = constants.score_components
self.sc... | the_stack_v2_python_sparse | graphinvent/ScoringFunction.py | JennyW5/GraphINVENT | train | 0 |
636166ce6e1fa3c54cfe991490a75026a7e87698 | [
"self.track_data_list = track_data_list\nnum_examples = [t.num_tracklet_pairs() for t in track_data_list]\ntotal_examples = sum(num_examples)\nself.prob = [float(n) / float(total_examples) for n in num_examples]\nself.queues = [Queue(128) for _ in track_data_list]\nself.procs = [Process(target=_random_example_from_... | <|body_start_0|>
self.track_data_list = track_data_list
num_examples = [t.num_tracklet_pairs() for t in track_data_list]
total_examples = sum(num_examples)
self.prob = [float(n) / float(total_examples) for n in num_examples]
self.queues = [Queue(128) for _ in track_data_list]
... | TrackletProvider | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrackletProvider:
def __init__(self, track_data_list, batch_size, timesteps):
"""Loads tracklet pair indices and sets variables for sampling them."""
<|body_0|>
def generate(self):
"""Builds a batch."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
s... | stack_v2_sparse_classes_36k_train_015618 | 3,173 | permissive | [
{
"docstring": "Loads tracklet pair indices and sets variables for sampling them.",
"name": "__init__",
"signature": "def __init__(self, track_data_list, batch_size, timesteps)"
},
{
"docstring": "Builds a batch.",
"name": "generate",
"signature": "def generate(self)"
}
] | 2 | null | Implement the Python class `TrackletProvider` described below.
Class description:
Implement the TrackletProvider class.
Method signatures and docstrings:
- def __init__(self, track_data_list, batch_size, timesteps): Loads tracklet pair indices and sets variables for sampling them.
- def generate(self): Builds a batch... | Implement the Python class `TrackletProvider` described below.
Class description:
Implement the TrackletProvider class.
Method signatures and docstrings:
- def __init__(self, track_data_list, batch_size, timesteps): Loads tracklet pair indices and sets variables for sampling them.
- def generate(self): Builds a batch... | fae655f396380dbe74413812a41b734e267faffe | <|skeleton|>
class TrackletProvider:
def __init__(self, track_data_list, batch_size, timesteps):
"""Loads tracklet pair indices and sets variables for sampling them."""
<|body_0|>
def generate(self):
"""Builds a batch."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrackletProvider:
def __init__(self, track_data_list, batch_size, timesteps):
"""Loads tracklet pair indices and sets variables for sampling them."""
self.track_data_list = track_data_list
num_examples = [t.num_tracklet_pairs() for t in track_data_list]
total_examples = sum(num... | the_stack_v2_python_sparse | train/siamese/providers/tracklet_provider.py | openem-team/openem | train | 11 | |
a60a9d1d7b4bed0bfd34ee413200b467037d3910 | [
"self.initial_backoff = initial_backoff\nself.increment_base = increment_base\nself.random_jitter_range = random_jitter_range\nsuper(ExponentialRetry, self).__init__(retry_total=retry_total, retry_to_secondary=retry_to_secondary, **kwargs)",
"random_generator = random.Random()\nbackoff = self.initial_backoff + (0... | <|body_start_0|>
self.initial_backoff = initial_backoff
self.increment_base = increment_base
self.random_jitter_range = random_jitter_range
super(ExponentialRetry, self).__init__(retry_total=retry_total, retry_to_secondary=retry_to_secondary, **kwargs)
<|end_body_0|>
<|body_start_1|>
... | Exponential retry. | ExponentialRetry | [
"MIT",
"LicenseRef-scancode-generic-cla",
"LGPL-2.1-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExponentialRetry:
"""Exponential retry."""
def __init__(self, initial_backoff=15, increment_base=3, retry_total=3, retry_to_secondary=False, random_jitter_range=3, **kwargs):
"""Constructs an Exponential retry object. The initial_backoff is used for the first retry. Subsequent retrie... | stack_v2_sparse_classes_36k_train_015619 | 26,717 | permissive | [
{
"docstring": "Constructs an Exponential retry object. The initial_backoff is used for the first retry. Subsequent retries are retried after initial_backoff + increment_power^retry_count seconds. For example, by default the first retry occurs after 15 seconds, the second after (15+3^1) = 18 seconds, and the th... | 2 | stack_v2_sparse_classes_30k_train_000500 | Implement the Python class `ExponentialRetry` described below.
Class description:
Exponential retry.
Method signatures and docstrings:
- def __init__(self, initial_backoff=15, increment_base=3, retry_total=3, retry_to_secondary=False, random_jitter_range=3, **kwargs): Constructs an Exponential retry object. The initi... | Implement the Python class `ExponentialRetry` described below.
Class description:
Exponential retry.
Method signatures and docstrings:
- def __init__(self, initial_backoff=15, increment_base=3, retry_total=3, retry_to_secondary=False, random_jitter_range=3, **kwargs): Constructs an Exponential retry object. The initi... | c2ca191e736bb06bfbbbc9493e8325763ba990bb | <|skeleton|>
class ExponentialRetry:
"""Exponential retry."""
def __init__(self, initial_backoff=15, increment_base=3, retry_total=3, retry_to_secondary=False, random_jitter_range=3, **kwargs):
"""Constructs an Exponential retry object. The initial_backoff is used for the first retry. Subsequent retrie... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExponentialRetry:
"""Exponential retry."""
def __init__(self, initial_backoff=15, increment_base=3, retry_total=3, retry_to_secondary=False, random_jitter_range=3, **kwargs):
"""Constructs an Exponential retry object. The initial_backoff is used for the first retry. Subsequent retries are retried... | the_stack_v2_python_sparse | sdk/eventhub/azure-eventhub-checkpointstoreblob-aio/azure/eventhub/extensions/checkpointstoreblobaio/_vendor/storage/blob/_shared/policies.py | Azure/azure-sdk-for-python | train | 4,046 |
f413c10154c22b1bf608fa618d1ca85f7482bb58 | [
"self.randomSampling = []\nself.dataRange = dataRange\nself.tot = tot\nself.len = len",
"try:\n isLegalNumList(self.dataRange)\nexcept Exception as err:\n print('An exception happened: ' + str(err))\n sys.exit()\nself.randomSampling = []\nwhile len(self.randomSampling) != self.tot:\n it = iter(self.da... | <|body_start_0|>
self.randomSampling = []
self.dataRange = dataRange
self.tot = tot
self.len = len
<|end_body_0|>
<|body_start_1|>
try:
isLegalNumList(self.dataRange)
except Exception as err:
print('An exception happened: ' + str(err))
... | elementSamplingFactory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class elementSamplingFactory:
def __init__(self, dataRange, tot, len=6):
""":param dataRange:数据范围 :param tot: 数据量 :param len: 若生成字符串，字符串长度"""
<|body_0|>
def randomIntSampling(self):
""":return: 生成数据"""
<|body_1|>
def randomFloatSampling(self):
""":retu... | stack_v2_sparse_classes_36k_train_015620 | 2,834 | no_license | [
{
"docstring": ":param dataRange:数据范围 :param tot: 数据量 :param len: 若生成字符串，字符串长度",
"name": "__init__",
"signature": "def __init__(self, dataRange, tot, len=6)"
},
{
"docstring": ":return: 生成数据",
"name": "randomIntSampling",
"signature": "def randomIntSampling(self)"
},
{
"docstring... | 4 | stack_v2_sparse_classes_30k_train_008326 | Implement the Python class `elementSamplingFactory` described below.
Class description:
Implement the elementSamplingFactory class.
Method signatures and docstrings:
- def __init__(self, dataRange, tot, len=6): :param dataRange:数据范围 :param tot: 数据量 :param len: 若生成字符串，字符串长度
- def randomIntSampling(self): :return: 生成数据... | Implement the Python class `elementSamplingFactory` described below.
Class description:
Implement the elementSamplingFactory class.
Method signatures and docstrings:
- def __init__(self, dataRange, tot, len=6): :param dataRange:数据范围 :param tot: 数据量 :param len: 若生成字符串，字符串长度
- def randomIntSampling(self): :return: 生成数据... | 661dba7ea846859056fd6ee7a310d352ca178e98 | <|skeleton|>
class elementSamplingFactory:
def __init__(self, dataRange, tot, len=6):
""":param dataRange:数据范围 :param tot: 数据量 :param len: 若生成字符串，字符串长度"""
<|body_0|>
def randomIntSampling(self):
""":return: 生成数据"""
<|body_1|>
def randomFloatSampling(self):
""":retu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class elementSamplingFactory:
def __init__(self, dataRange, tot, len=6):
""":param dataRange:数据范围 :param tot: 数据量 :param len: 若生成字符串，字符串长度"""
self.randomSampling = []
self.dataRange = dataRange
self.tot = tot
self.len = len
def randomIntSampling(self):
""":return... | the_stack_v2_python_sparse | 林一夫2017012923/平时作业1/Factory.py | wanghan79/2020_Python | train | 4 | |
bdbb3e95204c2bd3f5ca28ae7fb38a2111782372 | [
"self.pers = persistence\nself.logger = logging.getLogger('app')\nself.cfg = cfg",
"try:\n self.logger.debug('Password Reset attempt %s', user_id)\n password_reset_token = ''\n nosqldb = self.pers.nosql_db\n db_user_record = nosqldb['users'].find_one({'$or': [{'username': user_id}, {'email': user_id}]... | <|body_start_0|>
self.pers = persistence
self.logger = logging.getLogger('app')
self.cfg = cfg
<|end_body_0|>
<|body_start_1|>
try:
self.logger.debug('Password Reset attempt %s', user_id)
password_reset_token = ''
nosqldb = self.pers.nosql_db
... | Web user authentication database helper | PasswordResetDBHelper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PasswordResetDBHelper:
"""Web user authentication database helper"""
def __init__(self, persistence, cfg):
"""Class initialization"""
<|body_0|>
def create_password_reset_token(self, user_id):
"""Initiate a password reset with a complex token, multi step"""
... | stack_v2_sparse_classes_36k_train_015621 | 4,820 | no_license | [
{
"docstring": "Class initialization",
"name": "__init__",
"signature": "def __init__(self, persistence, cfg)"
},
{
"docstring": "Initiate a password reset with a complex token, multi step",
"name": "create_password_reset_token",
"signature": "def create_password_reset_token(self, user_i... | 4 | stack_v2_sparse_classes_30k_train_017612 | Implement the Python class `PasswordResetDBHelper` described below.
Class description:
Web user authentication database helper
Method signatures and docstrings:
- def __init__(self, persistence, cfg): Class initialization
- def create_password_reset_token(self, user_id): Initiate a password reset with a complex token... | Implement the Python class `PasswordResetDBHelper` described below.
Class description:
Web user authentication database helper
Method signatures and docstrings:
- def __init__(self, persistence, cfg): Class initialization
- def create_password_reset_token(self, user_id): Initiate a password reset with a complex token... | 3c774731b054c38a273371450a451c951d73b726 | <|skeleton|>
class PasswordResetDBHelper:
"""Web user authentication database helper"""
def __init__(self, persistence, cfg):
"""Class initialization"""
<|body_0|>
def create_password_reset_token(self, user_id):
"""Initiate a password reset with a complex token, multi step"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PasswordResetDBHelper:
"""Web user authentication database helper"""
def __init__(self, persistence, cfg):
"""Class initialization"""
self.pers = persistence
self.logger = logging.getLogger('app')
self.cfg = cfg
def create_password_reset_token(self, user_id):
... | the_stack_v2_python_sparse | genesis/passwordresetdbhelper.py | wbmartin/exodus-app | train | 0 |
9355c3cf48d3f7f9873a805d4c1be3cc53f9ad92 | [
"super(CustomerJourney, self).__init__(*args, **kwargs)\nself.endpoint = 'customer-journeys'\nself.journey_id = None\nself.step_id = None",
"self.journey_id = journey_id\nself.step_id = step_id\nif 'email_address' not in data:\n raise KeyError('The automation email queue must have an email_address')\ncheck_ema... | <|body_start_0|>
super(CustomerJourney, self).__init__(*args, **kwargs)
self.endpoint = 'customer-journeys'
self.journey_id = None
self.step_id = None
<|end_body_0|>
<|body_start_1|>
self.journey_id = journey_id
self.step_id = step_id
if 'email_address' not in da... | Manage specific customer journeys in your Mailchimp account. | CustomerJourney | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomerJourney:
"""Manage specific customer journeys in your Mailchimp account."""
def __init__(self, *args, **kwargs):
"""Initialize the endpoint"""
<|body_0|>
def trigger(self, journey_id, step_id, data):
"""Trigger a step in a customer journey. :param journey... | stack_v2_sparse_classes_36k_train_015622 | 1,605 | permissive | [
{
"docstring": "Initialize the endpoint",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Trigger a step in a customer journey. :param journey_id: The unique id for the Customer Journey :type journey_id: :py:class:`str` :param step_id: The unique id for ... | 2 | stack_v2_sparse_classes_30k_train_021465 | Implement the Python class `CustomerJourney` described below.
Class description:
Manage specific customer journeys in your Mailchimp account.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize the endpoint
- def trigger(self, journey_id, step_id, data): Trigger a step in a customer jo... | Implement the Python class `CustomerJourney` described below.
Class description:
Manage specific customer journeys in your Mailchimp account.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize the endpoint
- def trigger(self, journey_id, step_id, data): Trigger a step in a customer jo... | bf61cd602dc44cbff32fbf6f6dcdd33cf6f782e8 | <|skeleton|>
class CustomerJourney:
"""Manage specific customer journeys in your Mailchimp account."""
def __init__(self, *args, **kwargs):
"""Initialize the endpoint"""
<|body_0|>
def trigger(self, journey_id, step_id, data):
"""Trigger a step in a customer journey. :param journey... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomerJourney:
"""Manage specific customer journeys in your Mailchimp account."""
def __init__(self, *args, **kwargs):
"""Initialize the endpoint"""
super(CustomerJourney, self).__init__(*args, **kwargs)
self.endpoint = 'customer-journeys'
self.journey_id = None
... | the_stack_v2_python_sparse | mailchimp3/entities/customerjourney.py | VingtCinq/python-mailchimp | train | 190 |
5ccd97a8343aaecf0788b2cbf98b097521d70ab4 | [
"machine = machine_key.get()\nif not machine:\n raise endpoints.NotFoundException('CatalogMachineEntry not found')\nif not machine.instruction or machine.instruction.state == new_state:\n return\ntransition = (machine.instruction.state, new_state)\nif transition not in MachineEndpoints.ALLOWED_TRANSITIONS:\n ... | <|body_start_0|>
machine = machine_key.get()
if not machine:
raise endpoints.NotFoundException('CatalogMachineEntry not found')
if not machine.instruction or machine.instruction.state == new_state:
return
transition = (machine.instruction.state, new_state)
... | Implements cloud endpoints for Machine Provider's machines. | MachineEndpoints | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MachineEndpoints:
"""Implements cloud endpoints for Machine Provider's machines."""
def _update_instruction_state(machine_key, new_state):
"""Updates the state of the instruction for the given machine. The only updates allowed are: PENDING -> RECEIVED PENDING -> EXECUTED RECEIVED -> ... | stack_v2_sparse_classes_36k_train_015623 | 28,917 | permissive | [
{
"docstring": "Updates the state of the instruction for the given machine. The only updates allowed are: PENDING -> RECEIVED PENDING -> EXECUTED RECEIVED -> EXECUTED Args: machine_key: ndb.Key for a models.CatalogMachineEntry. new_state: One of models.InstructionStates, but not models.InstructionStates.PENDING... | 3 | stack_v2_sparse_classes_30k_train_004747 | Implement the Python class `MachineEndpoints` described below.
Class description:
Implements cloud endpoints for Machine Provider's machines.
Method signatures and docstrings:
- def _update_instruction_state(machine_key, new_state): Updates the state of the instruction for the given machine. The only updates allowed ... | Implement the Python class `MachineEndpoints` described below.
Class description:
Implements cloud endpoints for Machine Provider's machines.
Method signatures and docstrings:
- def _update_instruction_state(machine_key, new_state): Updates the state of the instruction for the given machine. The only updates allowed ... | 0a4fdfc25f89833026be6a8b29c0a27b8f3c5fc4 | <|skeleton|>
class MachineEndpoints:
"""Implements cloud endpoints for Machine Provider's machines."""
def _update_instruction_state(machine_key, new_state):
"""Updates the state of the instruction for the given machine. The only updates allowed are: PENDING -> RECEIVED PENDING -> EXECUTED RECEIVED -> ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MachineEndpoints:
"""Implements cloud endpoints for Machine Provider's machines."""
def _update_instruction_state(machine_key, new_state):
"""Updates the state of the instruction for the given machine. The only updates allowed are: PENDING -> RECEIVED PENDING -> EXECUTED RECEIVED -> EXECUTED Args... | the_stack_v2_python_sparse | appengine/machine_provider/handlers_endpoints.py | Swift1313/luci-py | train | 0 |
262e98326b7bed5d47ec3c5be15027ce3840b834 | [
"Node.__init__(self, name)\nself.args = []\n'\\n The arguments of the executable.\\n\\n :type: list[str]\\n '\nself.path = ''\n'\\n The path of the executable that must be run by this job.\\n\\n :type: str\\n '",
"command_job = SubElement(parent, 'CommandJob')\nself._gene... | <|body_start_0|>
Node.__init__(self, name)
self.args = []
'\n The arguments of the executable.\n\n :type: list[str]\n '
self.path = ''
'\n The path of the executable that must be run by this job.\n\n :type: str\n '
<|end_body_0|>
<|body_... | Class for generating XML messages for elements of type 'CommandJobType'. | CommandJobNode | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommandJobNode:
"""Class for generating XML messages for elements of type 'CommandJobType'."""
def __init__(self, name):
"""Object constructor. :param str name: The name of the node."""
<|body_0|>
def generate_xml(self, parent):
"""Generates the XML element for t... | stack_v2_sparse_classes_36k_train_015624 | 2,354 | permissive | [
{
"docstring": "Object constructor. :param str name: The name of the node.",
"name": "__init__",
"signature": "def __init__(self, name)"
},
{
"docstring": "Generates the XML element for this node. :param xml.etree.ElementTree.Element parent: The parent XML element.",
"name": "generate_xml",
... | 3 | stack_v2_sparse_classes_30k_train_018088 | Implement the Python class `CommandJobNode` described below.
Class description:
Class for generating XML messages for elements of type 'CommandJobType'.
Method signatures and docstrings:
- def __init__(self, name): Object constructor. :param str name: The name of the node.
- def generate_xml(self, parent): Generates ... | Implement the Python class `CommandJobNode` described below.
Class description:
Class for generating XML messages for elements of type 'CommandJobType'.
Method signatures and docstrings:
- def __init__(self, name): Object constructor. :param str name: The name of the node.
- def generate_xml(self, parent): Generates ... | eafd332e383f5f97dce4e35f6cff5a9a48dd3141 | <|skeleton|>
class CommandJobNode:
"""Class for generating XML messages for elements of type 'CommandJobType'."""
def __init__(self, name):
"""Object constructor. :param str name: The name of the node."""
<|body_0|>
def generate_xml(self, parent):
"""Generates the XML element for t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommandJobNode:
"""Class for generating XML messages for elements of type 'CommandJobType'."""
def __init__(self, name):
"""Object constructor. :param str name: The name of the node."""
Node.__init__(self, name)
self.args = []
'\n The arguments of the executable.\n\... | the_stack_v2_python_sparse | enarksh_lib/xml_generator/node/CommandJobNode.py | SetBased/py-enarksh-lib | train | 2 |
f6ecc30932191511adb31a71cfe95a6ff0bf2ec1 | [
"group_totals = dict()\nfor ocg in OffCycleCredits._offcycle_credit_groups:\n group_totals[ocg] = 0\ncost_cloud['cert_direct_offcycle_co2e_grams_per_mile'] = 0\ncost_cloud['cert_direct_offcycle_kwh_per_mile'] = 0\ncost_cloud['cert_indirect_offcycle_co2e_grams_per_mile'] = 0\nfor credit_column in OffCycleCredits.... | <|body_start_0|>
group_totals = dict()
for ocg in OffCycleCredits._offcycle_credit_groups:
group_totals[ocg] = 0
cost_cloud['cert_direct_offcycle_co2e_grams_per_mile'] = 0
cost_cloud['cert_direct_offcycle_kwh_per_mile'] = 0
cost_cloud['cert_indirect_offcycle_co2e_gram... | **Loads, stores and applies off-cycle credits to vehicle cost clouds** | OffCycleCredits | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OffCycleCredits:
"""**Loads, stores and applies off-cycle credits to vehicle cost clouds**"""
def calc_off_cycle_credits(calendar_year, vehicle, cost_cloud):
"""Calculate vehicle off-cycle credits for the vehicle's cost cloud Args: calendar_year (int): the year to calculate credits f... | stack_v2_sparse_classes_36k_train_015625 | 11,371 | no_license | [
{
"docstring": "Calculate vehicle off-cycle credits for the vehicle's cost cloud Args: calendar_year (int): the year to calculate credits for, usually the vehicle model year vehicle (Vehicle): the vehicle to apply off-cycle credits to cost_cloud (DataFrame): destination data set for off-cycle credits Returns: c... | 2 | stack_v2_sparse_classes_30k_train_010255 | Implement the Python class `OffCycleCredits` described below.
Class description:
**Loads, stores and applies off-cycle credits to vehicle cost clouds**
Method signatures and docstrings:
- def calc_off_cycle_credits(calendar_year, vehicle, cost_cloud): Calculate vehicle off-cycle credits for the vehicle's cost cloud A... | Implement the Python class `OffCycleCredits` described below.
Class description:
**Loads, stores and applies off-cycle credits to vehicle cost clouds**
Method signatures and docstrings:
- def calc_off_cycle_credits(calendar_year, vehicle, cost_cloud): Calculate vehicle off-cycle credits for the vehicle's cost cloud A... | afe912c57383b9de90ef30820f7977c3367a30c4 | <|skeleton|>
class OffCycleCredits:
"""**Loads, stores and applies off-cycle credits to vehicle cost clouds**"""
def calc_off_cycle_credits(calendar_year, vehicle, cost_cloud):
"""Calculate vehicle off-cycle credits for the vehicle's cost cloud Args: calendar_year (int): the year to calculate credits f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OffCycleCredits:
"""**Loads, stores and applies off-cycle credits to vehicle cost clouds**"""
def calc_off_cycle_credits(calendar_year, vehicle, cost_cloud):
"""Calculate vehicle off-cycle credits for the vehicle's cost cloud Args: calendar_year (int): the year to calculate credits for, usually t... | the_stack_v2_python_sparse | omega_model/policy/offcycle_credits.py | USEPA/EPA_OMEGA_Model | train | 17 |
e0c9f7d012343611c3c322118427951536e8a57a | [
"super().__init__(model=pref_model)\nself.add_module('outcome_model', outcome_model)\nself.num_samples = num_samples\nself.std_noise = std_noise\nself.std_normal = Normal(0, 1)",
"Y = X if self.outcome_model is None else self.outcome_model(X)\npref_posterior = self.model.posterior(Y)\npref_mean = pref_posterior.m... | <|body_start_0|>
super().__init__(model=pref_model)
self.add_module('outcome_model', outcome_model)
self.num_samples = num_samples
self.std_noise = std_noise
self.std_normal = Normal(0, 1)
<|end_body_0|>
<|body_start_1|>
Y = X if self.outcome_model is None else self.outc... | MC Bayesian Active Learning by Disagreement | PairwiseBayesianActiveLearningByDisagreement | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PairwiseBayesianActiveLearningByDisagreement:
"""MC Bayesian Active Learning by Disagreement"""
def __init__(self, pref_model: Model, outcome_model: Optional[DeterministicModel]=None, num_samples: Optional[int]=1024, std_noise: Optional[float]=0.0, **kwargs: Any) -> None:
"""Monte Ca... | stack_v2_sparse_classes_36k_train_015626 | 7,676 | permissive | [
{
"docstring": "Monte Carlo implementation of Bayesian Active Learning by Disagreement (BALD) proposed in [Houlsby2011bald]_. Args: pref_model: The preference model that maps the outcomes (i.e., Y) to scalar-valued utility. outcome_model: A deterministic model that maps parameters (i.e., X) to outcomes (i.e., Y... | 2 | null | Implement the Python class `PairwiseBayesianActiveLearningByDisagreement` described below.
Class description:
MC Bayesian Active Learning by Disagreement
Method signatures and docstrings:
- def __init__(self, pref_model: Model, outcome_model: Optional[DeterministicModel]=None, num_samples: Optional[int]=1024, std_noi... | Implement the Python class `PairwiseBayesianActiveLearningByDisagreement` described below.
Class description:
MC Bayesian Active Learning by Disagreement
Method signatures and docstrings:
- def __init__(self, pref_model: Model, outcome_model: Optional[DeterministicModel]=None, num_samples: Optional[int]=1024, std_noi... | 4cc5ed59b2e8a9c780f786830c548e05cc74d53c | <|skeleton|>
class PairwiseBayesianActiveLearningByDisagreement:
"""MC Bayesian Active Learning by Disagreement"""
def __init__(self, pref_model: Model, outcome_model: Optional[DeterministicModel]=None, num_samples: Optional[int]=1024, std_noise: Optional[float]=0.0, **kwargs: Any) -> None:
"""Monte Ca... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PairwiseBayesianActiveLearningByDisagreement:
"""MC Bayesian Active Learning by Disagreement"""
def __init__(self, pref_model: Model, outcome_model: Optional[DeterministicModel]=None, num_samples: Optional[int]=1024, std_noise: Optional[float]=0.0, **kwargs: Any) -> None:
"""Monte Carlo implement... | the_stack_v2_python_sparse | botorch/acquisition/preference.py | pytorch/botorch | train | 2,891 |
692f9333ad687865b45f0b70cad823afe9bc7f69 | [
"with self.Session() as session:\n stmt = sa.select(Obj).where(Obj.healpix.is_(None))\n count_stmt = sa.select(func.count()).select_from(stmt.distinct())\n total_missing = session.execute(count_stmt).scalar()\n stmt = sa.select(Obj).where(Obj.healpix.isnot(None))\n count_stmt = sa.select(func.count()... | <|body_start_0|>
with self.Session() as session:
stmt = sa.select(Obj).where(Obj.healpix.is_(None))
count_stmt = sa.select(func.count()).select_from(stmt.distinct())
total_missing = session.execute(count_stmt).scalar()
stmt = sa.select(Obj).where(Obj.healpix.isnot... | HealpixUpdateHandler | [
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HealpixUpdateHandler:
def get(self):
"""--- description: find the number of sources with and without a Healpix value tags: - source responses: 200: content: application/json: schema: allOf: - $ref: '#/components/schemas/Success' - type: object properties: data: type: object properties: t... | stack_v2_sparse_classes_36k_train_015627 | 4,688 | permissive | [
{
"docstring": "--- description: find the number of sources with and without a Healpix value tags: - source responses: 200: content: application/json: schema: allOf: - $ref: '#/components/schemas/Success' - type: object properties: data: type: object properties: totalWithoutHealpix: type: integer totalWithHealp... | 2 | null | Implement the Python class `HealpixUpdateHandler` described below.
Class description:
Implement the HealpixUpdateHandler class.
Method signatures and docstrings:
- def get(self): --- description: find the number of sources with and without a Healpix value tags: - source responses: 200: content: application/json: sche... | Implement the Python class `HealpixUpdateHandler` described below.
Class description:
Implement the HealpixUpdateHandler class.
Method signatures and docstrings:
- def get(self): --- description: find the number of sources with and without a Healpix value tags: - source responses: 200: content: application/json: sche... | 161d3532ba3ba059446addcdac58ca96f39e9636 | <|skeleton|>
class HealpixUpdateHandler:
def get(self):
"""--- description: find the number of sources with and without a Healpix value tags: - source responses: 200: content: application/json: schema: allOf: - $ref: '#/components/schemas/Success' - type: object properties: data: type: object properties: t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HealpixUpdateHandler:
def get(self):
"""--- description: find the number of sources with and without a Healpix value tags: - source responses: 200: content: application/json: schema: allOf: - $ref: '#/components/schemas/Success' - type: object properties: data: type: object properties: totalWithoutHea... | the_stack_v2_python_sparse | skyportal/handlers/api/healpix.py | skyportal/skyportal | train | 80 | |
e48422203f5bef271bc67c84d63a9ba56b00ef1c | [
"steps = self.filter(sequence=sequence_id, number__gte=step_number).order_by('number')\nfor i in steps:\n i.number = i.number + 1\n i.save()\nnew_step = self.create(sequence=sequence_id, number=step_number)\nreturn new_step",
"delete_step = self.filter(sequence=sequence_id, number=step_number).order_by('num... | <|body_start_0|>
steps = self.filter(sequence=sequence_id, number__gte=step_number).order_by('number')
for i in steps:
i.number = i.number + 1
i.save()
new_step = self.create(sequence=sequence_id, number=step_number)
return new_step
<|end_body_0|>
<|body_start_1|... | StepManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StepManager:
def insert(self, sequence_id, step_number):
"""Inserts a new step into a sequence at the given step number"""
<|body_0|>
def remove(self, sequence_id, step_number):
"""Removes a step at the current step number and moves the steps beyond the step number u... | stack_v2_sparse_classes_36k_train_015628 | 1,849 | no_license | [
{
"docstring": "Inserts a new step into a sequence at the given step number",
"name": "insert",
"signature": "def insert(self, sequence_id, step_number)"
},
{
"docstring": "Removes a step at the current step number and moves the steps beyond the step number up in the order",
"name": "remove"... | 2 | stack_v2_sparse_classes_30k_val_000559 | Implement the Python class `StepManager` described below.
Class description:
Implement the StepManager class.
Method signatures and docstrings:
- def insert(self, sequence_id, step_number): Inserts a new step into a sequence at the given step number
- def remove(self, sequence_id, step_number): Removes a step at the ... | Implement the Python class `StepManager` described below.
Class description:
Implement the StepManager class.
Method signatures and docstrings:
- def insert(self, sequence_id, step_number): Inserts a new step into a sequence at the given step number
- def remove(self, sequence_id, step_number): Removes a step at the ... | 31f6e4181b964c1ee7bbe3754b5004b34f91bca3 | <|skeleton|>
class StepManager:
def insert(self, sequence_id, step_number):
"""Inserts a new step into a sequence at the given step number"""
<|body_0|>
def remove(self, sequence_id, step_number):
"""Removes a step at the current step number and moves the steps beyond the step number u... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StepManager:
def insert(self, sequence_id, step_number):
"""Inserts a new step into a sequence at the given step number"""
steps = self.filter(sequence=sequence_id, number__gte=step_number).order_by('number')
for i in steps:
i.number = i.number + 1
i.save()
... | the_stack_v2_python_sparse | pinchart/managers.py | nunchuckBoP/systemmodel | train | 0 | |
b472bfead96051b3cba4d3d18f9122570dac3305 | [
"self._table = {}\nfor p in breadth_first_traversal(self._tree):\n self._table[p.index()] = [p]\n l = 0\n while l < self._tree.depth(p):\n u = self._table[p.index()][l]\n w = self._tree.parent(u)\n self._table[p.index()].append(w)\n l += 1",
"if isinstance(p, int):\n if k >... | <|body_start_0|>
self._table = {}
for p in breadth_first_traversal(self._tree):
self._table[p.index()] = [p]
l = 0
while l < self._tree.depth(p):
u = self._table[p.index()][l]
w = self._tree.parent(u)
self._table[p.index... | Concrete class implementing table indexing strategy. Every possible query (p, k) is precomputed and the result is stored in a table. The size of the table is n^2. Computation of the level ancestor is performed using bottom-up dynamic programming in O(n^2) time. Querying is performed by a simple table look-up in O(1) ti... | LA_table | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LA_table:
"""Concrete class implementing table indexing strategy. Every possible query (p, k) is precomputed and the result is stored in a table. The size of the table is n^2. Computation of the level ancestor is performed using bottom-up dynamic programming in O(n^2) time. Querying is performed ... | stack_v2_sparse_classes_36k_train_015629 | 15,776 | no_license | [
{
"docstring": "Precompute all n^2 possible queries and store them in a table.",
"name": "_preprocess",
"signature": "def _preprocess(self)"
},
{
"docstring": "Perform simple table look-up.",
"name": "_query",
"signature": "def _query(self, p, k)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011975 | Implement the Python class `LA_table` described below.
Class description:
Concrete class implementing table indexing strategy. Every possible query (p, k) is precomputed and the result is stored in a table. The size of the table is n^2. Computation of the level ancestor is performed using bottom-up dynamic programming... | Implement the Python class `LA_table` described below.
Class description:
Concrete class implementing table indexing strategy. Every possible query (p, k) is precomputed and the result is stored in a table. The size of the table is n^2. Computation of the level ancestor is performed using bottom-up dynamic programming... | 341bdc7d144d18b49917453006461d670109e706 | <|skeleton|>
class LA_table:
"""Concrete class implementing table indexing strategy. Every possible query (p, k) is precomputed and the result is stored in a table. The size of the table is n^2. Computation of the level ancestor is performed using bottom-up dynamic programming in O(n^2) time. Querying is performed ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LA_table:
"""Concrete class implementing table indexing strategy. Every possible query (p, k) is precomputed and the result is stored in a table. The size of the table is n^2. Computation of the level ancestor is performed using bottom-up dynamic programming in O(n^2) time. Querying is performed by a simple t... | the_stack_v2_python_sparse | Level_Ancestor/la.py | pi-tau/fun-with-algorithms | train | 0 |
d00355c451edba35b9798290b43d08f1cd5944e3 | [
"self._metadata_file = preproc_dir / 'metadata.yml'\nprev_metadata_file = prev_preproc_dir / 'metadata.yml'\nwith prev_metadata_file.open('rb') as file:\n prev_metadata = yaml.safe_load(file)\nproducts = set()\nfor prov_filename, attributes in prev_metadata.items():\n filename = str(prev_preproc_dir / Path(pr... | <|body_start_0|>
self._metadata_file = preproc_dir / 'metadata.yml'
prev_metadata_file = prev_preproc_dir / 'metadata.yml'
with prev_metadata_file.open('rb') as file:
prev_metadata = yaml.safe_load(file)
products = set()
for prov_filename, attributes in prev_metadata.... | Task for re-using preprocessor output files from a previous run. | ResumeTask | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResumeTask:
"""Task for re-using preprocessor output files from a previous run."""
def __init__(self, prev_preproc_dir, preproc_dir, name):
"""Create a resume task."""
<|body_0|>
def _run(self, _):
"""Return the result of a previous run."""
<|body_1|>
<|... | stack_v2_sparse_classes_36k_train_015630 | 29,602 | permissive | [
{
"docstring": "Create a resume task.",
"name": "__init__",
"signature": "def __init__(self, prev_preproc_dir, preproc_dir, name)"
},
{
"docstring": "Return the result of a previous run.",
"name": "_run",
"signature": "def _run(self, _)"
}
] | 2 | null | Implement the Python class `ResumeTask` described below.
Class description:
Task for re-using preprocessor output files from a previous run.
Method signatures and docstrings:
- def __init__(self, prev_preproc_dir, preproc_dir, name): Create a resume task.
- def _run(self, _): Return the result of a previous run. | Implement the Python class `ResumeTask` described below.
Class description:
Task for re-using preprocessor output files from a previous run.
Method signatures and docstrings:
- def __init__(self, prev_preproc_dir, preproc_dir, name): Create a resume task.
- def _run(self, _): Return the result of a previous run.
<|s... | d5187438fea2928644cb53ecb26c6adb1e4cc947 | <|skeleton|>
class ResumeTask:
"""Task for re-using preprocessor output files from a previous run."""
def __init__(self, prev_preproc_dir, preproc_dir, name):
"""Create a resume task."""
<|body_0|>
def _run(self, _):
"""Return the result of a previous run."""
<|body_1|>
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResumeTask:
"""Task for re-using preprocessor output files from a previous run."""
def __init__(self, prev_preproc_dir, preproc_dir, name):
"""Create a resume task."""
self._metadata_file = preproc_dir / 'metadata.yml'
prev_metadata_file = prev_preproc_dir / 'metadata.yml'
... | the_stack_v2_python_sparse | esmvalcore/_task.py | ESMValGroup/ESMValCore | train | 41 |
3b05b105b8f442f3e07ed76b144ee24fb0d6ce5a | [
"res = super(DebugPanelMiddleware, self).process_request(request)\ntry:\n res = resolve(request.path, urlconf=debug_panel.urls)\nexcept Resolver404:\n return res\nreturn res.func(request, *res.args, **res.kwargs)",
"toolbar = self.__class__.debug_toolbars.get(threading.current_thread().ident, None)\nrespons... | <|body_start_0|>
res = super(DebugPanelMiddleware, self).process_request(request)
try:
res = resolve(request.path, urlconf=debug_panel.urls)
except Resolver404:
return res
return res.func(request, *res.args, **res.kwargs)
<|end_body_0|>
<|body_start_1|>
t... | Middleware to set up Debug Panel on incoming request and render toolbar on outgoing response. | DebugPanelMiddleware | [
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DebugPanelMiddleware:
"""Middleware to set up Debug Panel on incoming request and render toolbar on outgoing response."""
def process_request(self, request):
"""Try to match the request with an URL from debug_panel application. If it matches, that means we are serving a view from deb... | stack_v2_sparse_classes_36k_train_015631 | 2,457 | permissive | [
{
"docstring": "Try to match the request with an URL from debug_panel application. If it matches, that means we are serving a view from debug_panel, and we can skip the debug_toolbar middleware. Otherwise we fallback to the default debug_toolbar middleware.",
"name": "process_request",
"signature": "def... | 2 | null | Implement the Python class `DebugPanelMiddleware` described below.
Class description:
Middleware to set up Debug Panel on incoming request and render toolbar on outgoing response.
Method signatures and docstrings:
- def process_request(self, request): Try to match the request with an URL from debug_panel application.... | Implement the Python class `DebugPanelMiddleware` described below.
Class description:
Middleware to set up Debug Panel on incoming request and render toolbar on outgoing response.
Method signatures and docstrings:
- def process_request(self, request): Try to match the request with an URL from debug_panel application.... | 4082346ef8d5e6a8365b05752be41186840dc868 | <|skeleton|>
class DebugPanelMiddleware:
"""Middleware to set up Debug Panel on incoming request and render toolbar on outgoing response."""
def process_request(self, request):
"""Try to match the request with an URL from debug_panel application. If it matches, that means we are serving a view from deb... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DebugPanelMiddleware:
"""Middleware to set up Debug Panel on incoming request and render toolbar on outgoing response."""
def process_request(self, request):
"""Try to match the request with an URL from debug_panel application. If it matches, that means we are serving a view from debug_panel, and... | the_stack_v2_python_sparse | desktop/core/ext-py/django-debug-panel-0.8.3/debug_panel/middleware.py | oyorooms/hue | train | 4 |
c9c20bac00aaa7aded58be2194a496c1dbe97725 | [
"k = k % len(nums)\nfor i in range(k):\n last = nums[-1]\n for j in range(len(nums) - 1, 0, -1):\n nums[j] = nums[j - 1]\n nums[0] = last",
"k = k % len(nums)\n\ndef reverse(nums, left, right):\n while left < right:\n nums[left], nums[right] = (nums[right], nums[left])\n left += 1... | <|body_start_0|>
k = k % len(nums)
for i in range(k):
last = nums[-1]
for j in range(len(nums) - 1, 0, -1):
nums[j] = nums[j - 1]
nums[0] = last
<|end_body_0|>
<|body_start_1|>
k = k % len(nums)
def reverse(nums, left, right):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rotate_1(self, nums: List[int], k: int) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def rotate_2(self, nums: List[int], k: int) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_015632 | 1,025 | no_license | [
{
"docstring": "Do not return anything, modify nums in-place instead.",
"name": "rotate_1",
"signature": "def rotate_1(self, nums: List[int], k: int) -> None"
},
{
"docstring": "Do not return anything, modify nums in-place instead.",
"name": "rotate_2",
"signature": "def rotate_2(self, n... | 2 | stack_v2_sparse_classes_30k_train_001368 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotate_1(self, nums: List[int], k: int) -> None: Do not return anything, modify nums in-place instead.
- def rotate_2(self, nums: List[int], k: int) -> None: Do not return an... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotate_1(self, nums: List[int], k: int) -> None: Do not return anything, modify nums in-place instead.
- def rotate_2(self, nums: List[int], k: int) -> None: Do not return an... | d3b6883bb8b5cef30369b606d6b3ea3029b798c7 | <|skeleton|>
class Solution:
def rotate_1(self, nums: List[int], k: int) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def rotate_2(self, nums: List[int], k: int) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rotate_1(self, nums: List[int], k: int) -> None:
"""Do not return anything, modify nums in-place instead."""
k = k % len(nums)
for i in range(k):
last = nums[-1]
for j in range(len(nums) - 1, 0, -1):
nums[j] = nums[j - 1]
... | the_stack_v2_python_sparse | Week_01/189_rotate_array.py | slsefe/-algorithm015 | train | 0 | |
4ef3ebb061767faf9fa84d80e6a3139e68532ea6 | [
"if request.method in permissions.SAFE_METHODS:\n return True\nreturn request.user.is_authenticated",
"if request.method in permissions.SAFE_METHODS:\n return True\nreturn obj.user == request.user"
] | <|body_start_0|>
if request.method in permissions.SAFE_METHODS:
return True
return request.user.is_authenticated
<|end_body_0|>
<|body_start_1|>
if request.method in permissions.SAFE_METHODS:
return True
return obj.user == request.user
<|end_body_1|>
| IsCreatorOrReadOnly | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IsCreatorOrReadOnly:
def has_permission(self, request, view):
"""Authenticated allowed to create ingredients on List page."""
<|body_0|>
def has_object_permission(self, request, view, obj):
"""Creator is allowed to Update/Delete. Others can only Retrieve."""
... | stack_v2_sparse_classes_36k_train_015633 | 640 | permissive | [
{
"docstring": "Authenticated allowed to create ingredients on List page.",
"name": "has_permission",
"signature": "def has_permission(self, request, view)"
},
{
"docstring": "Creator is allowed to Update/Delete. Others can only Retrieve.",
"name": "has_object_permission",
"signature": "... | 2 | stack_v2_sparse_classes_30k_train_016984 | Implement the Python class `IsCreatorOrReadOnly` described below.
Class description:
Implement the IsCreatorOrReadOnly class.
Method signatures and docstrings:
- def has_permission(self, request, view): Authenticated allowed to create ingredients on List page.
- def has_object_permission(self, request, view, obj): Cr... | Implement the Python class `IsCreatorOrReadOnly` described below.
Class description:
Implement the IsCreatorOrReadOnly class.
Method signatures and docstrings:
- def has_permission(self, request, view): Authenticated allowed to create ingredients on List page.
- def has_object_permission(self, request, view, obj): Cr... | 755e4b4f10aa7b1918b260a5823900fb41bfff1b | <|skeleton|>
class IsCreatorOrReadOnly:
def has_permission(self, request, view):
"""Authenticated allowed to create ingredients on List page."""
<|body_0|>
def has_object_permission(self, request, view, obj):
"""Creator is allowed to Update/Delete. Others can only Retrieve."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IsCreatorOrReadOnly:
def has_permission(self, request, view):
"""Authenticated allowed to create ingredients on List page."""
if request.method in permissions.SAFE_METHODS:
return True
return request.user.is_authenticated
def has_object_permission(self, request, view, ... | the_stack_v2_python_sparse | recipe_blog/ingredients/permissions.py | hossshakiba/recipe-blog-api | train | 3 | |
c933d66ac34d6796ab61d6495fdc343b87012891 | [
"for i in range(len(nums)):\n for j in range(i + 1, len(nums)):\n if nums[i] + nums[j] == target:\n return [i, j]\nreturn 0",
"for i in range(len(nums)):\n if target - nums[i] in nums:\n j = nums.index(target - nums[i])\n if i == j:\n continue\n return [i, j... | <|body_start_0|>
for i in range(len(nums)):
for j in range(i + 1, len(nums)):
if nums[i] + nums[j] == target:
return [i, j]
return 0
<|end_body_0|>
<|body_start_1|>
for i in range(len(nums)):
if target - nums[i] in nums:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def twoSum(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[int]"""
<|body_0|>
def twoSum1(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[int]"""
<|body_1|>
def twoSum2(self, nums, targe... | stack_v2_sparse_classes_36k_train_015634 | 1,437 | no_license | [
{
"docstring": ":type nums: List[int] :type target: int :rtype: List[int]",
"name": "twoSum",
"signature": "def twoSum(self, nums, target)"
},
{
"docstring": ":type nums: List[int] :type target: int :rtype: List[int]",
"name": "twoSum1",
"signature": "def twoSum1(self, nums, target)"
}... | 3 | stack_v2_sparse_classes_30k_train_014703 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def twoSum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]
- def twoSum1(self, nums, target): :type nums: List[int] :type target: int :rtype: List[... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def twoSum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]
- def twoSum1(self, nums, target): :type nums: List[int] :type target: int :rtype: List[... | 5b55e35f15c7bf098203a6aabbb7aad6b14579fa | <|skeleton|>
class Solution:
def twoSum(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[int]"""
<|body_0|>
def twoSum1(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[int]"""
<|body_1|>
def twoSum2(self, nums, targe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def twoSum(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[int]"""
for i in range(len(nums)):
for j in range(i + 1, len(nums)):
if nums[i] + nums[j] == target:
return [i, j]
return 0
def twoSum1... | the_stack_v2_python_sparse | leetcode/1.py | queryor/algorithms | train | 0 | |
4f95c96f5cfdd09c25091d7b66879f06782999c2 | [
"arr = self.linked_list_to_array(head)\nself.insertion_sort(arr)\nreturn self.array_to_linked_list(arr)",
"arr = []\nwhile head is not None:\n arr.append(head.val)\n head = head.next\nreturn arr",
"for i in range(1, len(arr)):\n j, tmp = (i, arr[i])\n while j and tmp < arr[j - 1]:\n arr[j] = ... | <|body_start_0|>
arr = self.linked_list_to_array(head)
self.insertion_sort(arr)
return self.array_to_linked_list(arr)
<|end_body_0|>
<|body_start_1|>
arr = []
while head is not None:
arr.append(head.val)
head = head.next
return arr
<|end_body_1|>
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def insertionSortList(self, head: ListNode) -> ListNode:
"""Time: O(n ** 2) Space: O(n)"""
<|body_0|>
def linked_list_to_array(self, head: ListNode) -> List[int]:
"""Time/Space: O(n)"""
<|body_1|>
def insertion_sort(self, arr: List[int]) -> Non... | stack_v2_sparse_classes_36k_train_015635 | 1,184 | no_license | [
{
"docstring": "Time: O(n ** 2) Space: O(n)",
"name": "insertionSortList",
"signature": "def insertionSortList(self, head: ListNode) -> ListNode"
},
{
"docstring": "Time/Space: O(n)",
"name": "linked_list_to_array",
"signature": "def linked_list_to_array(self, head: ListNode) -> List[int... | 4 | stack_v2_sparse_classes_30k_train_002401 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def insertionSortList(self, head: ListNode) -> ListNode: Time: O(n ** 2) Space: O(n)
- def linked_list_to_array(self, head: ListNode) -> List[int]: Time/Space: O(n)
- def inserti... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def insertionSortList(self, head: ListNode) -> ListNode: Time: O(n ** 2) Space: O(n)
- def linked_list_to_array(self, head: ListNode) -> List[int]: Time/Space: O(n)
- def inserti... | 359f3b78da90c41c7e42e5c9e13d49b4fc67fe41 | <|skeleton|>
class Solution:
def insertionSortList(self, head: ListNode) -> ListNode:
"""Time: O(n ** 2) Space: O(n)"""
<|body_0|>
def linked_list_to_array(self, head: ListNode) -> List[int]:
"""Time/Space: O(n)"""
<|body_1|>
def insertion_sort(self, arr: List[int]) -> Non... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def insertionSortList(self, head: ListNode) -> ListNode:
"""Time: O(n ** 2) Space: O(n)"""
arr = self.linked_list_to_array(head)
self.insertion_sort(arr)
return self.array_to_linked_list(arr)
def linked_list_to_array(self, head: ListNode) -> List[int]:
""... | the_stack_v2_python_sparse | problems/147. Insertion Sort List/1 - Back to Array.py | Vasilic-Maxim/LeetCode-Problems | train | 0 | |
76d7018000beb1395899c283c9337a14d1a6f293 | [
"nums = str(N)\nfor num in nums:\n if num == '0' or N % int(num) != 0:\n return False\nreturn True",
"sDN = []\nfor n in range(left, right + 1):\n if self.isselfDividingNumber(n):\n sDN.append(n)\nreturn sDN"
] | <|body_start_0|>
nums = str(N)
for num in nums:
if num == '0' or N % int(num) != 0:
return False
return True
<|end_body_0|>
<|body_start_1|>
sDN = []
for n in range(left, right + 1):
if self.isselfDividingNumber(n):
sDN.app... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isselfDividingNumber(self, N):
""":type N: int :rtype: Bool"""
<|body_0|>
def selfDividingNumbers(self, left, right):
""":type left: int :type right: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
nums = str(N)
... | stack_v2_sparse_classes_36k_train_015636 | 582 | no_license | [
{
"docstring": ":type N: int :rtype: Bool",
"name": "isselfDividingNumber",
"signature": "def isselfDividingNumber(self, N)"
},
{
"docstring": ":type left: int :type right: int :rtype: List[int]",
"name": "selfDividingNumbers",
"signature": "def selfDividingNumbers(self, left, right)"
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isselfDividingNumber(self, N): :type N: int :rtype: Bool
- def selfDividingNumbers(self, left, right): :type left: int :type right: int :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isselfDividingNumber(self, N): :type N: int :rtype: Bool
- def selfDividingNumbers(self, left, right): :type left: int :type right: int :rtype: List[int]
<|skeleton|>
class ... | 9752533bc76ce5ecb881f61e33a3bc4b20dcf666 | <|skeleton|>
class Solution:
def isselfDividingNumber(self, N):
""":type N: int :rtype: Bool"""
<|body_0|>
def selfDividingNumbers(self, left, right):
""":type left: int :type right: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isselfDividingNumber(self, N):
""":type N: int :rtype: Bool"""
nums = str(N)
for num in nums:
if num == '0' or N % int(num) != 0:
return False
return True
def selfDividingNumbers(self, left, right):
""":type left: int :type... | the_stack_v2_python_sparse | 728. Self Dividing Numbers/728. Self Dividing Numbers.py | 603lzy/LeetCode | train | 3 | |
67ae5c40dee00aed03cd47bea2f3706735148b56 | [
"nodes = [root]\nres = []\nwhile any(nodes):\n nodes = [node for node in nodes if node]\n res.append(max([node.val for node in nodes]))\n nodes = [n for node in nodes for n in (node.left, node.right)]\nreturn res",
"if not root:\n return []\nnodes = [(root, 0)]\nhigh_level = 0\ndict = {}\nwhile nodes:... | <|body_start_0|>
nodes = [root]
res = []
while any(nodes):
nodes = [node for node in nodes if node]
res.append(max([node.val for node in nodes]))
nodes = [n for node in nodes for n in (node.left, node.right)]
return res
<|end_body_0|>
<|body_start_1|>... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def largestValues(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_0|>
def largestValues2(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
nodes = [root]
res = [... | stack_v2_sparse_classes_36k_train_015637 | 1,901 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: List[int]",
"name": "largestValues",
"signature": "def largestValues(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: List[int]",
"name": "largestValues2",
"signature": "def largestValues2(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007349 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestValues(self, root): :type root: TreeNode :rtype: List[int]
- def largestValues2(self, root): :type root: TreeNode :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestValues(self, root): :type root: TreeNode :rtype: List[int]
- def largestValues2(self, root): :type root: TreeNode :rtype: List[int]
<|skeleton|>
class Solution:
... | 0fc4c7af59246e3064db41989a45d9db413a624b | <|skeleton|>
class Solution:
def largestValues(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_0|>
def largestValues2(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def largestValues(self, root):
""":type root: TreeNode :rtype: List[int]"""
nodes = [root]
res = []
while any(nodes):
nodes = [node for node in nodes if node]
res.append(max([node.val for node in nodes]))
nodes = [n for node in node... | the_stack_v2_python_sparse | 515. Find Largest Value in Each Tree Row/largest.py | Macielyoung/LeetCode | train | 1 | |
fc8c1de19640cb39897b96d5e16c10733dd75424 | [
"probeTable = HTML().table(border='1', klass=TABLE_ENV)\nheading = probeTable.thead.tr\nheading.th('Probe #')\nheading.th('Probe')\nheading.th('SysName')\ntbody = probeTable.tbody\nfor i, probe in enumerate(probes):\n row = tbody.tr\n row.td('{:,}'.format(i + 1), klass=TD_KEY)\n row.td('{} '.format(probe.n... | <|body_start_0|>
probeTable = HTML().table(border='1', klass=TABLE_ENV)
heading = probeTable.thead.tr
heading.th('Probe #')
heading.th('Probe')
heading.th('SysName')
tbody = probeTable.tbody
for i, probe in enumerate(probes):
row = tbody.tr
... | Builds markup for rendering profile info | ProfileInfoReportBuilder | [
"MIT",
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProfileInfoReportBuilder:
"""Builds markup for rendering profile info"""
def buildProbeTable(probes):
"""Builds a html table for the given list of probes"""
<|body_0|>
def buildPmcTable(pmcs):
"""Builds a html table for the given list of pmu events"""
<|b... | stack_v2_sparse_classes_36k_train_015638 | 3,411 | permissive | [
{
"docstring": "Builds a html table for the given list of probes",
"name": "buildProbeTable",
"signature": "def buildProbeTable(probes)"
},
{
"docstring": "Builds a html table for the given list of pmu events",
"name": "buildPmcTable",
"signature": "def buildPmcTable(pmcs)"
},
{
... | 4 | null | Implement the Python class `ProfileInfoReportBuilder` described below.
Class description:
Builds markup for rendering profile info
Method signatures and docstrings:
- def buildProbeTable(probes): Builds a html table for the given list of probes
- def buildPmcTable(pmcs): Builds a html table for the given list of pmu ... | Implement the Python class `ProfileInfoReportBuilder` described below.
Class description:
Builds markup for rendering profile info
Method signatures and docstrings:
- def buildProbeTable(probes): Builds a html table for the given list of probes
- def buildPmcTable(pmcs): Builds a html table for the given list of pmu ... | d6b67e98d4b640c98499a373425f1f009e5b9061 | <|skeleton|>
class ProfileInfoReportBuilder:
"""Builds markup for rendering profile info"""
def buildProbeTable(probes):
"""Builds a html table for the given list of probes"""
<|body_0|>
def buildPmcTable(pmcs):
"""Builds a html table for the given list of pmu events"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProfileInfoReportBuilder:
"""Builds markup for rendering profile info"""
def buildProbeTable(probes):
"""Builds a html table for the given list of probes"""
probeTable = HTML().table(border='1', klass=TABLE_ENV)
heading = probeTable.thead.tr
heading.th('Probe #')
h... | the_stack_v2_python_sparse | scripts/lib/xpedite/report/profileInfo.py | dendisuhubdy/Xpedite | train | 1 |
a44ab891e1e458dae00ecd5dff141abbdc0d3465 | [
"retflg, retstr = service.FileBusiness.upload_file_post(request)\nif retflg:\n return Response(retstr, status=status.HTTP_200_OK)\nelse:\n return Response(retstr, status=status.HTTP_500_INTERNAL_SERVER_ERROR)",
"retflg, retstr = service.FileBusiness.ft_dir_list_get(request)\nif retflg:\n return Response(... | <|body_start_0|>
retflg, retstr = service.FileBusiness.upload_file_post(request)
if retflg:
return Response(retstr, status=status.HTTP_200_OK)
else:
return Response(retstr, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
<|end_body_0|>
<|body_start_1|>
retflg, rets... | post:upload file get :get list for dir | FTList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FTList:
"""post:upload file get :get list for dir"""
def post(self, request, format=None):
"""post:upload file"""
<|body_0|>
def get(self, request, format=None):
"""list for dir"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
retflg, retstr = se... | stack_v2_sparse_classes_36k_train_015639 | 1,639 | no_license | [
{
"docstring": "post:upload file",
"name": "post",
"signature": "def post(self, request, format=None)"
},
{
"docstring": "list for dir",
"name": "get",
"signature": "def get(self, request, format=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012780 | Implement the Python class `FTList` described below.
Class description:
post:upload file get :get list for dir
Method signatures and docstrings:
- def post(self, request, format=None): post:upload file
- def get(self, request, format=None): list for dir | Implement the Python class `FTList` described below.
Class description:
post:upload file get :get list for dir
Method signatures and docstrings:
- def post(self, request, format=None): post:upload file
- def get(self, request, format=None): list for dir
<|skeleton|>
class FTList:
"""post:upload file get :get lis... | 7f801a569a396a27371d0831752595877c224a6b | <|skeleton|>
class FTList:
"""post:upload file get :get list for dir"""
def post(self, request, format=None):
"""post:upload file"""
<|body_0|>
def get(self, request, format=None):
"""list for dir"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FTList:
"""post:upload file get :get list for dir"""
def post(self, request, format=None):
"""post:upload file"""
retflg, retstr = service.FileBusiness.upload_file_post(request)
if retflg:
return Response(retstr, status=status.HTTP_200_OK)
else:
ret... | the_stack_v2_python_sparse | Python_projects/flask_projects/unicorn_project/ft/views.py | sdtimothy8/Coding | train | 0 |
ada60b98e0477bc8ef082f0deec0d2d0ee02f451 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn BroadcastMeetingSettings()",
"from .broadcast_meeting_audience import BroadcastMeetingAudience\nfrom .broadcast_meeting_caption_settings import BroadcastMeetingCaptionSettings\nfrom .broadcast_meeting_audience import BroadcastMeetingAu... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return BroadcastMeetingSettings()
<|end_body_0|>
<|body_start_1|>
from .broadcast_meeting_audience import BroadcastMeetingAudience
from .broadcast_meeting_caption_settings import BroadcastMeeti... | BroadcastMeetingSettings | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BroadcastMeetingSettings:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> BroadcastMeetingSettings:
"""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 cre... | stack_v2_sparse_classes_36k_train_015640 | 4,835 | 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: BroadcastMeetingSettings",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrimi... | 3 | null | Implement the Python class `BroadcastMeetingSettings` described below.
Class description:
Implement the BroadcastMeetingSettings class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> BroadcastMeetingSettings: Creates a new instance of the appropriate c... | Implement the Python class `BroadcastMeetingSettings` described below.
Class description:
Implement the BroadcastMeetingSettings class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> BroadcastMeetingSettings: Creates a new instance of the appropriate c... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class BroadcastMeetingSettings:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> BroadcastMeetingSettings:
"""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 cre... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BroadcastMeetingSettings:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> BroadcastMeetingSettings:
"""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... | the_stack_v2_python_sparse | msgraph/generated/models/broadcast_meeting_settings.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
cfdd8870b76449bc8100cb83d05fd6b05d279348 | [
"content = '\\n {{ invite_host_name }},\\n\\n I\\'m so excited to lead your Vinely Party on <b>{{ party.event_date|date:\"F j, o\" }}</b> at <b>{{ party.event_date|date:\"g:i A\" }}</b>!\\n Your party has been scheduled in the system using the information you provided (see below).\\n You... | <|body_start_0|>
content = '\n {{ invite_host_name }},\n\n I\'m so excited to lead your Vinely Party on <b>{{ party.event_date|date:"F j, o" }}</b> at <b>{{ party.event_date|date:"g:i A" }}</b>!\n Your party has been scheduled in the system using the information you provided (see below).\n ... | Migration | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Migration:
def forwards(self, orm):
"""Write your forwards methods here."""
<|body_0|>
def backwards(self, orm):
"""Write your backwards methods here."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
content = '\n {{ invite_host_name }},\n\n ... | stack_v2_sparse_classes_36k_train_015641 | 4,647 | no_license | [
{
"docstring": "Write your forwards methods here.",
"name": "forwards",
"signature": "def forwards(self, orm)"
},
{
"docstring": "Write your backwards methods here.",
"name": "backwards",
"signature": "def backwards(self, orm)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008265 | Implement the Python class `Migration` described below.
Class description:
Implement the Migration class.
Method signatures and docstrings:
- def forwards(self, orm): Write your forwards methods here.
- def backwards(self, orm): Write your backwards methods here. | Implement the Python class `Migration` described below.
Class description:
Implement the Migration class.
Method signatures and docstrings:
- def forwards(self, orm): Write your forwards methods here.
- def backwards(self, orm): Write your backwards methods here.
<|skeleton|>
class Migration:
def forwards(self,... | c5c7d8a0b1a297e07302870017d3fb03c5dbb009 | <|skeleton|>
class Migration:
def forwards(self, orm):
"""Write your forwards methods here."""
<|body_0|>
def backwards(self, orm):
"""Write your backwards methods here."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Migration:
def forwards(self, orm):
"""Write your forwards methods here."""
content = '\n {{ invite_host_name }},\n\n I\'m so excited to lead your Vinely Party on <b>{{ party.event_date|date:"F j, o" }}</b> at <b>{{ party.event_date|date:"g:i A" }}</b>!\n Your party has be... | the_stack_v2_python_sparse | cms/migrations/0015_new_party_host_confirmation_email.py | RSV3/nuvine | train | 0 | |
49df805c8e708079a2b0568c16a39742c110c305 | [
"red = [1.0, 0.0, 0.0, 1]\ngreen = [0.0, 1.0, 0.0, 1]\nblue = [0.0, 0.0, 1.0, 1]\nyellow = [1.0, 1.0, 0.0, 1]\nwhite = [1.0, 1.0, 1.0, 1]\nblack = [0.0, 0.0, 0.0, 0.0]\nscalingUnit = VPC.b / 6.0\nfuse_h = scalingUnit\nfuse_w = scalingUnit\nfuse_l1 = 2 * scalingUnit\nfuse_l2 = scalingUnit\nfuse_l3 = 4 * scalingUnit\... | <|body_start_0|>
red = [1.0, 0.0, 0.0, 1]
green = [0.0, 1.0, 0.0, 1]
blue = [0.0, 0.0, 1.0, 1]
yellow = [1.0, 1.0, 0.0, 1]
white = [1.0, 1.0, 1.0, 1]
black = [0.0, 0.0, 0.0, 0.0]
scalingUnit = VPC.b / 6.0
fuse_h = scalingUnit
fuse_w = scalingUnit
... | VehicleGeometry | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VehicleGeometry:
def __init__(self):
"""defines the vehicle in NED coordinates around the local body frame origin. Rotations and translations will be around the [0,0,0] point of this local frame. Has to be in NED or the rotation matrices will not work. Vehicle is scaled to match the wing... | stack_v2_sparse_classes_36k_train_015642 | 5,465 | no_license | [
{
"docstring": "defines the vehicle in NED coordinates around the local body frame origin. Rotations and translations will be around the [0,0,0] point of this local frame. Has to be in NED or the rotation matrices will not work. Vehicle is scaled to match the wing span of the actual vehicle, this all the points... | 2 | stack_v2_sparse_classes_30k_train_009756 | Implement the Python class `VehicleGeometry` described below.
Class description:
Implement the VehicleGeometry class.
Method signatures and docstrings:
- def __init__(self): defines the vehicle in NED coordinates around the local body frame origin. Rotations and translations will be around the [0,0,0] point of this l... | Implement the Python class `VehicleGeometry` described below.
Class description:
Implement the VehicleGeometry class.
Method signatures and docstrings:
- def __init__(self): defines the vehicle in NED coordinates around the local body frame origin. Rotations and translations will be around the [0,0,0] point of this l... | 5a1429df7d035a34145ec2191ddd3268553b1a73 | <|skeleton|>
class VehicleGeometry:
def __init__(self):
"""defines the vehicle in NED coordinates around the local body frame origin. Rotations and translations will be around the [0,0,0] point of this local frame. Has to be in NED or the rotation matrices will not work. Vehicle is scaled to match the wing... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VehicleGeometry:
def __init__(self):
"""defines the vehicle in NED coordinates around the local body frame origin. Rotations and translations will be around the [0,0,0] point of this local frame. Has to be in NED or the rotation matrices will not work. Vehicle is scaled to match the wing span of the a... | the_stack_v2_python_sparse | UAV_Payload_Delivery/CodeBase/ece163/Modeling/VehicleGeometry.py | rpsison/Projects | train | 2 | |
e950332936e16fe0704720e81b300c1cdc57962f | [
"netdata = self.loaddata(mriscan, attrname)\nnet = netattr.Net(netdata, self.atlasobj, mriscan, attrname)\nreturn net",
"ret = []\nfor mriscan in mriscans:\n ret.append(self.loadSingle(mriscan, attrname))\nreturn ret"
] | <|body_start_0|>
netdata = self.loaddata(mriscan, attrname)
net = netattr.Net(netdata, self.atlasobj, mriscan, attrname)
return net
<|end_body_0|>
<|body_start_1|>
ret = []
for mriscan in mriscans:
ret.append(self.loadSingle(mriscan, attrname))
return ret
<|e... | Net loader. | NetLoader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NetLoader:
"""Net loader."""
def loadSingle(self, mriscan, attrname='BOLD.net'):
"""Load the net object, with atlasobj."""
<|body_0|>
def loadMulti(self, mriscans, attrname='BOLD.net'):
"""Load a list of nets"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_015643 | 13,309 | no_license | [
{
"docstring": "Load the net object, with atlasobj.",
"name": "loadSingle",
"signature": "def loadSingle(self, mriscan, attrname='BOLD.net')"
},
{
"docstring": "Load a list of nets",
"name": "loadMulti",
"signature": "def loadMulti(self, mriscans, attrname='BOLD.net')"
}
] | 2 | stack_v2_sparse_classes_30k_train_020015 | Implement the Python class `NetLoader` described below.
Class description:
Net loader.
Method signatures and docstrings:
- def loadSingle(self, mriscan, attrname='BOLD.net'): Load the net object, with atlasobj.
- def loadMulti(self, mriscans, attrname='BOLD.net'): Load a list of nets | Implement the Python class `NetLoader` described below.
Class description:
Net loader.
Method signatures and docstrings:
- def loadSingle(self, mriscan, attrname='BOLD.net'): Load the net object, with atlasobj.
- def loadMulti(self, mriscans, attrname='BOLD.net'): Load a list of nets
<|skeleton|>
class NetLoader:
... | dabfabdeb2f922a3dcbdaf3fc46f0c4b40598279 | <|skeleton|>
class NetLoader:
"""Net loader."""
def loadSingle(self, mriscan, attrname='BOLD.net'):
"""Load the net object, with atlasobj."""
<|body_0|>
def loadMulti(self, mriscans, attrname='BOLD.net'):
"""Load a list of nets"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NetLoader:
"""Net loader."""
def loadSingle(self, mriscan, attrname='BOLD.net'):
"""Load the net object, with atlasobj."""
netdata = self.loaddata(mriscan, attrname)
net = netattr.Net(netdata, self.atlasobj, mriscan, attrname)
return net
def loadMulti(self, mriscans, ... | the_stack_v2_python_sparse | mmdps/proc/loader.py | geyunxiang/mmdps | train | 5 |
2e75f3f70ab13799d3b163d4f2873035a0de5839 | [
"Container.__init__(self, 'get_string_dialog', padding=5)\nself.callback = callback\nself.sub(Label('prompt', prompt, pygame.Rect((0, 0), (200, 30))))\ntextbox = TextBox('textbox', pygame.Rect((0, 0), (200, 30)), return_callback=self.return_key)\nself.sub(textbox)\ndisplay.key_sensitive(textbox)\nself.sub(Button('O... | <|body_start_0|>
Container.__init__(self, 'get_string_dialog', padding=5)
self.callback = callback
self.sub(Label('prompt', prompt, pygame.Rect((0, 0), (200, 30))))
textbox = TextBox('textbox', pygame.Rect((0, 0), (200, 30)), return_callback=self.return_key)
self.sub(textbox)
... | A combination of Container, Label, TextBox and Button that asks the user for a string. Additional attributes: GetStringDialog.callback The callback to be called callback(string) when the input is confirmed. | GetStringDialog | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetStringDialog:
"""A combination of Container, Label, TextBox and Button that asks the user for a string. Additional attributes: GetStringDialog.callback The callback to be called callback(string) when the input is confirmed."""
def __init__(self, prompt, callback, display):
"""Init... | stack_v2_sparse_classes_36k_train_015644 | 27,668 | permissive | [
{
"docstring": "Initialise. callback will be called callback(GetStringDialog.textbox.text) after the GetStringDialog is destroyed. It should call render() and flip the display to remove the GetStringDialog from the screen. display.key_sensitive() will be used to register the TextBox of this dialog.",
"name"... | 3 | stack_v2_sparse_classes_30k_train_009364 | Implement the Python class `GetStringDialog` described below.
Class description:
A combination of Container, Label, TextBox and Button that asks the user for a string. Additional attributes: GetStringDialog.callback The callback to be called callback(string) when the input is confirmed.
Method signatures and docstrin... | Implement the Python class `GetStringDialog` described below.
Class description:
A combination of Container, Label, TextBox and Button that asks the user for a string. Additional attributes: GetStringDialog.callback The callback to be called callback(string) when the input is confirmed.
Method signatures and docstrin... | c2fc3d4e9beedb8487cfa4bfa13bdf55ec36af97 | <|skeleton|>
class GetStringDialog:
"""A combination of Container, Label, TextBox and Button that asks the user for a string. Additional attributes: GetStringDialog.callback The callback to be called callback(string) when the input is confirmed."""
def __init__(self, prompt, callback, display):
"""Init... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetStringDialog:
"""A combination of Container, Label, TextBox and Button that asks the user for a string. Additional attributes: GetStringDialog.callback The callback to be called callback(string) when the input is confirmed."""
def __init__(self, prompt, callback, display):
"""Initialise. callb... | the_stack_v2_python_sparse | reference_scripts/clickndrag-0.4.1/clickndrag/gui.py | stivosaurus/rpi-snippets | train | 1 |
b39559e6583ac244ccf6b29f7eb6f4e899d379af | [
"article = get_object_or_404(Article, slug=slug)\nif Favourite.objects.filter(user=request.user, article=article).exists():\n raise serializers.ValidationError('You have already favourited ' + 'this article', 400)\nelse:\n request.data['user'] = request.user.id\n request.data['article'] = article\n seri... | <|body_start_0|>
article = get_object_or_404(Article, slug=slug)
if Favourite.objects.filter(user=request.user, article=article).exists():
raise serializers.ValidationError('You have already favourited ' + 'this article', 400)
else:
request.data['user'] = request.user.id
... | This class handles favourating and unfavourating articles | FavouriteView | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FavouriteView:
"""This class handles favourating and unfavourating articles"""
def post(self, request, slug):
"""This method adds an article to favourites"""
<|body_0|>
def delete(self, request, slug):
"""This method deletes an article from favourites"""
... | stack_v2_sparse_classes_36k_train_015645 | 2,936 | permissive | [
{
"docstring": "This method adds an article to favourites",
"name": "post",
"signature": "def post(self, request, slug)"
},
{
"docstring": "This method deletes an article from favourites",
"name": "delete",
"signature": "def delete(self, request, slug)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010079 | Implement the Python class `FavouriteView` described below.
Class description:
This class handles favourating and unfavourating articles
Method signatures and docstrings:
- def post(self, request, slug): This method adds an article to favourites
- def delete(self, request, slug): This method deletes an article from f... | Implement the Python class `FavouriteView` described below.
Class description:
This class handles favourating and unfavourating articles
Method signatures and docstrings:
- def post(self, request, slug): This method adds an article to favourites
- def delete(self, request, slug): This method deletes an article from f... | c38810e221f95567262034b860ee0512cf15f102 | <|skeleton|>
class FavouriteView:
"""This class handles favourating and unfavourating articles"""
def post(self, request, slug):
"""This method adds an article to favourites"""
<|body_0|>
def delete(self, request, slug):
"""This method deletes an article from favourites"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FavouriteView:
"""This class handles favourating and unfavourating articles"""
def post(self, request, slug):
"""This method adds an article to favourites"""
article = get_object_or_404(Article, slug=slug)
if Favourite.objects.filter(user=request.user, article=article).exists():
... | the_stack_v2_python_sparse | authors/apps/articles/views_favourites.py | andela/ah-backend-stark | train | 0 |
f5f9f58b9367a51e363dcd0b4dd2b63359ce6d1d | [
"self.ad_guid_pairs = ad_guid_pairs\nself.exclude_ldap_properties = exclude_ldap_properties\nself.ldap_properties = ldap_properties\nself.merge_multi_val_properties = merge_multi_val_properties",
"if dictionary is None:\n return None\nad_guid_pairs = None\nif dictionary.get('adGuidPairs') != None:\n ad_guid... | <|body_start_0|>
self.ad_guid_pairs = ad_guid_pairs
self.exclude_ldap_properties = exclude_ldap_properties
self.ldap_properties = ldap_properties
self.merge_multi_val_properties = merge_multi_val_properties
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
retur... | Implementation of the 'AdObjectAttributeParameters' model. AdObjectAttributeParameters are AD attribute recovery parameters for one or more AD objects Attributes: ad_guid_pairs (list of RestoreAdGuidPair): Specifies the array of source and destination object guid pairs to restore attributes. exclude_ldap_properties (li... | AdObjectAttributeParameters | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdObjectAttributeParameters:
"""Implementation of the 'AdObjectAttributeParameters' model. AdObjectAttributeParameters are AD attribute recovery parameters for one or more AD objects Attributes: ad_guid_pairs (list of RestoreAdGuidPair): Specifies the array of source and destination object guid p... | stack_v2_sparse_classes_36k_train_015646 | 4,187 | permissive | [
{
"docstring": "Constructor for the AdObjectAttributeParameters class",
"name": "__init__",
"signature": "def __init__(self, ad_guid_pairs=None, exclude_ldap_properties=None, ldap_properties=None, merge_multi_val_properties=None)"
},
{
"docstring": "Creates an instance of this model from a dicti... | 2 | stack_v2_sparse_classes_30k_train_012303 | Implement the Python class `AdObjectAttributeParameters` described below.
Class description:
Implementation of the 'AdObjectAttributeParameters' model. AdObjectAttributeParameters are AD attribute recovery parameters for one or more AD objects Attributes: ad_guid_pairs (list of RestoreAdGuidPair): Specifies the array ... | Implement the Python class `AdObjectAttributeParameters` described below.
Class description:
Implementation of the 'AdObjectAttributeParameters' model. AdObjectAttributeParameters are AD attribute recovery parameters for one or more AD objects Attributes: ad_guid_pairs (list of RestoreAdGuidPair): Specifies the array ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class AdObjectAttributeParameters:
"""Implementation of the 'AdObjectAttributeParameters' model. AdObjectAttributeParameters are AD attribute recovery parameters for one or more AD objects Attributes: ad_guid_pairs (list of RestoreAdGuidPair): Specifies the array of source and destination object guid p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdObjectAttributeParameters:
"""Implementation of the 'AdObjectAttributeParameters' model. AdObjectAttributeParameters are AD attribute recovery parameters for one or more AD objects Attributes: ad_guid_pairs (list of RestoreAdGuidPair): Specifies the array of source and destination object guid pairs to resto... | the_stack_v2_python_sparse | cohesity_management_sdk/models/ad_object_attribute_parameters.py | cohesity/management-sdk-python | train | 24 |
685fd25e3f51e11e3e834eb4a3367c6abae58635 | [
"if cut_baselines:\n fname_end = '-short' + str(lines[mol]['baseline_cutoff'])\nelse:\n fname_end = ''\nself.mol = mol\nself.path = './data/' + mol + '/' + mol + fname_end\nself.uvf = fits.open(self.path + '.uvf')\nself.fits = fits.open(self.path + '.fits')\nself.baseline_cutoff = 110\nself.rms = lines[mol]['... | <|body_start_0|>
if cut_baselines:
fname_end = '-short' + str(lines[mol]['baseline_cutoff'])
else:
fname_end = ''
self.mol = mol
self.path = './data/' + mol + '/' + mol + fname_end
self.uvf = fits.open(self.path + '.uvf')
self.fits = fits.open(self... | Make the whole observation/data processing shindig a Class. This incorporates everything from the path to the original data file to the final model. Running it will grab the appropriate data files and spit out a cleaned image and some other stuff. Only used in analysis.py/MCMC_Analysis I think? Probably doesn't do too ... | Observation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Observation:
"""Make the whole observation/data processing shindig a Class. This incorporates everything from the path to the original data file to the final model. Running it will grab the appropriate data files and spit out a cleaned image and some other stuff. Only used in analysis.py/MCMC_Ana... | stack_v2_sparse_classes_36k_train_015647 | 26,328 | no_license | [
{
"docstring": "Give some init values. Args: root (str): the name of the directory to source the data files from name (str): the name of the data files to grab from root rms (float): the rms noise of that particular observation",
"name": "__init__",
"signature": "def __init__(self, mol, cut_baselines=Tr... | 2 | stack_v2_sparse_classes_30k_train_011498 | Implement the Python class `Observation` described below.
Class description:
Make the whole observation/data processing shindig a Class. This incorporates everything from the path to the original data file to the final model. Running it will grab the appropriate data files and spit out a cleaned image and some other s... | Implement the Python class `Observation` described below.
Class description:
Make the whole observation/data processing shindig a Class. This incorporates everything from the path to the original data file to the final model. Running it will grab the appropriate data files and spit out a cleaned image and some other s... | f333f97a3d6f913037fa94b4b17ad1f2e5621b05 | <|skeleton|>
class Observation:
"""Make the whole observation/data processing shindig a Class. This incorporates everything from the path to the original data file to the final model. Running it will grab the appropriate data files and spit out a cleaned image and some other stuff. Only used in analysis.py/MCMC_Ana... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Observation:
"""Make the whole observation/data processing shindig a Class. This incorporates everything from the path to the original data file to the final model. Running it will grab the appropriate data files and spit out a cleaned image and some other stuff. Only used in analysis.py/MCMC_Analysis I think... | the_stack_v2_python_sparse | utils.py | Jonasori/Proplyd-Modeling | train | 2 |
ad722afc9686d54c1f309a94c6a2cc1b78803fbc | [
"assessment: Assessment = self.get_object()\npublished_only = get_published_only(assessment, request)\nqs = models.DataExtraction.objects.get_qs(assessment).published_only(published_only).complete()\nexporter = exports.EpiFlatComplete(qs, filename=f'{assessment}-epi')\nreturn Response(exporter.build_export())",
"... | <|body_start_0|>
assessment: Assessment = self.get_object()
published_only = get_published_only(assessment, request)
qs = models.DataExtraction.objects.get_qs(assessment).published_only(published_only).complete()
exporter = exports.EpiFlatComplete(qs, filename=f'{assessment}-epi')
... | EpiAssessmentViewSet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EpiAssessmentViewSet:
def export(self, request, pk):
"""Retrieve epidemiology complete export."""
<|body_0|>
def study_export(self, request, pk):
"""Retrieve epidemiology at the study level for assessment."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_015648 | 6,710 | permissive | [
{
"docstring": "Retrieve epidemiology complete export.",
"name": "export",
"signature": "def export(self, request, pk)"
},
{
"docstring": "Retrieve epidemiology at the study level for assessment.",
"name": "study_export",
"signature": "def study_export(self, request, pk)"
}
] | 2 | null | Implement the Python class `EpiAssessmentViewSet` described below.
Class description:
Implement the EpiAssessmentViewSet class.
Method signatures and docstrings:
- def export(self, request, pk): Retrieve epidemiology complete export.
- def study_export(self, request, pk): Retrieve epidemiology at the study level for ... | Implement the Python class `EpiAssessmentViewSet` described below.
Class description:
Implement the EpiAssessmentViewSet class.
Method signatures and docstrings:
- def export(self, request, pk): Retrieve epidemiology complete export.
- def study_export(self, request, pk): Retrieve epidemiology at the study level for ... | 51177c6fb9354cd028f7099fc10d83b1051fd50d | <|skeleton|>
class EpiAssessmentViewSet:
def export(self, request, pk):
"""Retrieve epidemiology complete export."""
<|body_0|>
def study_export(self, request, pk):
"""Retrieve epidemiology at the study level for assessment."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EpiAssessmentViewSet:
def export(self, request, pk):
"""Retrieve epidemiology complete export."""
assessment: Assessment = self.get_object()
published_only = get_published_only(assessment, request)
qs = models.DataExtraction.objects.get_qs(assessment).published_only(published_o... | the_stack_v2_python_sparse | hawc/apps/epiv2/api.py | shapiromatron/hawc | train | 25 | |
dd31799998d2fc189857bfb2804420549033e4da | [
"data = request.GET\nsize = int(data.get('size', 10))\npage = int(data.get('page', 1))\nresults = Mock.objects.all()\nret = CalcUtils.page_data(results, page, size)\nret['list'] = [model_to_dict(i) for i in ret['list']]\nreturn response_success(ret)",
"body = request.body\nif not body:\n return response_succes... | <|body_start_0|>
data = request.GET
size = int(data.get('size', 10))
page = int(data.get('page', 1))
results = Mock.objects.all()
ret = CalcUtils.page_data(results, page, size)
ret['list'] = [model_to_dict(i) for i in ret['list']]
return response_success(ret)
<|en... | MockListView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MockListView:
def get(self, request, *args, **kwargs):
"""代表获取mock列表 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""代表的是创建mock :param request: :param args: :param kwargs: :return:"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_015649 | 1,740 | no_license | [
{
"docstring": "代表获取mock列表 :param request: :param args: :param kwargs: :return:",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "代表的是创建mock :param request: :param args: :param kwargs: :return:",
"name": "post",
"signature": "def post(self, reques... | 2 | stack_v2_sparse_classes_30k_train_006051 | Implement the Python class `MockListView` described below.
Class description:
Implement the MockListView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 代表获取mock列表 :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): 代表的是创建mock :param req... | Implement the Python class `MockListView` described below.
Class description:
Implement the MockListView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 代表获取mock列表 :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): 代表的是创建mock :param req... | fa859efae69351ec79b3d8a56e9948797ab76b31 | <|skeleton|>
class MockListView:
def get(self, request, *args, **kwargs):
"""代表获取mock列表 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""代表的是创建mock :param request: :param args: :param kwargs: :return:"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MockListView:
def get(self, request, *args, **kwargs):
"""代表获取mock列表 :param request: :param args: :param kwargs: :return:"""
data = request.GET
size = int(data.get('size', 10))
page = int(data.get('page', 1))
results = Mock.objects.all()
ret = CalcUtils.page_dat... | the_stack_v2_python_sparse | django_interface_project/interface_main/views/mock/mock_list_view.py | harter123/test-dev2 | train | 7 | |
843997a3144c6987f63deabbe0e23cc852dc66aa | [
"self.name = 'ANNModel'\nsuper(ANNModel, self).__init__(self.name, use_logger)\nif self.use_logger:\n self.logger = ml.SciopeLogger().get_logger()\n self.logger.info('Artificial Neural Network regression model initialized')",
"self.scale_training_data(inputs, targets)\nself.model = MLPRegressor(solver='lbfg... | <|body_start_0|>
self.name = 'ANNModel'
super(ANNModel, self).__init__(self.name, use_logger)
if self.use_logger:
self.logger = ml.SciopeLogger().get_logger()
self.logger.info('Artificial Neural Network regression model initialized')
<|end_body_0|>
<|body_start_1|>
... | We use the sklearn MLP Regressor implementation here. | ANNModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ANNModel:
"""We use the sklearn MLP Regressor implementation here."""
def __init__(self, use_logger=False):
"""Initialize the model. Parameters ---------- use_logger : bool, optional Controls whether logging is enabled or disabled, by default False"""
<|body_0|>
def trai... | stack_v2_sparse_classes_36k_train_015650 | 2,694 | permissive | [
{
"docstring": "Initialize the model. Parameters ---------- use_logger : bool, optional Controls whether logging is enabled or disabled, by default False",
"name": "__init__",
"signature": "def __init__(self, use_logger=False)"
},
{
"docstring": "Train the ANN model given the data Parameters ---... | 3 | stack_v2_sparse_classes_30k_train_003853 | Implement the Python class `ANNModel` described below.
Class description:
We use the sklearn MLP Regressor implementation here.
Method signatures and docstrings:
- def __init__(self, use_logger=False): Initialize the model. Parameters ---------- use_logger : bool, optional Controls whether logging is enabled or disab... | Implement the Python class `ANNModel` described below.
Class description:
We use the sklearn MLP Regressor implementation here.
Method signatures and docstrings:
- def __init__(self, use_logger=False): Initialize the model. Parameters ---------- use_logger : bool, optional Controls whether logging is enabled or disab... | 5122107dedcee9c39458e83d853ec35f91268780 | <|skeleton|>
class ANNModel:
"""We use the sklearn MLP Regressor implementation here."""
def __init__(self, use_logger=False):
"""Initialize the model. Parameters ---------- use_logger : bool, optional Controls whether logging is enabled or disabled, by default False"""
<|body_0|>
def trai... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ANNModel:
"""We use the sklearn MLP Regressor implementation here."""
def __init__(self, use_logger=False):
"""Initialize the model. Parameters ---------- use_logger : bool, optional Controls whether logging is enabled or disabled, by default False"""
self.name = 'ANNModel'
super(... | the_stack_v2_python_sparse | sciope/models/ann_regressor.py | rmjiang7/sciope | train | 0 |
582c5f0ae0c7d06b002cfcc00aafd37ea54ad910 | [
"self._console = Console()\nself._status = self._console.status('Working on it ...', spinner='bouncingBall')\nself._stored_progress_reporter = RichConsoleProgressReporter(console=self._console, status=self._status, sections=[], created_entities=[], created_entities_stats=defaultdict(list), updated_entities=[], upda... | <|body_start_0|>
self._console = Console()
self._status = self._console.status('Working on it ...', spinner='bouncingBall')
self._stored_progress_reporter = RichConsoleProgressReporter(console=self._console, status=self._status, sections=[], created_entities=[], created_entities_stats=defaultdic... | A progress reporter factory that builds Rich progress reporters. | RichConsoleProgressReporterFactory | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RichConsoleProgressReporterFactory:
"""A progress reporter factory that builds Rich progress reporters."""
def __init__(self) -> None:
"""Constructor."""
<|body_0|>
def new_reporter(self, context: AppLoggedInUseCaseContext) -> ProgressReporter:
"""Create a new pr... | stack_v2_sparse_classes_36k_train_015651 | 19,842 | permissive | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Create a new progress reporter.",
"name": "new_reporter",
"signature": "def new_reporter(self, context: AppLoggedInUseCaseContext) -> ProgressReporter"
},
{
"docstring"... | 3 | null | Implement the Python class `RichConsoleProgressReporterFactory` described below.
Class description:
A progress reporter factory that builds Rich progress reporters.
Method signatures and docstrings:
- def __init__(self) -> None: Constructor.
- def new_reporter(self, context: AppLoggedInUseCaseContext) -> ProgressRepo... | Implement the Python class `RichConsoleProgressReporterFactory` described below.
Class description:
A progress reporter factory that builds Rich progress reporters.
Method signatures and docstrings:
- def __init__(self) -> None: Constructor.
- def new_reporter(self, context: AppLoggedInUseCaseContext) -> ProgressRepo... | 911ecd560142a9b4e57498f2b090f9469a0718a1 | <|skeleton|>
class RichConsoleProgressReporterFactory:
"""A progress reporter factory that builds Rich progress reporters."""
def __init__(self) -> None:
"""Constructor."""
<|body_0|>
def new_reporter(self, context: AppLoggedInUseCaseContext) -> ProgressReporter:
"""Create a new pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RichConsoleProgressReporterFactory:
"""A progress reporter factory that builds Rich progress reporters."""
def __init__(self) -> None:
"""Constructor."""
self._console = Console()
self._status = self._console.status('Working on it ...', spinner='bouncingBall')
self._stored... | the_stack_v2_python_sparse | src/cli/jupiter/cli/command/rendering.py | horia141/jupiter | train | 16 |
409220a3cf8b7ab8209f12d7ac382a7634617d34 | [
"super(AttentionLayer, self).__init__()\nself.h = n_head\nself.n_qk = N_qk\nself.n_v = N_v\nself.dim = N_in\nif N_qk is None:\n self.K = nn.Identity(N_in, N_in * n_head)\n self.Q = nn.Identity(N_in, N_in * n_head)\nelse:\n self.K = nn.Linear(N_in, N_qk * n_head)\n if queries:\n self.Q = nn.Linear... | <|body_start_0|>
super(AttentionLayer, self).__init__()
self.h = n_head
self.n_qk = N_qk
self.n_v = N_v
self.dim = N_in
if N_qk is None:
self.K = nn.Identity(N_in, N_in * n_head)
self.Q = nn.Identity(N_in, N_in * n_head)
else:
s... | AttentionLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttentionLayer:
def __init__(self, N_in, n_head=1, N_qk=None, N_v=None, queries=False):
"""Scaled dot-product attention, optional key, query, and value maps"""
<|body_0|>
def weights(self, x, mask):
"""x is shape (num_tok, *, dim_inp) expects padded inputs to be nans... | stack_v2_sparse_classes_36k_train_015652 | 45,005 | no_license | [
{
"docstring": "Scaled dot-product attention, optional key, query, and value maps",
"name": "__init__",
"signature": "def __init__(self, N_in, n_head=1, N_qk=None, N_v=None, queries=False)"
},
{
"docstring": "x is shape (num_tok, *, dim_inp) expects padded inputs to be nans!",
"name": "weigh... | 3 | stack_v2_sparse_classes_30k_train_013093 | Implement the Python class `AttentionLayer` described below.
Class description:
Implement the AttentionLayer class.
Method signatures and docstrings:
- def __init__(self, N_in, n_head=1, N_qk=None, N_v=None, queries=False): Scaled dot-product attention, optional key, query, and value maps
- def weights(self, x, mask)... | Implement the Python class `AttentionLayer` described below.
Class description:
Implement the AttentionLayer class.
Method signatures and docstrings:
- def __init__(self, N_in, n_head=1, N_qk=None, N_v=None, queries=False): Scaled dot-product attention, optional key, query, and value maps
- def weights(self, x, mask)... | 1d4c76920d50729680305a4e877c30e2b782d9d7 | <|skeleton|>
class AttentionLayer:
def __init__(self, N_in, n_head=1, N_qk=None, N_v=None, queries=False):
"""Scaled dot-product attention, optional key, query, and value maps"""
<|body_0|>
def weights(self, x, mask):
"""x is shape (num_tok, *, dim_inp) expects padded inputs to be nans... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttentionLayer:
def __init__(self, N_in, n_head=1, N_qk=None, N_v=None, queries=False):
"""Scaled dot-product attention, optional key, query, and value maps"""
super(AttentionLayer, self).__init__()
self.h = n_head
self.n_qk = N_qk
self.n_v = N_v
self.dim = N_in... | the_stack_v2_python_sparse | src/students.py | Kelarion/repler | train | 0 | |
d5f5e4f39c9167ab125aada9eb202f8e82365cf6 | [
"assert isinstance(module, torch.nn.Module), 'Must provide an instance of a nn.Module'\nself.module = module\nself.name_to_node = {}\nself.module_to_name = {}\nself.current_input = None\nsubmodules = module.named_children() if not submodules else submodules\nif isinstance(submodules, dict):\n submodules = submod... | <|body_start_0|>
assert isinstance(module, torch.nn.Module), 'Must provide an instance of a nn.Module'
self.module = module
self.name_to_node = {}
self.module_to_name = {}
self.current_input = None
submodules = module.named_children() if not submodules else submodules
... | Automatically construct a `ComputationGraph` from a `torch.nn.Module`. Currently, the constructor will automatically treat the submodules of type `torch.nn.Module` of the provided module as nodes in the computation graph. The constructor only works if the outputs of every submodule directly feeds into another one, with... | CompGraphConstructor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompGraphConstructor:
"""Automatically construct a `ComputationGraph` from a `torch.nn.Module`. Currently, the constructor will automatically treat the submodules of type `torch.nn.Module` of the provided module as nodes in the computation graph. The constructor only works if the outputs of every... | stack_v2_sparse_classes_36k_train_015653 | 6,065 | permissive | [
{
"docstring": "Initialize constructor from a pytorch module :param module: `torch.nn.Module` the module in question. :param submodules: see CompGraphConstructor.construct().",
"name": "__init__",
"signature": "def __init__(self, module, submodules=None)"
},
{
"docstring": "Construct a computati... | 5 | stack_v2_sparse_classes_30k_train_017670 | Implement the Python class `CompGraphConstructor` described below.
Class description:
Automatically construct a `ComputationGraph` from a `torch.nn.Module`. Currently, the constructor will automatically treat the submodules of type `torch.nn.Module` of the provided module as nodes in the computation graph. The constru... | Implement the Python class `CompGraphConstructor` described below.
Class description:
Automatically construct a `ComputationGraph` from a `torch.nn.Module`. Currently, the constructor will automatically treat the submodules of type `torch.nn.Module` of the provided module as nodes in the computation graph. The constru... | 860c12697211f4cb3c9ef7aa1c8dee247f2710f4 | <|skeleton|>
class CompGraphConstructor:
"""Automatically construct a `ComputationGraph` from a `torch.nn.Module`. Currently, the constructor will automatically treat the submodules of type `torch.nn.Module` of the provided module as nodes in the computation graph. The constructor only works if the outputs of every... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CompGraphConstructor:
"""Automatically construct a `ComputationGraph` from a `torch.nn.Module`. Currently, the constructor will automatically treat the submodules of type `torch.nn.Module` of the provided module as nodes in the computation graph. The constructor only works if the outputs of every submodule di... | the_stack_v2_python_sparse | antra/constructor.py | syyunn/antra | train | 0 |
d2b84e6e32b38af7ac2cc785661e39b7e9aadd43 | [
"def str_cmp(a, b):\n return int(a + b) - int(b + a)\nstrs = [str(i) for i in nums]\nstrs.sort(key=cmp_to_key(str_cmp), reverse=True)\nreturn str(int(''.join(strs)))",
"if len(nums) == 1:\n return str(nums[0])\nnum_strs = [str(i) for i in nums]\nmaxlen = len(max(num_strs, key=lambda _s: len(_s)))\npairs = [... | <|body_start_0|>
def str_cmp(a, b):
return int(a + b) - int(b + a)
strs = [str(i) for i in nums]
strs.sort(key=cmp_to_key(str_cmp), reverse=True)
return str(int(''.join(strs)))
<|end_body_0|>
<|body_start_1|>
if len(nums) == 1:
return str(nums[0])
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def largestNumber(self, nums):
"""思路：直接定义比较规则就好"""
<|body_0|>
def largestNumber_2(self, nums):
"""思路：一开始的解法，比较 ugly，把所有的数字扩充成一样长度，之后再比较 :type nums: List[int] :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def str_cmp(a, b):
... | stack_v2_sparse_classes_36k_train_015654 | 2,552 | permissive | [
{
"docstring": "思路：直接定义比较规则就好",
"name": "largestNumber",
"signature": "def largestNumber(self, nums)"
},
{
"docstring": "思路：一开始的解法，比较 ugly，把所有的数字扩充成一样长度，之后再比较 :type nums: List[int] :rtype: str",
"name": "largestNumber_2",
"signature": "def largestNumber_2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008941 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestNumber(self, nums): 思路：直接定义比较规则就好
- def largestNumber_2(self, nums): 思路：一开始的解法，比较 ugly，把所有的数字扩充成一样长度，之后再比较 :type nums: List[int] :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestNumber(self, nums): 思路：直接定义比较规则就好
- def largestNumber_2(self, nums): 思路：一开始的解法，比较 ugly，把所有的数字扩充成一样长度，之后再比较 :type nums: List[int] :rtype: str
<|skeleton|>
class Soluti... | 3469a79c34b6c08ae52797c3974b49fbfa8cca51 | <|skeleton|>
class Solution:
def largestNumber(self, nums):
"""思路：直接定义比较规则就好"""
<|body_0|>
def largestNumber_2(self, nums):
"""思路：一开始的解法，比较 ugly，把所有的数字扩充成一样长度，之后再比较 :type nums: List[int] :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def largestNumber(self, nums):
"""思路：直接定义比较规则就好"""
def str_cmp(a, b):
return int(a + b) - int(b + a)
strs = [str(i) for i in nums]
strs.sort(key=cmp_to_key(str_cmp), reverse=True)
return str(int(''.join(strs)))
def largestNumber_2(self, nums):... | the_stack_v2_python_sparse | 剑指offer/33_SortArrayForMinNumber(把数组排成最小的数).py | Mark24Code/python_data_structures_and_algorithms | train | 1 | |
c5e0dad4cc0eed51beef649c18f3b2d6113f20b9 | [
"self.n_kernels = n_kernels\nself.n_strides = n_strides\nself.norm_type = normalization\nself.activation_type = activation\nself.kernel_init = tf.random_normal_initializer(0.0, 0.02)",
"activation = Conv2D(filters=n_filters, kernel_size=self.n_kernels, strides=self.n_strides, padding='same', kernel_initializer=se... | <|body_start_0|>
self.n_kernels = n_kernels
self.n_strides = n_strides
self.norm_type = normalization
self.activation_type = activation
self.kernel_init = tf.random_normal_initializer(0.0, 0.02)
<|end_body_0|>
<|body_start_1|>
activation = Conv2D(filters=n_filters, kerne... | Class for the Down Convolution block for Unet. | DownConvBlock | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DownConvBlock:
"""Class for the Down Convolution block for Unet."""
def __init__(self, n_kernels, n_strides, activation, normalization):
"""Initialize the Down Convolution Block. Args: n_kernels (int): Number of kernels for Conv2D. n_strides (int): Stride size. activation (str): Type... | stack_v2_sparse_classes_36k_train_015655 | 11,636 | no_license | [
{
"docstring": "Initialize the Down Convolution Block. Args: n_kernels (int): Number of kernels for Conv2D. n_strides (int): Stride size. activation (str): Type of activation layer to use. normalization (str): Type of normalization layer to use.",
"name": "__init__",
"signature": "def __init__(self, n_k... | 2 | stack_v2_sparse_classes_30k_train_015343 | Implement the Python class `DownConvBlock` described below.
Class description:
Class for the Down Convolution block for Unet.
Method signatures and docstrings:
- def __init__(self, n_kernels, n_strides, activation, normalization): Initialize the Down Convolution Block. Args: n_kernels (int): Number of kernels for Con... | Implement the Python class `DownConvBlock` described below.
Class description:
Class for the Down Convolution block for Unet.
Method signatures and docstrings:
- def __init__(self, n_kernels, n_strides, activation, normalization): Initialize the Down Convolution Block. Args: n_kernels (int): Number of kernels for Con... | 1b953d87968dac46ebbc9b1d5c254ea9493ee328 | <|skeleton|>
class DownConvBlock:
"""Class for the Down Convolution block for Unet."""
def __init__(self, n_kernels, n_strides, activation, normalization):
"""Initialize the Down Convolution Block. Args: n_kernels (int): Number of kernels for Conv2D. n_strides (int): Stride size. activation (str): Type... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DownConvBlock:
"""Class for the Down Convolution block for Unet."""
def __init__(self, n_kernels, n_strides, activation, normalization):
"""Initialize the Down Convolution Block. Args: n_kernels (int): Number of kernels for Conv2D. n_strides (int): Stride size. activation (str): Type of activatio... | the_stack_v2_python_sparse | fmlwright/trainer/neural_networks/blocks.py | rgresia-umd/fml-wright | train | 0 |
1b9080a11beb1b3863b7e0dc95b160c886bc6207 | [
"self.id = id\nself.generator = generators[0]()\nself.effect_1 = effects[0]()\nself.effect_2 = effects[0]()\nself.effect_3 = effects[0]()\nlogging.info('Channel ' + str(self.id) + ' initialised')",
"self.generator = generators[int(settings[0])]()\nself.effect_1 = effects[int(settings[1])]()\nself.effect_2 = effec... | <|body_start_0|>
self.id = id
self.generator = generators[0]()
self.effect_1 = effects[0]()
self.effect_2 = effects[0]()
self.effect_3 = effects[0]()
logging.info('Channel ' + str(self.id) + ' initialised')
<|end_body_0|>
<|body_start_1|>
self.generator = generat... | Class for a channel | class_channel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class class_channel:
"""Class for a channel"""
def __init__(self, id=0):
"""initialises a channel by calling g_blank and e_blank"""
<|body_0|>
def set_settings(self, settings):
"""sets the settings of a channel"""
<|body_1|>
def render_frame(self, framecou... | stack_v2_sparse_classes_36k_train_015656 | 1,154 | no_license | [
{
"docstring": "initialises a channel by calling g_blank and e_blank",
"name": "__init__",
"signature": "def __init__(self, id=0)"
},
{
"docstring": "sets the settings of a channel",
"name": "set_settings",
"signature": "def set_settings(self, settings)"
},
{
"docstring": "render... | 3 | null | Implement the Python class `class_channel` described below.
Class description:
Class for a channel
Method signatures and docstrings:
- def __init__(self, id=0): initialises a channel by calling g_blank and e_blank
- def set_settings(self, settings): sets the settings of a channel
- def render_frame(self, framecounter... | Implement the Python class `class_channel` described below.
Class description:
Class for a channel
Method signatures and docstrings:
- def __init__(self, id=0): initialises a channel by calling g_blank and e_blank
- def set_settings(self, settings): sets the settings of a channel
- def render_frame(self, framecounter... | 29c0a54f8c8306c3d8154146037dab221d4c9419 | <|skeleton|>
class class_channel:
"""Class for a channel"""
def __init__(self, id=0):
"""initialises a channel by calling g_blank and e_blank"""
<|body_0|>
def set_settings(self, settings):
"""sets the settings of a channel"""
<|body_1|>
def render_frame(self, framecou... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class class_channel:
"""Class for a channel"""
def __init__(self, id=0):
"""initialises a channel by calling g_blank and e_blank"""
self.id = id
self.generator = generators[0]()
self.effect_1 = effects[0]()
self.effect_2 = effects[0]()
self.effect_3 = effects[0](... | the_stack_v2_python_sparse | channel.py | TillSchlemmermeier/l3d-controller-software | train | 3 |
6c41c3664c97b94a9784bb3a8a818a6be334354b | [
"if not p and (not q):\n return True\nif p == None and q != None:\n return False\nif p != None and q == None:\n return False\nif p.val != q.val:\n return False\nreturn self.isSameTree(p.left, q.left) and self.isSameTree(p.right, q.right)",
"def InOrder(root):\n if not root:\n return [None]\n... | <|body_start_0|>
if not p and (not q):
return True
if p == None and q != None:
return False
if p != None and q == None:
return False
if p.val != q.val:
return False
return self.isSameTree(p.left, q.left) and self.isSameTree(p.right,... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSameTree(self, p: TreeNode, q: TreeNode) -> bool:
"""递归法"""
<|body_0|>
def isSameTree_1(self, p: TreeNode, q: TreeNode) -> bool:
"""遍历法 颜色标记法会超时"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not p and (not q):
return... | stack_v2_sparse_classes_36k_train_015657 | 1,054 | no_license | [
{
"docstring": "递归法",
"name": "isSameTree",
"signature": "def isSameTree(self, p: TreeNode, q: TreeNode) -> bool"
},
{
"docstring": "遍历法 颜色标记法会超时",
"name": "isSameTree_1",
"signature": "def isSameTree_1(self, p: TreeNode, q: TreeNode) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_015251 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSameTree(self, p: TreeNode, q: TreeNode) -> bool: 递归法
- def isSameTree_1(self, p: TreeNode, q: TreeNode) -> bool: 遍历法 颜色标记法会超时 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSameTree(self, p: TreeNode, q: TreeNode) -> bool: 递归法
- def isSameTree_1(self, p: TreeNode, q: TreeNode) -> bool: 遍历法 颜色标记法会超时
<|skeleton|>
class Solution:
def isSame... | 3508e1ce089131b19603c3206aab4cf43023bb19 | <|skeleton|>
class Solution:
def isSameTree(self, p: TreeNode, q: TreeNode) -> bool:
"""递归法"""
<|body_0|>
def isSameTree_1(self, p: TreeNode, q: TreeNode) -> bool:
"""遍历法 颜色标记法会超时"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isSameTree(self, p: TreeNode, q: TreeNode) -> bool:
"""递归法"""
if not p and (not q):
return True
if p == None and q != None:
return False
if p != None and q == None:
return False
if p.val != q.val:
return Fals... | the_stack_v2_python_sparse | algorithm/leetcode/tree/11-相同的树.py | lxconfig/UbuntuCode_bak | train | 0 | |
7825bce2939e5497b80a55388ca1a44e6ff11b3c | [
"conn, cursor = get_db_cursor()\nbuild = 'toy_build'\ne_dict = init_refs.make_edge_dict(cursor, build)\nconn.close()\nassert len(e_dict.keys()) == 31",
"conn, cursor = get_db_cursor()\nbuild = 'toy_build'\ne_dict = init_refs.make_edge_dict(cursor, build, chrom='chr1', start=1, end=1000)\nconn.close()\nassert len(... | <|body_start_0|>
conn, cursor = get_db_cursor()
build = 'toy_build'
e_dict = init_refs.make_edge_dict(cursor, build)
conn.close()
assert len(e_dict.keys()) == 31
<|end_body_0|>
<|body_start_1|>
conn, cursor = get_db_cursor()
build = 'toy_build'
e_dict = i... | TestEdgeDict | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestEdgeDict:
def test_all_edges(self):
"""Get all edges in the database"""
<|body_0|>
def test_interval_edges(self):
"""Get all edges in the database within the specified interval"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
conn, cursor = get_d... | stack_v2_sparse_classes_36k_train_015658 | 1,153 | permissive | [
{
"docstring": "Get all edges in the database",
"name": "test_all_edges",
"signature": "def test_all_edges(self)"
},
{
"docstring": "Get all edges in the database within the specified interval",
"name": "test_interval_edges",
"signature": "def test_interval_edges(self)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000462 | Implement the Python class `TestEdgeDict` described below.
Class description:
Implement the TestEdgeDict class.
Method signatures and docstrings:
- def test_all_edges(self): Get all edges in the database
- def test_interval_edges(self): Get all edges in the database within the specified interval | Implement the Python class `TestEdgeDict` described below.
Class description:
Implement the TestEdgeDict class.
Method signatures and docstrings:
- def test_all_edges(self): Get all edges in the database
- def test_interval_edges(self): Get all edges in the database within the specified interval
<|skeleton|>
class T... | 8014faed5f982e5e106ec05239e47d65878e76c3 | <|skeleton|>
class TestEdgeDict:
def test_all_edges(self):
"""Get all edges in the database"""
<|body_0|>
def test_interval_edges(self):
"""Get all edges in the database within the specified interval"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestEdgeDict:
def test_all_edges(self):
"""Get all edges in the database"""
conn, cursor = get_db_cursor()
build = 'toy_build'
e_dict = init_refs.make_edge_dict(cursor, build)
conn.close()
assert len(e_dict.keys()) == 31
def test_interval_edges(self):
... | the_stack_v2_python_sparse | testing_suite/test_make_edge_dict.py | kopardev/TALON | train | 0 | |
48b9789fb5808e7e8fe03e2b8c2c7fa1f47145ce | [
"response = requests.get(cls.search_url, params=query, allow_redirects=False)\n_response_error_handler(response.status_code, response.text)\nreturn response.json()",
"res: [Dict] = []\nfor page_num in range(num_of_pages):\n query['page'] = page_num\n page = cls.execute_query(query)\n for dct in page['dat... | <|body_start_0|>
response = requests.get(cls.search_url, params=query, allow_redirects=False)
_response_error_handler(response.status_code, response.text)
return response.json()
<|end_body_0|>
<|body_start_1|>
res: [Dict] = []
for page_num in range(num_of_pages):
que... | Rapid7 vulnerability database Search API ETL utilities. | Search | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Search:
"""Rapid7 vulnerability database Search API ETL utilities."""
def execute_query(cls, query: Dict) -> Dict:
"""Executes API query by sending a request to API and extracting the result as a python data structure :param query: free form text :return: Data as dictionary"""
... | stack_v2_sparse_classes_36k_train_015659 | 5,775 | permissive | [
{
"docstring": "Executes API query by sending a request to API and extracting the result as a python data structure :param query: free form text :return: Data as dictionary",
"name": "execute_query",
"signature": "def execute_query(cls, query: Dict) -> Dict"
},
{
"docstring": "Iterates over avai... | 4 | null | Implement the Python class `Search` described below.
Class description:
Rapid7 vulnerability database Search API ETL utilities.
Method signatures and docstrings:
- def execute_query(cls, query: Dict) -> Dict: Executes API query by sending a request to API and extracting the result as a python data structure :param qu... | Implement the Python class `Search` described below.
Class description:
Rapid7 vulnerability database Search API ETL utilities.
Method signatures and docstrings:
- def execute_query(cls, query: Dict) -> Dict: Executes API query by sending a request to API and extracting the result as a python data structure :param qu... | 718d15ca36c57231bb89df0aebc53d0210db400c | <|skeleton|>
class Search:
"""Rapid7 vulnerability database Search API ETL utilities."""
def execute_query(cls, query: Dict) -> Dict:
"""Executes API query by sending a request to API and extracting the result as a python data structure :param query: free form text :return: Data as dictionary"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Search:
"""Rapid7 vulnerability database Search API ETL utilities."""
def execute_query(cls, query: Dict) -> Dict:
"""Executes API query by sending a request to API and extracting the result as a python data structure :param query: free form text :return: Data as dictionary"""
response = ... | the_stack_v2_python_sparse | plugins/rapid7_vulndb/komand_rapid7_vulndb/util/extract.py | rapid7/insightconnect-plugins | train | 61 |
9f894e08ff4ef54a9b8346171a857b123e3e2b02 | [
"max_1 = 0\nsets = set(nums)\nwhile sets:\n count = 1\n a = list(sets)[0]\n sets.remove(a)\n i = a - 1\n while sets and i in sets:\n sets.remove(i)\n count += 1\n i -= 1\n j = a + 1\n while sets and j in sets:\n sets.remove(j)\n count += 1\n j += 1\n ... | <|body_start_0|>
max_1 = 0
sets = set(nums)
while sets:
count = 1
a = list(sets)[0]
sets.remove(a)
i = a - 1
while sets and i in sets:
sets.remove(i)
count += 1
i -= 1
j = a + ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestConsecutive(self, nums):
""":type nums: List[int] :rtype: int 39ms"""
<|body_0|>
def longestConsecutive_1(self, nums):
""":type nums: List[int] :rtype: int 35ms"""
<|body_1|>
def longestConsecutive_2(self, nums):
"""35ms :par... | stack_v2_sparse_classes_36k_train_015660 | 1,951 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int 39ms",
"name": "longestConsecutive",
"signature": "def longestConsecutive(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int 35ms",
"name": "longestConsecutive_1",
"signature": "def longestConsecutive_1(self, nums)"
},
... | 3 | stack_v2_sparse_classes_30k_train_016524 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestConsecutive(self, nums): :type nums: List[int] :rtype: int 39ms
- def longestConsecutive_1(self, nums): :type nums: List[int] :rtype: int 35ms
- def longestConsecutive... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestConsecutive(self, nums): :type nums: List[int] :rtype: int 39ms
- def longestConsecutive_1(self, nums): :type nums: List[int] :rtype: int 35ms
- def longestConsecutive... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def longestConsecutive(self, nums):
""":type nums: List[int] :rtype: int 39ms"""
<|body_0|>
def longestConsecutive_1(self, nums):
""":type nums: List[int] :rtype: int 35ms"""
<|body_1|>
def longestConsecutive_2(self, nums):
"""35ms :par... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestConsecutive(self, nums):
""":type nums: List[int] :rtype: int 39ms"""
max_1 = 0
sets = set(nums)
while sets:
count = 1
a = list(sets)[0]
sets.remove(a)
i = a - 1
while sets and i in sets:
... | the_stack_v2_python_sparse | LongestConsecutiveSequence_HARD_128.py | 953250587/leetcode-python | train | 2 | |
bbb42a497b0e963b76753a2f3e3c564521062add | [
"res = ''\nif root is not None:\n queue = deque()\n queue.append((root, 1))\n index_to_el_dict = {}\n while queue:\n node, index = queue.popleft()\n if node:\n index_to_el_dict[index] = node.val\n queue.append((node.left, 2 * index))\n queue.append((node.ri... | <|body_start_0|>
res = ''
if root is not None:
queue = deque()
queue.append((root, 1))
index_to_el_dict = {}
while queue:
node, index = queue.popleft()
if node:
index_to_el_dict[index] = node.val
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_015661 | 3,532 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_010285 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 120ea2cd7a29e173d1a8a87eac85b2939c03c06d | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
res = ''
if root is not None:
queue = deque()
queue.append((root, 1))
index_to_el_dict = {}
while queue:
node, ind... | the_stack_v2_python_sparse | tree/297_serialize_and_deserialize_binary_tree.py | tsimafeip/leetcode | train | 0 | |
3106cb9233ae0604f22467633ca8f556cb0207f5 | [
"self._r1 = r1\nself._r2 = r2\nself._direction = direction",
"delev, dazim = self._direction\nr1 = self._r1\nr2 = self._r2\nx1 = -r1 * np.cos(elev) * np.cos(azim)\ny1 = -r1 * np.cos(elev) * np.sin(azim)\nz1 = r1 * np.sin(elev)\ndx1 = -np.cos(delev) * np.cos(dazim)\ndy1 = -np.cos(delev) * np.sin(dazim)\ndz1 = np.s... | <|body_start_0|>
self._r1 = r1
self._r2 = r2
self._direction = direction
<|end_body_0|>
<|body_start_1|>
delev, dazim = self._direction
r1 = self._r1
r2 = self._r2
x1 = -r1 * np.cos(elev) * np.cos(azim)
y1 = -r1 * np.cos(elev) * np.sin(azim)
z1 = ... | SphereToCylinderMap | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SphereToCylinderMap:
def __init__(self, r1, r2, direction):
"""r1: sphere radius r2: cylinder radius direction: can be a tuple or a list of 2 elements elevation, azimuth"""
<|body_0|>
def map(self, elev, azim):
"""z = 0 corresponds to azimuth 0 or pi z = max at azimu... | stack_v2_sparse_classes_36k_train_015662 | 16,243 | permissive | [
{
"docstring": "r1: sphere radius r2: cylinder radius direction: can be a tuple or a list of 2 elements elevation, azimuth",
"name": "__init__",
"signature": "def __init__(self, r1, r2, direction)"
},
{
"docstring": "z = 0 corresponds to azimuth 0 or pi z = max at azimuth -pi/2 axes were chosen ... | 3 | stack_v2_sparse_classes_30k_train_010820 | Implement the Python class `SphereToCylinderMap` described below.
Class description:
Implement the SphereToCylinderMap class.
Method signatures and docstrings:
- def __init__(self, r1, r2, direction): r1: sphere radius r2: cylinder radius direction: can be a tuple or a list of 2 elements elevation, azimuth
- def map(... | Implement the Python class `SphereToCylinderMap` described below.
Class description:
Implement the SphereToCylinderMap class.
Method signatures and docstrings:
- def __init__(self, r1, r2, direction): r1: sphere radius r2: cylinder radius direction: can be a tuple or a list of 2 elements elevation, azimuth
- def map(... | fdab351e6c5530c8f051193158856ba6ef11d715 | <|skeleton|>
class SphereToCylinderMap:
def __init__(self, r1, r2, direction):
"""r1: sphere radius r2: cylinder radius direction: can be a tuple or a list of 2 elements elevation, azimuth"""
<|body_0|>
def map(self, elev, azim):
"""z = 0 corresponds to azimuth 0 or pi z = max at azimu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SphereToCylinderMap:
def __init__(self, r1, r2, direction):
"""r1: sphere radius r2: cylinder radius direction: can be a tuple or a list of 2 elements elevation, azimuth"""
self._r1 = r1
self._r2 = r2
self._direction = direction
def map(self, elev, azim):
"""z = 0 ... | the_stack_v2_python_sparse | retina/screen/map/mapimpl.py | neurokernel/retina | train | 5 | |
40b1e63017e6d987de88ee28b1e8ad47c400bbef | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn PlannerTaskDetails()",
"from .entity import Entity\nfrom .planner_checklist_items import PlannerChecklistItems\nfrom .planner_external_references import PlannerExternalReferences\nfrom .planner_preview_type import PlannerPreviewType\nf... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return PlannerTaskDetails()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .planner_checklist_items import PlannerChecklistItems
from .planner_external_references impor... | PlannerTaskDetails | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlannerTaskDetails:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerTaskDetails:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the obje... | stack_v2_sparse_classes_36k_train_015663 | 3,591 | 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: PlannerTaskDetails",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_... | 3 | null | Implement the Python class `PlannerTaskDetails` described below.
Class description:
Implement the PlannerTaskDetails class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerTaskDetails: Creates a new instance of the appropriate class based on disc... | Implement the Python class `PlannerTaskDetails` described below.
Class description:
Implement the PlannerTaskDetails class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerTaskDetails: Creates a new instance of the appropriate class based on disc... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class PlannerTaskDetails:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerTaskDetails:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the obje... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlannerTaskDetails:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PlannerTaskDetails:
"""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: Pl... | the_stack_v2_python_sparse | msgraph/generated/models/planner_task_details.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
4cb1f4fc2217cad4fc531b53081abd7590cf9906 | [
"self.capacity = capacity\nself.c_size = 0\nself.history = []\nself._data = {}",
"if self._data.get(key, None) is None:\n return -1\nelse:\n self.history.remove(key)\n self.history.append(key)\nreturn self._data.get(key)",
"if self._data.get(key, -1) == -1:\n if self.c_size == self.capacity:\n ... | <|body_start_0|>
self.capacity = capacity
self.c_size = 0
self.history = []
self._data = {}
<|end_body_0|>
<|body_start_1|>
if self._data.get(key, None) is None:
return -1
else:
self.history.remove(key)
self.history.append(key)
... | LRUCache_On_List_sol | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache_On_List_sol:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k_train_015664 | 4,563 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: nothing",
"name": "set",
"sig... | 3 | stack_v2_sparse_classes_30k_train_010335 | Implement the Python class `LRUCache_On_List_sol` described below.
Class description:
Implement the LRUCache_On_List_sol class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: n... | Implement the Python class `LRUCache_On_List_sol` described below.
Class description:
Implement the LRUCache_On_List_sol class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: n... | d308e0e41c288f23a846b8505e572943d30b1392 | <|skeleton|>
class LRUCache_On_List_sol:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache_On_List_sol:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.c_size = 0
self.history = []
self._data = {}
def get(self, key):
""":rtype: int"""
if self._data.get(key, None) is None:
return -... | the_stack_v2_python_sparse | python/146_LRU_Cache.py | HankerZheng/LeetCode-Problems | train | 2 | |
8443889d7c9b25c593b4fdd14bbcb8cae5e2e6f8 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn DeviceComplianceActionItem()",
"from .device_compliance_action_type import DeviceComplianceActionType\nfrom .entity import Entity\nfrom .device_compliance_action_type import DeviceComplianceActionType\nfrom .entity import Entity\nfield... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return DeviceComplianceActionItem()
<|end_body_0|>
<|body_start_1|>
from .device_compliance_action_type import DeviceComplianceActionType
from .entity import Entity
from .device_complia... | Scheduled Action Configuration | DeviceComplianceActionItem | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeviceComplianceActionItem:
"""Scheduled Action Configuration"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceComplianceActionItem:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node t... | stack_v2_sparse_classes_36k_train_015665 | 3,346 | 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: DeviceComplianceActionItem",
"name": "create_from_discriminator_value",
"signature": "def create_from_discri... | 3 | stack_v2_sparse_classes_30k_train_000449 | Implement the Python class `DeviceComplianceActionItem` described below.
Class description:
Scheduled Action Configuration
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceComplianceActionItem: Creates a new instance of the appropriate class based ... | Implement the Python class `DeviceComplianceActionItem` described below.
Class description:
Scheduled Action Configuration
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceComplianceActionItem: Creates a new instance of the appropriate class based ... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class DeviceComplianceActionItem:
"""Scheduled Action Configuration"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceComplianceActionItem:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeviceComplianceActionItem:
"""Scheduled Action Configuration"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceComplianceActionItem:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read... | the_stack_v2_python_sparse | msgraph/generated/models/device_compliance_action_item.py | microsoftgraph/msgraph-sdk-python | train | 135 |
0c1b699d18933ca76dcc0358803abd2a50a8c082 | [
"feats = self.hparams.compute_stft(wavs)\nfeats = self.hparams.spectral_magnitude(feats)\nreturn torch.log1p(feats)",
"noisy = noisy.to(self.device)\nnoisy_features = self.compute_features(noisy)\nif lengths is not None:\n mask = self.modules.enhance_model(noisy_features, lengths=lengths)\nelse:\n mask = se... | <|body_start_0|>
feats = self.hparams.compute_stft(wavs)
feats = self.hparams.spectral_magnitude(feats)
return torch.log1p(feats)
<|end_body_0|>
<|body_start_1|>
noisy = noisy.to(self.device)
noisy_features = self.compute_features(noisy)
if lengths is not None:
... | A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfixture("tmpdir") >>> enhancer = SpectralMaskEnhanceme... | SpectralMaskEnhancement | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference",
"GPL-1.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SpectralMaskEnhancement:
"""A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfix... | stack_v2_sparse_classes_36k_train_015666 | 35,100 | permissive | [
{
"docstring": "Compute the log spectral magnitude features for masking. Arguments --------- wavs : torch.tensor A batch of waveforms to convert to log spectral mags.",
"name": "compute_features",
"signature": "def compute_features(self, wavs)"
},
{
"docstring": "Enhance a batch of noisy wavefor... | 3 | stack_v2_sparse_classes_30k_train_010801 | Implement the Python class `SpectralMaskEnhancement` described below.
Class description:
A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbra... | Implement the Python class `SpectralMaskEnhancement` described below.
Class description:
A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbra... | 92acc188d3a0f634de58463b6676e70df83ef808 | <|skeleton|>
class SpectralMaskEnhancement:
"""A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfix... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SpectralMaskEnhancement:
"""A ready-to-use model for speech enhancement. Arguments --------- See ``Pretrained``. Example ------- >>> import torchaudio >>> from speechbrain.pretrained import SpectralMaskEnhancement >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfixture("tmpdir"... | the_stack_v2_python_sparse | ACL_PyTorch/contrib/audio/tdnn/interfaces.py | Ascend/ModelZoo-PyTorch | train | 23 |
9ef58a2323237dd6cdbddb991ff2d0980acc14b8 | [
"super(IperfSession, self).__init__()\nself.iperf_test = iperf_test\nself.nodes = nodes\nself.filename_base = filename_base\nself.tpc = tpc\nself._to_node_expression = None\nself._from_node_expression = None\nself.poll = None\nreturn",
"if self._from_node_expression is None:\n self._from_node_expression = re.c... | <|body_start_0|>
super(IperfSession, self).__init__()
self.iperf_test = iperf_test
self.nodes = nodes
self.filename_base = filename_base
self.tpc = tpc
self._to_node_expression = None
self._from_node_expression = None
self.poll = None
return
<|end_... | A bundler of nodes and the iperftest | IperfSession | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IperfSession:
"""A bundler of nodes and the iperftest"""
def __init__(self, iperf_test, nodes, tpc, filename_base=None):
"""IperfSession Constructor :param: - `iperf_test`: a bundle of parameters and storage - `nodes`: id:device pairs - `tpc`: traffic PC device - `filename_base`: An ... | stack_v2_sparse_classes_36k_train_015667 | 4,629 | permissive | [
{
"docstring": "IperfSession Constructor :param: - `iperf_test`: a bundle of parameters and storage - `nodes`: id:device pairs - `tpc`: traffic PC device - `filename_base`: An optional string to add to the filename",
"name": "__init__",
"signature": "def __init__(self, iperf_test, nodes, tpc, filename_b... | 6 | null | Implement the Python class `IperfSession` described below.
Class description:
A bundler of nodes and the iperftest
Method signatures and docstrings:
- def __init__(self, iperf_test, nodes, tpc, filename_base=None): IperfSession Constructor :param: - `iperf_test`: a bundle of parameters and storage - `nodes`: id:devic... | Implement the Python class `IperfSession` described below.
Class description:
A bundler of nodes and the iperftest
Method signatures and docstrings:
- def __init__(self, iperf_test, nodes, tpc, filename_base=None): IperfSession Constructor :param: - `iperf_test`: a bundle of parameters and storage - `nodes`: id:devic... | b4d1c77e1d611fe2b30768b42bdc7493afb0ea95 | <|skeleton|>
class IperfSession:
"""A bundler of nodes and the iperftest"""
def __init__(self, iperf_test, nodes, tpc, filename_base=None):
"""IperfSession Constructor :param: - `iperf_test`: a bundle of parameters and storage - `nodes`: id:device pairs - `tpc`: traffic PC device - `filename_base`: An ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IperfSession:
"""A bundler of nodes and the iperftest"""
def __init__(self, iperf_test, nodes, tpc, filename_base=None):
"""IperfSession Constructor :param: - `iperf_test`: a bundle of parameters and storage - `nodes`: id:device pairs - `tpc`: traffic PC device - `filename_base`: An optional stri... | the_stack_v2_python_sparse | apetools/tools/iperfsession.py | russell-n/oldape | train | 0 |
54b887145ecf4fba69389346118b7358b4dc77cb | [
"super(FeaturePyramid, self).__init__()\nif levels is None:\n levels = [3, 4, 5, 6, 7]\nself.levels = levels\nif resnet == 18:\n self.backbone = resnet18(pretrained)\nelif resnet == 34:\n self.backbone = resnet34(pretrained)\nelif resnet == 50:\n self.backbone = resnet50(pretrained)\nelif resnet == 101:... | <|body_start_0|>
super(FeaturePyramid, self).__init__()
if levels is None:
levels = [3, 4, 5, 6, 7]
self.levels = levels
if resnet == 18:
self.backbone = resnet18(pretrained)
elif resnet == 34:
self.backbone = resnet34(pretrained)
elif ... | Feature pyramid network. It takes the natural architecture of a convolutional network to generate a pyramid of features with little extra effort. It merges the outputs of each one of the ResNet layers with the previous one to give more semantically meaning (the deepest the layer the more semantic meaning it has) and th... | FeaturePyramid | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeaturePyramid:
"""Feature pyramid network. It takes the natural architecture of a convolutional network to generate a pyramid of features with little extra effort. It merges the outputs of each one of the ResNet layers with the previous one to give more semantically meaning (the deepest the laye... | stack_v2_sparse_classes_36k_train_015668 | 27,489 | no_license | [
{
"docstring": "Initialize the network. It init a ResNet with the given depth and use 'features' as the number of channels for each feature map. Args: resnet (int): Indicates the depth of the resnet backbone. features (int): Indicates the depth (number of channels) of each feature map of the pyramid. levels (li... | 2 | null | Implement the Python class `FeaturePyramid` described below.
Class description:
Feature pyramid network. It takes the natural architecture of a convolutional network to generate a pyramid of features with little extra effort. It merges the outputs of each one of the ResNet layers with the previous one to give more sem... | Implement the Python class `FeaturePyramid` described below.
Class description:
Feature pyramid network. It takes the natural architecture of a convolutional network to generate a pyramid of features with little extra effort. It merges the outputs of each one of the ResNet layers with the previous one to give more sem... | a22aa5b00369c2692bf4fa537bce20144d14d5cb | <|skeleton|>
class FeaturePyramid:
"""Feature pyramid network. It takes the natural architecture of a convolutional network to generate a pyramid of features with little extra effort. It merges the outputs of each one of the ResNet layers with the previous one to give more semantically meaning (the deepest the laye... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeaturePyramid:
"""Feature pyramid network. It takes the natural architecture of a convolutional network to generate a pyramid of features with little extra effort. It merges the outputs of each one of the ResNet layers with the previous one to give more semantically meaning (the deepest the layer the more se... | the_stack_v2_python_sparse | torchsight/models/retinanet.py | SetaSouto/torchsight | train | 2 |
beb0bec6f5df14be6ea91c0e06c3c2b13193bb36 | [
"self._handshakeMD5 = hashlib.md5()\nself._handshakeSHA = hashlib.sha1()\nself._handshakeSHA224 = hashlib.sha224()\nself._handshakeSHA256 = hashlib.sha256()\nself._handshakeSHA384 = hashlib.sha384()\nself._handshakeSHA512 = hashlib.sha512()\nself._handshake_buffer = bytearray()",
"text = compat26Str(data)\nself._... | <|body_start_0|>
self._handshakeMD5 = hashlib.md5()
self._handshakeSHA = hashlib.sha1()
self._handshakeSHA224 = hashlib.sha224()
self._handshakeSHA256 = hashlib.sha256()
self._handshakeSHA384 = hashlib.sha384()
self._handshakeSHA512 = hashlib.sha512()
self._handsh... | Store and calculate necessary hashes for handshake protocol Calculates message digests of messages exchanged in handshake protocol of SSLv3 and TLS. | HandshakeHashes | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HandshakeHashes:
"""Store and calculate necessary hashes for handshake protocol Calculates message digests of messages exchanged in handshake protocol of SSLv3 and TLS."""
def __init__(self):
"""Create instance"""
<|body_0|>
def update(self, data):
"""Add `data` ... | stack_v2_sparse_classes_36k_train_015669 | 4,137 | permissive | [
{
"docstring": "Create instance",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Add `data` to hash input. :param bytearray data: serialized TLS handshake message",
"name": "update",
"signature": "def update(self, data)"
},
{
"docstring": "Calculate and ... | 5 | null | Implement the Python class `HandshakeHashes` described below.
Class description:
Store and calculate necessary hashes for handshake protocol Calculates message digests of messages exchanged in handshake protocol of SSLv3 and TLS.
Method signatures and docstrings:
- def __init__(self): Create instance
- def update(sel... | Implement the Python class `HandshakeHashes` described below.
Class description:
Store and calculate necessary hashes for handshake protocol Calculates message digests of messages exchanged in handshake protocol of SSLv3 and TLS.
Method signatures and docstrings:
- def __init__(self): Create instance
- def update(sel... | 541f58da464296001109f9cfbb879256957b3819 | <|skeleton|>
class HandshakeHashes:
"""Store and calculate necessary hashes for handshake protocol Calculates message digests of messages exchanged in handshake protocol of SSLv3 and TLS."""
def __init__(self):
"""Create instance"""
<|body_0|>
def update(self, data):
"""Add `data` ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HandshakeHashes:
"""Store and calculate necessary hashes for handshake protocol Calculates message digests of messages exchanged in handshake protocol of SSLv3 and TLS."""
def __init__(self):
"""Create instance"""
self._handshakeMD5 = hashlib.md5()
self._handshakeSHA = hashlib.sha... | the_stack_v2_python_sparse | code/default/lib/noarch/tlslite/handshakehashes.py | XX-net/XX-Net | train | 40,250 |
f8c75968f4903ed95adf9b87115d353d39277f83 | [
"WAS_ZERO = 'zero'\n\ndef mark(val):\n return WAS_ZERO if val == 0 else 0\nfor row in range(len(matrix)):\n for value in matrix[row]:\n if value == 0:\n matrix[row] = [mark(v) for v in matrix[row]]\n break\nfor row in range(len(matrix)):\n for col, value in enumerate(matrix[row... | <|body_start_0|>
WAS_ZERO = 'zero'
def mark(val):
return WAS_ZERO if val == 0 else 0
for row in range(len(matrix)):
for value in matrix[row]:
if value == 0:
matrix[row] = [mark(v) for v in matrix[row]]
break
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def setZeroes(self, matrix: List[List[int]]) -> None:
"""Do not return anything, modify matrix in-place instead."""
<|body_0|>
def setZeroes2(self, matrix: List[List[int]]) -> None:
"""Do not return anything, modify matrix in-place instead."""
<|bod... | stack_v2_sparse_classes_36k_train_015670 | 3,470 | no_license | [
{
"docstring": "Do not return anything, modify matrix in-place instead.",
"name": "setZeroes",
"signature": "def setZeroes(self, matrix: List[List[int]]) -> None"
},
{
"docstring": "Do not return anything, modify matrix in-place instead.",
"name": "setZeroes2",
"signature": "def setZeroe... | 3 | stack_v2_sparse_classes_30k_test_000591 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def setZeroes(self, matrix: List[List[int]]) -> None: Do not return anything, modify matrix in-place instead.
- def setZeroes2(self, matrix: List[List[int]]) -> None: Do not retu... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def setZeroes(self, matrix: List[List[int]]) -> None: Do not return anything, modify matrix in-place instead.
- def setZeroes2(self, matrix: List[List[int]]) -> None: Do not retu... | 77fcd5520b018bd96dd253300b66c6d5dc06fa05 | <|skeleton|>
class Solution:
def setZeroes(self, matrix: List[List[int]]) -> None:
"""Do not return anything, modify matrix in-place instead."""
<|body_0|>
def setZeroes2(self, matrix: List[List[int]]) -> None:
"""Do not return anything, modify matrix in-place instead."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def setZeroes(self, matrix: List[List[int]]) -> None:
"""Do not return anything, modify matrix in-place instead."""
WAS_ZERO = 'zero'
def mark(val):
return WAS_ZERO if val == 0 else 0
for row in range(len(matrix)):
for value in matrix[row]:
... | the_stack_v2_python_sparse | leetcode/set-matrix-zeroes.py | murbar/code-challenges | train | 0 | |
9bea945aefe2568c360c83b43c0e59a3f5fafed0 | [
"if not self.options.physical.user or not self.options.physical.password:\n raise CuckooCriticalError('Physical machine credentials are missing, please add it to the config file')\nfor machine in self.machines():\n if self._status(machine.label) != self.RUNNING:\n raise CuckooCriticalError('Physical ma... | <|body_start_0|>
if not self.options.physical.user or not self.options.physical.password:
raise CuckooCriticalError('Physical machine credentials are missing, please add it to the config file')
for machine in self.machines():
if self._status(machine.label) != self.RUNNING:
... | Manage physical sandboxes. | Physical | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Physical:
"""Manage physical sandboxes."""
def _initialize_check(self):
"""Ensures that credentials have been entered into the config file. @raise CuckooCriticalError: if no credentials were provided or if one or more physical machines are offline."""
<|body_0|>
def _get... | stack_v2_sparse_classes_36k_train_015671 | 5,469 | no_license | [
{
"docstring": "Ensures that credentials have been entered into the config file. @raise CuckooCriticalError: if no credentials were provided or if one or more physical machines are offline.",
"name": "_initialize_check",
"signature": "def _initialize_check(self)"
},
{
"docstring": "Retrieve all ... | 6 | stack_v2_sparse_classes_30k_train_000899 | Implement the Python class `Physical` described below.
Class description:
Manage physical sandboxes.
Method signatures and docstrings:
- def _initialize_check(self): Ensures that credentials have been entered into the config file. @raise CuckooCriticalError: if no credentials were provided or if one or more physical ... | Implement the Python class `Physical` described below.
Class description:
Manage physical sandboxes.
Method signatures and docstrings:
- def _initialize_check(self): Ensures that credentials have been entered into the config file. @raise CuckooCriticalError: if no credentials were provided or if one or more physical ... | 36434f6b8f80833b2328eb096ac239f7932a1eb3 | <|skeleton|>
class Physical:
"""Manage physical sandboxes."""
def _initialize_check(self):
"""Ensures that credentials have been entered into the config file. @raise CuckooCriticalError: if no credentials were provided or if one or more physical machines are offline."""
<|body_0|>
def _get... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Physical:
"""Manage physical sandboxes."""
def _initialize_check(self):
"""Ensures that credentials have been entered into the config file. @raise CuckooCriticalError: if no credentials were provided or if one or more physical machines are offline."""
if not self.options.physical.user or ... | the_stack_v2_python_sparse | modules/machinery/physical.py | open-nsm/dockoo-cuckoo | train | 5 |
35721b281e378d157b40921d19fa4301e55ff95d | [
"url = 'os-quota-sets/%s' % tenant_id\nif user_id:\n url += '?user_id=%s' % user_id\nresp, body = self.get(url)\nbody = json.loads(body)\nself.validate_response(schema.get_quota_set, resp, body)\nreturn rest_client.ResponseBody(resp, body)",
"url = 'os-quota-sets/%s/defaults' % tenant_id\nresp, body = self.get... | <|body_start_0|>
url = 'os-quota-sets/%s' % tenant_id
if user_id:
url += '?user_id=%s' % user_id
resp, body = self.get(url)
body = json.loads(body)
self.validate_response(schema.get_quota_set, resp, body)
return rest_client.ResponseBody(resp, body)
<|end_body_... | QuotasClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QuotasClient:
def show_quota_set(self, tenant_id, user_id=None):
"""List the quota set for a tenant."""
<|body_0|>
def show_default_quota_set(self, tenant_id):
"""List the default quota set for a tenant."""
<|body_1|>
def update_quota_set(self, tenant_id... | stack_v2_sparse_classes_36k_train_015672 | 2,700 | permissive | [
{
"docstring": "List the quota set for a tenant.",
"name": "show_quota_set",
"signature": "def show_quota_set(self, tenant_id, user_id=None)"
},
{
"docstring": "List the default quota set for a tenant.",
"name": "show_default_quota_set",
"signature": "def show_default_quota_set(self, ten... | 4 | stack_v2_sparse_classes_30k_train_006729 | Implement the Python class `QuotasClient` described below.
Class description:
Implement the QuotasClient class.
Method signatures and docstrings:
- def show_quota_set(self, tenant_id, user_id=None): List the quota set for a tenant.
- def show_default_quota_set(self, tenant_id): List the default quota set for a tenant... | Implement the Python class `QuotasClient` described below.
Class description:
Implement the QuotasClient class.
Method signatures and docstrings:
- def show_quota_set(self, tenant_id, user_id=None): List the quota set for a tenant.
- def show_default_quota_set(self, tenant_id): List the default quota set for a tenant... | 78c71b3bc74144ee5d2a77707d7f195b96ad09b4 | <|skeleton|>
class QuotasClient:
def show_quota_set(self, tenant_id, user_id=None):
"""List the quota set for a tenant."""
<|body_0|>
def show_default_quota_set(self, tenant_id):
"""List the default quota set for a tenant."""
<|body_1|>
def update_quota_set(self, tenant_id... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QuotasClient:
def show_quota_set(self, tenant_id, user_id=None):
"""List the quota set for a tenant."""
url = 'os-quota-sets/%s' % tenant_id
if user_id:
url += '?user_id=%s' % user_id
resp, body = self.get(url)
body = json.loads(body)
self.validate_r... | the_stack_v2_python_sparse | tempest/lib/services/compute/quotas_client.py | microsoft/LIS-Tempest | train | 1 | |
6f137be05acf7b2d58e24805acf1ad00abfe6b62 | [
"super().__init__(entry, controller, poolObject, **kwargs)\nself._bodies = set(poolObject[BODY_ATTR].split(' '))\nself._attr_icon = 'mdi:fire-circle'",
"for bodyObjnam in self._bodies:\n body = self._controller.model[bodyObjnam]\n if body[STATUS_ATTR] == 'ON' and body[HEATER_ATTR] == self._poolObject.objnam... | <|body_start_0|>
super().__init__(entry, controller, poolObject, **kwargs)
self._bodies = set(poolObject[BODY_ATTR].split(' '))
self._attr_icon = 'mdi:fire-circle'
<|end_body_0|>
<|body_start_1|>
for bodyObjnam in self._bodies:
body = self._controller.model[bodyObjnam]
... | Representation of a Heater binary sensor. | HeaterBinarySensor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HeaterBinarySensor:
"""Representation of a Heater binary sensor."""
def __init__(self, entry: ConfigEntry, controller: ModelController, poolObject: PoolObject, **kwargs):
"""Initialize."""
<|body_0|>
def is_on(self) -> bool:
"""Return true if sensor is on."""
... | stack_v2_sparse_classes_36k_train_015673 | 4,015 | no_license | [
{
"docstring": "Initialize.",
"name": "__init__",
"signature": "def __init__(self, entry: ConfigEntry, controller: ModelController, poolObject: PoolObject, **kwargs)"
},
{
"docstring": "Return true if sensor is on.",
"name": "is_on",
"signature": "def is_on(self) -> bool"
},
{
"d... | 3 | stack_v2_sparse_classes_30k_train_018079 | Implement the Python class `HeaterBinarySensor` described below.
Class description:
Representation of a Heater binary sensor.
Method signatures and docstrings:
- def __init__(self, entry: ConfigEntry, controller: ModelController, poolObject: PoolObject, **kwargs): Initialize.
- def is_on(self) -> bool: Return true if... | Implement the Python class `HeaterBinarySensor` described below.
Class description:
Representation of a Heater binary sensor.
Method signatures and docstrings:
- def __init__(self, entry: ConfigEntry, controller: ModelController, poolObject: PoolObject, **kwargs): Initialize.
- def is_on(self) -> bool: Return true if... | 625290c164c60611f501ee773583c06a85281300 | <|skeleton|>
class HeaterBinarySensor:
"""Representation of a Heater binary sensor."""
def __init__(self, entry: ConfigEntry, controller: ModelController, poolObject: PoolObject, **kwargs):
"""Initialize."""
<|body_0|>
def is_on(self) -> bool:
"""Return true if sensor is on."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HeaterBinarySensor:
"""Representation of a Heater binary sensor."""
def __init__(self, entry: ConfigEntry, controller: ModelController, poolObject: PoolObject, **kwargs):
"""Initialize."""
super().__init__(entry, controller, poolObject, **kwargs)
self._bodies = set(poolObject[BODY... | the_stack_v2_python_sparse | custom_components/intellicenter/binary_sensor.py | ntalekt/homeassistant | train | 213 |
25b33e171a3d0eed61c12b13e6221e0e5a0ecd3e | [
"if not one_step:\n return\nnode_info = one_step.split(', ')\nnode_name, node_start, node_end, queue_size = ('', 0, 0, 0)\nif node_info:\n node_name = node_info[0].replace('Node:', '')\nif len(node_info) > 3:\n if 'queue' in node_info[1]:\n queue_size = node_info[1].replace('queue size:', '')\n ... | <|body_start_0|>
if not one_step:
return
node_info = one_step.split(', ')
node_name, node_start, node_end, queue_size = ('', 0, 0, 0)
if node_info:
node_name = node_info[0].replace('Node:', '')
if len(node_info) > 3:
if 'queue' in node_info[1]:... | Minddata Aicpu Parser. | MinddataParser | [
"Apache-2.0",
"LicenseRef-scancode-proprietary-license",
"MPL-1.0",
"OpenSSL",
"LGPL-3.0-only",
"LicenseRef-scancode-warranty-disclaimer",
"BSD-3-Clause-Open-MPI",
"MIT",
"MPL-2.0-no-copyleft-exception",
"NTP",
"BSD-3-Clause",
"GPL-1.0-or-later",
"0BSD",
"MPL-2.0",
"LicenseRef-scancode-f... | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MinddataParser:
"""Minddata Aicpu Parser."""
def parse_step_minddata_aicpu_data(one_step, result):
"""Parse step mind_data ai_cpu data. Args: one_step (str): The mind_data step info text, it is one of two structures. Type queue: node_name,queue_size,run_start,run_end Type run: node_n... | stack_v2_sparse_classes_36k_train_015674 | 5,228 | permissive | [
{
"docstring": "Parse step mind_data ai_cpu data. Args: one_step (str): The mind_data step info text, it is one of two structures. Type queue: node_name,queue_size,run_start,run_end Type run: node_name,run_start,run_end,queue_size result ([[node_name, node_start, node_end, queue_size]]): Step info list.",
"... | 3 | null | Implement the Python class `MinddataParser` described below.
Class description:
Minddata Aicpu Parser.
Method signatures and docstrings:
- def parse_step_minddata_aicpu_data(one_step, result): Parse step mind_data ai_cpu data. Args: one_step (str): The mind_data step info text, it is one of two structures. Type queue... | Implement the Python class `MinddataParser` described below.
Class description:
Minddata Aicpu Parser.
Method signatures and docstrings:
- def parse_step_minddata_aicpu_data(one_step, result): Parse step mind_data ai_cpu data. Args: one_step (str): The mind_data step info text, it is one of two structures. Type queue... | 54acb15d435533c815ee1bd9f6dc0b56b4d4cf83 | <|skeleton|>
class MinddataParser:
"""Minddata Aicpu Parser."""
def parse_step_minddata_aicpu_data(one_step, result):
"""Parse step mind_data ai_cpu data. Args: one_step (str): The mind_data step info text, it is one of two structures. Type queue: node_name,queue_size,run_start,run_end Type run: node_n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MinddataParser:
"""Minddata Aicpu Parser."""
def parse_step_minddata_aicpu_data(one_step, result):
"""Parse step mind_data ai_cpu data. Args: one_step (str): The mind_data step info text, it is one of two structures. Type queue: node_name,queue_size,run_start,run_end Type run: node_name,run_start... | the_stack_v2_python_sparse | mindspore/python/mindspore/profiler/parser/minddata_parser.py | mindspore-ai/mindspore | train | 4,178 |
22a8adaa22876d2062cd7afdba35ab59f7a23a67 | [
"context = context or {}\nres = {}\nuom = context and context.get('uom', False) or False\nif 'uom' in fields:\n res.update({'uom': uom})\nif 'sign' in fields:\n res.update({'sign': '+'})\nif 'variation' in fields:\n res.update({'variation': 0.0})\nreturn res",
"context = context or {}\norder_line_obj = s... | <|body_start_0|>
context = context or {}
res = {}
uom = context and context.get('uom', False) or False
if 'uom' in fields:
res.update({'uom': uom})
if 'sign' in fields:
res.update({'sign': '+'})
if 'variation' in fields:
res.update({'va... | consignment_variation_po | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class consignment_variation_po:
def default_get(self, cr, uid, fields, context=None):
"""-Process -To set default value"""
<|body_0|>
def to_update(self, cr, uid, ids, context=None):
"""- Process - update variation on lines, just for only information purpose"""
<|b... | stack_v2_sparse_classes_36k_train_015675 | 2,646 | no_license | [
{
"docstring": "-Process -To set default value",
"name": "default_get",
"signature": "def default_get(self, cr, uid, fields, context=None)"
},
{
"docstring": "- Process - update variation on lines, just for only information purpose",
"name": "to_update",
"signature": "def to_update(self,... | 2 | stack_v2_sparse_classes_30k_train_002810 | Implement the Python class `consignment_variation_po` described below.
Class description:
Implement the consignment_variation_po class.
Method signatures and docstrings:
- def default_get(self, cr, uid, fields, context=None): -Process -To set default value
- def to_update(self, cr, uid, ids, context=None): - Process ... | Implement the Python class `consignment_variation_po` described below.
Class description:
Implement the consignment_variation_po class.
Method signatures and docstrings:
- def default_get(self, cr, uid, fields, context=None): -Process -To set default value
- def to_update(self, cr, uid, ids, context=None): - Process ... | d3daac105636ac4e146816232c616298dc5bb742 | <|skeleton|>
class consignment_variation_po:
def default_get(self, cr, uid, fields, context=None):
"""-Process -To set default value"""
<|body_0|>
def to_update(self, cr, uid, ids, context=None):
"""- Process - update variation on lines, just for only information purpose"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class consignment_variation_po:
def default_get(self, cr, uid, fields, context=None):
"""-Process -To set default value"""
context = context or {}
res = {}
uom = context and context.get('uom', False) or False
if 'uom' in fields:
res.update({'uom': uom})
if... | the_stack_v2_python_sparse | l10n_in_mrp_subcontract/wizard/consignment_variation_po.py | Odoo-India/odoo-india | train | 10 | |
3c81595b2388cde33c8b97271b8bfaaf153718d3 | [
"self._init_attrs()\nself.data = data\nself.delimiter = delimiter\nself.indent = indent\nself.sep = sep",
"data = self.data.strip()\nsections = data.split(self.sep)\nret_dict = {}\nfor section in sections:\n section_data = self._parse_as_dict(section)\n ret_dict.update(section_data)\nreturn ret_dict",
"da... | <|body_start_0|>
self._init_attrs()
self.data = data
self.delimiter = delimiter
self.indent = indent
self.sep = sep
<|end_body_0|>
<|body_start_1|>
data = self.data.strip()
sections = data.split(self.sep)
ret_dict = {}
for section in sections:
... | Class to parse cmd output Tailored to 'netsh {interface} show networks mode=bssid' Also works for showing interfaces | CmdSoup | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CmdSoup:
"""Class to parse cmd output Tailored to 'netsh {interface} show networks mode=bssid' Also works for showing interfaces"""
def __init__(self, data, delimiter=':', indent=' ', sep='\n\n'):
"""Initialises self."""
<|body_0|>
def as_dict(self):
"""Parses... | stack_v2_sparse_classes_36k_train_015676 | 2,802 | no_license | [
{
"docstring": "Initialises self.",
"name": "__init__",
"signature": "def __init__(self, data, delimiter=':', indent=' ', sep='\\n\\n')"
},
{
"docstring": "Parses self.data, returning a dictionary of its contents",
"name": "as_dict",
"signature": "def as_dict(self)"
},
{
"docs... | 5 | stack_v2_sparse_classes_30k_train_017489 | Implement the Python class `CmdSoup` described below.
Class description:
Class to parse cmd output Tailored to 'netsh {interface} show networks mode=bssid' Also works for showing interfaces
Method signatures and docstrings:
- def __init__(self, data, delimiter=':', indent=' ', sep='\n\n'): Initialises self.
- def ... | Implement the Python class `CmdSoup` described below.
Class description:
Class to parse cmd output Tailored to 'netsh {interface} show networks mode=bssid' Also works for showing interfaces
Method signatures and docstrings:
- def __init__(self, data, delimiter=':', indent=' ', sep='\n\n'): Initialises self.
- def ... | 7d370342f34e26e6e66718ae397eb1d81253cd8a | <|skeleton|>
class CmdSoup:
"""Class to parse cmd output Tailored to 'netsh {interface} show networks mode=bssid' Also works for showing interfaces"""
def __init__(self, data, delimiter=':', indent=' ', sep='\n\n'):
"""Initialises self."""
<|body_0|>
def as_dict(self):
"""Parses... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CmdSoup:
"""Class to parse cmd output Tailored to 'netsh {interface} show networks mode=bssid' Also works for showing interfaces"""
def __init__(self, data, delimiter=':', indent=' ', sep='\n\n'):
"""Initialises self."""
self._init_attrs()
self.data = data
self.delimite... | the_stack_v2_python_sparse | yatwin/onekeywifi/network/netsh/cmdsoup/CmdSoup.py | andre95d/python-yatwin | train | 0 |
0c2c64f1f55803c137a85775fefc1ad648fb8969 | [
"self.id_db_int_DBI = id_db_int_DBI\nself.designation_source = designation_source\nself.database_name = database_name",
"listOfDomainsSources = []\nsqlObj = _DB_interaction_DDI_SQL(db_name=self.database_name)\nresults = sqlObj.select_all_sources_DDI_name()\nfor element in results:\n listOfDomainsSources.append... | <|body_start_0|>
self.id_db_int_DBI = id_db_int_DBI
self.designation_source = designation_source
self.database_name = database_name
<|end_body_0|>
<|body_start_1|>
listOfDomainsSources = []
sqlObj = _DB_interaction_DDI_SQL(db_name=self.database_name)
results = sqlObj.sel... | This class treat the source that give the information about the DDI object has it exists in DB_interaction_DDI table database By default, all FK are in the lasts positions in the parameters declaration | DB_interaction_DDI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DB_interaction_DDI:
"""This class treat the source that give the information about the DDI object has it exists in DB_interaction_DDI table database By default, all FK are in the lasts positions in the parameters declaration"""
def __init__(self, id_db_int_DBI=-1, designation_source='', data... | stack_v2_sparse_classes_36k_train_015677 | 2,715 | permissive | [
{
"docstring": "Constructor of the DDI source data object. All the parameters have a default value :param id_db_int_DBI: id of DDI interaction - -1 if unknown :param designation_source: id of the domain A :param database_name: name of the database. See Factory_databases_access :type id_db_int_DBI: int - not req... | 4 | stack_v2_sparse_classes_30k_train_020633 | Implement the Python class `DB_interaction_DDI` described below.
Class description:
This class treat the source that give the information about the DDI object has it exists in DB_interaction_DDI table database By default, all FK are in the lasts positions in the parameters declaration
Method signatures and docstrings... | Implement the Python class `DB_interaction_DDI` described below.
Class description:
This class treat the source that give the information about the DDI object has it exists in DB_interaction_DDI table database By default, all FK are in the lasts positions in the parameters declaration
Method signatures and docstrings... | 862eb85746e8a3a9bbc0d6aef9abbd5eebe9765f | <|skeleton|>
class DB_interaction_DDI:
"""This class treat the source that give the information about the DDI object has it exists in DB_interaction_DDI table database By default, all FK are in the lasts positions in the parameters declaration"""
def __init__(self, id_db_int_DBI=-1, designation_source='', data... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DB_interaction_DDI:
"""This class treat the source that give the information about the DDI object has it exists in DB_interaction_DDI table database By default, all FK are in the lasts positions in the parameters declaration"""
def __init__(self, id_db_int_DBI=-1, designation_source='', database_name='IN... | the_stack_v2_python_sparse | objects_new/DB_interaction_DDI_new.py | diogo1790/inphinity | train | 1 |
b07d1f7fa062e4a5ce29e994de29f7ff6e33facb | [
"_validate_epsilon_delta(total_epsilon, total_delta)\nself._total_epsilon = total_epsilon\nself._total_delta = total_delta\nself._mechanisms = []",
"if count != 1 or noise_standard_deviation is not None:\n raise NotImplementedError('Count and noise standard deviation have not been implemented yet.')\nif mechan... | <|body_start_0|>
_validate_epsilon_delta(total_epsilon, total_delta)
self._total_epsilon = total_epsilon
self._total_delta = total_delta
self._mechanisms = []
<|end_body_0|>
<|body_start_1|>
if count != 1 or noise_standard_deviation is not None:
raise NotImplementedE... | Manages the privacy budget. | NaiveBudgetAccountant | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NaiveBudgetAccountant:
"""Manages the privacy budget."""
def __init__(self, total_epsilon: float, total_delta: float):
"""Constructs a NaiveBudgetAccountant. Args: total_epsilon: epsilon for the entire pipeline. total_delta: delta for the entire pipeline. Raises: A ValueError if eith... | stack_v2_sparse_classes_36k_train_015678 | 14,723 | permissive | [
{
"docstring": "Constructs a NaiveBudgetAccountant. Args: total_epsilon: epsilon for the entire pipeline. total_delta: delta for the entire pipeline. Raises: A ValueError if either argument is out of range.",
"name": "__init__",
"signature": "def __init__(self, total_epsilon: float, total_delta: float)"... | 3 | stack_v2_sparse_classes_30k_train_014812 | Implement the Python class `NaiveBudgetAccountant` described below.
Class description:
Manages the privacy budget.
Method signatures and docstrings:
- def __init__(self, total_epsilon: float, total_delta: float): Constructs a NaiveBudgetAccountant. Args: total_epsilon: epsilon for the entire pipeline. total_delta: de... | Implement the Python class `NaiveBudgetAccountant` described below.
Class description:
Manages the privacy budget.
Method signatures and docstrings:
- def __init__(self, total_epsilon: float, total_delta: float): Constructs a NaiveBudgetAccountant. Args: total_epsilon: epsilon for the entire pipeline. total_delta: de... | fdc64a80a35336ed9e6a0956b23c118c6012ca01 | <|skeleton|>
class NaiveBudgetAccountant:
"""Manages the privacy budget."""
def __init__(self, total_epsilon: float, total_delta: float):
"""Constructs a NaiveBudgetAccountant. Args: total_epsilon: epsilon for the entire pipeline. total_delta: delta for the entire pipeline. Raises: A ValueError if eith... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NaiveBudgetAccountant:
"""Manages the privacy budget."""
def __init__(self, total_epsilon: float, total_delta: float):
"""Constructs a NaiveBudgetAccountant. Args: total_epsilon: epsilon for the entire pipeline. total_delta: delta for the entire pipeline. Raises: A ValueError if either argument i... | the_stack_v2_python_sparse | pipeline_dp/budget_accounting.py | lovroprepolec/PipelineDP | train | 0 |
305572fe9bd59dbccba654884516e2bf5b0421d3 | [
"super().__init__()\nhypo_parameters = hypo_module.meta_named_parameters()\nself.names = []\nself.nets = nn.ModuleList()\nself.param_shapes = []\nfor name, param in hypo_parameters:\n self.names.append(name)\n self.param_shapes.append(param.size())\n hn = custom_layers.FCBlock(in_features=hyper_in_features... | <|body_start_0|>
super().__init__()
hypo_parameters = hypo_module.meta_named_parameters()
self.names = []
self.nets = nn.ModuleList()
self.param_shapes = []
for name, param in hypo_parameters:
self.names.append(name)
self.param_shapes.append(param.... | HyperNetwork | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HyperNetwork:
def __init__(self, hyper_in_features, hyper_hidden_layers, hyper_hidden_features, hypo_module, siren=False):
"""Args: hyper_in_features: In features of hypernetwork hyper_hidden_layers: Number of hidden layers in hypernetwork hyper_hidden_features: Number of hidden units in... | stack_v2_sparse_classes_36k_train_015679 | 7,605 | no_license | [
{
"docstring": "Args: hyper_in_features: In features of hypernetwork hyper_hidden_layers: Number of hidden layers in hypernetwork hyper_hidden_features: Number of hidden units in hypernetwork hypo_module: MetaModule. The module whose parameters are predicted.",
"name": "__init__",
"signature": "def __in... | 2 | stack_v2_sparse_classes_30k_train_013431 | Implement the Python class `HyperNetwork` described below.
Class description:
Implement the HyperNetwork class.
Method signatures and docstrings:
- def __init__(self, hyper_in_features, hyper_hidden_layers, hyper_hidden_features, hypo_module, siren=False): Args: hyper_in_features: In features of hypernetwork hyper_hi... | Implement the Python class `HyperNetwork` described below.
Class description:
Implement the HyperNetwork class.
Method signatures and docstrings:
- def __init__(self, hyper_in_features, hyper_hidden_layers, hyper_hidden_features, hypo_module, siren=False): Args: hyper_in_features: In features of hypernetwork hyper_hi... | 1c2ba87c6b2cf89f14ea43ec14b179579cbc9220 | <|skeleton|>
class HyperNetwork:
def __init__(self, hyper_in_features, hyper_hidden_layers, hyper_hidden_features, hypo_module, siren=False):
"""Args: hyper_in_features: In features of hypernetwork hyper_hidden_layers: Number of hidden layers in hypernetwork hyper_hidden_features: Number of hidden units in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HyperNetwork:
def __init__(self, hyper_in_features, hyper_hidden_layers, hyper_hidden_features, hypo_module, siren=False):
"""Args: hyper_in_features: In features of hypernetwork hyper_hidden_layers: Number of hidden layers in hypernetwork hyper_hidden_features: Number of hidden units in hypernetwork ... | the_stack_v2_python_sparse | colf/supplementary_colf/code/hyperlayers.py | cameronosmith/cameronosmith.github.io | train | 0 | |
1cc4658348411f1a556b9ab87261f4116b5edb15 | [
"count = task_obj.find({'status': 1}).count()\nprint('queue status:1 count: {}'.format(count))\nwhile count >= max_enqueue_num:\n time.sleep(int(count) / 6)\n count = task_obj.find({'status': 1}).count()",
"try:\n print('获取爬虫: {} 的同步数据'.format(obj.__class__.__name__))\n result = obj.get_result()\n ... | <|body_start_0|>
count = task_obj.find({'status': 1}).count()
print('queue status:1 count: {}'.format(count))
while count >= max_enqueue_num:
time.sleep(int(count) / 6)
count = task_obj.find({'status': 1}).count()
<|end_body_0|>
<|body_start_1|>
try:
... | 爬虫项目的工具类 | CrawlerHelper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CrawlerHelper:
"""爬虫项目的工具类"""
def delay_by_task_count(task_obj, max_enqueue_num=200):
"""根据task数量缓慢写入抓取任务 :param task_obj: 查询队列的task mongo实例 :param max_enqueue_num: 写入队列的队列最大值 :return:"""
<|body_0|>
def get_sync_result(cls, obj, retry=3, delay_time=20):
"""通用的获取同... | stack_v2_sparse_classes_36k_train_015680 | 1,942 | permissive | [
{
"docstring": "根据task数量缓慢写入抓取任务 :param task_obj: 查询队列的task mongo实例 :param max_enqueue_num: 写入队列的队列最大值 :return:",
"name": "delay_by_task_count",
"signature": "def delay_by_task_count(task_obj, max_enqueue_num=200)"
},
{
"docstring": "通用的获取同步爬虫请求的方法 :param obj: 同步爬虫的实例 :param retry: 重试次数，默认重试3次 :... | 3 | null | Implement the Python class `CrawlerHelper` described below.
Class description:
爬虫项目的工具类
Method signatures and docstrings:
- def delay_by_task_count(task_obj, max_enqueue_num=200): 根据task数量缓慢写入抓取任务 :param task_obj: 查询队列的task mongo实例 :param max_enqueue_num: 写入队列的队列最大值 :return:
- def get_sync_result(cls, obj, retry=3, d... | Implement the Python class `CrawlerHelper` described below.
Class description:
爬虫项目的工具类
Method signatures and docstrings:
- def delay_by_task_count(task_obj, max_enqueue_num=200): 根据task数量缓慢写入抓取任务 :param task_obj: 查询队列的task mongo实例 :param max_enqueue_num: 写入队列的队列最大值 :return:
- def get_sync_result(cls, obj, retry=3, d... | 29ba13905c73081097df9ef646a5c8194eb024be | <|skeleton|>
class CrawlerHelper:
"""爬虫项目的工具类"""
def delay_by_task_count(task_obj, max_enqueue_num=200):
"""根据task数量缓慢写入抓取任务 :param task_obj: 查询队列的task mongo实例 :param max_enqueue_num: 写入队列的队列最大值 :return:"""
<|body_0|>
def get_sync_result(cls, obj, retry=3, delay_time=20):
"""通用的获取同... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CrawlerHelper:
"""爬虫项目的工具类"""
def delay_by_task_count(task_obj, max_enqueue_num=200):
"""根据task数量缓慢写入抓取任务 :param task_obj: 查询队列的task mongo实例 :param max_enqueue_num: 写入队列的队列最大值 :return:"""
count = task_obj.find({'status': 1}).count()
print('queue status:1 count: {}'.format(count))
... | the_stack_v2_python_sparse | pyspider/helper/crawler_utils.py | UoToGK/crawler-pyspider | train | 0 |
439f37b9d5652347b1f8a2ced50d36ca0c819500 | [
"super().__init__()\nself.cnn_layers = nn.Sequential()\nself.fc_layers = nn.Sequential()\nself.loss_criterion = None\nself.cnn_layers = nn.Sequential(nn.Conv2d(1, 10, 5), nn.MaxPool2d(3, 3), nn.ReLU(), nn.Conv2d(10, 20, 5), nn.MaxPool2d(3, 3), nn.ReLU(), nn.Flatten())\nself.fc_layers = nn.Sequential(nn.Dropout(0.1)... | <|body_start_0|>
super().__init__()
self.cnn_layers = nn.Sequential()
self.fc_layers = nn.Sequential()
self.loss_criterion = None
self.cnn_layers = nn.Sequential(nn.Conv2d(1, 10, 5), nn.MaxPool2d(3, 3), nn.ReLU(), nn.Conv2d(10, 20, 5), nn.MaxPool2d(3, 3), nn.ReLU(), nn.Flatten())... | SimpleNetDropout | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleNetDropout:
def __init__(self):
"""Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means"""
<|body_0|>
def forward(self, x: torch.tensor) -> torch.tensor:
"""Pe... | stack_v2_sparse_classes_36k_train_015681 | 1,913 | no_license | [
{
"docstring": "Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Perform the forward pass with the net Note: do not... | 2 | stack_v2_sparse_classes_30k_train_008447 | Implement the Python class `SimpleNetDropout` described below.
Class description:
Implement the SimpleNetDropout class.
Method signatures and docstrings:
- def __init__(self): Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand w... | Implement the Python class `SimpleNetDropout` described below.
Class description:
Implement the SimpleNetDropout class.
Method signatures and docstrings:
- def __init__(self): Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand w... | bc48e09844c70f28fc82cdbead405219a964f5aa | <|skeleton|>
class SimpleNetDropout:
def __init__(self):
"""Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means"""
<|body_0|>
def forward(self, x: torch.tensor) -> torch.tensor:
"""Pe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleNetDropout:
def __init__(self):
"""Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means"""
super().__init__()
self.cnn_layers = nn.Sequential()
self.fc_layers = nn.Sequen... | the_stack_v2_python_sparse | Computer Vision/proj2_part2_release/proj2_code/simple_net_dropout.py | karan-sarkar/GT | train | 1 | |
5b85be2512696f7634a2fb349bbeaaa342cf6875 | [
"crypto_meta_json = self._response_header_value(header_name)\nif crypto_meta_json is None:\n return None\ncrypto_meta = load_crypto_meta(crypto_meta_json)\nif check:\n self.crypto.check_crypto_meta(crypto_meta)\nreturn crypto_meta",
"try:\n return self.crypto.unwrap_key(wrapping_key, crypto_meta['body_ke... | <|body_start_0|>
crypto_meta_json = self._response_header_value(header_name)
if crypto_meta_json is None:
return None
crypto_meta = load_crypto_meta(crypto_meta_json)
if check:
self.crypto.check_crypto_meta(crypto_meta)
return crypto_meta
<|end_body_0|>
<... | BaseDecrypterContext | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseDecrypterContext:
def get_crypto_meta(self, header_name, check=True):
"""Extract a crypto_meta dict from a header. :param header_name: name of header that may have crypto_meta :param check: if True validate the crypto meta :return: A dict containing crypto_meta items :raises Encrypti... | stack_v2_sparse_classes_36k_train_015682 | 20,452 | permissive | [
{
"docstring": "Extract a crypto_meta dict from a header. :param header_name: name of header that may have crypto_meta :param check: if True validate the crypto meta :return: A dict containing crypto_meta items :raises EncryptionException: if an error occurs while parsing the crypto meta",
"name": "get_cryp... | 5 | stack_v2_sparse_classes_30k_train_016988 | Implement the Python class `BaseDecrypterContext` described below.
Class description:
Implement the BaseDecrypterContext class.
Method signatures and docstrings:
- def get_crypto_meta(self, header_name, check=True): Extract a crypto_meta dict from a header. :param header_name: name of header that may have crypto_meta... | Implement the Python class `BaseDecrypterContext` described below.
Class description:
Implement the BaseDecrypterContext class.
Method signatures and docstrings:
- def get_crypto_meta(self, header_name, check=True): Extract a crypto_meta dict from a header. :param header_name: name of header that may have crypto_meta... | f06e5369579599648cc78e4b556887bc6d978c2b | <|skeleton|>
class BaseDecrypterContext:
def get_crypto_meta(self, header_name, check=True):
"""Extract a crypto_meta dict from a header. :param header_name: name of header that may have crypto_meta :param check: if True validate the crypto meta :return: A dict containing crypto_meta items :raises Encrypti... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseDecrypterContext:
def get_crypto_meta(self, header_name, check=True):
"""Extract a crypto_meta dict from a header. :param header_name: name of header that may have crypto_meta :param check: if True validate the crypto meta :return: A dict containing crypto_meta items :raises EncryptionException: i... | the_stack_v2_python_sparse | swift/common/middleware/crypto/decrypter.py | openstack/swift | train | 2,370 | |
2cae8616761bbb55d187f020fa1593b431af4d99 | [
"self.benchmark_op = benchmark_op\nself.iterations_warmup = iterations_warmup\nself.iterations_benchmark = iterations_benchmark\nself.iterations_timeline = iterations_timeline\nself.graph = graph\nself.config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.Optimizer... | <|body_start_0|>
self.benchmark_op = benchmark_op
self.iterations_warmup = iterations_warmup
self.iterations_benchmark = iterations_benchmark
self.iterations_timeline = iterations_timeline
self.graph = graph
self.config = tf.ConfigProto(graph_options=tf.GraphOptions(optim... | Class for running the benchmarks | benchmark | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class benchmark:
"""Class for running the benchmarks"""
def __init__(self, benchmark_op, iterations_warmup, iterations_benchmark, iterations_timeline, graph):
"""Initialize benchmark Args: benchmark_op: tf tensor, operation to be executed in benchmark iterations_warmup: Number of iteration... | stack_v2_sparse_classes_36k_train_015683 | 3,384 | permissive | [
{
"docstring": "Initialize benchmark Args: benchmark_op: tf tensor, operation to be executed in benchmark iterations_warmup: Number of iterations for warm-up iterations_benchmark: Number of iterations for benchmark iterations_timeline: Number of iterations for generation of timeline graph: tf graph",
"name"... | 4 | stack_v2_sparse_classes_30k_train_007655 | Implement the Python class `benchmark` described below.
Class description:
Class for running the benchmarks
Method signatures and docstrings:
- def __init__(self, benchmark_op, iterations_warmup, iterations_benchmark, iterations_timeline, graph): Initialize benchmark Args: benchmark_op: tf tensor, operation to be exe... | Implement the Python class `benchmark` described below.
Class description:
Class for running the benchmarks
Method signatures and docstrings:
- def __init__(self, benchmark_op, iterations_warmup, iterations_benchmark, iterations_timeline, graph): Initialize benchmark Args: benchmark_op: tf tensor, operation to be exe... | c177ed3b01b3849de2d0266a3dd673c47bd754f8 | <|skeleton|>
class benchmark:
"""Class for running the benchmarks"""
def __init__(self, benchmark_op, iterations_warmup, iterations_benchmark, iterations_timeline, graph):
"""Initialize benchmark Args: benchmark_op: tf tensor, operation to be executed in benchmark iterations_warmup: Number of iteration... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class benchmark:
"""Class for running the benchmarks"""
def __init__(self, benchmark_op, iterations_warmup, iterations_benchmark, iterations_timeline, graph):
"""Initialize benchmark Args: benchmark_op: tf tensor, operation to be executed in benchmark iterations_warmup: Number of iterations for warm-up... | the_stack_v2_python_sparse | benchmark/utils_tf/run_benchmark.py | profnote/ml-performance-prediction | train | 0 |
b7ae12437727c5f89d006f643674c018db505e04 | [
"audio = np.empty((1,))\nsecs_loaded = 0\nfiles_loaded = 0\nfiles = glob.glob(path + '*.wav')\nfor file in files:\n sr, samples = wavfile.read(file)\n audio = np.concatenate((audio, samples))\n dur = len(samples) / sr\n secs_loaded = secs_loaded + dur\n files_loaded = files_loaded + 1\n if secs_lo... | <|body_start_0|>
audio = np.empty((1,))
secs_loaded = 0
files_loaded = 0
files = glob.glob(path + '*.wav')
for file in files:
sr, samples = wavfile.read(file)
audio = np.concatenate((audio, samples))
dur = len(samples) / sr
secs_loa... | Spectrogram data from the Vox Celeb Dataset. | VoxCeleb | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VoxCeleb:
"""Spectrogram data from the Vox Celeb Dataset."""
def __init__(self, secs, path, concat=True):
"""Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. Multiple .wav files will be combined if necessary. path : ... | stack_v2_sparse_classes_36k_train_015684 | 3,492 | no_license | [
{
"docstring": "Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. Multiple .wav files will be combined if necessary. path : string Path to folder containing .wav file(s) concat : bool Whether or not to concatenate multiple files. If false, only ... | 2 | stack_v2_sparse_classes_30k_test_000909 | Implement the Python class `VoxCeleb` described below.
Class description:
Spectrogram data from the Vox Celeb Dataset.
Method signatures and docstrings:
- def __init__(self, secs, path, concat=True): Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. M... | Implement the Python class `VoxCeleb` described below.
Class description:
Spectrogram data from the Vox Celeb Dataset.
Method signatures and docstrings:
- def __init__(self, secs, path, concat=True): Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. M... | eabdb6ca44cb8f44f0cfb2d94561c9d4de9bb413 | <|skeleton|>
class VoxCeleb:
"""Spectrogram data from the Vox Celeb Dataset."""
def __init__(self, secs, path, concat=True):
"""Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. Multiple .wav files will be combined if necessary. path : ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VoxCeleb:
"""Spectrogram data from the Vox Celeb Dataset."""
def __init__(self, secs, path, concat=True):
"""Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. Multiple .wav files will be combined if necessary. path : string Path t... | the_stack_v2_python_sparse | cmfpy/datasets/vox_celeb.py | degleris1/cmfpy | train | 1 |
c5ae46f540fcd83f656bdecc2373f998a1158310 | [
"self.epsilon = epsilon\nself.epsilon_decay = epsilon_decay\nself.epsilon_min = epsilon_min\nself.possible_actions = possible_actions\nself.continuous = continuous",
"if self.epsilon < self.epsilon_min:\n self.epsilon = self.epsilon_min\nif random.random() < self.epsilon:\n return True\nreturn False",
"se... | <|body_start_0|>
self.epsilon = epsilon
self.epsilon_decay = epsilon_decay
self.epsilon_min = epsilon_min
self.possible_actions = possible_actions
self.continuous = continuous
<|end_body_0|>
<|body_start_1|>
if self.epsilon < self.epsilon_min:
self.epsilon = ... | An implementation of epsilon greedy exploration. Epsilon greedy exploration selects an explorative action with probability epsilon at each step. The value of epsilon decays at a rate 'epsilon_decay', until it hits the minimum value of 'epsilon_min' | EpsilonGreedyExplorer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EpsilonGreedyExplorer:
"""An implementation of epsilon greedy exploration. Epsilon greedy exploration selects an explorative action with probability epsilon at each step. The value of epsilon decays at a rate 'epsilon_decay', until it hits the minimum value of 'epsilon_min'"""
def __init__(s... | stack_v2_sparse_classes_36k_train_015685 | 1,590 | no_license | [
{
"docstring": "Initialises the parameters for the explorer",
"name": "__init__",
"signature": "def __init__(self, possible_actions, continuous=False, epsilon=0.1, epsilon_decay=0, epsilon_min=0.1)"
},
{
"docstring": "Determines whether or not to explore in the current time step by comparing eps... | 3 | stack_v2_sparse_classes_30k_train_000431 | Implement the Python class `EpsilonGreedyExplorer` described below.
Class description:
An implementation of epsilon greedy exploration. Epsilon greedy exploration selects an explorative action with probability epsilon at each step. The value of epsilon decays at a rate 'epsilon_decay', until it hits the minimum value ... | Implement the Python class `EpsilonGreedyExplorer` described below.
Class description:
An implementation of epsilon greedy exploration. Epsilon greedy exploration selects an explorative action with probability epsilon at each step. The value of epsilon decays at a rate 'epsilon_decay', until it hits the minimum value ... | 6dfb342efda4c5ae0dff72bb132a6ce12fbfd8b8 | <|skeleton|>
class EpsilonGreedyExplorer:
"""An implementation of epsilon greedy exploration. Epsilon greedy exploration selects an explorative action with probability epsilon at each step. The value of epsilon decays at a rate 'epsilon_decay', until it hits the minimum value of 'epsilon_min'"""
def __init__(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EpsilonGreedyExplorer:
"""An implementation of epsilon greedy exploration. Epsilon greedy exploration selects an explorative action with probability epsilon at each step. The value of epsilon decays at a rate 'epsilon_decay', until it hits the minimum value of 'epsilon_min'"""
def __init__(self, possible... | the_stack_v2_python_sparse | Software/Agents/Policies/Explorers/EpsilonGreedyExplorer.py | meelement/ArtificialIntelligence | train | 0 |
8c8168740babbb789e1e46bca079cc88686e178d | [
"self.metrics = {}\nqueries = {'visits': PiwikQueryReportEventMetricVisits(**self.params), 'unique_visits': PiwikQueryReportEventMetricUniqueVisits(**self.params), 'visit_duration': PiwikQueryReportEventMetricVisitDuration(**self.params), 'referrers': PiwikQueryReportEventMetricReferrers(**self.params), 'peak': Piw... | <|body_start_0|>
self.metrics = {}
queries = {'visits': PiwikQueryReportEventMetricVisits(**self.params), 'unique_visits': PiwikQueryReportEventMetricUniqueVisits(**self.params), 'visit_duration': PiwikQueryReportEventMetricVisitDuration(**self.params), 'referrers': PiwikQueryReportEventMetricReferrers(... | ReportGeneral | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReportGeneral:
def _build_report(self):
"""Build the report by performing queries to Piwik"""
<|body_0|>
def _fetch_contribution_info(self):
"""Build the list of information entries for contributions of the event"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_015686 | 5,751 | permissive | [
{
"docstring": "Build the report by performing queries to Piwik",
"name": "_build_report",
"signature": "def _build_report(self)"
},
{
"docstring": "Build the list of information entries for contributions of the event",
"name": "_fetch_contribution_info",
"signature": "def _fetch_contrib... | 2 | stack_v2_sparse_classes_30k_train_002286 | Implement the Python class `ReportGeneral` described below.
Class description:
Implement the ReportGeneral class.
Method signatures and docstrings:
- def _build_report(self): Build the report by performing queries to Piwik
- def _fetch_contribution_info(self): Build the list of information entries for contributions o... | Implement the Python class `ReportGeneral` described below.
Class description:
Implement the ReportGeneral class.
Method signatures and docstrings:
- def _build_report(self): Build the report by performing queries to Piwik
- def _fetch_contribution_info(self): Build the list of information entries for contributions o... | 2d7ca9489e1e9b6c66005384528b5fe51e2ed9e3 | <|skeleton|>
class ReportGeneral:
def _build_report(self):
"""Build the report by performing queries to Piwik"""
<|body_0|>
def _fetch_contribution_info(self):
"""Build the list of information entries for contributions of the event"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReportGeneral:
def _build_report(self):
"""Build the report by performing queries to Piwik"""
self.metrics = {}
queries = {'visits': PiwikQueryReportEventMetricVisits(**self.params), 'unique_visits': PiwikQueryReportEventMetricUniqueVisits(**self.params), 'visit_duration': PiwikQueryRe... | the_stack_v2_python_sparse | piwik/indico_piwik/reports.py | indico/indico-plugins | train | 26 | |
84850fa004c26d22771f16c787d44dadf1cfbfd5 | [
"parser.add_argument('BUCKET_ID', help='ID of the bucket to create.')\nparser.add_argument('--display-name', help='A textual name to display for the bucket.')\nparser.add_argument('--description', help='A textual description for the bucket.')\nparser.add_argument('--retention-days', type=int, help='The period logs ... | <|body_start_0|>
parser.add_argument('BUCKET_ID', help='ID of the bucket to create.')
parser.add_argument('--display-name', help='A textual name to display for the bucket.')
parser.add_argument('--description', help='A textual description for the bucket.')
parser.add_argument('--retentio... | Creates a bucket. | Create | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Create:
"""Creates a bucket."""
def Args(parser):
"""Register flags for this command."""
<|body_0|>
def Run(self, args):
"""This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this c... | stack_v2_sparse_classes_36k_train_015687 | 2,655 | permissive | [
{
"docstring": "Register flags for this command.",
"name": "Args",
"signature": "def Args(parser)"
},
{
"docstring": "This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: The created... | 2 | null | Implement the Python class `Create` described below.
Class description:
Creates a bucket.
Method signatures and docstrings:
- def Args(parser): Register flags for this command.
- def Run(self, args): This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that we... | Implement the Python class `Create` described below.
Class description:
Creates a bucket.
Method signatures and docstrings:
- def Args(parser): Register flags for this command.
- def Run(self, args): This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that we... | 85bb264e273568b5a0408f733b403c56373e2508 | <|skeleton|>
class Create:
"""Creates a bucket."""
def Args(parser):
"""Register flags for this command."""
<|body_0|>
def Run(self, args):
"""This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Create:
"""Creates a bucket."""
def Args(parser):
"""Register flags for this command."""
parser.add_argument('BUCKET_ID', help='ID of the bucket to create.')
parser.add_argument('--display-name', help='A textual name to display for the bucket.')
parser.add_argument('--desc... | the_stack_v2_python_sparse | google-cloud-sdk/lib/surface/logging/buckets/create.py | bopopescu/socialliteapp | train | 0 |
aa3da683a2b93dcec2afbefb352ff374eebfd878 | [
"super(RLConfiguration, self).__init__(**kwargs)\nself.device = 'cpu'\nself.save_model_config = None\nself.save_result_config = None\nself.encoding_config = None\nself.monitor_dir = './logs'\nself.model_load_path = ''\nself.buffer_load_path = ''\nself.encoding_load_path = ''\nself.extra_load_path = ''\nself.log_int... | <|body_start_0|>
super(RLConfiguration, self).__init__(**kwargs)
self.device = 'cpu'
self.save_model_config = None
self.save_result_config = None
self.encoding_config = None
self.monitor_dir = './logs'
self.model_load_path = ''
self.buffer_load_path = ''
... | class stores the rl experiment configuration | RLConfiguration | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RLConfiguration:
"""class stores the rl experiment configuration"""
def __init__(self, **kwargs):
"""initialize settings"""
<|body_0|>
def set_necessary_configs(self, **kwargs):
"""set rl configs that necessarily provided by user"""
<|body_1|>
def se... | stack_v2_sparse_classes_36k_train_015688 | 5,332 | no_license | [
{
"docstring": "initialize settings",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "set rl configs that necessarily provided by user",
"name": "set_necessary_configs",
"signature": "def set_necessary_configs(self, **kwargs)"
},
{
"docstring": ... | 3 | null | Implement the Python class `RLConfiguration` described below.
Class description:
class stores the rl experiment configuration
Method signatures and docstrings:
- def __init__(self, **kwargs): initialize settings
- def set_necessary_configs(self, **kwargs): set rl configs that necessarily provided by user
- def set_un... | Implement the Python class `RLConfiguration` described below.
Class description:
class stores the rl experiment configuration
Method signatures and docstrings:
- def __init__(self, **kwargs): initialize settings
- def set_necessary_configs(self, **kwargs): set rl configs that necessarily provided by user
- def set_un... | b0e8f66b3ade742445a41d3d5667032a931d94d2 | <|skeleton|>
class RLConfiguration:
"""class stores the rl experiment configuration"""
def __init__(self, **kwargs):
"""initialize settings"""
<|body_0|>
def set_necessary_configs(self, **kwargs):
"""set rl configs that necessarily provided by user"""
<|body_1|>
def se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RLConfiguration:
"""class stores the rl experiment configuration"""
def __init__(self, **kwargs):
"""initialize settings"""
super(RLConfiguration, self).__init__(**kwargs)
self.device = 'cpu'
self.save_model_config = None
self.save_result_config = None
self... | the_stack_v2_python_sparse | config/rl_config.py | wz139704646/MBRL_on_VAEs | train | 1 |
942f7e2ac06f33815a91e6f04e0527deb23a6d66 | [
"x, y = RecursiveFilter()._validate_coefficients(self.cube, self.smoothing_coefficients)\nself.assertEqual(x.name(), self.x_name)\nself.assertEqual(y.name(), self.y_name)\nx, y = RecursiveFilter()._validate_coefficients(self.cube, self.smoothing_coefficients[::-1])\nself.assertEqual(x.name(), self.x_name)\nself.ass... | <|body_start_0|>
x, y = RecursiveFilter()._validate_coefficients(self.cube, self.smoothing_coefficients)
self.assertEqual(x.name(), self.x_name)
self.assertEqual(y.name(), self.y_name)
x, y = RecursiveFilter()._validate_coefficients(self.cube, self.smoothing_coefficients[::-1])
s... | Test the _validate_coefficients method | Test__validate_coefficients | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test__validate_coefficients:
"""Test the _validate_coefficients method"""
def test_return_order(self):
"""Test that the coefficients cubes are returned in x, y order."""
<|body_0|>
def test_smoothing_coefficients_wrong_name(self):
"""Test that an error is raised ... | stack_v2_sparse_classes_36k_train_015689 | 22,857 | permissive | [
{
"docstring": "Test that the coefficients cubes are returned in x, y order.",
"name": "test_return_order",
"signature": "def test_return_order(self)"
},
{
"docstring": "Test that an error is raised if the smoothing_coefficients_cube has an incorrect name",
"name": "test_smoothing_coefficien... | 5 | null | Implement the Python class `Test__validate_coefficients` described below.
Class description:
Test the _validate_coefficients method
Method signatures and docstrings:
- def test_return_order(self): Test that the coefficients cubes are returned in x, y order.
- def test_smoothing_coefficients_wrong_name(self): Test tha... | Implement the Python class `Test__validate_coefficients` described below.
Class description:
Test the _validate_coefficients method
Method signatures and docstrings:
- def test_return_order(self): Test that the coefficients cubes are returned in x, y order.
- def test_smoothing_coefficients_wrong_name(self): Test tha... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test__validate_coefficients:
"""Test the _validate_coefficients method"""
def test_return_order(self):
"""Test that the coefficients cubes are returned in x, y order."""
<|body_0|>
def test_smoothing_coefficients_wrong_name(self):
"""Test that an error is raised ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test__validate_coefficients:
"""Test the _validate_coefficients method"""
def test_return_order(self):
"""Test that the coefficients cubes are returned in x, y order."""
x, y = RecursiveFilter()._validate_coefficients(self.cube, self.smoothing_coefficients)
self.assertEqual(x.name... | the_stack_v2_python_sparse | improver_tests/nbhood/recursive_filter/test_RecursiveFilter.py | metoppv/improver | train | 101 |
0d2788a3d6283dcb7879f0d7cac3aee0add23639 | [
"super(Highway, self).__init__()\nself.embedded_char_size = embedded_char_size\nself.proj_projection = nn.Linear(in_features=embedded_char_size, out_features=embedded_char_size)\nself.gate_projection = nn.Linear(in_features=embedded_char_size, out_features=embedded_char_size)\nself.relu = nn.ReLU()\nself.sigmoid = ... | <|body_start_0|>
super(Highway, self).__init__()
self.embedded_char_size = embedded_char_size
self.proj_projection = nn.Linear(in_features=embedded_char_size, out_features=embedded_char_size)
self.gate_projection = nn.Linear(in_features=embedded_char_size, out_features=embedded_char_size... | HighWay Layer, i.e. a layer of highway network that takes the output of convolutional network as input | Highway | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Highway:
"""HighWay Layer, i.e. a layer of highway network that takes the output of convolutional network as input"""
def __init__(self, embedded_char_size):
"""Init HighWay Instance. @param embedded_char_size: int"""
<|body_0|>
def forward(self, x_conv_out):
"""... | stack_v2_sparse_classes_36k_train_015690 | 2,058 | no_license | [
{
"docstring": "Init HighWay Instance. @param embedded_char_size: int",
"name": "__init__",
"signature": "def __init__(self, embedded_char_size)"
},
{
"docstring": "Run a forward step that map a batch of x_conv_out to x_high_way @param x_conv_out: tensor of (max_sentence_length * batch_size, emb... | 2 | stack_v2_sparse_classes_30k_train_007058 | Implement the Python class `Highway` described below.
Class description:
HighWay Layer, i.e. a layer of highway network that takes the output of convolutional network as input
Method signatures and docstrings:
- def __init__(self, embedded_char_size): Init HighWay Instance. @param embedded_char_size: int
- def forwar... | Implement the Python class `Highway` described below.
Class description:
HighWay Layer, i.e. a layer of highway network that takes the output of convolutional network as input
Method signatures and docstrings:
- def __init__(self, embedded_char_size): Init HighWay Instance. @param embedded_char_size: int
- def forwar... | a883935d779dca3a3cc443c3fa6d6a455f21e87a | <|skeleton|>
class Highway:
"""HighWay Layer, i.e. a layer of highway network that takes the output of convolutional network as input"""
def __init__(self, embedded_char_size):
"""Init HighWay Instance. @param embedded_char_size: int"""
<|body_0|>
def forward(self, x_conv_out):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Highway:
"""HighWay Layer, i.e. a layer of highway network that takes the output of convolutional network as input"""
def __init__(self, embedded_char_size):
"""Init HighWay Instance. @param embedded_char_size: int"""
super(Highway, self).__init__()
self.embedded_char_size = embed... | the_stack_v2_python_sparse | stanford_nlp/a5/highway.py | guocongyun/ml-projects | train | 0 |
1977e5bc5f765b35a9cfaf773ad9cf978bd40b22 | [
"self.datum = data\nself.left = None\nself.right = None",
"if self:\n if value < self.datum:\n if self.left is None:\n self.left = Node(value)\n else:\n self.left + value\n elif value > self.datum:\n if self.right is None:\n self.right = Node(value)\n ... | <|body_start_0|>
self.datum = data
self.left = None
self.right = None
<|end_body_0|>
<|body_start_1|>
if self:
if value < self.datum:
if self.left is None:
self.left = Node(value)
else:
self.left + value... | Node | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Node:
def __init__(self, data):
"""This constructor initializes the instance variable datum to the data and the instance variable left anf right to None."""
<|body_0|>
def __add__(self, value):
"""This magic method inserts a new item with self as a root, if the item ... | stack_v2_sparse_classes_36k_train_015691 | 4,969 | no_license | [
{
"docstring": "This constructor initializes the instance variable datum to the data and the instance variable left anf right to None.",
"name": "__init__",
"signature": "def __init__(self, data)"
},
{
"docstring": "This magic method inserts a new item with self as a root, if the item is not alr... | 5 | stack_v2_sparse_classes_30k_train_014474 | Implement the Python class `Node` described below.
Class description:
Implement the Node class.
Method signatures and docstrings:
- def __init__(self, data): This constructor initializes the instance variable datum to the data and the instance variable left anf right to None.
- def __add__(self, value): This magic me... | Implement the Python class `Node` described below.
Class description:
Implement the Node class.
Method signatures and docstrings:
- def __init__(self, data): This constructor initializes the instance variable datum to the data and the instance variable left anf right to None.
- def __add__(self, value): This magic me... | e773e87668af057c8adb1e012aa5d81f42c70f2a | <|skeleton|>
class Node:
def __init__(self, data):
"""This constructor initializes the instance variable datum to the data and the instance variable left anf right to None."""
<|body_0|>
def __add__(self, value):
"""This magic method inserts a new item with self as a root, if the item ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Node:
def __init__(self, data):
"""This constructor initializes the instance variable datum to the data and the instance variable left anf right to None."""
self.datum = data
self.left = None
self.right = None
def __add__(self, value):
"""This magic method inserts ... | the_stack_v2_python_sparse | HW/HW5/hw5.py | SiddhantBhardwaj2018/ISTA-350 | train | 0 | |
9491a39cf700a2c335c03197b903861e6eaca755 | [
"head = ListNode(0)\nl3 = head\naddon = 0\nwhile l1 is not None or l2 is not None:\n if l1 is None:\n tmp = l2.val + addon * 1\n elif l2 is None:\n tmp = l1.val + addon * 1\n else:\n tmp = l1.val + l2.val + addon * 1\n addon = 0\n if tmp >= 10:\n l3.val = tmp - 10\n ... | <|body_start_0|>
head = ListNode(0)
l3 = head
addon = 0
while l1 is not None or l2 is not None:
if l1 is None:
tmp = l2.val + addon * 1
elif l2 is None:
tmp = l1.val + addon * 1
else:
tmp = l1.val + l2.va... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addTwoNumbers(self, l1, l2) -> ListNode:
"""最直接的方法：遍历两个链表到最后，执行加操作 Q：l1，l2为空之后，继续执行增加一个节点，导致最后多一位0 A：加一个判断，都为空直接结束 Q：其中一个链表对应的数位数多一位 1 8 + 2 A：有一个不为空则继续加，只不过不加为空的那个链表的值，None.val报错， 遍历的时候也忽略为空的那个 Q：还有最后可能也有进位 5 + 5 A: 判断都为空的时候再判断进位标志位是否为1，是的话一个节点值为1"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_015692 | 3,769 | no_license | [
{
"docstring": "最直接的方法：遍历两个链表到最后，执行加操作 Q：l1，l2为空之后，继续执行增加一个节点，导致最后多一位0 A：加一个判断，都为空直接结束 Q：其中一个链表对应的数位数多一位 1 8 + 2 A：有一个不为空则继续加，只不过不加为空的那个链表的值，None.val报错， 遍历的时候也忽略为空的那个 Q：还有最后可能也有进位 5 + 5 A: 判断都为空的时候再判断进位标志位是否为1，是的话一个节点值为1",
"name": "addTwoNumbers",
"signature": "def addTwoNumbers(self, l1, l2) -> ListNod... | 3 | stack_v2_sparse_classes_30k_train_002705 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addTwoNumbers(self, l1, l2) -> ListNode: 最直接的方法：遍历两个链表到最后，执行加操作 Q：l1，l2为空之后，继续执行增加一个节点，导致最后多一位0 A：加一个判断，都为空直接结束 Q：其中一个链表对应的数位数多一位 1 8 + 2 A：有一个不为空则继续加，只不过不加为空的那个链表的值，None.val... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addTwoNumbers(self, l1, l2) -> ListNode: 最直接的方法：遍历两个链表到最后，执行加操作 Q：l1，l2为空之后，继续执行增加一个节点，导致最后多一位0 A：加一个判断，都为空直接结束 Q：其中一个链表对应的数位数多一位 1 8 + 2 A：有一个不为空则继续加，只不过不加为空的那个链表的值，None.val... | 95dddb78bccd169d9d219a473627361fe739ab5e | <|skeleton|>
class Solution:
def addTwoNumbers(self, l1, l2) -> ListNode:
"""最直接的方法：遍历两个链表到最后，执行加操作 Q：l1，l2为空之后，继续执行增加一个节点，导致最后多一位0 A：加一个判断，都为空直接结束 Q：其中一个链表对应的数位数多一位 1 8 + 2 A：有一个不为空则继续加，只不过不加为空的那个链表的值，None.val报错， 遍历的时候也忽略为空的那个 Q：还有最后可能也有进位 5 + 5 A: 判断都为空的时候再判断进位标志位是否为1，是的话一个节点值为1"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def addTwoNumbers(self, l1, l2) -> ListNode:
"""最直接的方法：遍历两个链表到最后，执行加操作 Q：l1，l2为空之后，继续执行增加一个节点，导致最后多一位0 A：加一个判断，都为空直接结束 Q：其中一个链表对应的数位数多一位 1 8 + 2 A：有一个不为空则继续加，只不过不加为空的那个链表的值，None.val报错， 遍历的时候也忽略为空的那个 Q：还有最后可能也有进位 5 + 5 A: 判断都为空的时候再判断进位标志位是否为1，是的话一个节点值为1"""
head = ListNode(0)
l... | the_stack_v2_python_sparse | LinkListOperation/addTwoNumbers.py | Philex5/codingPractice | train | 0 | |
cffaf3e28c1eb069a6d6feba625891bf4363bc36 | [
"super().__init__(freq=freq, context_length=context_length, prediction_length=prediction_length, num_feat_dynamic_real=num_feat_dynamic_real, num_feat_static_real=num_feat_static_real, num_feat_static_cat=num_feat_static_cat, cardinality=cardinality, embedding_dimension=embedding_dimension, num_layers=num_layers, h... | <|body_start_0|>
super().__init__(freq=freq, context_length=context_length, prediction_length=prediction_length, num_feat_dynamic_real=num_feat_dynamic_real, num_feat_static_real=num_feat_static_real, num_feat_static_cat=num_feat_static_cat, cardinality=cardinality, embedding_dimension=embedding_dimension, num_... | MQF2MultiHorizonModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MQF2MultiHorizonModel:
def __init__(self, freq: str, context_length: int, prediction_length: int, num_feat_dynamic_real: int, num_feat_static_real: int, num_feat_static_cat: int, cardinality: List[int], distr_output: Optional[DistributionOutput]=None, embedding_dimension: Optional[List[int]]=Non... | stack_v2_sparse_classes_36k_train_015693 | 6,947 | permissive | [
{
"docstring": "Model class for the model MQF2 proposed in the paper ``Multivariate Quantile Function Forecaster`` by Kan, Aubet, Januschowski, Park, Benidis, Ruthotto, Gasthaus This is the multi-horizon (multivariate in time step) variant of MQF2 This class is based on gluonts.torch.model.deepar.module.DeepARM... | 3 | stack_v2_sparse_classes_30k_train_004327 | Implement the Python class `MQF2MultiHorizonModel` described below.
Class description:
Implement the MQF2MultiHorizonModel class.
Method signatures and docstrings:
- def __init__(self, freq: str, context_length: int, prediction_length: int, num_feat_dynamic_real: int, num_feat_static_real: int, num_feat_static_cat: i... | Implement the Python class `MQF2MultiHorizonModel` described below.
Class description:
Implement the MQF2MultiHorizonModel class.
Method signatures and docstrings:
- def __init__(self, freq: str, context_length: int, prediction_length: int, num_feat_dynamic_real: int, num_feat_static_real: int, num_feat_static_cat: i... | a818f69dc049c1c1d57e09d2ccb8b5f7a0cff656 | <|skeleton|>
class MQF2MultiHorizonModel:
def __init__(self, freq: str, context_length: int, prediction_length: int, num_feat_dynamic_real: int, num_feat_static_real: int, num_feat_static_cat: int, cardinality: List[int], distr_output: Optional[DistributionOutput]=None, embedding_dimension: Optional[List[int]]=Non... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MQF2MultiHorizonModel:
def __init__(self, freq: str, context_length: int, prediction_length: int, num_feat_dynamic_real: int, num_feat_static_real: int, num_feat_static_cat: int, cardinality: List[int], distr_output: Optional[DistributionOutput]=None, embedding_dimension: Optional[List[int]]=None, num_layers:... | the_stack_v2_python_sparse | src/gluonts/torch/model/mqf2/module.py | kashif/gluon-ts | train | 5 | |
909682b53f3183c561aa0ced34e09240014b39fb | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn Task()",
"from ..entity import Entity\nfrom ..key_value_pair import KeyValuePair\nfrom .lifecycle_task_category import LifecycleTaskCategory\nfrom .task_processing_result import TaskProcessingResult\nfrom ..entity import Entity\nfrom .... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return Task()
<|end_body_0|>
<|body_start_1|>
from ..entity import Entity
from ..key_value_pair import KeyValuePair
from .lifecycle_task_category import LifecycleTaskCategory
fr... | Task | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Task:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Task:
"""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: Task"""
... | stack_v2_sparse_classes_36k_train_015694 | 5,225 | 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: Task",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(parse_no... | 3 | stack_v2_sparse_classes_30k_train_014926 | Implement the Python class `Task` described below.
Class description:
Implement the Task class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Task: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The pars... | Implement the Python class `Task` described below.
Class description:
Implement the Task class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Task: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The pars... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class Task:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Task:
"""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: Task"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Task:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Task:
"""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: Task"""
if not parse_n... | the_stack_v2_python_sparse | msgraph/generated/models/identity_governance/task.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
ff8739d3bf20714b39dbbb763c9b52194206ca2a | [
"self.layers = ModuleList([ConditionalDetrTransformerDecoderLayer(**self.layer_cfg) for _ in range(self.num_layers)])\nself.embed_dims = self.layers[0].embed_dims\nself.post_norm = build_norm_layer(self.post_norm_cfg, self.embed_dims)[1]\nself.query_scale = MLP(self.embed_dims, self.embed_dims, self.embed_dims, 2)\... | <|body_start_0|>
self.layers = ModuleList([ConditionalDetrTransformerDecoderLayer(**self.layer_cfg) for _ in range(self.num_layers)])
self.embed_dims = self.layers[0].embed_dims
self.post_norm = build_norm_layer(self.post_norm_cfg, self.embed_dims)[1]
self.query_scale = MLP(self.embed_di... | Decoder of Conditional DETR. | ConditionalDetrTransformerDecoder | [
"Apache-2.0",
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConditionalDetrTransformerDecoder:
"""Decoder of Conditional DETR."""
def _init_layers(self) -> None:
"""Initialize decoder layers and other layers."""
<|body_0|>
def forward(self, query: Tensor, key: Tensor=None, query_pos: Tensor=None, key_pos: Tensor=None, key_padding... | stack_v2_sparse_classes_36k_train_015695 | 7,563 | permissive | [
{
"docstring": "Initialize decoder layers and other layers.",
"name": "_init_layers",
"signature": "def _init_layers(self) -> None"
},
{
"docstring": "Forward function of decoder. Args: query (Tensor): The input query with shape (bs, num_queries, dim). key (Tensor): The input key with shape (bs,... | 2 | null | Implement the Python class `ConditionalDetrTransformerDecoder` described below.
Class description:
Decoder of Conditional DETR.
Method signatures and docstrings:
- def _init_layers(self) -> None: Initialize decoder layers and other layers.
- def forward(self, query: Tensor, key: Tensor=None, query_pos: Tensor=None, k... | Implement the Python class `ConditionalDetrTransformerDecoder` described below.
Class description:
Decoder of Conditional DETR.
Method signatures and docstrings:
- def _init_layers(self) -> None: Initialize decoder layers and other layers.
- def forward(self, query: Tensor, key: Tensor=None, query_pos: Tensor=None, k... | 8d5f9a2d49ab8f9e85ccf058cb02c2fda287afc6 | <|skeleton|>
class ConditionalDetrTransformerDecoder:
"""Decoder of Conditional DETR."""
def _init_layers(self) -> None:
"""Initialize decoder layers and other layers."""
<|body_0|>
def forward(self, query: Tensor, key: Tensor=None, query_pos: Tensor=None, key_pos: Tensor=None, key_padding... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConditionalDetrTransformerDecoder:
"""Decoder of Conditional DETR."""
def _init_layers(self) -> None:
"""Initialize decoder layers and other layers."""
self.layers = ModuleList([ConditionalDetrTransformerDecoderLayer(**self.layer_cfg) for _ in range(self.num_layers)])
self.embed_d... | the_stack_v2_python_sparse | ai/mmdetection/mmdet/models/layers/transformer/conditional_detr_layers.py | alldatacenter/alldata | train | 774 |
bf2cb6ce763e0e859c5699c57c1a5b7a28b81ecb | [
"model_params = {'architecture': 'ResNet101', 'pooling': 'gem', 'whitening': False, 'pretrained': True}\nmodel = global_model.GlobalFeatureNet(**model_params)\nexpected_meta = {'architecture': 'ResNet101', 'pooling': 'gem', 'whitening': False, 'outputdim': 2048}\nself.assertEqual(expected_meta, model.meta)",
"mod... | <|body_start_0|>
model_params = {'architecture': 'ResNet101', 'pooling': 'gem', 'whitening': False, 'pretrained': True}
model = global_model.GlobalFeatureNet(**model_params)
expected_meta = {'architecture': 'ResNet101', 'pooling': 'gem', 'whitening': False, 'outputdim': 2048}
self.assert... | Tests for the GlobalFeatureNet backbone. | GlobalFeatureNetTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GlobalFeatureNetTest:
"""Tests for the GlobalFeatureNet backbone."""
def testInitModel(self):
"""Testing GlobalFeatureNet initialization."""
<|body_0|>
def testExtractVectors(self):
"""Tests extraction of global descriptors from list."""
<|body_1|>
d... | stack_v2_sparse_classes_36k_train_015696 | 3,230 | permissive | [
{
"docstring": "Testing GlobalFeatureNet initialization.",
"name": "testInitModel",
"signature": "def testInitModel(self)"
},
{
"docstring": "Tests extraction of global descriptors from list.",
"name": "testExtractVectors",
"signature": "def testExtractVectors(self)"
},
{
"docstr... | 3 | null | Implement the Python class `GlobalFeatureNetTest` described below.
Class description:
Tests for the GlobalFeatureNet backbone.
Method signatures and docstrings:
- def testInitModel(self): Testing GlobalFeatureNet initialization.
- def testExtractVectors(self): Tests extraction of global descriptors from list.
- def t... | Implement the Python class `GlobalFeatureNetTest` described below.
Class description:
Tests for the GlobalFeatureNet backbone.
Method signatures and docstrings:
- def testInitModel(self): Testing GlobalFeatureNet initialization.
- def testExtractVectors(self): Tests extraction of global descriptors from list.
- def t... | d3507b550a3ade40cade60a79eb5b8978b56c7ae | <|skeleton|>
class GlobalFeatureNetTest:
"""Tests for the GlobalFeatureNet backbone."""
def testInitModel(self):
"""Testing GlobalFeatureNet initialization."""
<|body_0|>
def testExtractVectors(self):
"""Tests extraction of global descriptors from list."""
<|body_1|>
d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GlobalFeatureNetTest:
"""Tests for the GlobalFeatureNet backbone."""
def testInitModel(self):
"""Testing GlobalFeatureNet initialization."""
model_params = {'architecture': 'ResNet101', 'pooling': 'gem', 'whitening': False, 'pretrained': True}
model = global_model.GlobalFeatureNet... | the_stack_v2_python_sparse | research/delf/delf/python/training/model/global_model_test.py | jianzhnie/models | train | 2 |
8fdae510c84f4cc1e84b670792eb671cd0bb5021 | [
"class ExampleDataset(Dataset):\n\n def __init__(self, size):\n self.size = size\n self.data = torch.arange(size) + 100\n\n def __getitem__(self, index):\n if torch.is_tensor(index):\n index = index.tolist()\n return self.data[index]\n\n def __len__(self):\n re... | <|body_start_0|>
class ExampleDataset(Dataset):
def __init__(self, size):
self.size = size
self.data = torch.arange(size) + 100
def __getitem__(self, index):
if torch.is_tensor(index):
index = index.tolist()
... | two types of Dataset: map-style, iterable-style map-style: 基类是Dataset，代表kv类型的数据集 iterable-style: 基类是IterableDataset, 代表list类型的数据集 | DatasetTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatasetTest:
"""two types of Dataset: map-style, iterable-style map-style: 基类是Dataset，代表kv类型的数据集 iterable-style: 基类是IterableDataset, 代表list类型的数据集"""
def test_dataset(self):
"""Dataset: map-style抽象基类; 提供方法ds[key], len(ds) 子类必须实现 def __getitem__(self, index) def __len__(self)"""
... | stack_v2_sparse_classes_36k_train_015697 | 2,805 | no_license | [
{
"docstring": "Dataset: map-style抽象基类; 提供方法ds[key], len(ds) 子类必须实现 def __getitem__(self, index) def __len__(self)",
"name": "test_dataset",
"signature": "def test_dataset(self)"
},
{
"docstring": "IterableDataset: iterable-style抽象类， 提供iter方法 子类必须实现： __iter__() Note: 一般在Dataloader进行多进程加载时， ds会被复... | 2 | stack_v2_sparse_classes_30k_train_001097 | Implement the Python class `DatasetTest` described below.
Class description:
two types of Dataset: map-style, iterable-style map-style: 基类是Dataset，代表kv类型的数据集 iterable-style: 基类是IterableDataset, 代表list类型的数据集
Method signatures and docstrings:
- def test_dataset(self): Dataset: map-style抽象基类; 提供方法ds[key], len(ds) 子类必须实现... | Implement the Python class `DatasetTest` described below.
Class description:
two types of Dataset: map-style, iterable-style map-style: 基类是Dataset，代表kv类型的数据集 iterable-style: 基类是IterableDataset, 代表list类型的数据集
Method signatures and docstrings:
- def test_dataset(self): Dataset: map-style抽象基类; 提供方法ds[key], len(ds) 子类必须实现... | 4f4bd55d7f0502c188976dda2f95fd25614283f3 | <|skeleton|>
class DatasetTest:
"""two types of Dataset: map-style, iterable-style map-style: 基类是Dataset，代表kv类型的数据集 iterable-style: 基类是IterableDataset, 代表list类型的数据集"""
def test_dataset(self):
"""Dataset: map-style抽象基类; 提供方法ds[key], len(ds) 子类必须实现 def __getitem__(self, index) def __len__(self)"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatasetTest:
"""two types of Dataset: map-style, iterable-style map-style: 基类是Dataset，代表kv类型的数据集 iterable-style: 基类是IterableDataset, 代表list类型的数据集"""
def test_dataset(self):
"""Dataset: map-style抽象基类; 提供方法ds[key], len(ds) 子类必须实现 def __getitem__(self, index) def __len__(self)"""
class Examp... | the_stack_v2_python_sparse | com.xulf.learn.ml.pytorch/th_data/dataset_test.py | sankoudai/py-knowledge-center | train | 0 |
098488790d31900f44208d9cd2279a4afd2423c9 | [
"self.seg = WordSegmentation(stop_words_file=stop_words_file, allow_speech_tags=allow_speech_tags)\nself.wc_background = get_default_wc_background()\nif type(wc_background) is str:\n self.wc_background = wc_background\nself.font_path = get_default_font_path()\nif type(font_path) is str:\n self.font_path = fon... | <|body_start_0|>
self.seg = WordSegmentation(stop_words_file=stop_words_file, allow_speech_tags=allow_speech_tags)
self.wc_background = get_default_wc_background()
if type(wc_background) is str:
self.wc_background = wc_background
self.font_path = get_default_font_path()
... | WordCloud | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordCloud:
def __init__(self, stop_words_file=None, allow_speech_tags=conf.allow_speech_tags, wc_background=None, font_path=None, max_words=200, max_font_size=100, save_path=None, wc_name=None, topK=10):
""":param stop_words_file: -- str，停用词文件路径，若不是str则使用默认停用词文件 :param allow_speech_tags:... | stack_v2_sparse_classes_36k_train_015698 | 4,561 | no_license | [
{
"docstring": ":param stop_words_file: -- str，停用词文件路径，若不是str则使用默认停用词文件 :param allow_speech_tags: -- 词性列表 :param wc_background: 词云图背景图片 :param max_words: 最多显示词数，默认200 :param max_font_size: 字体最大值，默认100 :param save_path: 词云图保存地址，默认当前文件夹",
"name": "__init__",
"signature": "def __init__(self, stop_words_fil... | 4 | stack_v2_sparse_classes_30k_train_019268 | Implement the Python class `WordCloud` described below.
Class description:
Implement the WordCloud class.
Method signatures and docstrings:
- def __init__(self, stop_words_file=None, allow_speech_tags=conf.allow_speech_tags, wc_background=None, font_path=None, max_words=200, max_font_size=100, save_path=None, wc_name... | Implement the Python class `WordCloud` described below.
Class description:
Implement the WordCloud class.
Method signatures and docstrings:
- def __init__(self, stop_words_file=None, allow_speech_tags=conf.allow_speech_tags, wc_background=None, font_path=None, max_words=200, max_font_size=100, save_path=None, wc_name... | a477c2926e97c86135623a2c7c844812be3be696 | <|skeleton|>
class WordCloud:
def __init__(self, stop_words_file=None, allow_speech_tags=conf.allow_speech_tags, wc_background=None, font_path=None, max_words=200, max_font_size=100, save_path=None, wc_name=None, topK=10):
""":param stop_words_file: -- str，停用词文件路径，若不是str则使用默认停用词文件 :param allow_speech_tags:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordCloud:
def __init__(self, stop_words_file=None, allow_speech_tags=conf.allow_speech_tags, wc_background=None, font_path=None, max_words=200, max_font_size=100, save_path=None, wc_name=None, topK=10):
""":param stop_words_file: -- str，停用词文件路径，若不是str则使用默认停用词文件 :param allow_speech_tags: -- 词性列表 :para... | the_stack_v2_python_sparse | WordCloud/word_cloud/word_cloud.py | FredZhao04/chinese-nlp | train | 5 | |
53ea4d4d18cd81e6cd5cda7d78ab6f7cd27dc050 | [
"if (len(nums1) + len(nums2)) % 2 == 1:\n return self.findKth(nums1, nums2, (len(nums1) + len(nums2) + 1) // 2)\nelse:\n median1 = self.findKth(nums1, nums2, (len(nums1) + len(nums2)) // 2)\n median2 = self.findKth(nums1, nums2, (len(nums1) + len(nums2)) // 2 + 1)\n return (median1 + median2) / 2.0",
... | <|body_start_0|>
if (len(nums1) + len(nums2)) % 2 == 1:
return self.findKth(nums1, nums2, (len(nums1) + len(nums2) + 1) // 2)
else:
median1 = self.findKth(nums1, nums2, (len(nums1) + len(nums2)) // 2)
median2 = self.findKth(nums1, nums2, (len(nums1) + len(nums2)) // 2... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
<|body_0|>
def findKth(self, nums1, nums2, k):
"""Find the k th largest number in the union of `nums1` and `nums2`"""
<|body_1|>
<|end... | stack_v2_sparse_classes_36k_train_015699 | 3,611 | no_license | [
{
"docstring": ":type nums1: List[int] :type nums2: List[int] :rtype: float",
"name": "findMedianSortedArrays",
"signature": "def findMedianSortedArrays(self, nums1, nums2)"
},
{
"docstring": "Find the k th largest number in the union of `nums1` and `nums2`",
"name": "findKth",
"signatur... | 2 | stack_v2_sparse_classes_30k_train_013222 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int] :rtype: float
- def findKth(self, nums1, nums2, k): Find the k th largest number in ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int] :rtype: float
- def findKth(self, nums1, nums2, k): Find the k th largest number in ... | 69a960dd8f39e9c8435a3678852071e1085fcb72 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
<|body_0|>
def findKth(self, nums1, nums2, k):
"""Find the k th largest number in the union of `nums1` and `nums2`"""
<|body_1|>
<|end... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
if (len(nums1) + len(nums2)) % 2 == 1:
return self.findKth(nums1, nums2, (len(nums1) + len(nums2) + 1) // 2)
else:
median1 = self.findKth(... | the_stack_v2_python_sparse | python/binary_search/lc4.py | chao-ji/LeetCode | train | 1 |
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