text stringlengths 190 325k |
|---|
Imports:
```python
import typing
```
Type definitions:
Input Types: float, np.ndarray, Any
Output Type: np.ndarray
Dependencies:
```python
def v0(v1: float, v2: np.ndarray) -> np.ndarray:
return v1 * v2
```
Function Name: v3
Function:
```python
def v3(v4: float, v5: np.ndarray, v6=6.0) -> np.ndarray:
v7 = v0(v... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Callable[[Sequence[str]], List[str]]
Dependencies:
```python
def v0(v1):
return split_by_regex('[A-Z][a-z0-9]+')(v1)
```
Function Name: v2
Function:
```python
def v2() -> Callable[[Sequence[str]], List[str]]:
@apply_to_each
... |
Imports:
```python
from hashlib import sha256
import hmac
import base64
import typing
```
Type definitions:
```python
v0 = Union[str, bytes, bytearray]
```
Input Types: v0, v0, str
Output Type: bytes
Dependencies:
Function Name: v1
Function:
```python
def v1(v2: v0, v3: v0, v4: str='utf-8') -> bytes:
if isinstance... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> bool:
if self.open[v1.code]:
for v2 in range(len(self.outcomming)):
v3 = next(self.outcomming_cicle)
if v3.code != v1.re... |
Imports:
```python
import numpy
import typing
```
Type definitions:
Input Types: 'qcelemental.models.results.WavefunctionProperties', int
Output Type: numpy.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: 'qcelemental.models.results.WavefunctionProperties', v2: int) -> numpy.ndarray:
v3 = g... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.assert_subtype(self.fx.e, self.fx.f)
self.assert_equivalent(self.fx.f, self.fx.f)
self.assert_not_subtype(self.fx.a, self.fx.f)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
if len(v1) == 4:
return '0000{0}-0000-1000-8000-00805f9b34fb'.format(v1.lower())
else:
return v1.lower()
``` |
Imports:
```python
import datetime as dt
import os
import re
import typing
```
Type definitions:
Input Types: str
Output Type: list[Any]
Dependencies:
```python
def v0(v1: str) -> list[Any]:
v2: list[Any] = []
if len(v1.split('_')[0]) > 15:
v2.append(f'Landsat {int(v1[2])}')
v2.append(lsat_sens... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> None:
self.msg_edit.setText(v1)
self.show()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: JobStatus
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> JobStatus:
self._update_status_queue_info_error()
return self._status
``` |
Imports:
```python
import logging
import typing
```
Type definitions:
Input Types: dict, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: dict, v2: str) -> None:
v1['domains'][v2] = {'mapped': 0, 'next_neuron_id': 0, 'last_bmu_id': None, 'ema_error': None, 'ema_variance': 0.0, ... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any, Any, Any
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2, v3) -> dict:
(v4, v5) = np.split(v1, [int(0.9 * v1.shape[0])])
(v6, v7) = np.split(v2, [int(0.9 * v2.shape[0])])
v8 = {'... |
Imports:
```python
import typing
```
Type definitions:
Input Types: yahoo_tv.Schedule
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: yahoo_tv.Schedule):
v2 = v1.get_all_station()
if not v2:
print('放送局一覧を取得できませんでした。')
return 1
for v3 in v2:
print(v3)... |
Imports:
```python
import random
import numpy as np
import typing
```
Type definitions:
Input Types: Optional[int]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Optional[int]=None):
if v1 is not None:
np.random.seed(v1)
random.seed(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> bool:
v2 = self.client.get(f'/containers/{v1}/exists')
return v2.ok
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int) -> list:
v2 = [0] * 4
v3 = 3
while v1 != 0 and v3 > -1:
v2[v3] = v1 % 10
v1 //= 10
v3 -= 1
return v2
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: int=1):
v3 = v1.shape[0]
v4 = np.atleast_2d(v1)
v5 = v4 + np.random.laplace(scale=self.eps, size=(v2, v3))
return v5... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list[int], list[int]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list[int], v2: list[int]):
if v1[0] == 0:
v2[v1[3]] = v2[v1[1]] + v2[v1[2]]
elif v1[0] == 1:
v2[v1[3]] = v2[v1[1]] + v1[2]
... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: bool=False):
v3 = [fname for v4 in sorted(os.listdir(v1))]
if v2:
v3 = [os.path.join(v1, v4) for v4 in v3]
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: carla.Client, int
Output Type: Any
Dependencies:
```python
def v0(v1: carla.Client, v2: int) -> carla.ServerSideSensor:
v3 = v1.get_world()
v3.wait_for_tick()
v4 = v3.get_actor(v2)
v3.wait_for_tick()
if v4 is None:
raise Va... |
Imports:
```python
import tensorflow as tf
import numpy as np
import typing
```
Type definitions:
Input Types: tuple
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: tuple) -> dict:
v2 = {'num_detections': tf.convert_to_tensor(np.array([float(len(tf.convert_to_tensor(v1[2])))], dty... |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray) -> np.ndarray:
v2 = self._expectation_value_components[0, 0](v1)
for v3 in range(1, self._sp.num_energy_states):
v2 +=... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray
Output Type: NoReturn
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray, v2: np.ndarray) -> NoReturn:
self.weights_ = np.zeros((self.iterations_,))
if len(v1.shape) == 1:... |
Imports:
```python
from qiskit import QuantumCircuit, QuantumRegister
import typing
```
Type definitions:
Input Types:
Output Type: QuantumCircuit
Dependencies:
Function Name: v0
Function:
```python
def v0() -> QuantumCircuit:
v1 = QuantumRegister(4, 'q')
v2 = QuantumCircuit(v1)
v2.h(v1[0])
v2.h(v1[1... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str):
v2 = ':green:`py`\\ :gray:`throttle`'
v1 = v1.strip()
v1 = v1.split('\n', 2)[-1]
return v2 + '\n' + '=' * len(v2) + '\n' + v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: 'MonitorTask'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> 'MonitorTask':
self._task_name = v1
return self
``` |
Imports:
```python
import math
import folium
import typing
```
Type definitions:
```python
v0 = Tuple[float, float]
```
Input Types: folium.Map, List[v0], bool, float, int
Output Type: None
Dependencies:
```python
def v1(v2: v0, v3: v0) -> float:
return math.sqrt((v2[0] - v3[0]) ** 2 + (v2[1] - v3[1]) ** 2)
```
```... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> None:
if not self._max_calls or v1 < self._max_calls:
self._max_calls = v1
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray, float
Output Type: Tuple[np.ndarray, np.ndarray, float]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray, v2: np.ndarray, v3: float=0.001) -> Tuple[np.ndarray, np.ndarray, float]:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> int:
if v1 == 'pc':
return 1
elif v1 == 'android':
return 2
elif v1 == 'ios':
return 4
elif v1 == 'mac':
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self) -> None:
for (v1, v2) in self._heartbeat_future_dict.items():
if not v2.done() and (not v2.cancelled()):
v2.cancel()
await self._cli... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str):
if v1.islower() or v1.isupper():
return 'Invalid calculation'
try:
return eval(v1)
except ValueError:
return 'Invalid calcul... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> bool:
if self.war is not None:
return self.should_send_mii_notification
return False
``` |
Imports:
```python
import torch
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.env.reset()
self.network.reset_state_variables()
self.accumulated_reward = 0.0
self.step_count = 0
self.overlay_start =... |
Imports:
```python
import inspect
import typing
```
Type definitions:
Input Types:
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(cls: type) -> List[str]:
v1 = inspect.signature(cls.__init__)
v2 = dict(v1.parameters)
v2.pop('self')
return list(v2.keys())
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, bool
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: bool=False) -> None:
v4 = self.container_client.get_blob_client(v2)
with open(v1, 'rb') as v5:
v4.upload_blob(v5, ... |
Imports:
```python
import os
import os.path
from os.path import expanduser
import typing
```
Type definitions:
Input Types: Optional[str]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[str]=None) -> None:
if v1 is None:
v1 = ''
if self.path is None:... |
Imports:
```python
import glob
import random
import typing
```
Type definitions:
Input Types:
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0() -> list:
v1: list = glob.glob('../data/VIDEO/*')
v2: list = []
for v3 in v1:
for v4 in glob.glob(v3 + '/*.csv'):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: collections.Counter, list[str], list[tuple[int, str]]
Output Type: tuple[collections.Counter, dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: collections.Counter, v2: list[str], v3: list[tuple[int, str]]) -> tuple[collectio... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: tuple
Dependencies:
```python
def v0(v1: dict=GRAPH_ATTR, v2: str=OUTPUT_FORMAT, v3: str=OUTPUT_PATH) -> str:
v4 = f'{v3}/media_processing'
with Diagram('Media Processing', show=False, outformat=v2, filename=v4):
v5 = Pyt... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types:
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> float:
v1 = self.label_histograms
v2 = np.log(self._internal_bin_confidences())
return -(v1[..., 1, :] * v2).sum() / v1[..., 1, :].... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Optional[float]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Optional[float]:
if not self.queue:
return None
v1 = self.get_oldest()
assert v1 is not None
return self.queue[v1]
``` |
Imports:
```python
import numpy
from numpy.linalg import norm
from scipy.fft import idstn, idctn
from scipy.ndimage import convolve
from scipy.ndimage import median_filter, gaussian_filter
import typing
```
Type definitions:
Input Types: Any, float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
... |
Imports:
```python
import sqlite3
import typing
```
Type definitions:
```python
v0 = Tuple[int, str, str, str, str, str, str, str, str, Dict[str, Any]]
```
Input Types: str
Output Type: None
Dependencies:
```python
def v1() -> sqlite3.Connection:
v2 = wn.config.database_path
v3 = v2.is_file()
v4 = sqlite3.c... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = typing.MutableMapping[tree.Node, tree.Node]
```
Input Types: v0, v0
Output Type: v0
Dependencies:
Function Name: v1
Function:
```python
def v1(v2: v0, v3: v0) -> v0:
v4 = {}
for v5 in set(v2.keys()).intersection(set(v3.keys())):
v4[v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Tensor
Output Type: Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tensor) -> Tensor:
(v2, v3, v4, v5, v5) = self.image_sizes
return self.tile.backward_indexed(v1, v2, v3, v4)
``` |
Imports:
```python
import json
import os
import typing
```
Type definitions:
Input Types: str, int, int
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: int, v3: int) -> str:
v4 = os.path.join(self.repo_dir, '{}:{}:{}'.format(v1, v2, v3))
(v5, v6, v7) = self.run_c... |
Imports:
```python
import json
import typing
```
Type definitions:
Input Types: Pipeline.PipeLineConfigDTO
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Pipeline.PipeLineConfigDTO):
v2 = self._pl.get_all_pipeline_configs()
if v2.status_code < 300:
try:
... |
Imports:
```python
from io import BytesIO
from PIL import Image, UnidentifiedImageError
import typing
```
Type definitions:
Input Types: dataiku.Folder, dataiku.Folder, List[AnyStr], AnyStr
Output Type: AnyStr
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dataiku.Folder, v2: dataiku.Folder, v3:... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> int:
if v1[0] != v1[-1]:
while v1[0] != v1[-1]:
v1 = str(int(v1) - 1)
v2 = len(v1)
v3 = 0
if v2 == 1:
return int(v... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> int:
v3 = v1.split(' ')
v4 = [i for v5 in range(0, len(v3)) if v3[v5][1:] == v2]
if len(v4) == 0:
return False
... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
os.environ.clear()
os.environ.update(self._orig_env)
if os.path.isfile(self.outfile):
os.unlink(self.outfile)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self._context['reversed']:
self._move_to_last_line()
self._vim.command('normal! zb')
else:
self._move_to_first_line()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: float, bool
Output Type: Any
Dependencies:
```python
def v0(v1: tf.Tensor, v2: tf.Tensor):
v2 *= dm_alpha
return tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.SUM)(v1, v2)
```
Function Name... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str, v3: str, v4: str):
v5 = " {}: Optional[Union[Sequence[Union[{}, Reference]], {}, Reference]]\n if '{}' not in d:\n {} = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: archs.HyperInfo
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2) -> archs.HyperInfo:
v3 = self._encoder(v1, training=self.training)
return self._entropy_model(v3, image_shape=v2, mode=self._mode)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: ('Dry run', 'flag', 'd')
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: ('Dry run', 'flag', 'd')):
if v1:
print('Doing nothing')
else:
print('Doing something')
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: bool, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool, v2=True):
self.active_this_session = v1
if v2:
self.save()
``` |
Imports:
```python
import warnings
import typing
```
Type definitions:
Input Types: int, Optional[Dict[str, float]]
Output Type: Dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int=None, v2: Optional[Dict[str, float]]=None) -> Dict:
if v2 is None:
v2 = {}
v3 = 0
v4: Optio... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: str, int
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: int) -> list:
v3 = 120
v4 = []
if v2 > v3:
for v5 in range(1, int(np.floor(v2 / 120)) + 1):
v6 = v3 ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Path
Output Type: Path
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Path) -> Path:
v2 = next((v1 / 'lib').glob('*'))
if v2.name != 'site-packages':
v2 /= 'site-packages'
return v2
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1, v2, v3, v4, v5, v6, v7=[]):
self._url: str = v1
self._route: str = v2
self._format: str = v3
self._content: str = v4
self._title: str = v5
self._slug: str = v6
s... |
Imports:
```python
import cv2
import typing
```
Type definitions:
Input Types: Any, List
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2: List) -> None:
for v3 in v2:
for v4 in v3:
cv2.circle(v1, v4, 1, (255, 0, 0))
``` |
Imports:
```python
from math import atan, degrees, gcd
import typing
```
Type definitions:
```python
v0 = Tuple[int, int]
```
```python
v1 = List[v0]
```
Input Types: v0, v1
Output Type: Any
Dependencies:
Function Name: v2
Function:
```python
def v2(v3: v0, v4: v1):
v5 = set()
for v6 in v4:
if v6 == v3... |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.array, float, float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.array=None, v2: float=0.01, v3: float=0.005):
if v1 is None or (v1[0] == 0 and v1[1] == 0):
self.curr_reward -= v2
v4 ... |
Imports:
```python
import re
import typing
```
Type definitions:
```python
v0 = namedtuple('CalendarEvent', ['title', 'start', 'end', 'duration', 'categories'])
```
Input Types: List[v0], str, str
Output Type: Dict[str, List[v0]]
Dependencies:
Function Name: v1
Function:
```python
def v1(v2: List[v0], v3: str, v4: str... |
Imports:
```python
import ast
import typing
```
Type definitions:
Input Types: Path
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Path):
v2 = {}
if v1.exists():
with open(v1, 'r') as v3:
v4 = v3.read()
v2 = ast.literal_eval(v4)
return v2
``... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = TypeVar('Decorated')
```
Input Types: v0
Output Type: v0
Dependencies:
Function Name: v1
Function:
```python
def v1(v2: v0) -> v0:
v2.auth_allow_anonymous_access = True
return v2
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: Any
Dependencies:
```python
def v0(v1: int, v2: np.ndarray, v3: np.ndarray):
v4 = 0
v5 = (0, 0)
while True:
v6 = v4
v3[v1] = 0
v7 = np.sum(np.array((v2 - v2[v1]) ** 2), 1)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict, Dict[str, str], bool
Output Type: Dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict, v2: Dict[str, str], v3: bool=False) -> Dict:
if not v3:
v1 = dict(v1)
for (v4, v5) in v2.items():
if v4 in v1:
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray, np.ndarray, np.ndarray
Output Type: Tuple[np.ndarray, np.ndarray]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray, v2: np.ndarray, v3: np.ndarray, v4: np.ndarray) -> Tuple[np.... |
Imports:
```python
import typing
```
Type definitions:
Input Types: brawlstats.models.Player
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: brawlstats.models.Player):
v2 = 0
for v3 in v1.raw_data['brawlers']:
if 550 <= v3.get('trophies') <= 599:
v2 = v2 + 7... |
Imports:
```python
import typing
```
Type definitions:
Input Types: ndarray, ndarray, float
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: ndarray, v2: ndarray, v3: float) -> None:
for v4 in range(v2.shape[0]):
for v5 in range(v2.shape[1]):
v1[v4, v5] = 1 if v... |
Imports:
```python
import typing
```
Type definitions:
```python
@dataclass
class v0(EmbeddedDocument):
v1: ClassVar[Manager]
@property
def v2(self):
return getattr(self, self.Meta.pk_field, None)
def v3(self) -> v0:
return self.documents.create(obj=self)
def v4(self) -> None:
... |
Imports:
```python
import pandas as pd
import numpy as np
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: dict):
v2 = [np.ndarray, list, tuple, pd.Series]
v3 = []
v4 = ''
for v5 in v1.values():
if type(v5) no... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self.__field__ == '':
self.__collection__.__collection__.delete_one({'_id': self.__id__})
else:
self.__collection__.__collection__... |
Imports:
```python
import logging
import typing
```
Type definitions:
Input Types: str, Any
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2='latest') -> int:
if (v1 in self._nonce_dict) is False:
self._nonce_dict[v1] = self.web3.eth.get_transaction_count(v1, v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional[Tuple[int, ...]], Optional[Tuple[int, ...]]
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[Tuple[int, ...]]=None, v2: Optional[Tuple[int, ...]]=None) -> bool:
if not self.server_version:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, str]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict[str, str]):
v2 = v1.copy()
v2['cache-control'] = 'no-cache'
return v2
``` |
Imports:
```python
import torch
import torch.cuda
import torch.nn
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: torch.LongTensor, Tuple[Any, ...], Optional[Dict[str, Any]]
Output Type: Tuple[torch.Tensor, Optional[Dict[str, Any]]]
Dependencies:
Function Name: v0
Function:
```python
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: dict):
try:
v3 = self.dao.update(id=v1, update_data=v2)
return v3
except Exception as e:
raise
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict[str, Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Dict[str, Any]:
v1 = super().get_xgb_params()
v1['num_parallel_tree'] = self.n_estimators
return v1
``` |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
self.file_path = v1
v2 = pd.read_csv(v1)
v3 = str(input('Input the text Column Name Please ? : '))
self.corpus_list = ... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = TypeVar('T', bound=Tuple[Any, ...])
```
Input Types: str
Output Type: v0
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: str) -> v0:
v3 = getattr(self.tuple_type, '_fields', None)
v4 = getattr(self.tuple_type, '__annotat... |
Imports:
```python
from copy import deepcopy
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = deepcopy(self.parsed_mkt_data_buffer)
v2 = self.get_internal_data()
v3 = deepcopy(self.parsed_volume_data_buffer)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = self._api_get(f'workspaces/{self._workspace_id}/projects')
for v2 in v1:
self._projects_by_name[v2['name']] = v2
self._projects_... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
v1 = self.lex.get_token()
if not re.match('[a-zA-Z]+', v1):
raise Exception('expected identifier found %s' % v1)
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
```python
def v0(v1: ClauseElement) -> str:
v2 = get_sqlalchemy_connection().dialect
v3 = v1.compile(dialect=v2)
return str(v3)
```
Function Name: v4
Function:
```python
def v4(self) -> None:
v5 = dict(... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, str
Output Type: 'list[str]'
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str=' ', v3: str='"') -> 'list[str]':
v4 = []
v5 = ''
v6 = False
v7 = False
def v8():
nonlocal v, unitStarted
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float) -> None:
self.min_brightness = v1
self.max_brightness = max(self.max_brightness, v1)
``` |
Imports:
```python
import torch
import functools
import typing
```
Type definitions:
Input Types: list
Output Type: Any
Dependencies:
```python
def v0(v1: int, v2: int=0):
v3 = IntersimpleLidarFlatIncrementingAgent(loc=v2, track=v1, n_rays=5, reward=functools.partial(speed_reward, collision_penalty=0))
v4 = No... |
Imports:
```python
import folium
from folium.plugins import MarkerCluster, FeatureGroupSubGroup
import typing
```
Type definitions:
Input Types: Iterable[Tuple[float, float]], str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Iterable[Tuple[float, float]], v2: str, **v3):
v... |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: v1
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: v1):
v2 = pd.DataFrame.from_dict(v1)
v2.to_csv(self.output_file_name, index=False, sep='\t')
``` |
Imports:
```python
import torch
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: torch.Tensor, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor, v2: int):
assert v1.dtype in {torch.float16, torch.float32}
v3 = v1.dtype == torch.fl... |
Imports:
```python
import tensorflow as tf
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self._built:
return
self._built = True
self._global_step = tf.Variable(0, trainable=False)
self._tf_optimi... |
Imports:
```python
import glob
import os
import pandas as pd
import typing
```
Type definitions:
Input Types: bool, str
Output Type: tuple
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool=False, v2: str=None) -> tuple:
self.errors = []
self.warnings = []
v3 = [d for v4 in os.listd... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: typing.Optional[set[tanjun_abc.SlashHooks]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> typing.Optional[set[tanjun_abc.SlashHooks]]:
v1: typing.Optional[set[tanjun_abc.SlashHooks]] = None
if self._hooks a... |
Imports:
```python
from collections import namedtuple, defaultdict
from inspect import signature
import typing
```
Type definitions:
```python
v0 = Iterable[Item]
```
```python
v1 = Any
```
Input Types: Iterable[v1], Callable[[v1], Hashable], Optional[Callable[[v1], Any]], Callable[[], v0], Callable[[Any, Any], bool], ... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: List[Dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> List[Dict]:
v1 = 'projects/%s' % self.project_id
v2 = self.service.projects().processors().list(parent=v1).execute()
return v2['processors']
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> None:
v1.add_argument('--output_dir', default=None, type=str, required=True, help='The output directory where the model predictions and checkpoints w... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.