text stringlengths 190 325k |
|---|
Imports:
```python
import typing
```
Type definitions:
Input Types: List[int], int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int], v2: int) -> int:
if v2 == 1:
return 0
v3 = sum(v1)
v4 = v3 % v2
if v4 == 0:
return 0
v5 = {0: -1}
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str) -> str:
if v1 == '':
return self._cr_config['credentials'][v2]
return self._cr_config[v1]['credentials'][v2]
``` |
Imports:
```python
from tensorflow.compiler.xla import xla_data_pb2
from tensorflow.compiler.xla.experimental.xla_sharding import xla_sharding
import typing
```
Type definitions:
Input Types: tf.Variable
Output Type: tf.Variable
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: tf.Variable) -> tf.V... |
Imports:
```python
import json
from http import HTTPStatus
from json import JSONDecodeError
import typing
```
Type definitions:
Input Types: Response
Output Type: Any
Dependencies:
```python
def v0(v1: str):
try:
v2 = json.loads(v1)
if isinstance(v2, dict) and 'detail' in v2:
return v2[... |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: pd.DataFrame
Output Type: Union[pd.Series, ndarray, list[ndarray]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: pd.DataFrame) -> Union[pd.Series, ndarray, list[ndarray]]:
assert self.estimatorPool is no... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
if self.display.nrql:
v1 = f' {self.display.nrql}'
else:
v1 = ''
if self.condition:
v2 = f' WHERE {self.condition.nrql}'
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, *v1: Any, **v2: Any) -> Any:
v1 = self._input_to_device(v1)
v2 = self._input_to_device(v2)
return self.module(*v1, **v2)
``` |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = TypeVar('_Member', bound=Optional['IntegrityBase'])
```
Input Types: v0, str
Output Type: None
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: v0, v3: str) -> None:
v4 = False
for v5 in self.manifest['entries']:
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, Tuple[int, int]
Output Type: Any
Dependencies:
```python
def v0(v1: np.ndarray):
(v2, v3, v4, v5) = [i[:, 0] for v6 in np.split(v1, [1, 2, 3], axis=1)]
v7 = [v2 - 0.5 * v4, v3 - 0.5 * v5, v2 + 0.5 * v4, v3 + 0.5 ... |
Imports:
```python
from tqdm import tqdm
import typing
```
Type definitions:
Input Types: str, Optional[dict], Optional[dict], str, Optional[Callable], int, bool, str, bool
Output Type: list[dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: Optional[dict]=None, v3: Optional[dict]=Non... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'List[int]', 'Optional[str]'
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: 'List[int]', v2: 'Optional[str]'=None) -> bool:
if not self.check_connection():
return False
if not v1:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, List[str], str, str, int, Sequence[str], bool
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: List[str], v3: str, v4: str, v5: int, v6: Sequence[str], v7: bool) -> str:
v1 += self._add_comments(... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[str]
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[str]) -> List[str]:
v2 = []
for v3 in v1:
if v3.endswith('.so'):
v3 = v3[:-3]
if v3.startswith('lib'):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: pd.DataFrame
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: pd.DataFrame) -> pd.DataFrame:
v2 = v1.drop(list(self.columns), axis='columns', errors='ignore').copy()
for v3 in self.columns:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Tuple[Dataset, Dataset]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Tuple[Dataset, Dataset]:
(v1, v2) = super().pre_process_data()
if 'agent_position' in self.config['data_features']:
v1.subsets[0... |
Imports:
```python
import numpy as np
from numpy.random import choice
import typing
```
Type definitions:
Input Types: Any, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: int=None):
if v2 is None:
v2 = self.batch_size
v3 = self.data
v4 = self.starts[v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional[Type]
Output Type: Optional[Tuple]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Optional[Type]) -> Optional[Tuple]:
if v1 and hasattr(v1, '__args__'):
return v1.__args__
return None
``` |
Imports:
```python
import re
import requests
import typing
```
Type definitions:
Input Types: str
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> dict:
v2 = {}
v3 = {'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,applic... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str='.'):
for v2 in self.timeseries_metric.keys():
self.timeseries_metric[v2]['TimeStamps'] = self.timestamps
self.export(self.metric, exportpat... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: Sequence[str], str, str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Sequence[str], v2: str, v3: str) -> bool:
if v3 == '^$':
return True
(v4, v5) = (re.compile(v2), re.compile(v3))
fo... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self: 'ContextElement', v1: int) -> int:
for (v2, v3) in self._dilatations[::-1]:
if v2 <= v1:
v1 -= v3
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Any) -> Any:
v2 = v1
for (v3, v4) in enumerate(self):
if v4.__class__.__name__ == 'ModulePointer':
v2 = v4.forward(x=v2)[0]
... |
Imports:
```python
import base64
import logging
from base64 import b64encode
from urllib.parse import urlencode, quote
import typing
```
Type definitions:
Input Types: str
Output Type: Dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Dict:
v2 = {'key': self.key if self.key is not ... |
Imports:
```python
import copy
import typing
```
Type definitions:
Input Types: Dict[str, Any], Dict[str, Any]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict[str, Any], v2: Dict[str, Any]):
v1 = copy.deepcopy(v1)
for (v3, v4) in v2.items():
v5 = v3.split('.')
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float, Sequence[int]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float, v2: Sequence[int]) -> str:
v3 = 'parts'
if v1 > 90.0:
v3 = 'lost'
elif v1 < 50.0:
if v2[0] / len(v2) < 0.3:
... |
Imports:
```python
import random
import typing
```
Type definitions:
Input Types: str, List[float]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: List[float]) -> str:
v3 = random.randint(0, 3)
if v2 is None or len(v2) != 4:
v2 = self.alphas
v4 = self... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
v2 = ''
for v3 in range(len(v1)):
if v3 == 0:
v2 += v1[v3].upper()
elif v1[v3 - 1] in [' ', '-']:
v2 += v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: str):
assert v1
await self._ensure_subscribed('trade', v1)
return self.data[v1]['trade']
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.array, int
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.array, v2: int) -> dict:
v3 = {}
v4 = v1.shape[1] / v2
v5 = {1: 1, 2: 3, 4: 5}
for v6 in v5:
(v7, v8, v9) = (v1 == v6).non... |
Imports:
```python
import math
from statistics import mean, harmonic_mean
import typing
```
Type definitions:
Input Types: ty.Sequence[ty.Set], ty.Sequence[ty.Set]
Output Type: ty.Tuple[float, float, float]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: ty.Sequence[ty.Set], v2: ty.Sequence[ty.Set]) ->... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float, float
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float, v2: float) -> float:
v3 = v1 * v2 + (1 - v1) * (1 - v2) / 3
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
with self._lock:
self.is_disposed = True
self.singles = []
self.exception = None
self._value = (False, None)
``` |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: pd.DataFrame, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: pd.DataFrame, v2: int):
v3 = pd.DataFrame(v1['outputs'].tolist(), columns=[f'output{i}' for v4 in range(v2)])
v1 = pd.concat... |
Imports:
```python
import numpy as np
from numpy import dot
from numpy.linalg import norm
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> float:
v3 = dot(v1, v2) / (norm(v1) * norm(v2))
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[torch.Tensor], Any
Output Type: Tuple[torch.Tensor, torch.Tensor]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[torch.Tensor], v2) -> Tuple[torch.Tensor, torch.Tensor]:
v3 = self.evaluator_1(*v1)
v4 = self.eva... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
v1: 'ColumnAccessor'
v2: Optional[cudf.core.index.BaseIndex]
v3: Optional[List]
def __init__(self, v4=None, v5=None):
if v4 is None:
v4 = {}
self._data = cudf.core.column_accessor.ColumnAccessor(v4)
... |
Imports:
```python
import inspect
import typing
```
Type definitions:
Input Types: typing.Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: typing.Any) -> bool:
v2 = getattr(v1, '__aiter__', None)
return inspect.isfunction(v2) or inspect.ismethod(v2)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: bool):
v3 = self._instance.media_new_location(v1) if v2 else self._instance.media_new_path(v1)
self._player.set_media(v3)
v3.release(... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.set_falling_piece()
v1 = self.playfield.clear_full_lines()
if v1 > 0:
self.raise_on_lines_cleared_event(v1)
self.get_next_piece_... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int
Output Type: Dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int) -> Dict:
v3 = self._raw_jira.sprint_info(v1, v2)
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, int
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: int) -> str:
while len(v1) > v2:
v3 = []
for v4 in range(0, len(v1), v2):
v5 = 0
for v6 in range(v4, m... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, bool
Output Type: Optional[chess.Move]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: bool) -> Optional[chess.Move]:
v3 = -9999
v4: Optional[chess.Move] = None
for v5 in self.board.legal_moves:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int=-1) -> int:
if v1 == -1:
return super().get_id()
else:
self._used.add(v1)
return v1
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray):
v2 = np.float64
v3 = np.array(np.append(v1, np.fliplr(v1), axis=1), v2)
v3 = np.array(np.append(v3, np.flipud(v3), ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any, Any
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2, v3) -> float:
v4 = v1[v1[v2] == v3]
return len(v4.index) / len(v1.index)
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Union[None, Iterable[int]]
Output Type: Optional[Tuple[float, float, float, float]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[None, Iterable[int]]=None) -> Optional[Tuple[float, float, float, float]... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: int, v2: dict):
self.add_next(v1, v2)
await self.play(ignore_shuffle=True)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: torch.Tensor, torch.Tensor
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:
v3 = self.class_weights[v2].to(v2.device)
v4 = v1 * v3
return v4
``... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> int:
v1 = self.inner_widget.hex.get_selection()
if v1 is None:
return 0
(v2, v3) = v1
v4 = v3 - v2 + 1
return v4
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'QGraphicsSceneHoverEvent'
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'QGraphicsSceneHoverEvent') -> None:
self.hovered = False
self.update()
self.socket.socket_label.hide()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, bool, Optional[str], bool, Optional[str]
Output Type: str
Dependencies:
```python
@lru_cache()
def v0(v1: str, v2: str=SINGULAR_NAME_SUFFIX) -> str:
v3 = inflect_engine.singular_noun(v1)
if v3 is False:
v3 = f'{v1}{v2}'
return... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, Dict[str, List[Union[float, str]]], Callable[[np.ndarray], np.ndarray], List[str], Dict[int, List[str]], Dict[int, Dict[str, float]], Dict[str, List[float]], List[str], Any
Output Type: List[np.ndarray]
Dependencies:
Fu... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict, str
Output Type: Dict
Dependencies:
```python
@requiresMcVersion(2529, 'Support for hexadecimal color values was added in 1.16 (20w17a)')
def v0(v1: Dict, v2: str) -> Dict:
if len(v2) != 7:
raise ValueError(f'Required hexadecimal str... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[Tuple[int]]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[Tuple[int]]=None):
v2 = self._create_placeholders(v1)
v3 = [self._self] + v2 if self.is_method else v2
self._build(v3)
``` |
Imports:
```python
from datetime import datetime, timedelta
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str):
v2 = datetime.now()
return '_'.join([v1, str(v2.minute), str(v2.second)])
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, int
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: int) -> str:
if v2 >= len(v1):
return '0'
if '0' in v1:
v3 = v1.index('0')
if v2 >= v3:
return self.re... |
Imports:
```python
import os
import shlex
import stat
from pathlib import Path
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
```python
def v0(v1: str, *v2) -> plumbum.commands.BaseCommand:
def v3(v4) -> bool:
return command_path.is_file() and bool(stat.S_IMODE(v4.stat()... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: List[Dict[str, Any]], Optional[float]
Output Type: str
Dependencies:
```python
def v0(v1: List[Dict[str, Any]]) -> Tuple[float, float, float]:
v2: List[float] = []
for (v3, v4) in enumerate(v1):
v5 = v4['index']
v6 = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional[dict]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[dict]=None):
if self.log_metrics:
v1 = v1 or {}
v2 = v1.get('gp_model', None)
self.our_step = self.model.n_evals... |
Imports:
```python
import asyncio
import cv2
import typing
```
Type definitions:
Input Types:
Output Type: Optional[str]
Dependencies:
```python
async def v0() -> Optional[str]:
v1 = _start_camera()
v2 = (640, 480)
v3 = None
v4 = None
v5 = 'Signature Request QR Code Scanner'
cv2.namedWindow(v5... |
Imports:
```python
from pathlib import Path
import pandas as pd
import typing
```
Type definitions:
Input Types: pd.DataFrame
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: pd.DataFrame) -> None:
v2 = Path(self._get_save_path(self._filepath, self._version))
v2.parent.mk... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str, Any
Output Type: List[str]
Dependencies:
```python
def v0(v1: str) -> List[str]:
v2 = []
for v3 in os.listdir(v1):
v4 = os.fsdecode(v3)
if v4.endswith(cts.MD_EXT) or v4.endswith(cts.TXT_EXT):
v2.appen... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Callable[..., Any]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Callable[..., Any]) -> None:
v2 = [((self.table.key, self.key), False), ((self.table.key, self.name), True)]
for (v3, v4) in v2:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, str, dict, bool, list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: str='mean', v4: dict={}, v5: bool=False, v6: list=['organ', 'model_type']):
v7 = []
v8 = []
for (v9, v... |
Imports:
```python
import random
import typing
```
Type definitions:
Input Types: List[int], int, int, int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int], v2: int, v3: int, v4: int) -> int:
if v2 == v3:
return v1[v2]
v5 = random.randint(v2, v3)
v5 =... |
Imports:
```python
from PIL import Image
import typing
```
Type definitions:
Input Types: List[str], List[Tuple[int, int]]
Output Type: List[Tuple[str, str]]
Dependencies:
```python
def v0(v1):
try:
if v1 == (1920, 1080):
return 'Desktop'
elif v1 == (1080, 1920):
return 'Mob... |
Imports:
```python
from collections import OrderedDict
import typing
```
Type definitions:
Input Types: OrderedDict, OrderedDict
Output Type: Tuple[OrderedDict, OrderedDict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: OrderedDict, v2: OrderedDict) -> Tuple[OrderedDict, OrderedDict]:
v3 = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float, float
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float, v2: float) -> None:
if v2 > 0:
self.zoom /= 1.5
else:
self.zoom *= 1.5
self.zoom = max(0.0025, min(1, self.zoom))
... |
Imports:
```python
import torch
from torch import nn
from torch.nn.utils import rnn
import typing
```
Type definitions:
Input Types: torch.LongTensor, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.LongTensor, v2: int):
v3 = torch.arange(v2, device=self.device)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: str) -> None:
await self.clear_temporaries(v1)
await super().remove_sid(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: tuple, tuple
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: tuple, v2: tuple) -> list:
(v3, v4, v5, v6, v7, v8, v9, v10) = v1[:8]
(v11, v12, v13, v14, v15, v16, v17, v18) = v2
v19 = v11 - v12 / v16 - v12 ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
if v1[len(v1) - 1] != '.':
return v1[0].upper() + v1[1:] + '.'
return v1[0].upper() + v1[1:]
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> np.ndarray:
if self.__eigsolcalled:
(v1, v2) = self.__eig_solver.getSolution()
else:
v1 = None
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1=0, v2=0, v3=0, v4=0, v5=0):
self.pos = v1
if v2 == 2 or v3 == 2:
v2 -= 1
v3 -= 1
self.hap = v2
self.hap1 = v3
self.ps = v4
self.idx = v5
def ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> list:
if v1 in self.pred_dict:
return self.pred_dict[v1]
return []
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(BaseClient):
def __init__(self, v1: str, v2: str, v3: str, v4: str, v5: bool, v6: bool):
super().__init__(base_url=v2, verify=v5)
self._username = v3
self._password = v4
self._tenant = v1
self._proxies... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list):
v2 = {}
for v3 in v1:
v4 = v3.get('code', 0)
if v4 == -4004:
v3['value'] = None
elif v4 != 0:
co... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, Any], str, Optional[bool]
Output Type: str
Dependencies:
```python
def v0(v1: Dict[str, Any], v2: str, v3: Optional[bool]=False) -> str:
v2 += ' on'
return get_pull_request_event_message(get_user_username(v1), v2, v1['pullrequest']['... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: List[dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> List[dict]:
v2 = []
try:
v3 = int(v1)
except ValueError:
v3 = 3
for v4 in range(v3):
v2.append({'name': f'comma... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = List[Inch]
```
Input Types: str
Output Type: v0
Dependencies:
```python
def v1(v2: str) -> Dict[str, int]:
v3 = pattern.match(v2)
if not v3:
raise ValueError(f"Could not parse '{v2}'")
return {'x': int(v3.group('x')), 'y': int(v3.... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[int, float]
Output Type: NoReturn
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[int, float]) -> NoReturn:
if self.floating_action_button:
self.floating_action_button.text = self.action_text_button
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[Dict[str, Union[float, np.ndarray]], List, Tuple, np.ndarray]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[Dict[str, Union[float, np.ndarray]], List, Tuple, np.ndarray]=None):
if v1 is None:
... |
Imports:
```python
from pathlib import Path
import typing
```
Type definitions:
Input Types: tp.Optional[Path], bool
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: tp.Optional[Path]=None, v2: bool=False) -> None:
v3 = self.format_filetypes.get(self.format, 'txt')
if v1 ... |
Imports:
```python
from torch.optim import Optimizer
import typing
```
Type definitions:
Input Types: Union[Type[Optimizer], Tuple[Type[Optimizer], ...]], str, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[Type[Optimizer], Tuple[Type[Optimizer], ...]], v2: str='optim... |
Imports:
```python
import typing
```
Type definitions:
Input Types: KoiParser.LineContext
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: KoiParser.LineContext):
self.file_contents.append(self.current_line)
self.current_line = []
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Callable[..., None]
Output Type: 'BasePopulation'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Callable[..., None], **v2) -> 'BasePopulation':
self.evaluate(lazy=True)
v1(self, **v2)
return self
``` |
Imports:
```python
from PIL import Image
import numpy as np
import sys
import os
import typing
```
Type definitions:
Input Types: pathlib.Path
Output Type: Any
Dependencies:
```python
def v0(v1: np.ndarray, v2: str, v3: np.ndarray, v4: str, v5=np.mean):
if len(v2) == len(v4):
for (v6, v7) in zip(v2, v4):
... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = Union[int, slice, Indices1D, Indices2D]
```
Input Types: Dict[str, Any], v0
Output Type: Any
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: Dict[str, Any], v3: v0):
if not self.isnull:
self._assert_keys_exist(v2.key... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = self.doc.objects
for (v2, v3) in self.items():
v1.delete_entity(v3)
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Union[List[Any], np.ndarray], int, Any
Output Type: Union[List[Any], np.ndarray]
Dependencies:
```python
def v0(v1: List[Any], v2: int, v3: List[Any]) -> List[Any]:
v1 = v1[:v2]
while len(v1) < v2:
v1.append(v3)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self.request_type == 'shell':
self.write('# ')
elif self.request_type == 'exec':
assert self.chan is not None
self.chan.cl... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list, v2: str):
with open(v2, 'w') as v3:
print('Writing solution file ' + v2 + '...')
for v4 in v1:
v3.write(f'{v4[0]} {v4[1]}\... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.g_service.startThread()
self.gui.quit()
``` |
Imports:
```python
from scipy.io import wavfile
from scipy.fft import fft, fftfreq
from scipy.stats.mstats import gmean
import numpy as np
import typing
```
Type definitions:
Input Types:
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> float:
v1 = gmean(self.fft)
v2 = n... |
Imports:
```python
import warnings
import numpy as np
import typing
```
Type definitions:
Input Types:
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> float:
warnings.filterwarnings('ignore', category=RuntimeWarning)
v1 = self.true * np.log2(2 * self.true / (self.true +... |
Imports:
```python
from collections import defaultdict
import typing
```
Type definitions:
Input Types: str, Any, Optional[str], int
Output Type: dict
Dependencies:
```python
def v0(v1: str, v2: int=4, v3: bool=False) -> Tuple[int, dict]:
v4 = v1.split()
v5 = defaultdict(int)
for v6 in range(1, v2 + 1):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int=600) -> None:
v2 = 'yes | petalinux-config'
if 'plnx_config_component' in self.config:
v2 += f" -c {self.config['plnx_config_component']}"
... |
Imports:
```python
import torch
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: Tuple[torch.Tensor, float, torch.Tensor]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2='bilinear') -> Tuple[torch.Tensor, float, torch.Tensor]:
(v3, v4) = (self.image_height, self.image... |
Imports:
```python
import numpy as np
import os
from tqdm import tqdm
import math
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any, Any, Any, Any, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2, v3, v4, v5, v6, v7, v8: bool=False):
v9 = 0
v10 = 0
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: th.Tensor, th.Tensor
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: th.Tensor, v2: th.Tensor):
(v3, v4) = self.forward(v1, v2, self.action_dist_num)
v2 = v3.mean(dim=1)
return v2
``` |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.