text stringlengths 190 325k |
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Imports:
```python
import torch
from torch.autograd import Variable
import torch.distributed as dist
from torch.distributed import ProcessGroup
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: torch.Tensor
Output Type: Tuple[torch.Ten... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str='id', v2: str=''):
v3 = f' ORDER BY {v1} {v2} '
self.statement_helper(v3)
return self
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1) -> str:
v2 = v1.rstrip('0123456789')
v3 = v1[len(v2):].zfill(3)
v4 = v2 + v3
return str(v4)
``` |
Imports:
```python
from itertools import chain
import typing
```
Type definitions:
Input Types: Any, dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: dict):
for v3 in chain(self.dtc_dops, self.data_object_props):
v3._resolve_references(v2)
for v4 in chain(... |
Imports:
```python
import logging
import typing
```
Type definitions:
Input Types: bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: bool=False):
logging.addLevelName(logging.DEBUG, 'DBG')
logging.addLevelName(logging.INFO, 'INF')
logging.addLevelName(logging.WARNING, 'W... |
Imports:
```python
from pathlib import Path
import typing
```
Type definitions:
Input Types: nox.sessions.Session
Output Type: Path
Dependencies:
```python
def v0(v1: str, v2: bool=False) -> Path:
v3 = Path() / 'requirements' / 'nox.lock'
v4 = '{py_string}-{platform}.lock'
if v2:
v5 = '{platform}'
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray
Output Type: float
Dependencies:
```python
def v0(v1: np.ndarray, v2: np.ndarray) -> float:
assert v1.shape == v2.shape, "Shape of 'a' must match shape of 'b'"
return np.linalg.norm(v1 - v2, axis=-1)
`... |
Imports:
```python
import argparse
import json
import sys
import textwrap
import typing
```
Type definitions:
Input Types:
Output Type: int
Dependencies:
```python
def v0(v1: argparse.ArgumentParser, v2: str) -> None:
v1.add_argument('--sas', help='SAS Token for the storage account.', required=True)
v1.add_ar... |
Imports:
```python
import tempfile
import typing
```
Type definitions:
Input Types: Path
Output Type: tempfile.TemporaryFile
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Path) -> tempfile.TemporaryFile:
v2 = tempfile.TemporaryFile()
with open(v1, encoding='utf-8') as v3:
for v4 in v3... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: list
Dependencies:
```python
def v0(v1: dict, v2: int):
if v1['channel'].isdigit():
v3 = str(int(v1['channel']) + 100 * v2)
else:
v3 = str(float(v1['channel']) + 100 * v2)
return (v3, v1['callSign'].replace(v1... |
Imports:
```python
import math
import numpy as np
import statsmodels.stats.power as pwr
import typing
```
Type definitions:
Input Types: float, float, float, float, float, float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float, v2: float, v3: float, v4: float=0.5, v5: float=0.9, v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, int, 'wrapper.MjData'
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2: int, v3: 'wrapper.MjData'):
v1._physics.free()
v1._physics._reload_from_data(v3.deepcopy())
v1._hooks._episode_step_count = 0
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, Any, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list, v2=None, v3=None):
if v1:
if len(v1) > 0:
if type(v2) is int:
if len(v1) > v2:
return v1.... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, int, int, int, int, int, int, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int, v3: int, v4: int, v5: int=0, v6: int=0, v7: int=100, v8: int=0, v9: int=1):
v10 = self.moodle.post('co... |
Imports:
```python
from copy import deepcopy
import tensorflow as tf
from tensorflow.python.keras import backend
from tensorflow.python.tpu.tpu_embedding_v2_utils import FeatureConfig, TableConfig
import typing
```
Type definitions:
Input Types: FeatureConfig
Output Type: Dict[str, Any]
Dependencies:
```python
def v0(... |
Imports:
```python
import torch
import torch.nn as nn
from torch import optim
from torch.utils.data import DataLoader, RandomSampler
import typing
```
Type definitions:
Input Types: list
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list) -> dict:
v2 = 0
v3 = 0
v4 ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.array, int, int
Output Type: np.array
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.array, v2: int, v3: int) -> np.array:
(v4, v5) = v1.shape
return v1.reshape(v4 // v2, v2, -1, v3).swapaxes(1, 2).reshape(-1, v2, ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[int], List[int]
Output Type: List[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int], v2: List[int]) -> List[int]:
v3 = (sum(v1) - sum(v2)) // 2
v4 = set(v2)
for v5 in v1:
if v5 - v3 in v4:
... |
Imports:
```python
from typing import List, Tuple, Union
import torch.nn
import torch
import typing
```
Type definitions:
Input Types: torch.nn.Module, Union[Tuple, List[Tuple]], Any, Any
Output Type: Any
Dependencies:
```python
def v0(v1: Union[Tuple, List[Tuple]]) -> List[torch.Tensor]:
if isinstance(v1, List):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Tensor, Tensor, Tensor, Optional[Tensor]
Output Type: Tuple[Tensor, Tensor]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tensor, v2: Tensor, v3: Tensor, v4: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]:
v5 = v1.size(0)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> bool:
self.determine_cooldown()
if self._tokens == 0:
return False
return True
``` |
Imports:
```python
import math
import typing
```
Type definitions:
Input Types: int, int, int, int, int
Output Type: float
Dependencies:
```python
def v0(v1: int, v2: int) -> float:
(v3, v4) = divmod(v1, v2)
v5 = math.gcd(v4, v2)
return float(v3) + v4 // v5 / (v2 // v5)
```
Function Name: v6
Function:
```p... |
Imports:
```python
import json
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> None:
self.msg = json.loads(v1)
self.handle_message()
self.handle_presence_change()
self.handle_dnd_change()
``` |
Imports:
```python
from tqdm import tqdm
import typing
```
Type definitions:
Input Types: str
Output Type: Tuple[Dict[str, int], Dict[int, str], Dict[str, int], Dict[int, str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> Tuple[Dict[str, int], Dict[int, str], Dict[str, int], Dict[int, str]]:... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> bool:
v2 = 0
for v3 in v1:
if v3.isalpha():
v2 += 1
if v2 > 2:
return True
return False
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
```python
v0 = Tuple[str, str, int]
```
Input Types: np.ndarray
Output Type: v0
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: np.ndarray) -> v0:
(v3, v4) = ([], [])
for (v5, v6) in self.graph.items():
for (... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
v1 = f'<title> {self.title} </title>\n <link rel="icon" href="{self.icon}" type="image/x-icon">\n <meta charset="{self.meta_c... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'Directory'
Output Type: Optional['Entry']
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'Directory') -> Optional['Entry']:
if self.get_parent():
self.get_parent().remove_entry(self)
v2 = v1
v3 = sel... |
Imports:
```python
from collections import namedtuple, defaultdict
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: List[Point]) -> None:
assert len(v1) == 2
self._endpoints = v1
def v2(self) -> Iterable[Point]:
(v3, v4) = sorted(self._endpoints, key=lambd... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, list
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list, v2: list) -> None:
self._add_table_header_or_row([(column[0], column[1]) for v3 in v1])
self._add_table_header_separator([(v3[2], v3[1]) f... |
Imports:
```python
import os
import subprocess
import typing
```
Type definitions:
Input Types:
Output Type: Tuple[int, str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Tuple[int, str]:
for (v1, v2) in self.env_vars.items():
os.environ[v1] = str(v2)
v3 = subprocess.run(self.ge... |
Imports:
```python
import torch
import torch.nn as nn
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:
v1.names = ('B', 'N', 'E')
v3 = v1.f... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> None:
super().leave_chat(v1)
self.__WebSocketClient.update_channelid_sub_pair()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: List[Dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> List[Dict]:
v1 = self.session.portfolio_account_positions(account_id=self.account, page_id=0)
for v2 in v1:
if 'ticker' not in v2:
... |
Imports:
```python
import difflib
import typing
```
Type definitions:
Input Types: list, list
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list, v2: list) -> list:
v3 = list()
for v4 in v1:
v5 = difflib.get_close_matches(v4, v2, n=1)
if v5:
v3.ap... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str, str, int
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str, v3: int=0) -> str:
v4 = re.search(v2, v1)
if v4:
return v4.group(v3)
return ''
``` |
Imports:
```python
import rasterio
from rasterio.transform import rowcol
from rasterio.warp import transform_bounds
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: (float, float, float, float)
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> (float, float, float, float):
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Union[pd.Series, xr.DataArray], Union[float, xr.DataArray]
Output Type: Union[pd.Series, xr.DataArray]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union[pd.Series, xr.DataArray], v2: Union[float, xr.DataArray]) -... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> str:
v2 = self._get_icd_10_article_chapter(v1)
if v2 == 'not found':
return 'not found'
v3: str = 'Chapter {} of ICD-10 deals with'.... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'Param'
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'Param') -> None:
self.id = ', '.join([str(self), str(v1)])
self.args = self.args + v1.args
self.arg_names = self.arg_names + v1.arg_names
``` |
Imports:
```python
import queue
import threading
import typing
```
Type definitions:
Input Types: ray.data.Dataset
Output Type: Any
Dependencies:
```python
def v0():
v1 = threading.Thread(target=producer)
v1.start()
while True:
v2 = q.get(block=True)
if v2 is None:
break
... |
Imports:
```python
from datetime import date, datetime, time, timezone
import typing
```
Type definitions:
Input Types: datetime | date | None, str | None, Callable
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: datetime | date | None, v2: str | None=None, v3: Callable=iso_format) ->... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'DataSymbol'
Output Type: Generator['DataSymbol', None, None]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'DataSymbol') -> Generator['DataSymbol', None, None]:
if self.updated_sym is v1:
yield from v1.children
... |
Imports:
```python
import requests
import typing
```
Type definitions:
Input Types: str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str):
self._parameters['action'] = 'download'
self._parameters['language'] = v1
v3 = requests.get(self._URL, params=se... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict[int, Dict[str, Any]]
Dependencies:
```python
def v0(v1: Dict[str, Any], v2: str) -> Any:
v3 = v1.get(v2)
assert v3 is not None, 'KeyError: ' + v2 + ' not found in ' + str(v1)
return v3
```
Function Name: v4
Function:
```... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> DataFrame:
if not isinstance(v1, int):
raise TypeError('Period parameter is not perioderic.')
if v1 < 7 or v1 > 21:
raise Value... |
Imports:
```python
import os
import glob
import pandas as pd
import numpy as np
from os.path import basename, isdir, isfile, splitext
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any, Any
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2, v3, v4, v5) -> pd.... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> float:
v2 = filter(lambda sale: sale.salesman_cpf == v1, self._storage.values())
v3 = float(0)
for v4 in v2:
v3 += v4.cashback.tot... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional[Union[str, StringIO]], Optional[str], int, bool, bool, Optional[bool], bool, bool
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[Union[str, StringIO]]=None, v2: Optional[str]=None, v3: int=8, ... |
Imports:
```python
from multiprocessing.pool import Pool
from functools import partial
from itertools import chain
from tqdm import tqdm
import numpy as np
import librosa
import typing
```
Type definitions:
Input Types: Path, Path, int, bool, Any
Output Type: Any
Dependencies:
```python
def v0(v1, v2: Path, v3: bool, ... |
Imports:
```python
import torch
import torch.nn.functional as F
from torch import Tensor, nn
import typing
```
Type definitions:
Input Types: Union[int, List[int]], Optional[Tensor]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[int, List[int]], v2: Optional[Tensor]=None)... |
Imports:
```python
from datetime import datetime as dt
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1: str = dt.now().strftime('%A %d %B %H:%M:%S %Y')
self.__clock_label.setText(v1)
``` |
Imports:
```python
from sklearn.model_selection import GridSearchCV
import typing
```
Type definitions:
```python
v0 = pd.DataFrame
```
```python
v1 = np.ndarray
```
Input Types: v0, v1, dict, int, Any, str
Output Type: tuple
Dependencies:
Function Name: v2
Function:
```python
def v2(self, v3: v0, v4: v1, v5: dict, v6... |
Imports:
```python
from tqdm import tqdm
import typing
```
Type definitions:
Input Types: list, int
Output Type: Any
Dependencies:
```python
def v0(v1, v2, v3):
if v2 == v3:
return v1 + 1
else:
return 0
```
Function Name: v4
Function:
```python
def v4(self, v5: list, v6: int=5):
v7 = float(... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Tuple[int, int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Tuple[int, int]:
(v1, v2) = self.last_moves
return (self._get_repetition(v1), self._get_repetition(v2))
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, float
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray, v2: float=293.15) -> np.ndarray:
v1 = np.array(v1, dtype=float)
v1[v1 < 0] = np.nan
v2 -= 273.15
v... |
Imports:
```python
from timeit import default_timer as time
import typing
```
Type definitions:
Input Types: bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool=True):
if v1 or self._start_time is None:
self._start_time = time()
self.elapsed_time = None
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, float, int, int, int, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: float=0.0, v3: int=4, v4: int=60, v5: int=600, v6: bool=True):
if v2:
return self.summmarizer(v1, ratio=v2, min_leng... |
Imports:
```python
from datetime import datetime, timedelta
import typing
```
Type definitions:
Input Types: str
Output Type: tuple
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str=DEFAULT_MARKET) -> tuple:
if not self._isMarketValid(v1):
raise TypeError('Binance market required.')... |
Imports:
```python
import gzip
import bz2
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str):
if v1.endswith('.gz'):
return gzip.open
if v1.endswith('.bz2'):
return bz2.BZ2File
return open
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: list[str]
Output Type: set[str]
Dependencies:
```python
def v0(v1: str) -> str:
if v1 in TOPIC_KEYWORDS:
for v2 in MAP_TOPICS:
if v1 in MAP_TOPICS[v2]:
return v2
return '_'
```
Function Name: v3
Function:
``... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
with self.get_cursor() as v2:
v2.execute('SELECT cur_datatype, cur_blackbox from cur_blackbox WHERE cur_paper = %s', (v1,))
v3 = v2.fe... |
Imports:
```python
import os
import torch.cuda as cuda
import torch
import torch.nn as nn
import torch.optim as optim
import typing
```
Type definitions:
Input Types: str, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str='checkpoints') -> None:
self.model.loa... |
Imports:
```python
from random import randint
import typing
```
Type definitions:
Input Types: list[int], int, int
Output Type: int
Dependencies:
```python
def v0(v1: int, v2: int):
return randint(v1, v2)
```
Function Name: v3
Function:
```python
def v3(v4: list[int], v5: int, v6: int) -> int:
v7 = v0(v5, v6)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional[float], Optional[float]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[float]=None, v2: Optional[float]=None):
(v3, v4, v5, v6) = self.bb
v1 = v5 - v3 if v1 is None else v1
v2 = v6 ... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(ProperType):
v1 = ('arg_types', 'arg_kinds', 'arg_names', 'min_args', 'is_ellipsis_args', 'variables')
def __init__(self, v2: Sequence[Type], v3: List[ArgKind], v4: Sequence[Optional[str]], *, v5: Optional[Sequence[TypeVarLikeType]]=None... |
Imports:
```python
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import _LRScheduler
import torch
from torch import Tensor
from torch.nn import Module
import typing
```
Type definitions:
Input Types: Module, DataLoader, Union[str, torch.device], Any
Output Type: float
Dependencies:
Function Na... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: str) -> str:
if v1 == 'yes':
return 'Yes.'
elif v1 == 'no':
return 'NOPE'
elif v1 == 'maybe':
return 'maaaaaaybe?'
... |
Imports:
```python
import json
from json import load
import typing
```
Type definitions:
Input Types: object, object
Output Type: object
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: object, v2: object) -> object:
with open(v2, 'w', encoding='utf-8') as v3:
json.dump(v1, v3, indent=4)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Iterator[bool]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> Iterator[bool]:
while len(self.buf) < self.pos + v1:
yield False
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.registry.set(20, 'twenty')
self.registry.set(20, '20')
self.assertEqual(self.registry.current_keys, [20])
self.assertEqual(self.registry... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, list, list
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: list, v3: list) -> bool:
if v1 not in self.data:
if len(v2) != len(v3):
print('長さが違う')
return False
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[int, float], Dict, List
Output Type: Tuple[Dict, List]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union[int, float], v2: Dict, v3: List) -> Tuple[Dict, List]:
v2.update({'level': v1})
return (v2, v3)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: [str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> [str]:
v1 = self.GetCurrentIndex()
if v1 == -1:
return []
return [self.model().index(v1, col_index).data() for v2 in range(self.columnCount())... |
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.id != v1.id:
return False
if self.type != v1.type:
return False
v2 = sorted([str(prop) for v3 in self.properties])... |
Imports:
```python
import gc
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self._lock.lock()
try:
del self._current_runnable
self._current_runnable = None
gc.collect()
finally:
s... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> bool:
v1.writeto(self.dev_address, bytes([6, 255]))
v1.writeto(self.dev_address, bytes([7, 255]))
return True
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = self._network.add_person('Bill')
v2 = v1.make_post('Greetings.')
self.assertEqual(1, v1.post_count)
self.assertEqual(1, self._network.po... |
Imports:
```python
from contextlib import suppress
import typing
```
Type definitions:
Input Types: List[str]
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[str], *v2: str) -> int:
for v3 in v2:
with suppress(ValueError):
return v1.index(v3)
return -1
... |
Imports:
```python
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Conv2d, Linear
from torch.optim.lr_scheduler import ReduceLROnPlateau
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None... |
Imports:
```python
import typing
```
Type definitions:
Input Types: bytes, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bytes, v2=True):
self._ser.write(v1)
if v2:
self.monitor.write(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: str=None):
self.optimizing_target_func = v1
if v2 is None:
v2 = '适应值'
self.set_optimizing_target(v2)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: typing.Dict[str, typing.Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> typing.Dict[str, typing.Any]:
v1: typing.Dict[str, typing.Any] = dict()
for v2 in self.items:
v3 = v1.get(v2.product.name, ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[str]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[str]):
if len(v1) == 0:
raise ValueError(f'Video names list is empty, no video to analyze')
if not all((isinstance(n, str) for v2 in v1)):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: str=' ') -> str:
v3 = self.m_rows[v1]
v4 = f'{v2}{v3:<{self.m_maxlen}}'
return v4
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[np.ndarray, Any], Union[np.ndarray, Any]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[np.ndarray, Any], v2: Union[np.ndarray, Any]) -> None:
self.train_data = v1
self.d_train_data = v2
... |
Imports:
```python
import smtplib
import typing
```
Type definitions:
Input Types: MIMEMultipart
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: MIMEMultipart) -> None:
v2 = smtplib.SMTP_SSL('smtp.gmail.com', 465)
v2.ehlo()
v2.login(self.email, self.password)
v2.... |
Imports:
```python
import cv2
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import os
import torch.nn.functional as F
from torch import optim
from torch.utils.data import Dataset, DataLoader
from torch.utils.tensorboard import SummaryWriter
import typing
```
Typ... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(Operation):
v1: str = '/platform/admin/namespaces/{namespace}/currencies/{currencyCode}'
v2: str = 'DELETE'
v3: List[str] = []
v4: List[str] = ['application/json']
v5: List[List[str]] = [['BEARER_AUTH'], ['BEARER_AUTH']]
v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: (float, float, float)
Output Type: (float, float)
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: (float, float, float)) -> (float, float):
v2 = self.camera_relative_coordinates(v1)
return self.camera.perspective_pixel(v2... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: List[Dict[str, str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> List[Dict[str, str]]:
v1: List[Dict[str, str]] = []
for v2 in self.__file_list:
v3 = {'label': v2, 'value': self.__load_location + ... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(ProperType):
v1 = ('type', 'args', 'erased', 'invalid', 'type_ref', 'last_known_value')
def __init__(self, v2: mypy.nodes.TypeInfo, v3: Sequence[Type], v4: int=-1, v5: int=-1, v6: bool=False, v7: Optional['LiteralType']=None) -> None:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: [str]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: [str]):
v2 = []
if self.parent != None:
v2.extend(self.parent.get_parents_properties(v1))
v2.extend(self.get_properties(v1))
else:... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self.name == '<lambda>':
self.consts[self.get_const_key(None)] = 0
elif not self.name.startswith('<') and (not self.klass):
if sel... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
v1 = ('dst', 'jumpkind')
v2: Optional[int]
v3: str
def __init__(self, v4: Optional[int], v5: str):
self.dst = v4
self.jumpkind = v5
```
Input Types: Iterable, Optional[int], Any, Optional[str], Optional[bool], Op... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Sequence[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Sequence[int]:
v1 = self.lp_model._lp_model['var_type_index_type_beg']
v2 = self.lp_model._lp_model['var_type_index_type_cnt']
v3 = self.lp_mo... |
Imports:
```python
from collections import Counter
import typing
```
Type definitions:
Input Types: list[int], int
Output Type: int
Dependencies:
```python
def v0(v1: list[int], v2: int) -> dict[int, int]:
v3: dict[int, int] = {}
for v4 in range(v2, -9, -1):
v5 = puzzle_1.get_days_when_fish_spawned(sta... |
Imports:
```python
import numpy as np
from scipy.linalg import block_diag
import scipy.optimize as sciopt
import typing
```
Type definitions:
Input Types: np.ndarray, Dict
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray=None, v2: Dict=None) -> pd.DataFrame:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = '\n def f(\n x, # not-a-type-comment\n # also-not-a-type-comment\n y = 42, # type: int\n *args,... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str, Dict[str, str]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: Dict[str, str]) -> str:
for (v3, v4) in v2.items():
v1 = re.sub(f'"{v3}"', f'"{v4}"', v1, flags=re.MULTILINE)
retur... |
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