text stringlengths 190 325k |
|---|
Imports:
```python
import typing
```
Type definitions:
```python
v0 = tuple[int, int]
```
Input Types: v0
Output Type: Any
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: v0):
(v3, v4) = v2
if v3 == self.max_r:
return (0, v4)
return (v3 + 1, v4)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, Any
Output Type: List[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int, v3=1) -> List[int]:
v4 = [0] * v2
while v1:
for v5 in range(v2):
v6 = min(v3, v1)
v4[v5] +... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'pd.DataFrame', str
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'pd.DataFrame', v2: str) -> list:
self.X_columns = [col for v3 in v1.columns if v3 != v2]
v4 = []
v5 = list(v1.columns).index(v2)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Vector4
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Vector4):
self.write_float(v1.X)
self.write_float(v1.Y)
self.write_float(v1.Z)
self.write_float(v1.W)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Dict[str, Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Dict[str, Any]:
v2 = [{'$eq': [chan, '$$d.channel']} for v3 in self.channel]
v2.insert(0, {'$not': ['$$d.channel']})
return {'$fi... |
Imports:
```python
import glob
import logging
import os
import re
import typing
```
Type definitions:
Input Types: Path, int, int
Output Type: Any
Dependencies:
```python
def v0(v1: Path) -> List[str]:
v2 = list(glob.glob(f'{v1}/checkpoint-[0-9]*.pt'))
v3 = re.compile('checkpoint-([0-9]+).pt')
v4 = [(int(v... |
Imports:
```python
import json
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0() -> str:
if (KeyError, json.JSONDecodeError, AssertionError, ValueError):
return (json.dumps({'error': 'Check input'}), 400)
else:
retur... |
Imports:
```python
import numpy as np
from pandas._typing import Axis, FrameOrSeries, FrameOrSeriesUnion, IndexLabel, Scalar
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import doc
import pandas as pd
from pandas.api.types import is_list_like
from pandas.core import generi... |
Imports:
```python
import torch
import torch.nn as nn
import typing
```
Type definitions:
Input Types: torch.nn.Module, torch.Tensor
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.nn.Module, v2: torch.Tensor):
v1.eval()
if isinstance(v2, torch.Tensor):
v2 = [v2]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: list[str | None]
Dependencies:
```python
def v0(v1: str) -> None:
result.append(v1.strip().lstrip('*'))
```
Function Name: v2
Function:
```python
def v2(v3: str) -> list[str | None]:
def v4(v5: str) -> None:
result.ap... |
Imports:
```python
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras import Input
import typing
```
Type definitions:
Input Types: bool
Output Type: tf.keras.Model
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool=False) -> tf.keras.Model:
v2 = []
v3 = ... |
Imports:
```python
import requests
import typing
```
Type definitions:
Input Types: str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str):
v3 = requests.get('https://coreos.com/dist/aws/aws-{}.json'.format(v1), timeout=5)
v3.raise_for_status()
v4 = v3.json(... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: 'Field'
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> 'Field':
self.value = None
self.subfields = []
return self
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict, int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict, v2: int=DEFAULT_TIMEOUT) -> None:
v3 = self._add_many_sql.format(', '.join([f'(:key{n}, :value{n}, :exp{n})' for v4 in range(len(v1))]))
v5... |
Imports:
```python
import torch
from torch import Tensor
import typing
```
Type definitions:
```python
class v0(SlateSlotObjects[SlateItem]):
def v1(self, v2: SlateItems, v3=None) -> Tensor:
v4 = torch.zeros((len(self), len(v2)), dtype=torch.double, device=v3)
for (v5, v6) in zip(v4, self._values):... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, str, Any, Any
Output Type: Any
Dependencies:
```python
def v0(v1: list, v2: APIinfoCenter):
if len(v1) <= 1:
return None
v3 = [(api, v2.get_api_community_score(api)) for v4 in v1]
v5 = sorted(v3, reverse=True, key=lambda s: s... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(Enum):
v1 = 'DIRECT'
v2 = 'FAN_IN'
v3 = 'DYNAMIC_COLLECT'
```
```python
class v4(namedtuple('_SolidInputHandle', 'solid input_def')):
def v5(cls, v6: Solid, v7: InputDefinition):
return super(v4, cls).__new__(cls, check.i... |
Imports:
```python
import os
import numpy as np
import pandas as pd
import typing
```
Type definitions:
Input Types: str, dict, list
Output Type: Any
Dependencies:
```python
def v0(v1: str):
v2 = v1[10:18]
v3 = v1[-12:-4]
v4 = v1[19:27]
v5 = v1[28:31]
return (v2, v3, v4, v5)
```
```python
def v6(v7... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, int, int, int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str, v3: int=0, v4: int=None, v5: int=0) -> int:
v4 = v4 if v4 is not None else len(v1)
if not v5:
return v1.find(v2, v3,... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> dict:
v2 = {'zero': 'ноль', 'one': 'один', 'two': 'два', 'three': 'три', 'four': 'четыре', 'five': 'пять', 'six': 'шесть', 'seven': 'семь', 'eight': 'вос... |
Imports:
```python
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 = len(v1)
for (v5, v6) in enumerate(v2):
(v7, v8) = v6
v9 = v5 + v4
v1[v9] = {'hash': v8, 'fpath': v7, ... |
Imports:
```python
import asyncio
import logging
import typing
```
Type definitions:
Input Types: Dict[str, Any]
Output Type: None
Dependencies:
```python
async def v0(v1: asyncio.Queue) -> NoReturn:
while True:
v2 = await v1.get()
logging.info(f"Starting the '{v2.name}' job")
v3 = getattr(... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'Node'
Output Type: List[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'Node') -> List[int]:
v2: List[int] = []
self.preorderImpl(v1, v2)
return v2
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: None
Dependencies:
```python
def v0(self, v1) -> dict:
v2 = {}
for v3 in v1:
v2[v3] = v2.get(v3, 0) + 1
return v2
```
Function Name: v4
Function:
```python
def v4(self, v5) -> None:
v6 = v0(v5)
for v7 in so... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> None:
v2 = self.train_lst_path if v1 == 'train' else self.valid_lst_path
with v2.open('r') as v3:
self.images = []
for v4 in v3... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = typ.Dict[Key, Entry]
```
```python
class v1(typ.NamedTuple):
v2: str
v3: str
v4: str
v5: str
v6: str
v7: str
v8: str
v9: MaybeSourceText
```
```python
class v10(typ.NamedTuple):
v11: str
v12: str
v13: str
... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: pathlib.Path, pathlib.Path
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: pathlib.Path, v2: pathlib.Path) -> None:
for v3 in os.listdir(v1):
os.rename(v1 / v3, v2 / v3)
``` |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: list, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list, v2: str) -> None:
v3 = pd.concat(v1)
v3.to_csv(v2, index=False, sep='\t')
``` |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: paramiko.SSHClient
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: paramiko.SSHClient):
v2: dict = {}
(v3, v4, v5) = v1.exec_command('top -H -b -d 1 -n 10')
(v6, v7) = (v4.read(), v5.read())
v... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Tuple[str, ...]
Dependencies:
```python
def v0():
v1 = {' ': 'space'}
if len(self._accept_keys) == 1:
v2 = '<' + self._accept_keys[0] + '>'
else:
v2 = '(' + ', '.join(('<' + accept_key + '>' for v3 in self._ac... |
Imports:
```python
from concurrent.futures import FIRST_COMPLETED, Future, ThreadPoolExecutor, wait
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> bool:
(v1, v2) = wait(self._futures[:], return_when=FIRST_COMPLETED)
for v... |
Imports:
```python
from math import sqrt
import typing
```
Type definitions:
Input Types: int
Output Type: int
Dependencies:
```python
def v0(v1: int) -> bool:
if 1 < v1 < 4:
return True
elif v1 < 2 or not v1 % 2:
return False
v2: Iterable = range(3, int(sqrt(v1) + 1), 2)
return not any... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: str) -> None:
self.new_paragraph()
self.new_line('#' * v1 + ' ' + v2)
self.new_paragraph()
``` |
Imports:
```python
import torch
import typing
```
Type definitions:
Input Types: Any
Output Type: np.array
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> np.array:
if not v1.strip():
raise ValueError('No input text')
v2 = self.tokenizer(v1, padding=True, truncation=True, add_s... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, jnp.ndarray, int
Output Type: jnp.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: jnp.ndarray, v3: int=0) -> jnp.ndarray:
try:
return v1.apply_transport_from_potentials(self.f, self.g, v2, axis=v3)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> dict:
v1 = dict(allowedflowdir='positive', crestwidth='3.45', uselimitflowpos='true', limitflowpos='6.78', uselimitflowneg='true', limitflowneg='7.89')
v1.... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(Schedule):
def __init__(self, v1: str, v2: Optional[datetime], v3: str, v4: str, v5: str='', v6: datetime=None):
self.title = v1
self.date = v2
self.type = v3
self.description = v4
self.url = v5
... |
Imports:
```python
import os
import subprocess
import tempfile
from urllib.request import urlopen
import typing
```
Type definitions:
Input Types: List[str]
Output Type: int
Dependencies:
```python
def v0(v1: str) -> Optional[bytes]:
try:
v2 = urlopen(v1)
if v2.getcode() != 200:
return ... |
Imports:
```python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import typing
```
Type definitions:
Input Types: LRScheduler, int, Optional[str]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: LRScheduler, v2: int, v3: Optional[str]=None) -> None:
v4: ... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: int) -> None:
self.val = v1
self.next: Optional[v0] = None
```
Input Types: v0
Output Type: None
Dependencies:
Function Name: v2
Function:
```python
def v2(self, v3: v0) -> None:
assert v3.next is... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Sequence[Union[str, Sequence[str]]]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Sequence[Union[str, Sequence[str]]]) -> None:
for v2 in reversed(v1):
if isinstance(v2, str):
self.writ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, Any
Output Type: Any
Dependencies:
```python
def v0(v1: str):
return v1 == '*' or v1 == '/'
```
```python
def v2(v3: str):
return not str.isnumeric(v3)
```
```python
def v4(v5: str):
v6 = v5.split()
v7 = v6[0]
v8 = v6[1]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: torch.Tensor
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor) -> torch.Tensor:
assert self._imitator is not None
assert self._policy is not None
assert self._perturbation is not ... |
Imports:
```python
import numpy
from nltk import FreqDist
import typing
```
Type definitions:
Input Types: int
Output Type: Iterable[numpy.ndarray]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> Iterable[numpy.ndarray]:
self._prepare()
v2 = numpy.array([w for (v3, v4) in self._av... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[str], str, Any
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[str], v2: str, v3=False) -> List[str]:
while True:
v4 = next((i for (v5, v6) in enumerate(v1) if v6.startswith(v2)), None)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> None:
if v1 == 'InstanceID':
self._current_instance = None
``` |
Imports:
```python
import resource
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> str:
(v3, v4) = resource.getrlimit(resource.RLIMIT_NOFILE)
if v3 > v1:
return 'FD soft limit: {} is above desired limt: {}... |
Imports:
```python
import datetime
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import sklearn.ensemble as sk_ensemble
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
impo... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self._score = 0
self._grid = np.zeros_like(self._grid, dtype='bool')
self._colour_grid = np.zeros_like(self._colour_grid, dtyp... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[str], str
Output Type: Dict
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: List[str], v2: str) -> Dict:
v3 = {'uids': v1}
return await self.request('POST', f'chat/thread/{v2}/member/invite', v3)
``` |
Imports:
```python
import torch
from torch import Tensor
from torch.nn import functional as F
import typing
```
Type definitions:
Input Types: Tensor
Output Type: Tuple[Tensor, ...]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Tensor) -> Tuple[Tensor, ...]:
assert v1.shape[-1] in [4, 5, 8, 12]
... |
Imports:
```python
from datetime import datetime
import typing
```
Type definitions:
Input Types:
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> list:
try:
v1 = str(self.dataset.year)
v2 = str(self.dataset.month).zfill(2)
v3 = str(self.dataset.day).z... |
Imports:
```python
from pandas._config import get_option
from pandas._libs import algos as libalgos, lib
from pandas.compat import PY36, raise_with_traceback
from pandas.compat.numpy import function as nv
from pandas.util._decorators import Appender, Substitution, deprecate_kwarg, rewrite_axis_style_signature
from pand... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict):
if v1['parent_sampled'] is not None:
return v1['parent_sampled']
if v1.get('wsgi_environ', {}).get('PATH_INFO') == '/martor/markdownify/':
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, **v1) -> int:
v2 = self._rest.GET(url='/api/v1/Dimensions/$count', **v1)
return int(v2.text)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Mapping[bytes, bytes]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Mapping[bytes, bytes]) -> None:
v2 = list(sorted(v1.items()))
v3 = [i[0] for v4 in v2]
v5 = [v4[1] for v4 in v2]
self.add_kno... |
Imports:
```python
import platform
import signal
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
```python
def v0(v1: int, v2) -> None:
log.critical('Ignoring CTRL+BREAK (signal {}); use the GUI to quit', v1)
```
```python
def v3(v4: int, v5) -> None:
log.critical('Ignoring CT... |
Imports:
```python
import ast
from ast import AST
import typing
```
Type definitions:
Input Types: Any
Output Type: List[Tuple[str, Any]]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Any) -> List[Tuple[str, Any]]:
if isinstance(v1, list):
return [(str(i), a) for (v2, v3) in enumerate(v1)... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, int, str, str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2: int, v3: str, v4: str, v5: str):
v6 = {'job_id': v2, 'last_job_id': v3, 'method': v4, 'resource_url': v5}
v1.emit('update', v6, namesp... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
```python
def v0(v1: str) -> str:
return v1.replace('\n', '%0A')
```
```python
def v2(v3: str) -> str:
return re.sub('\x1b\\[(K|.*?m)', '', v3)
```
Function Name: v4
Function:
```python
def v4(v5: s... |
Imports:
```python
import torch
import torch.nn as nn
import torch.nn.functional as functional
import typing
```
Type definitions:
Input Types: torch.Tensor
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor) -> torch.Tensor:
v1 = functional.relu(self.FC1(v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> None:
for v2 in range(10):
v3 = v1[0][v2]
self.bars[v2]['value'] = v3 * 1000
self.gui.update()
``` |
Imports:
```python
import secrets
import typing
```
Type definitions:
Input Types: dict, Any
Output Type: Any
Dependencies:
```python
def v0(v1: str, v2):
v3 = v2.execute('SELECT * FROM tasks WHERE shortname=%s', (v1,))
assert v3 == 1
v4 = v2.fetchall()
assert len(v4) == 1
v5 = v4[0][0]
return ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float):
self.amount_bid *= v1
self.amount_ask *= v1
self.amount_low *= v1
self.amount_high *= v1
self.amount_prev_close *= v1
self.del... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
if v1:
self.trace.append(v1)
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
```python
@unique
class v0(Enum):
v1 = 'to_class'
v2 = 'to_instance'
```
```python
class v3:
def __init__(self, v4: Optional[dict]=None):
self.binds = {} if v4 is None else v4
def v5(self, v6: str, v7: v0, v8: Any) -> v3:
self.bind... |
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 is None:
return None
elif v1.endswith('M'):
return int(v1[:-1]) * 1000000
elif v1.endswith('k'):
return int(v1[... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, Any]
Output Type: Dict[str, Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Dict[str, Any]) -> Dict[str, Any]:
v1.update(lr_scheduler=self.scheduler, step_interval=self.step_interval)
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = TypeVar('T')
```
Input Types: v0, str
Output Type: v0
Dependencies:
Function Name: v1
Function:
```python
def v1(v2: v0, v3: str) -> v0:
if not v2:
raise ValueError(f'{v3} cannot be a value that evaluates to false')
return v2
``` |
Imports:
```python
import os
import sys
import logging
import shutil
from glob import glob
import typing
```
Type definitions:
Input Types: bool, bool, [str], bool, bool
Output Type: Any
Dependencies:
```python
def v0(v1: bool=False, v2: bool=True, v3: bool=False):
if v3:
logger.setLevel(logging.DEBUG)
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types:
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> np.ndarray:
v1 = np.sum(self.__q_prof * self.__colden)
v2 = self.__q_prof * self.__colden * self.__OD / v1
return (v2 * self._... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Tuple[Optional[str], Optional[str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Tuple[Optional[str], Optional[str]]:
v2 = self.get_valorant_api(f'/v1/seasons/{v1}')
if v2 is None:
retu... |
Imports:
```python
import operator
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2=False) -> bool:
v3 = operator.le
if v2:
v3 = operator.ge
return all((v3(v1[i], v1[i + 1]) for v4 in range(len(v1) - 1)))
... |
Imports:
```python
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import roc_auc_score, average_precision_score, accuracy_score, f1_score
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline imp... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> np.ndarray:
v2 = self.camera.calib_mat[0, 0]
v3 = self.get_idepth_image()
v4 = v3 * v1 * v2
return v4
``` |
Imports:
```python
import urllib.parse as urlparse
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
```python
def v0(v1):
v2 = urlparse.urlparse(v1)
v3 = []
for v4 in v2.query.split('&'):
(v5, v6) = v4.split('=')
if v5 not in ['_nc_rid', 'ccb']:
... |
Imports:
```python
import hashlib
import os
import sys
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
```python
def v0() -> str:
try:
v1 = __file__
except NameError:
v1 = sys.argv[0]
v1 = os.path.abspath(v1)
return v1
```
```python
def v2(v3: str) -> st... |
Imports:
```python
import asyncio
from asyncio import Event as _asyncio_Event, Lock as _asyncio_Lock
from contextvars import copy_context, Context
import typing
```
Type definitions:
```python
v0 = TypeVar('RetT')
```
Input Types: Awaitable[v0]
Output Type: v0
Dependencies:
Function Name: v1
Function:
```python
async ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(v1) -> pd.DataFrame:
v2 = v1[['SHOT_DISTANCE', 'SHOT_MADE_FLAG']]
v2 = v2.groupby('SHOT_DISTANCE').agg(['mean', 'count']).reset_index()
return v2
``` |
Imports:
```python
import datetime
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
```python
def v0(v1: str) -> None:
f.write(v1.encode('utf-8'))
```
Function Name: v2
Function:
```python
def v2(self, v3: str) -> None:
with open(self.filename, 'ab') as v4:
def v5(v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: torch.Tensor, Optional[torch.Tensor]
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor, v2: Optional[torch.Tensor]=None) -> torch.Tensor:
(v3, v4) = v1.split([3, 1], dim=-1)
if v2 is None:
... |
Imports:
```python
import pandas as pd
from pandas import DataFrame
import typing
```
Type definitions:
Input Types: datetime, datetime, List[dict], bool
Output Type: DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: datetime, v2: datetime, v3: List[dict], v4: bool=True) -> DataFrame:
v5 = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> int:
v1 = 2
if self.feat_map_size:
v1 += 2
if self.feat_abstime:
v1 += 2
if self.feat_rule_msdm:
v1 += 1
if self.feat_ru... |
Imports:
```python
import typing
```
Type definitions:
Input Types: set
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: set) -> None:
v2 = set(v1) - self._known_peers
if self._p2pfactory is not None:
for v3 in v2:
self._p2pfactory.connect_peer(v3)
... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: Optional[int]
Output Type: Tuple[str, str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[int]=None) -> Tuple[str, str]:
if v1 is None:
v1 = self.input_file_index
v2 = self.input_files[v1]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, bool
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: bool=False) -> str:
v3 = 2 if not v2 else 1
v4 = self._position
self._position += v1 * v3
if self._position > len(self._data):
... |
Imports:
```python
import numpy as np
from numpy.random import multivariate_normal
import typing
```
Type definitions:
Input Types: int, float, str
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, *v4: int, v2: float=0, v3: str='float') -> np.ndarray:
if v3 == 'float':
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.MESSAGE = 'New comment:\n> It is better\n* here'
self.do_test(expected_message='New comment:\n> It is better\n* here')
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> dict:
v1 = {}
for (v2, v3) in self._settings.items():
v1[v2] = {'json_schema': v3.schema_json(), 'model': v3}
return v1
``` |
Imports:
```python
import pandas as pd
import os
import typing
```
Type definitions:
Input Types: xr.Dataset, str
Output Type: str
Dependencies:
```python
def v0(v1: xr.Dataset) -> List[str]:
return list(v1.data_vars.keys())[0]
```
Function Name: v2
Function:
```python
def v2(v3: xr.Dataset, v4: str) -> str:
v... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
print('\nSTOCK\t\t\t\t\tINITIALS\tPRICE/SHARE($)')
for (v1, v2) in self.data.items():
if v1 != 'LBJ':
print('{}\t\t\t{}\t\t{}'.fo... |
Imports:
```python
import torch
import typing
```
Type definitions:
Input Types: torch.Tensor, torch.Tensor, torch.Tensor
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor, v2: torch.Tensor, v3: torch.Tensor):
self._sort_state(v3)
self._scores = v2
self._hy... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: object
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> object:
v2 = self._redis.get(self._encode_key(v1))
if v2:
return self._serializer.loads(v2)
``` |
Imports:
```python
import pandas as pd
from pandas.api.types import is_numeric_dtype
import typing
```
Type definitions:
Input Types: pd.Series, bool
Output Type: pd.Series
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: pd.Series, v2: bool) -> pd.Series:
if v2:
v1 = v1.astype(pd.StringDtyp... |
Imports:
```python
import typing
```
Type definitions:
Input Types: PySide2.QtGui.QMouseEvent
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: PySide2.QtGui.QMouseEvent) -> None:
self.start_editing()
return super().mouseDoubleClickEvent(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Iterator[Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Iterator[Any]:
for (v1, v2) in self.items():
yield v2
``` |
Imports:
```python
from random import randint
import typing
```
Type definitions:
Input Types:
Output Type: 15
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> 15:
self._move_head()
v1 = self.intify(self._get())
self._move_head()
v2 = self.intify(self._get())
if v2 >= 0:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, int, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: int, v2: int, v3: int, v4: int):
v5 = {'eastE6': v1, 'westE6': v2, 'southE6': v3, 'northE6': v4}
return await self.request(method='... |
Imports:
```python
import base64
import math
import secrets
import zlib
import typing
```
Type definitions:
Input Types: bytes, str
Output Type: Any
Dependencies:
```python
def v0(v1, v2):
return bytes([_a ^ _b for (v3, v4) in zip(v1, v2)])
```
Function Name: v5
Function:
```python
def v5(v6: bytes, v7: str):
... |
Imports:
```python
from math import ceil, gcd, lcm
import typing
```
Type definitions:
Input Types: int, int
Output Type: tuple[int, int, int]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: int) -> tuple[int, int, int]:
(v3, v4) = (v1, v2)
(v5, v6) = (1, 0)
(v7, v8) = (0, 1)
w... |
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