text stringlengths 190 325k |
|---|
Imports:
```python
import typing
```
Type definitions:
Input Types: json
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: json):
v2 = []
v3 = v1['dialog_nodes']
for v4 in v3:
v5 = v4.get('dialog_node')
if v4.get('disabled') == True or v4.get('disabled')... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict):
v1 = {'embeds': [v1]}
self._send_to_webhook(v1)
``` |
Imports:
```python
from pandas._config import config
from pandas._libs import Timestamp, iNaT, properties
from pandas.compat import set_function_name
from pandas.compat._optional import import_optional_dependency
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
from pandas.ut... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[Path]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[Path]):
for v2 in v1:
if not v2.exists():
v2.mkdir(mode=493)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: object
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: object) -> str:
if v1:
v2 = str(v1)
v2 = v2.replace("'", "''")
v2 = f"'{v2}'"
return v2
else:
return ''
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: bytes
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bytes) -> int:
if isinstance(v1, str):
v1 = v1.encode(self.encoding)
self._stdin.write(v1)
self._stdin.flush()
return len(v1)
``` |
Imports:
```python
import matplotlib.pyplot as plt
import typing
```
Type definitions:
Input Types: Union[ndarray, Tuple[float], List[float]], Union[ndarray, tuple, list], str, str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union[ndarray, Tuple[float], List[float]], v2: Union... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Optional[Dict[str, str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Optional[Dict[str, str]]:
if not self._credentials:
return None
return {'token': self._credentials.token, 'url': self._credenti... |
Imports:
```python
import json
import typing
```
Type definitions:
Input Types: pathlib.Path
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: pathlib.Path) -> dict:
v2 = None
v2 = json.loads(v1.read_text())
return v2
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(Model):
v1: int
v2: int
v3: int
v4: int
def v5(self, v6: int) -> v0:
self.busy_count = v6
return self
def v7(self, v8: int) -> v0:
self.creating_count = v8
return self
def v9(self, v1... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, tuple
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: tuple) -> None:
if not isinstance(v2, (tuple, list)) or len(v2) != 2:
raise ValueError('Arg "pos" must be an iterable of length 2')... |
Imports:
```python
import torch as th
import torch.nn as nn
import typing
```
Type definitions:
Input Types: th.Tensor, int
Output Type: List[th.Tensor]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: th.Tensor, v2: int) -> List[th.Tensor]:
(v3, v4) = self.xfmr(v1, None)
v5 = self.mask(v3... |
Imports:
```python
import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import boston_housing
from tensorflow.keras.initializers import Constant
from tensorflow.keras.initializers import RandomUniform
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dense
from tens... |
Imports:
```python
from pathlib import Path
import typing
```
Type definitions:
Input Types: 'argparse.ArgumentParser'
Output Type: 'argparse.ArgumentParser'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'argparse.ArgumentParser') -> 'argparse.ArgumentParser':
v1.add_argument('-l', '--repo-... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, List[str]
Output Type: List[Dict[str, str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: List[str]) -> List[Dict[str, str]]:
print(f'Attempting to parse rows: ```\n{v1}\n```')
v3: List[Dict[str, str]] = []
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray, v2: np.ndarray=None):
for v3 in self.processors:
v3.fit(v1, v2)
``` |
Imports:
```python
from functools import cmp_to_key
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1):
self.val = v1
self.left = None
self.right = None
```
Input Types: v0
Output Type: List[List[int]]
Dependencies:
```python
def v2(v3, v4):
v5 = v3[1][0]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str=None):
if not v1:
v1 = self.current_party
if v1 not in self.parties:
self.build_party(v1)
return self.parties[v1]
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self) -> None:
self.logger.log_register('ID')
self.assertTrue('ID' in self.logger.logTunnels)
self.logger.log_close('ID')
self.assertTrue('ID' not in ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict, dict
Output Type: Any
Dependencies:
```python
def v0(v1: str, v2: list):
v3 = getattr(sys.modules[__name__], v1)(*v2)
return v3
```
```python
def v4():
v5: str = input('>>: ')
return v5
```
Function Name: v6
Function:
```python
d... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Optional[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Optional[str]:
v2 = self.sydent.db.cursor()
v3 = v2.execute('SELECT sender FROM invite_tokens WHERE token = ?', (v1,))
v4: List[Tu... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
async def v0(self) -> bool:
if self.skip is not None and self.skip.check():
return True
if self.skip_until is not None and (not self.skip_until.check()):
r... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self.trainer.testing:
self.trainer.reset_test_dataloader()
elif self.trainer.val_dataloaders is None or self.trainer._data_connector._shou... |
Imports:
```python
import torch, os, pickle
import typing
```
Type definitions:
Input Types: List[Tuple], Union[BertTokenizer, AlbertTokenizer], Any, Any
Output Type: Any
Dependencies:
```python
def v0(v1, v2, v3: int):
while True:
v4 = len(v1) + len(v2)
if v4 <= v3:
break
if le... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int):
v2 = self.ffi.new('unsigned int*')
v2[0] = v1
self.lib.prussdrv_pru_enable(v2[0])
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, List[str]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: List[str]):
if self.CC is not None:
self.CC.logging.log('Processing Staying Times From Beacon')
self.listing_all_staying... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: 'Matrix4'
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> 'Matrix4':
v1 = self.elements
v2 = None
v2 = v1[1]
v1[1] = v1[4]
v1[4] = v2
v2 = v1[2]
v1[2] = v1[8]
v1[8] = v2
v2 = v1[6]... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> str:
if not v1:
return self._namespace
v2 = '/'.join((i for v3 in v1.split('/') if v3))
if v1.startswith('/'):
v4 = '/' + v2... |
Imports:
```python
import io
import torch
import typing
```
Type definitions:
Input Types: torch.Tensor
Output Type: bytes
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor) -> bytes:
v2 = io.BytesIO()
torch.save(v1, v2)
return v2.getvalue()
``` |
Imports:
```python
import string
import typing
```
Type definitions:
Input Types: dict, list
Output Type: Any
Dependencies:
```python
def v0(v1: str):
v2 = []
v1 = v1.lower()
v1 = v1.translate(str.maketrans('', '', string.punctuation))
v1 = v1.translate(str.maketrans('', '', '1234567890'))
print(v1... |
Imports:
```python
import typing
```
Type definitions:
```python
@dataclass
class v0:
v1: str = ''
v2: int = 80
v3: int = 100
v4: int = 0
v5: bool = False
v6: str = None
v7: str = 'http://'
v8: str = None
v9: list = None
v10: list = None
v11 = None
v12: datetime = None
... |
Imports:
```python
import numpy as np
from pandas._libs import NaT, algos as libalgos, lib
from pandas._typing import ArrayLike, DtypeObj, npt
from pandas.util._validators import validate_bool_kwarg
from pandas.core.dtypes.astype import astype_array_safe
from pandas.core.dtypes.cast import ensure_dtype_can_hold_na, inf... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, bool
Output Type: list
Dependencies:
```python
def v0(v1: str) -> tuple:
v1 = v1.replace('\n', '').replace(' ', '').split(',')
v1[0] = int(v1[0])
v1[1] = float(v1[1])
return (v1[0], v1[1], v1[2:])
```
Function Name: v2
Function:
`... |
Imports:
```python
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rcParams
import typing
```
Type definitions:
Input Types: Any, int, list, list, tuple, int, float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2: int, v3: list, v4: list=['R', 'PMX', ... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, int, int, int, bool
Output Type: Any
Dependencies:
```python
def v0(v1: np.ndarray, v2=None) -> np.ndarray:
v1 = np.array(v1)
assert v1.ndim == 1
v3 = np.where(~np.isnan(v1), np.arange(v1.shape[0]), 0)
np... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
```python
def v0(v1: str, v2: str):
v3 = 1
while v3 < len(v1):
v4 = v1[v3]
v3 += 1
if v4 == '\\':
v3 += 1
elif v4 == v2:
return v3
else:
ret... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
if isinstance(v1, int):
v1 = self.get_clip(v1 + 2)
v2 = os.path.abspath(os.path.join(self.__pth, v1))
self.__pth = v2 if os.... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Module, Tuple[Tensor]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Module, v2: Tuple[Tensor]) -> None:
self._check_output_is_scalar(v1)
self._check_loss_has_not_been_modified(v1, v2)
``` |
Imports:
```python
import typing
```
Type definitions:
```python
@dataclass
class v0:
v1: str
v2: str
v3: str
v4: str
v5: int
v6: int
v7: str
v8: str
v9: str
v10: str
v11: str
v12: int
v13: int
v14: int
v15: Optional[str]
v16: List[str]
v17: str
d... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(ABC):
v1 = None
v2 = None
v3 = threading.Lock()
def __init__(self, v4: str, v5: str):
self._address = v4
self._session_id = v5
@property
def v6(self):
return self._address
@property
def v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[Any]
Output Type: List[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[Any]) -> List[int]:
if len(v1) == 0 or isinstance(v1[0], int):
return v1
return [cap[0] for v2 in v1]
``` |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = Union[Type[Resource], CodeableConceptRefType]
```
```python
class v1(Enum):
v2 = 'concepts'
v3 = 'symptom_disease_ind'
v4 = 'medication_ind'
```
```python
class v5(NamedTuple):
v6: acd.AttributeValueAnnotation
v7: AttributeSource
... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(TreeBase):
v1 = ['_raw_definition', '_definition', '_label', '_parent', '_children', '_mongoquery']
def __init__(self, v2: Dict[Any, Any], v3: str='#root', v4: v0=None, v5: OrderedDict[str, v0]=None):
self._raw_definition: Dict[A... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: list=None):
v4 = {'readByQuery': {'object': self.__dimension, 'fields': ','.join(v3) if v3 else '*', 'query': "{0} = '{1}'".for... |
Imports:
```python
from os import path
import typing
```
Type definitions:
Input Types: str, str, int, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: int, v4: str) -> None:
with open(path.join(self.outdir, 'output.txt'), 'a', encoding='utf-8') as v5:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional['DbExperimentDataV1']
Output Type: 'DbExperimentDataV1'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional['DbExperimentDataV1']=None) -> 'DbExperimentDataV1':
if v1 is None:
v1 = self.__class__()
v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: datasets.TextDataset, float, float, float, Optional[str], bool
Output Type: models.Model
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: datasets.TextDataset, v2: float=0.8, v3: float=0.1, v4: float=0.1, v5: Optional[str]=None, v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: WebElement, WebElement
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: WebElement, v2: WebElement) -> None:
self.assertFalse(v2.is_displayed())
v1.click()
self.assertTrue(v2.is_displayed())
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, /) -> str:
if not self.id:
return self.name
return f'{self.name}:{self.id}'
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: IO[str]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: IO[str]) -> None:
v2 = self.get_connection(self.gcp_conn_id)
v3 = v2.extra_dejson['extra__google_cloud_platform__keyfile_dict']
v1.write(v3)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any, Any, Any
Output Type: str
Dependencies:
```python
def v0(v1):
v2 = [bin(x)[2:].rjust(v1, '0') for v3 in range(2 ** v1)]
return [[int(a) for v4 in s] for v5 in v2]
```
```python
def v6(v7, v8):
v9 = v0(v7)
return [[*... |
Imports:
```python
from scipy.interpolate import InterpolatedUnivariateSpline
from scipy.stats import norm
import numpy as np
import typing
```
Type definitions:
Input Types: pd.DataFrame, float, bool, Callable[[np.ndarray], float]
Output Type: Tuple[np.ndarray, np.ndarray]
Dependencies:
```python
def v0(v1: np.ndarra... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, int
Output Type: v7
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: int) -> v7:
v1 = list(v1.replace('-', '').upper())
v3 = len(v1)
v4 = []
while v3 > 0:
v5 = v3 - v2
v6 = v3
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self._playing = True
self.after(0, self._on_play_step)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, Dict[str, Dict[str, str]], str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: Dict[str, Dict[str, str]], v3: str):
assert type(v1) == str
v4 = []
v5 = v2[v1]['title'].strip()
v6 = v2[v1][... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, int
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray, v2: int) -> np.ndarray:
v3 = np.zeros(int(v2 * (v2 + 1) / 2))
v4 = 0
for v5 in range(v2):
v3[v4]... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict[str, float]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Dict[str, float]:
v1 = self.simulator.state.copy()
v1['distance_to_target'] = v1['target_pole_position'] - v1['cart_position']
return v1
``... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> bool:
self.__state = True
if not self.is_connected():
self.connect()
self.start()
return True
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: o3d.geometry.PointCloud, Any, Any, Any, Any
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: o3d.geometry.PointCloud, v2, v3, v4, v5) -> np.ndarray:
v6 = np.zeros(shape=(v2 * v3, v2 *... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[int]
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int]) -> int:
v2 = 2
for v3 in range(2, len(v1)):
if v1[v2 - 2] != v1[v3]:
v1[v2] = v1[v3]
v2 += 1
return... |
Imports:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset
from torch.nn.utils.rnn import pack_sequence
from torch.nn.utils.rnn import pad_packed_sequence
from torch.nn.util... |
Imports:
```python
import typing
```
Type definitions:
Input Types: bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool=False):
for v2 in self.lockfile():
if self.environment == 'production' and v2['category'] == 'dev':
continue
if not v1:
... |
Imports:
```python
from configparser import ConfigParser
import typing
```
Type definitions:
Input Types: Path, str
Output Type: Dict[str, Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Path, v2: str) -> Dict[str, Any]:
v3 = ConfigParser()
with v1.open() as v4:
v3.read_file(v4)
... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(JobBuilder):
def __init__(self):
super().__init__(ServingJobType.TRTServingJob)
def v1(self, v2: int) -> v0:
self._options.append(StringField('--http-port', str(v2)))
return self
def v3(self, v4: int) -> v0:... |
Imports:
```python
import geopandas as gpd
import numpy as np
from geopandas.sindex import PyGEOSSTRTreeIndex
from shapely import prepared
from shapely.affinity import scale
from shapely.geometry import LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, box
from shapely.geometry.base import BaseGeom... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> str:
if self._needs_update:
v2 = self._lookup_data['site']
v3 = self._lookup_data['species']
v4 = self.get_sources(site=v2, ... |
Imports:
```python
from queue import Queue
import collections
import typing
```
Type definitions:
Input Types: int, List[List[int]]
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: List[List[int]]) -> bool:
if len(v2) != v1 - 1:
return False
v3 = collecti... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Sequence[float], Sequence[float]
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Sequence[float], v2: Sequence[float]) -> float:
if len(v1) <= 0:
raise ValueError('Length of episode_rewards must be greate... |
Imports:
```python
import torch
import numpy as np
import typing
```
Type definitions:
Input Types: int, int
Output Type: Tuple[torch.Tensor, torch.Tensor]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: int) -> Tuple[torch.Tensor, torch.Tensor]:
v3 = torch.rand([v1, 1, v2])
v4 = torch... |
Imports:
```python
import torch as t
import typing
```
Type definitions:
Input Types: Union[str, t.device]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[str, t.device]):
for v2 in self._major_attr:
v3 = getattr(self, v2)
for (v4, v5) in v3.items():
... |
Imports:
```python
import random
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
v2: str = ''
while len(v1) > 0:
v3 = len(v1)
v4 = random.randrange(v3)
v5 = v1[v4]
v2 += v5
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: dict) -> None:
for (v2, v3) in v1.items():
self.updating[v2] = v3
await self.async_request_refresh()
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
v1 = ('action',)
v2: _t.Callable
v3: _t.Callable
v4: _t.Callable
v5: _t.Callable
v6: _t.Text
v7: drf_request.Request
v8: _t.Text
v9: _t.Text
v10: bool
v11: _t.Text
v12: _t.Union[_t.Text, int]
v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[List[int]]
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[List[int]]) -> int:
v2 = []
v3 = []
v4 = 0
for (v5, v6) in enumerate(v1):
v2.append(set())
v7 = 0
for v8 in v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: bool=True):
v3 = 0.0
if self.ohlc:
if v2:
self.get_account_balance()
v4 = float(self.balance['USDT'])
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[str]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[str]) -> None:
for v2 in v1:
self.add_melting_temperature_path(v2)
``` |
Imports:
```python
import numpy as np
from scipy.optimize import fsolve
from scipy.stats import norm
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray
Output Type: tuple[np.ndarray, float]
Dependencies:
```python
def v0(v1: float, *v2: tuple[np.ndarray, np.ndarray]) -> np.ndarray:
return dual... |
Imports:
```python
import numpy
import numpy.linalg
import typing
```
Type definitions:
Input Types: float
Output Type: numpy.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float) -> numpy.ndarray:
v2 = numpy.cos(v1, dtype=numpy.float64)
v3 = numpy.sin(v1, dtype=numpy.float64)
retu... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0() -> int:
global __my_enum_auto_id
v1 = __my_enum_auto_id
v2 += 1
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: typing.NoReturn
Dependencies:
Function Name: v0
Function:
```python
def v0() -> typing.NoReturn:
(v1, v2, v3) = map(int, input().split())
v4 = v1 < pow(v3, v2)
print('Yes' if v4 else 'No')
``` |
Imports:
```python
import warnings
from pandas._libs import algos as libalgos, index as libindex, lib
import pandas._libs.join as libjoin
from pandas._libs.lib import is_datetime_array, no_default
from pandas._libs.tslibs import IncompatibleFrequency, NaTType, OutOfBoundsDatetime, Timestamp, tz_compare
from pandas._typ... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: v1
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: v1) -> int:
try:
v1 = re.search('(-?[\\d]+)', v1, re.M | re.I).group(1)
except Exception as e:
return 0
v2 = int(v1)
if... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
v1: int = 0
v2: Optional = None
v3: Optional['Link'] = None
v4: Optional['Link'] = None
def __init__(self, v5: int, v6, v7: Optional['Link']=None, v8: Optional['Link']=None):
self.key = v5
self.value = v6
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> list:
v1 = self._store._database_connection.cursor()
v1.execute('SELECT key FROM dimension WHERE metric_id=? GROUP BY key ORDER BY key ASC', [self._metric_... |
Imports:
```python
import torch
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: torch.Tensor, torch.Tensor
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:
v3 = v2.shape
v2 = v2.view(v1.sh... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[List[Any], Any], int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[List[Any], Any], v2: int) -> None:
if isinstance(v1, list):
self._extend(v1, v2)
self._periodic_thruput_monito... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0() -> str:
if not os.getenv('SPACK_ROOT', False):
print('Please provide a information about SPACK_ROOT')
exit(1)
v1 = os.path.relpath('var/s... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(list):
def __init__(self):
self._children: dict[str, v0] = dict()
self._used = False
def v1(self, v2: AppID) -> v0:
if v2 is None:
return self
if v2.head not in self._children:
sel... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'SimpleConfig', str, str
Output Type: Optional[str]
Dependencies:
```python
def v0(v1):
return v1.get('block_explorer', 'system default')
```
```python
def v2():
from . import constants
return mainnet_block_explorers if not constants.net.T... |
Imports:
```python
import os
from pathlib import Path
import typing
```
Type definitions:
Input Types: configparser.ConfigParser
Output Type: None
Dependencies:
```python
def v0() -> Path:
v1 = os.environ.get('XDG_CONFIG_HOME') or os.environ.get('APPDATA')
if v1:
v2 = Path(v1)
else:
v2 = Pa... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float) -> float:
if 5 <= v1 <= 10:
v2 = 1
else:
v2 = 0
return v2
``` |
Imports:
```python
import torch
from torch import nn
from torch.nn.utils import prune
import typing
```
Type definitions:
Input Types: nn.Module
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: nn.Module):
(v2, v3) = (0.0, 0.0)
for v4 in v1.modules():
if isinstance(v4, (... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> str:
if v1 == 'map':
self.expect(self.read_char() == '<', "expected '<'")
v2 = self.read_data_type()
self.expect(self.read_c... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, Any], Dict[str, Any]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict[str, Any], v2: Dict[str, Any]) -> None:
if 'limit' in v1:
v1['limit'] = int(v1['limit'])
if v1['limit'] < 1:
... |
Imports:
```python
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import check_random_state
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = self.data.columns.str.contains('user... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types:
Output Type: Optional[float]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Optional[float]:
if self._stats:
return np.sqrt(self.variance())
else:
return None
``` |
Imports:
```python
import cv2
import numpy as np
import typing
```
Type definitions:
Input Types: Any, list
Output Type: dict
Dependencies:
```python
def v0(v1: np.ndarray) -> np.ndarray:
v2 = cv2.cvtColor(v1, cv2.COLOR_RGB2HSV)
v3 = v2[:, :, 0] * 2
v4 = v2[:, :, 1] / 2.55
v5 = v2[:, :, 2] / 2.55
r... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, List[List[int]]
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int, v3: List[List[int]]) -> int:
(v4, v5) = (v1, v2)
for v6 in v3:
v4 = min(v6[0], v4)
v5 = min(v6[1], v... |
Imports:
```python
import torch
from torch import optim, nn
from torch.utils.data import DataLoader
import typing
```
Type definitions:
Input Types: torch.Tensor, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor, v2: str):
v3 = torch.sum((v1 - self.hypersphere... |
Imports:
```python
import numpy as np
import qiskit
import typing
```
Type definitions:
Input Types: _circuit.Circuit
Output Type: qiskit.QuantumCircuit
Dependencies:
```python
def v0(v1: _gates.ControlledGate, v2, v3, v4):
if not isinstance(v1, _gates.ControlledGate):
raise ValueError(f"Can't export gate ... |
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