text stringlengths 190 325k |
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Imports:
```python
import numpy as np
import os
from scipy.sparse import csr_matrix, diags, load_npz
import torch
import typing
```
Type definitions:
Input Types: str
Output Type: Tuple[torch.sparse.FloatTensor, torch.FloatTensor, torch.LongTensor]
Dependencies:
```python
def v0(v1: csr_matrix) -> csr_matrix:
v2 =... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: int):
if self._model_cache and len(self._model_cache) >= v1:
return self._model_cache[v1]
v2 = self.copy(offset=v1, limit=1)
v3 = awai... |
Imports:
```python
from collections.abc import Sequence, Mapping
from numbers import Integral
import typing
```
Type definitions:
Input Types: Any, str, str, Any
Output Type: Any
Dependencies:
```python
def v0(v1: Any, v2: SequenceType[Any], v3: Any=None) -> Any:
v4 = v1
for v5 in v2:
if isinstance(v4,... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> None:
if 12 == v1.state and v1.keysym == 'c':
return
else:
return 'break'
``` |
Imports:
```python
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any
Output Type: nn.Sequential
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2, v3, v4=2) -> nn.Sequential:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> list:
v1 = {}
v2 = self.s.request('GET', url=f'{self.endpoint}/card-accounts', data=v1)
return [i.get('resourceId') for v3 in v2.json().get('cardAccoun... |
Imports:
```python
import itertools
import typing
```
Type definitions:
Input Types: tf.compat.v1.data.Dataset, int
Output Type: Any
Dependencies:
```python
def v0(v1: tf.compat.v1.data.Dataset) -> Iterator[tf.Tensor]:
if v1 in _tf_dataset_iterables:
v2 = _tf_dataset_iterables[v1]
for v3 in v2:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: typing.List[float]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: typing.List[float]) -> None:
v2 = sum(v1)
for v3 in range(len(v1)):
v1[v3] /= v2
``` |
Imports:
```python
from warnings import warn
import typing
```
Type definitions:
Input Types: str, sc.Variable, str, Dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: sc.Variable, v3: str, v4: Dict):
v3 = v3.split('/')
v5 = -2
v6 = False
v7 = False
while... |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
```python
v0 = Union[np.ndarray, pd.DataFrame]
```
Input Types: v0
Output Type: Dict[str, pd.Series]
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: v0) -> Dict[str, pd.Series]:
if not isinstance(v2, pd.DataFrame):
... |
Imports:
```python
import pprint
import typing
```
Type definitions:
Input Types: Any, Any, list, Any
Output Type: None
Dependencies:
```python
def v0(v1: dict, v2, v3: str='') -> None:
for (v4, v5) in v1.items():
v6 = v3 + ('/' if v3 else '') + v4
if 'expected' in v5 and 'actual' in v5:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list):
v2 = []
v3 = []
v4 = []
for v5 in v1:
v2.extend(v5[0])
v3.extend(v5[1])
v4.extend(v5[2])
return (v2, v3, v4)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int):
while v1 * 2 <= self.size:
v2: int = self.getMinIndex(v1)
if self.heap[v1].weight > self.heap[v2].weight:
(self.heap[v1], ... |
Imports:
```python
import statistics
import typing
```
Type definitions:
Input Types: int, str
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: str) -> float:
v3 = self[v1]
if v2 not in v3.prefs:
return 1
v4 = v3.prefs[v2]
if not v4:
retu... |
Imports:
```python
import matplotlib.figure
from matplotlib import pyplot as plt
import typing
```
Type definitions:
Input Types: mne.time_frequency.AverageTFR, Optional[str], Optional[Union[str, Path]], bool
Output Type: matplotlib.figure.Figure
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: mne.time... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: str):
v2 = f'{self._address}/api/session/{self._session_id}/task/{v1}'
await self._request_url(path=v2, method='DELETE')
``` |
Imports:
```python
from numpy import floor, maximum, mean, minimum, nan, ndarray, round
from numpy import sum as np_sum
from numpy import where
import typing
```
Type definitions:
Input Types: int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> None:
if not isinstanc... |
Imports:
```python
import logging
import typing
```
Type definitions:
Input Types: str, str, bool, str, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: bool=True, v4: str=None, v5: str=None, **v6) -> None:
self.firestore.delete_document(self.report_type... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Lambda
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Lambda) -> Any:
for v2 in v1.args.defaults:
self.visit(v2)
for v2 in v1.args.kw_defaults:
if v2:
self.visit(v2)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: list
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list) -> int:
v2 = [0 for v3 in range(len(v1))]
v4 = [0 for v3 in range(len(v1))]
v5 = 0
for v6 in range(len(v1)):
v5 = max((v5, v1[v6]... |
Imports:
```python
import pathlib
import torch
from torch import nn as nn
from torch.nn import functional as F
import typing
```
Type definitions:
Input Types: Union[str, pathlib.Path]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[str, pathlib.Path]):
v2 = {'state_dic... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types:
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> int:
v1 = self.readByte()
v2 = 0
if v1 == 253:
v3 = re.findall('.{1,2}', self.read(2))
for v4 in reversed(v3):
v2 ... |
Imports:
```python
from itertools import tee, _tee, islice, chain, combinations
import typing
```
Type definitions:
Input Types: Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Any):
v2 = [v1]
self._inert = chain(self._inert, v2)
return self
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
v1: str
v2: Any
def __init__(self, v3: str, v4: Any):
"""
Construct an NbtTag instance.
:param name: The name of the NbtTag.
:param value: The value of the NbtTag.
"""
self.name = v3
... |
Imports:
```python
import cv2
import typing
```
Type definitions:
Input Types: Path, int, zivid.Frame, np.array
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Path, v2: int, v3: zivid.Frame, v4: np.array):
v3.save(v1 / f'img{v2:02d}.zdf')
v5 = cv2.FileStorage(str(v1 / f'pos{v2... |
Imports:
```python
import asyncio
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self) -> None:
self._logger.debug('Server close started')
v1 = [connection.close() for v2 in self._connections]
await asyncio.gather(*v1)
... |
Imports:
```python
from tensorflow.keras import Model, layers
import typing
```
Type definitions:
Input Types: int, Union[str, Callable], bool, str, str
Output Type: List[layers.Layer]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: Union[str, Callable]=None, v3: bool=False, v4: str='same', v5... |
Imports:
```python
import typing
```
Type definitions:
Input Types: bool
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: bool) -> str:
if v1:
return '\n <p>If you need help or have questions about EasyCLA, you can\n <a href="https://docs.linuxfoundatio... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> bool:
v2 = v1.split(':')
try:
return len(v2) == 8 and all([re.match('^[0-9a-fA-F]{1,4}$', part) and 0 <= int(part, 16) <= 655... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, pymap.Observation], Dict[str, pymap.Observation]
Output Type: List[Tuple[str, str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict[str, pymap.Observation], v2: Dict[str, pymap.Observation]) -> List[Tuple[str, str]]:
... |
Imports:
```python
import cgi
import typing
```
Type definitions:
Input Types: cgi.FieldStorage, str
Output Type: Optional[bytes]
Dependencies:
```python
def v0(v1: cgi.FieldStorage, v2: str) -> Tuple[Optional[str], Optional[bytes]]:
if not v2 in v1:
log.warning('get_cgi_parameter_file: form has no key {}'... |
Imports:
```python
import random
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.cuda.amp import GradScaler
from torch.optim import Adam
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DistributedSampler, DataLoader
from torch.u... |
Imports:
```python
import numpy as np
import torch
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import SequentialSampler, DataLoader, Dataset
from tqdm import tqdm, trange
import typing
```
Type definitions:
```python
@dataclass
class v0:
v1: int
v2: float
v3: int
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: webdriver.Chrome, str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: webdriver.Chrome, v2: str) -> str:
v1.get(v2)
v3 = f'web_page.png'
v1.get_screenshot_as_file(v3)
return str(v3)
``` |
Imports:
```python
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, LSTM, RepeatVector, TimeDistributed, Input
from tensorflow.keras.callbacks import EarlyStopping
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: Model
Dependencies:
Function Name: v0
Function:... |
Imports:
```python
import typing
```
Type definitions:
Input Types: bool, bool
Output Type: Union[int, str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool=False, v2: bool=False) -> Union[int, str]:
v3 = self._start
return self._human_precise(v2, v1, v3)
``` |
Imports:
```python
from datetime import datetime
import random
import typing
```
Type definitions:
Input Types: str, int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: int) -> int:
v3 = random.randint(100000, 999999)
if self.db.find_one({'verif_code': v3}):
... |
Imports:
```python
import logging
import os
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> None:
logging.debug('Removing temporary file:\t%s', v1)
os.remove(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: 'IO_TYPE'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str='r') -> 'IO_TYPE':
v1 = self.join(self.root, v1)
with open(v1, v2) as v3:
return v3.read()
``` |
Imports:
```python
import sympy as sp
import sympy.printing.cxxcode as cxxcode
from sympy.matrices.immutable import ImmutableDenseMatrix
from sympy.matrices.dense import MutableDenseMatrix
import typing
```
Type definitions:
Input Types: sp.Basic
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
d... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[str, List[str]], str, int, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[str, List[str]], v2: str, v3: int, v4: str) -> None:
if type(v1) is list:
for v5 in v1:
v6: str ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: None
Dependencies:
```python
def v0(v1: Tuple) -> None:
assert v1[2] == 3, f'Received image array with shape: {v1}, expected image array shape is (x, y, 3)'
```
```python
def v2(v3: Tuple) -> None:
raise ValueError(f'Re... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: List[np.array], int, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[np.array], v2: int, v3: int):
v4 = []
for v5 in v1:
for v6 in range(1, int(np.unique(v5)[-1]) + 1):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: Iterator[Tuple[str, str, List[str], str]]
Dependencies:
```python
def v0(v1):
v2 = []
print('loading examples from {0}'.format(v1))
with jsonlines.open(v1) as v3:
for v4 in v3:
v2.append(v4)
return ... |
Imports:
```python
import os
import sys
import typing
```
Type definitions:
Input Types: str
Output Type: List
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str='/proc') -> List:
if sys.platform != 'linux':
return []
v2 = []
for v3 in os.listdir(v1):
try:
if os... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int):
if not 0 <= v1 < 256:
raise ValueError(f'color {v1} not between 0 and 255')
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'ScheduleCache'
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'ScheduleCache') -> None:
for (v2, v3, v4) in v1:
self.put(v2, v3, v4)
``` |
Imports:
```python
import pickle
import numpy as np
import pandas as pd
import typing
```
Type definitions:
Input Types: str
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> pd.DataFrame:
v2 = '/glade/scratch/jframe/neuralhydrology/data/'
with open(v2 + 'full_pe... |
Imports:
```python
import queue
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str='default') -> str:
try:
return self.outputQueues[v1]
except KeyError:
self.outputQueues[v1] = queue.Queue(self.qsize)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: KolibriDaemonDBus.MainSkeleton, Gio.DBusMethodInvocation
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: KolibriDaemonDBus.MainSkeleton, v2: Gio.DBusMethodInvocation) -> bool:
self.__application.reset_inacti... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
```python
def v0(v1: str):
v2 = {}
for v3 in v1.split('\n'):
if '=' not in v3 or len(v3) == 0:
continue
v4 = v3.split('=')
v2[v4[0]] = v4[1]
return v2
```
Function Name... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int, v3: int):
self.__script.action(v1, v2, v3)
return self.__data
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: torch.Tensor, Any
Output Type: Any
Dependencies:
```python
def v0(v1: torch.Tensor, v2):
v3 = list(v2)
for v4 in range(len(v3)):
if v3[v4] != v4:
v1 = v1.transpose(v4, v3[v4])
v5 = v3.index(v4)
(v3[v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> bool:
v2 = [0, 0]
v3 = 0
while True:
for v4 in v1:
if v4 == 'G':
if v3 in [0, 2]:
v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1=None) -> None:
if v1 is not None:
v2 = self.table[v1]
v2.scaling()
print('Column {} was scaled'.format(v1))
else:
if sel... |
Imports:
```python
import logging
import sys
import typing
```
Type definitions:
Input Types: Optional[logging.LogRecord]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[logging.LogRecord]):
if not v1 or v1.levelno < logging.INFO:
return
print(v1.message,... |
Imports:
```python
import ctypes
import typing
```
Type definitions:
```python
class v0(ctypes.Structure):
v1 = [('p_forw', ctypes.c_uint64), ('p_back', ctypes.c_uint64), ('p_paddr', ctypes.c_uint64), ('p_addr', ctypes.c_uint64), ('p_fd', ctypes.c_uint64), ('p_cwdi', ctypes.c_uint64), ('p_stats', ctypes.c_uint64), ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, ParameterId
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: int, v2: ParameterId):
v3 = await self.get_parameter_raw(v1, v2)
self.add_parameter_extensions(v3)
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> None:
self.controller.handle_cmdline_input(v1)
self.hide_cmdline()
``` |
Imports:
```python
import requests
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str):
v2 = requests.get(url=v1)
return v2.content.decode(encoding='utf-8')
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: 'Widget'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str='widget') -> 'Widget':
assert isinstance(v1, str)
if v1 in self._kwargs.keys():
raise KeyError('duplicated key')
self._kwargs[v1] =... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> list:
v1 = ['Midrange Long Strafes Invincible', 'air far long strafes', 'Vertical Long Strafes', 'Bounce 180 Tracking Large', '1wall6targets TE', 'Tile Frenzy ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, int
Output Type: str
Dependencies:
```python
def v0(v1, v2):
if v1 > n:
if n % v2 == 0:
for v3 in range(1, v2 + 1):
yield alphabet[a[v3]]
else:
a[v1] = a[v1 - v2]
for v4 in v0(v1 + 1, v2... |
Imports:
```python
import os
import base64
from base64 import b64encode, b64decode
import typing
```
Type definitions:
Input Types: str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(v1: str, v2: str):
v3 = v1
v4 = v3.encode('ascii')
v5 = base64.b64decode(v4, valida... |
Imports:
```python
import typing
```
Type definitions:
```python
@dataclass(frozen=True)
class v0:
def v1(self, v2: v0) -> v0:
return v2
```
Input Types:
Output Type: None
Dependencies:
```python
def v3(v4) -> Union[ta.BaseColumn, v0, None]:
if v4 is None:
return None
assert isinstance(v4,... |
Imports:
```python
from math import factorial
import typing
```
Type definitions:
Input Types: int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int) -> int:
v2 = 0
"\n math.factorial returns an integer which isn't iterable so convert to a string which is.\n "
for v... |
Imports:
```python
import tensorflow as tf
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
```python
def v0(v1, v2, v3: str, v4=identity):
assert len(v2) == 2
v5 = tf.get_variable(v3, initializer=tf.glorot_normal_initializer(), shape=v2)
v6 = tf.Variable(tf.zeros(v2[1]))... |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types:
Output Type: pd.Series
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> pd.Series:
v1 = self._check_fillna(self._lband, value=-1)
if self._offset != 0:
v1 = v1.shift(self._offset)
return pd.... |
Imports:
```python
from datetime import datetime, time
from math import floor
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
```python
def v0(v1, v2):
return v2 * round(floor(v1 / v2))
```
Function Name: v3
Function:
```python
def v3(self) -> str:
v4 = datetime.now()
v5 = ... |
Imports:
```python
import os
import numpy as np
from numpy.compat.py3k import npy_load_module
import typing
```
Type definitions:
Input Types: str
Output Type: List[np.ndarray]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> List[np.ndarray]:
v1 = os.path.splitext(os.path.basename(v1)... |
Imports:
```python
import collections
import ctypes as ct
import typing
```
Type definitions:
Input Types:
Output Type: Optional[Dict[str, Any]]
Dependencies:
```python
def v0(v1: Optional[ct._Pointer]) -> bool:
if v1:
return False
return ct.cast(v1, ct.c_void_p).value is None
```
Function Name: v2
Fu... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, Any
Output Type: Optional[bool]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: Any=DEFAULT) -> Optional[bool]:
v3 = self.pop(v1, v2)
if v3 is None:
return None
elif isinstance(v3, bool):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: torch.Tensor, int
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor, v2: int) -> torch.Tensor:
v3 = v1.new_ones(v1.size(0), v2)
v4 = v3.cumsum(dim=1)
return (v1.unsqueeze(1) >= v4).long(... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict, str, Any, dict
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(*, v1: dict, v2: str, v3, v4: dict=None) -> dict:
v5 = {'attrs': v1, 'time_format': v2, 'dimensions': {'*': v3.copy()}}
if v4:
v5['addit... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1) -> bool:
for v2 in v1.split(' '):
if not re.match('(^[a-z][a-z0-9-]*$)|(^[0-9]+$)', v2) and (not re.match('-[AFGMPagpunr]+$', v2)):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Dict[int, int]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int) -> Dict[int, int]:
v2 = {}
for v3 in range(-9, 10):
v2[v3] = 20 - v3 - v1
return v2
``` |
Imports:
```python
import random
import string
import typing
```
Type definitions:
```python
@value.value_equality
class v0:
def __init__(self, v1: Optional[str]=None, v2: Optional[str]=None, v3: Optional[str]=None) -> None:
"""Configuration for a job that is run on Quantum Engine.
Args:
... |
Imports:
```python
import torch
from torch import Tensor
import torch.distributed as dist
from torch.distributed.distributed_c10d import _get_global_rank, group
from torch.nn import Module
from torch.nn import Parameter
import typing
```
Type definitions:
Input Types: Tensor, Tensor, Any
Output Type: Any
Dependencies:... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = self.cell.seconds_since_birth
v2 = self.cell.migrate(delta=self.default_delta)
self.assertEqual(v2.seconds_since_birth, v1 + self.default_de... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, List[int]
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: List[int]) -> int:
v3 = {0: 1}
for v4 in range(1, v1 + 1):
v3[v4] = 0
v2.sort()
for v5 in v2:
for v6 in range(0, v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict):
v2 = v1['id']
for v3 in self.connections:
if v3.user_id == v2:
v3.groups = v1['groups']
v3.permissions = v1['per... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list) -> int:
v2: List[List[int]] = [1 for v3 in range(len(v1))]
for v4 in range(1, len(v2)):
for v5 in range(v4):
if v1[v5] <= v1[v4] an... |
Imports:
```python
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import init
import torch.distributions
import typing
```
Type definitions:
```python
@dataclass
class v0:
v1: str = 'constant'
v2: float = 0.0
```
Input Types: v0
Output Type: Any
Dependencies:
F... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, dict
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: dict={}) -> None:
for v4 in self.__tags['device_serial_number']:
for v5 in self.__measurements:
if v5 == '... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> List[str]:
v3 = []
for v4 in v1:
v5 = []
for v6 in range(len(v4)):
v5.append(''.join(map(chr,... |
Imports:
```python
import numbers
from typing import Any, Callable, Dict, Iterable, Mapping, Optional, Sequence, Tuple, Union, cast
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.optimizer import Optimizer
from torch.utils.data.distributed import DistributedSampler... |
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 self._scene:
self._scene.shutdown()
self._scene = self._scene_factory(v1)
self._scene.startup()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: BaseEstimator, List, List, List, List, Optional[Dict], Optional[Dict]
Output Type: Any
Dependencies:
```python
def v0(**v1):
return lime_tabular.LimeTabularExplainer(discretize_continuous=False, **v1)
```
```python
def v2(v3: Explanation):
ret... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: int, **v2) -> None:
await self.fetch_user_badges(v1)
v3 = []
v4 = []
for (v5, v6) in enumerate(v2.items()):
(v7, v8) = v6
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray) -> np.ndarray:
v2 = np.zeros((v1.shape[0], v1.shape[1], 3), dtype=np.uint8)
v2[np.where(v1 == 0)] = (255, 0, 0)
... |
Imports:
```python
import pandas as pd
from pandas._typing import DataFrame
import typing
```
Type definitions:
Input Types: DataFrame, Optional[List[str]]
Output Type: DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: DataFrame, v2: Optional[List[str]]=None) -> DataFrame:
if v2 is not None... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, Any
Output Type: np.ndarray
Dependencies:
```python
def v0(v1: np.ndarray) -> np.ndarray:
return v1 / v1.sum(axis=1, keepdims=True)
```
```python
def v2(v3: np.ndarray, v4=1.0) -> np.ndarray:
return v3 + v4
```
F... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Enum
Output Type: Optional[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Enum) -> Optional[int]:
v2 = self.RAM_INPUT_MAP.get(v1)
if v2:
return self.ram[v2]
return None
``` |
Imports:
```python
from re import UNICODE, compile
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> bool:
if len(v1) < 3 or len(v1) > 20:
return False
v2 = compile('\\A[\\w-]+\\Z', UNICODE)
if not v2.match... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> None:
self.forms[v1] += 1
self.count += 1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'Node'
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'Node'):
v1.nexts.add(self)
self.prevs.add(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: list[list[int]]
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list[list[int]]) -> bool:
if len(v1) % 2 != 0 or len(v1) != 20:
return False
for v2 in v1:
if len(v2) != len(v1):
ret... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Any) -> dict:
if not isinstance(v1, str):
v2 = False
else:
try:
(v1, v3) = v1.split('|')
except ValueError:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = self.forward_only()
v2 = self._criterion(v1, self._labels)
v2.backward()
self._module.zero_grad()
``` |
Imports:
```python
import matplotlib.pyplot as plt
import typing
```
Type definitions:
Input Types: List
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List=None):
if v1 is None:
v1 = plt.gcf().get_axes()
for v2 in v1:
v2.set_axis_off()
``` |
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