text stringlengths 190 325k |
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Imports:
```python
import torch.backends as backends
import torch.cuda as cuda
import torch.nn as nn
import torch.utils.checkpoint as torchcheckpoint
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0() -> None:
assert cuda.is_available()... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2):
v3 = re.search(v1, v2.text)
v4 = 0
while v3:
yield v3
v5 = v3.span(0)[0]
if v4 != v5:
v4 = v5... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[int, str]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: Union[int, str]) -> str:
v2 = await self._export_chat_invite_link__make_request(chat_id=v1)
return self._export_chat_invite_link__proc... |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: pd.DataFrame, pd.DataFrame, Union[List[str], str]
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: pd.DataFrame, v2: pd.DataFrame, v3: Union[List[str], str], **v4: Any) -> pd.DataFrame:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: io.TextIOWrapper
Output Type: Tuple[str, str, str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: io.TextIOWrapper) -> Tuple[str, str, str]:
v2 = next(v1).replace('\n', '')
v3 = next(v1).replace('\n', '')
next(v1)
... |
Imports:
```python
import tensorflow as tf
from tensorflow.python.keras import layers
from tensorflow.python.keras.utils import data_utils
import typing
```
Type definitions:
Input Types: bool, str, Tuple[int], Optional[str], int
Output Type: tf.keras.Model
Dependencies:
```python
def v0(v1: Callable[[tf.Tensor], tf.T... |
Imports:
```python
from math import inf as infinito
import typing
```
Type definitions:
Input Types: list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list):
v2 = -infinito
v3 = []
for v4 in range(len(v1) - 1, -1, -1):
if v1[v4] > v2:
v2 = v1[v4]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.df_rater.drop_duplicates(subset=['Rater', 'Frame', 'Trial'], keep='last', inplace=True)
self.df_algo.columns = ['Trial', 'Label', '1', '2', '3',... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types:
Output Type: Tuple[float, float]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Tuple[float, float]:
if self.beta > 0:
if self._H is False:
v1 = 2 / self.beta
v2 = 0
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, Optional[transport.PTransportSettings]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int, v3: Optional[transport.PTransportSettings]=None) -> str:
v4 = self.delete(f'/users/{v1}/sessions/... |
Imports:
```python
import typing
```
Type definitions:
Input Types: torch.Tensor
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor) -> None:
self.weight = self.make_weight(v1)[:, :, None, None].add_(1)
self.bias = self.make_bias(v1)[:, :, None, None]
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, List[str], Dict, Any
Output Type: Dict
Dependencies:
```python
def v0(v1, v2):
for (v3, v4) in zip(v1, v2):
if v3 != v4:
if v3.startswith('_'):
return False
if not (v4.startswith('{') and v4.end... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, Any], str, Optional[str]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Dict[str, Any], v2: str, v3: Optional[str]=None) -> str:
if v2 in v1:
return self.__filterNonPrintable(v1[v2].strip()... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, int, str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: int, v3: int, v4: str) -> str:
if abs(v1) < 25:
return ('N', 'neutral')
v1 = v1 * -1 if v3 % 2 == 0 else v1
v2 = v2 * -1 i... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: str):
self._manager = config.ConfigManager(v1)
self._log_path = self._manager.get_file(LOGFILE_NAME)
self._old_log_path = self._manager.get_file(LOGFILE_NAME_OLD)
def v2(self, v3: str) -> ... |
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._sim
v3 = v2.vehicle_index.actor_id_from_vehicle_id(v1)
v4 = v2.vehicle_index.shadow_actor_id_from_vehicle_id(v1)
v5 = v1... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: float
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float=1e-05) -> float:
v2 = self.q_vector
if v2 is not None:
v3 = np.sqrt(np.sum(v2 * v2))
return v3 if v3 > v1 e... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
self.theming['text'] = v1
self.build_text_layout()
self.redraw_all_states()
``` |
Imports:
```python
import numpy as np
from pandas._libs import lib
import pandas._libs.sparse as splib
from pandas._libs.sparse import BlockIndex, IntIndex, SparseIndex
from pandas._libs.tslibs import NaT
from pandas._typing import ArrayLike, AstypeArg, Dtype, NpDtype, PositionalIndexer, Scalar, ScalarIndexer, Sequence... |
Imports:
```python
import torch
from torch import nn
import torch.nn.functional as F
from torch.distributions import Normal
import typing
```
Type definitions:
Input Types: Tuple[torch.Tensor], bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tuple[torch.Tensor], v2: bool):
... |
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):
if self._is_free(v2):
self.pos = v2
``` |
Imports:
```python
import logging
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str='./log.runlog'):
v2 = v1
v3 = '%(asctime)s %(name)s:%(levelname)s:%(message)s'
v4 = logging.FileHandler(filename=v2, encoding='utf-8', ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
```python
def v0(v1, v2):
return ec2.instances.filter(Filters=[{'Name': v1, 'Values': [v2]}])
```
```python
def v3(v4='t4g.2xlarge'):
return v0('instance-type', v4)
```
Function Name: v5
Function:
```python
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.reqid += 1
v1 = {}
v1['requestID'] = self.reqid
v1['accountID'] = self.accountid
self.reqStockQryStockStaticInfo(v1)
``` |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
```python
def v0() -> Tuple[str, str]:
try:
v1 = os.environ['NETMIKO_DIR']
except KeyError:
v1 = NETMIKO_BASE_DIR
v1 = os.path.expanduser(v1)
if v1 == '/':
raise Valu... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> dict:
v1 = {}
if hasattr(self, 'checksum'):
v1['checksum'] = self.checksum
if hasattr(self, 'custom_attribute'):
v1['customAttribute'] ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union['futures.Future[_T]', 'Future[_T]'], Callable[..., None]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union['futures.Future[_T]', 'Future[_T]'], v2: Callable[..., None]) -> None:
if v1.done():
v2(... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
v1 = bytearray(self.state.phone_id, 'ascii')
v2 = 0
for v3 in range(len(v1)):
v2 += v1[v3]
return f'2{v2}'
``` |
Imports:
```python
from collections import deque
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: Any
Dependencies:
Function Name: v2
Function:
```python
def v2(self, v3: v0):
... |
Imports:
```python
from datetime import datetime, timedelta
import typing
```
Type definitions:
Input Types: datetime, DataFrame, int
Output Type: Tuple[int, int, int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: datetime, v2: DataFrame, v3: int) -> Tuple[int, int, int]:
v4 = v1 + timedelt... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> bool:
v1 = self.ensure_tree(v1)
if self.is_empty():
self.node = v1.node
self.left = v1.left
self.right = v1.right
re... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, **v1: Any) -> None:
self.__instance_params = v1
for (v2, v3) in v1.items():
setattr(self, v2, v3)
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: FloatArray, FloatArray
Output Type: Tuple[FloatArray, FloatArray, IntArray]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: FloatArray, v2: FloatArray) -> Tuple[FloatArray, FloatArray, IntArray]:
(v3, v4) =... |
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:
v1 = [col for v2 in [self.o, self.h, self.l, self.c, self.vwap] if v2 is not None]
v3 = np.any(np.isnan(v1), axis=0)
self.vali... |
Imports:
```python
import seaborn as sns
import matplotlib.pyplot as plt
import typing
```
Type definitions:
Input Types: list
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list) -> None:
v2 = 1
plt.figure(figsize=(24, 16))
for v3 in self.dataframe[v1].columns:
... |
Imports:
```python
import math
import numpy as np
import scipy.sparse as sp
import numpy.random as rnd
import typing
```
Type definitions:
Input Types: int, int, float, float, Optional[float], float, Optional[float], str
Output Type: Tuple[Tuple[sp.spmatrix, sp.spmatrix], np.array]
Dependencies:
```python
def v0(v1: s... |
Imports:
```python
import torch
from torch import device as _device
import typing
```
Type definitions:
Input Types: Optional[_device], bool, bool
Output Type: int
Dependencies:
```python
def v0() -> int:
if torch.cuda.device_count() > 0:
return torch.cuda.current_device()
return -1
```
Function Name: ... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
v1: bool
def __init__(self) -> None:
self.highlight = False
def __repr__(self) -> str:
"""From the top level node, the tree is traversed and `linearized` to
produce a representation with exactly one node def... |
Imports:
```python
import requests
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> bool:
v1 = self.nodes
v2 = None
v3 = len(self.chain)
print('get here')
print(v1)
for v4 in v1:
v5 = requests.get(f'... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
```python
def v0():
v1 = tuple((field.strip().lower() for v2 in lines[0].split('|')))
for v3 in lines[1:]:
v4 = tuple((v2.strip() for v2 in v3.split('|')))
if len(v4) == 0:
continu... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str=None, v2: str=None):
self._check_legal_access(v1, v2)
if v1 is None:
return self.dataset
elif v2 is None:
return self.datas... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
```python
def v0(v1: str) -> bool:
v2 = True
for v3 in v1:
for v4 in ['left', 'right']:
v5 = v3[0]
if v5 == '*':
pass
else:
v5 = in... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, Set[str]
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: Set[str]) -> bool:
if v1 in v2:
return True
v3 = v1.split('.')
v3.append('*')
while v3:
v3[-1] = '*'
if '.... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Iterable[int], dict[int, set[int]]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Iterable[int], v2: dict[int, set[int]]) -> None:
self._highest = v3 = self._highest + 1
self._mod_seqs_order.append(v3)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Optional[str]
Dependencies:
Function Name: v0
Function:
```python
async def v0(self) -> Optional[str]:
if not self.serial.isOpen():
return
self.buffer += self.serial.read_all().decode()
if '\n' in self.buffer:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int
Output Type: bytes
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int) -> bytes:
v1 += self.X86_64_OFFSET
v3 = self.object.Read(v1, v2, dbus_interface='org.example.TestsInterface')
return bytes(v3)
... |
Imports:
```python
import pathlib
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: pathlib.Path
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2) -> pathlib.Path:
v3 = self.get_install_root(v1)
v4 = self.get_install_prefix(v1)
if v2 == 'sysconf':
return... |
Imports:
```python
import subprocess
from subprocess import PIPE, Popen, STDOUT
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
v2 = f'gpg -K {v1}'.split()
try:
subprocess.run(v2, check=True)
retur... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> bool:
self._get_userdata_classes()
return v1 in self.userdataclasses
``` |
Imports:
```python
from os import path, makedirs
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
for v1 in self.subdirs:
print(f'INFO Saving {v1}/{self.html_name}')
makedirs(v1, exist_ok=True)
wit... |
Imports:
```python
from polars import internals as pli
from polars.internals.construction import arrow_to_pyseries, numpy_to_pyseries, pandas_to_pyseries, sequence_to_pyseries, series_to_pyseries
from polars.datatypes import Boolean, DataType, Date, Datetime, Duration, Float32, Float64, Int8, Int16, Int32, Int64
from p... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
super().dry_run()
v1: List[SftpFile] = self.get_sftp_files_map()
for v2 in v1:
self.log.info('Process will upload file from (SFTP) %s to ... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str
Output Type: Union[int, None]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Union[int, None]:
try:
v2 = int(re.sub('[^0-9]', '', str(v1)))
except ValueError:
v2 = None
return v2... |
Imports:
```python
from collections import deque
import typing
```
Type definitions:
Input Types: str
Output Type: Iterator[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> Iterator[str]:
v2 = deque(list(v1))
while v2:
v3 = v2.popleft()
assert v3 in ['e', 'w', 's', '... |
Imports:
```python
import inspect
import logging
from typing import Any, Callable, List, Optional, Type, TypeVar, Union, cast, overload
import typing
```
Type definitions:
```python
class v0:
v1: _FUNC_TYPE = None
v2: Optional[str] = None
v3: int = logging.NOTSET
v4: Any = None
v5: Optional[str] = N... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int):
self.build_layouts_toolbar()
self.update_hex_viewer_actions()
``` |
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:
super().reset()
self.next_observations = np.zeros((self.buffer_size,) + self.obs_shape, dtype=self.env.observation_space.dtype)
``... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, List[List[int]]
Output Type: int
Dependencies:
```python
def v0(v1):
if v1 in visited:
return visited[v1]
visited[v1] = -1
v2 = 1
for v3 in graph[v1]:
v4 = v0(v3)
if v4 == -1:
return -1
... |
Imports:
```python
import json
import os
import typing
```
Type definitions:
Input Types: list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list):
v2 = ['success_count', 'failure_count']
v3 = json.loads(os.getenv('RESULTS'))
assert len(v3) == len(v1), f'results: {v3}\n\n... |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.array, int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.array, v2: int) -> None:
(v3, v4, v5) = self._interpret_action_continuous(v1, v2)
if v3:
self.add_line(v4, v5)
else:
s... |
Imports:
```python
import torch
from torch import nn
from torch.nn.functional import cross_entropy
from torch.optim import Adam
from torch.utils.data import DataLoader
import typing
```
Type definitions:
Input Types: Dict[str, torch.Tensor], Dict
Output Type: Dict[str, torch.Tensor]
Dependencies:
Function Name: v0
Fu... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, str, int
Output Type: t.List[t.List[dict]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: str, v4: int=1) -> t.List[t.List[dict]]:
v5 = self.client.get(path=f'/projects/{v1}/{v2}/results', params=... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> dict:
v1 = self._get_enviornment_variable(self._environment_keys.PIP_KEY)
v2 = {'Content-Type': 'application/vnd.az.batch.v1+json', 'x-api-key': v1, 'Accep... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Path
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Path) -> List[str]:
v2 = []
with v1.open() as v3:
for v4 in v3:
v4.strip()
v5 = v4.split(' ')
v2.extend(v5)... |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray) -> bool:
v2 = v1[..., 1].max()
return v2 < 0.5
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> bool:
v1 = False
v2 = None
try:
v2 = self.ui_theme.get_image('normal_image', self.combined_element_ids)
except LookupError:
v2 = No... |
Imports:
```python
import requests
import shutil
import os
import typing
```
Type definitions:
Input Types: str, str
Output Type: image
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str) -> image:
v3 = requests.get(v1, stream=True)
v4 = f'./Data/Images'
if not os.path.exist... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(object):
v1 = ['_key', '_parent', '_left', '_right', '_color']
def __init__(self, v2: int, v3, v4: bool):
"""
Constructor with parameter.
:param key: int
:param parent: Node
:param color: boolean
... |
Imports:
```python
import pandas as pd
import string
import itertools
import datetime
import typing
```
Type definitions:
Input Types: pd.DataFrame, snowquery.Connector, str, bool
Output Type: bool
Dependencies:
```python
def v0(v1: str, v2: snowquery.Connector) -> list:
v3 = f"SELECT\n ORDINAL_POSI... |
Imports:
```python
import math
import typing
```
Type definitions:
Input Types: int
Output Type: bytes
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> bytes:
assert v1 in [2, 3]
v2 = self.__send_cmd(198, parameter=[v1], response_length=2, echo_expected=False)
v3 = int.from_byt... |
Imports:
```python
import subprocess
import sys
import typing
```
Type definitions:
Input Types: str, bool, Optional[str]
Output Type: Tuple[int, str, str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: bool=False, v3: Optional[str]=None) -> Tuple[int, str, str]:
if v2:
sys.stdout... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: 'Dataset'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2, **v3) -> 'Dataset':
self._check_dim(v2)
return self.obj.map(v1, dim=v2, **v3)
``` |
Imports:
```python
import sqlite3
from contextlib import closing
import typing
```
Type definitions:
Input Types: int, dict
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: dict) -> None:
if v2 == {}:
return
with closing(sqlite3.connect('./taskmanager/data/task... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> int:
v2 = 0
v3 = -1
v4 = ''
for (v5, v6) in enumerate(v1):
if v6 not in v1[v2:v3 + 1]:
v3 += 1
if len(v1... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: list):
v3 = ['%s[%d].lambdify()(x)*(%s<=x<%s)' % (v1, i, v2[i], v2[i + 1]) for v4 in range(len(v2) - 1)]
return eval('lambda x: %s' % '+'.join(... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.array, np.array, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.array, v2: np.array, v3):
v1 = v1.squeeze()
v2 = v2.squeeze()
if v3 == 'origin':
return np.concatenate([... |
Imports:
```python
import cv2
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: cv2.VideoWriter
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2) -> cv2.VideoWriter:
print('Creating writer %s' % v1)
print('Fourcc: %s' % v2)
v2 = cv2.VideoWriter_fourcc(*v2)
v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2) -> dict:
v3 = {}
for (v4, v5) in v1:
v3[v4] = v2[v5].copy()
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int=100):
v2 = {}
v3 = {}
(v4, v5) = self.process_protocol(v1)
v2.update(v4)
v3.update(v5)
return (v2, v3)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> bool:
v1 = self.state
if v1 is None:
self._logger.debug('Not got enough state to start yet... waiting... no state.')
elif v1.dungeon_map is Non... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(NamedTuple):
v1: TraceFrameQueryResult
v2: int = 1
v3: bool = False
v4: bool = False
```
Input Types: v0
Output Type: Tuple[str, str]
Dependencies:
Function Name: v5
Function:
```python
def v5(self, v6: v0) -> Tuple[str, str]:
... |
Imports:
```python
import gzip
import pickle
import logging
import typing
```
Type definitions:
Input Types: list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list):
try:
v2 = gzip.open('cache.gz', 'wb')
v1 = pickle.dump(v1, v2)
v2.close()
print('... |
Imports:
```python
import typing
```
Type definitions:
Input Types: pd.DataFrame
Output Type: pd.DataFrame
Dependencies:
```python
def v0(v1: pd.DataFrame) -> pd.DataFrame:
if v1['subtype_id'] in [81, 36, 21, 90, 91]:
v2 = 'other'
elif v1['subtype_id'] == 82:
v2 = 'head'
elif v1['type_id'] ... |
Imports:
```python
import math
import typing
```
Type definitions:
Input Types: List[str]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[str]) -> None:
self.idf_col = list()
self.vocabulary = list()
v2 = self.preprocess(v1)
for v3 in v2:
for v4 in v... |
Imports:
```python
import random
import requests
import typing
```
Type definitions:
Input Types:
Output Type: List[Dict]
Dependencies:
```python
def v0(v1: str) -> requests.Response:
return requests.get(v1, headers={'User-Agent': REDDIT_USER_AGENT})
```
Function Name: v2
Function:
```python
def v2() -> List[Dict... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, type
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: type):
if not isinstance(v1, str):
raise TypeError('parameter_name must be a str')
if not isinstance(v2, type):
raise Typ... |
Imports:
```python
import datetime
import asyncio
import typing
```
Type definitions:
Input Types: datetime.time
Output Type: bool
Dependencies:
```python
def v0(*v2, v1: float=None):
v3 = asyncio.get_event_loop()
if not v2:
if v3.is_running():
return
v3.run_forever()
v4 = a... |
Imports:
```python
import torch
from torch import Tensor
import torch.distributions as D
from torch.nn.functional import mse_loss
import typing
```
Type definitions:
Input Types: dict, dict
Output Type: Tuple[Tensor, Tensor]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict, v2: dict) -> Tuple... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: float, float, float, int, float, float
Output Type: float
Dependencies:
```python
def v0(v1, v2, v3, v4):
v1[0] = v2
v1[1] = v3
v1[2] = v4
```
```python
def v5(v6: float, v7: float, v8: float) -> float:
v9 = [0, 0, 0... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Optional[calibration.Calibration]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> Optional[calibration.Calibration]:
if not self._calibrations:
return None
return self._calibrations[max(se... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = Tuple[List[hikari.Embed], str]
```
Input Types: int
Output Type: v0 | None
Dependencies:
Function Name: v1
Function:
```python
async def v1(self, v2: int) -> v0 | None:
if v2 < 0:
return None
self.index = v2
while self.index > le... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Dict):
for v2 in self.per_ep_group_data.keys():
if v2 not in v1:
v1[v2] = np.nan
self.per_ep_group_data[v2].... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Tuple[str, int]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int) -> Tuple[str, int]:
v2 = chr(v1 & 255)
v3 = v1 >> 8 << 8
return (v2, v3)
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: bytearray):
"""
Initialize an instance of Genotype
:param array_of_bytes: The underlying representation of the genotype. For some
application, each byte can represent an ascii c... |
Imports:
```python
from collections import OrderedDict
import typing
```
Type definitions:
Input Types:
Output Type: List[OrderedDict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> List[OrderedDict]:
v1: List[OrderedDict] = []
for (v2, v3) in self.component_configurations.items():
... |
Imports:
```python
import torch
from torch._C import dtype
import torch.nn as nn
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: torch.Tensor, Any, Any, int, int, Any
Output Type: Any
Dependencies:
```python
def v0(v1: torch.Tensor, v2: torch.Tensor, v3=None, v4=1):
(v5, v6, v7, v8... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[str, int, List[Union[str, int]], Set[Union[str, int]]], Union[str, int, List[Union[str, int]], Set[Union[str, int]]], bool
Output Type: Union[str, int, List[Union[str, int]], Set[Union[str, int]]]
Dependencies:
Function Name: v0
Function:
```py... |
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 'CSW' in v1:
v1 = 'CSW'
elif 'Unittest' in v1:
v1 = 'Unittest'
elif 'Coverage' in v1:
v1 = 'Coverage'
el... |
Imports:
```python
import torch
from torch import Tensor
from torch.jit.annotations import List, Tuple
import typing
```
Type definitions:
Input Types: Tensor, int, int, int, int, List[int], int
Output Type: Tensor
Dependencies:
```python
def v0(v1: Tensor, v2: int, v3: int, v4: int, v5: int) -> Tensor:
if not isi... |
Imports:
```python
import PIL.Image as Image
import torch
from torch.autograd import Variable
from PIL.ImageOps import invert
import typing
```
Type definitions:
Input Types: Union[torch.Tensor, List[torch.Tensor]], int, int, bool, Optional[Tuple[int, int]], bool, int
Output Type: Any
Dependencies:
```python
def v0(v1... |
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