text stringlengths 190 325k |
|---|
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
if self._attribute_path:
v1 = '.' + '.'.join([str(x[1]) for v2 in self._attribute_path])
else:
v1 = ''
return str(self.datablock_st... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = 'Demo Project / MR #1 Make a trivial change to the README.'
v2 = '\nHemanth V. Alluri created [MR #1](https://gitlab.com/Hypro999/demo-project/-... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Optional[int]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[int]=None):
v2 = self.sheet.get_worksheet(2)
v1 = v2.col_count - 1
if v1 < 2:
return np.array([])
... |
Imports:
```python
import torch
import gzip
import lzma
import typing
```
Type definitions:
Input Types: Union[str, IO]
Output Type: Union[IO, gzip.GzipFile]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union[str, IO]) -> Union[IO, gzip.GzipFile]:
if not isinstance(v1, torch._six.string_classes)... |
Imports:
```python
import typing
```
Type definitions:
Input Types: torch.Tensor, str
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor, v2: str='') -> torch.Tensor:
v3 = v1.shape
if not v2:
return 1
v4 = 1
if 'b' in v2:
v4 /= v3[0]
... |
Imports:
```python
from collections import UserDict
import torch
import typing
```
Type definitions:
Input Types: torch.LongTensor, torch.FloatTensor, torch.LongTensor, torch.LongTensor, torch.FloatTensor, Optional[int], Optional[int]
Output Type: Tuple[torch.Tensor]
Dependencies:
Function Name: v0
Function:
```pytho... |
Imports:
```python
from datetime import date, datetime, timedelta
from polars.utils import _timedelta_to_pl_duration
from polars import internals as pli
from polars.datatypes import DataType, Date, Datetime, Float64, Int32, Object, UInt32, py_type_to_dtype
import typing
```
Type definitions:
```python
class v0:
de... |
Imports:
```python
import logging
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict):
v2 = self._get_settings()
for (v3, v4) in v1.items():
if len(v3.split('settings.')) > 1:
v3 = v3.split('setti... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> bool:
for v1 in self.widgets:
if getattr(self.widgets[v1], '_terminate', None):
self.widgets[v1]._terminate()
self.widgets = {}
sel... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: str, Sequence[int]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: Sequence[int]) -> None:
if hasattr(self, v1):
raise ValueError(f'invalid parameter name: {v1}')
v3 =... |
Imports:
```python
import h5py
import numpy as np
import plotly.graph_objects as go
import typing
```
Type definitions:
Input Types: Any, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1=np.linspace(0, 20000000.0, 50), v2: str='initial_source.h5'):
v3 = h5py.File(v2, 'r')
v4 ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
if type(v1) != str:
self.fail('invalid_type')
return v1
``` |
Imports:
```python
import subprocess
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0() -> bool:
v1 = subprocess.run(['git', 'rev-parse', '--is-inside-work-tree'], stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
if v1.returncode ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: str):
v3 = self.cpu
v4 = v3.x
v3.memory[v1] = v4
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
```python
def v0(v1: str | None) -> float:
if not v1:
return 0
v1 = v1.lstrip('#')
v2 = tuple((int(v1[i:i + 2], 16) / 255.0 for v3 in (0, 2, 4)))
v4 = 0.2126 * v2[0] + 0.7152 * v2[1] + 0.0722 * ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray
Output Type: Tuple[np.ndarray, np.ndarray]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray, v2: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
v3 = v1.round().astype(int)
v4 = v2.round().a... |
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._rel_path = v1
self._target_adapters.clear()
``` |
Imports:
```python
import numpy as np
from netCDF4 import Dataset
from ..logging import debug, log
import typing
```
Type definitions:
Input Types: str, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: bool=True):
with Dataset(v1, 'r') as v3:
try:
v... |
Imports:
```python
import json
import os
import typing
```
Type definitions:
Input Types:
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> dict:
if not os.path.exists(self.files_path):
return {}
return json.load(open(self.index_path, 'r')) if self.index_filename i... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, list, Any, Any, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1, v2: list, v3, v4, v5=False):
v6 = [v2[x:x + 10] for v7 in range(0, len(v2), 10)]
if v3 > len(v6):
v3 = 0
if v3:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: pd.DataFrame, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: pd.DataFrame, v2: int):
if v2 == -1:
v3 = v1.iloc[:, :-1]._get_numeric_data()
v4 = v1.iloc[:, -1]
else:
v5 = [x for v6 i... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> dict:
v1 = {'type': self.type, 'expirationMs': self.expiration_ms, 'field': self.field}
return v1
``` |
Imports:
```python
import torch
import torch.nn as nn
import typing
```
Type definitions:
Input Types: Dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Dict):
(v2, v3) = (v1['continuous'], v1['categorical'])
v1 = None
if len(self.hparams.categorical_cols) > 0:
... |
Imports:
```python
import pandas as pd
from pandas.io.formats.style import Styler
import numpy as np
import typing
```
Type definitions:
Input Types: bool, bool, bool
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool=False, v2: bool=True, v3: bool=True) -> pd.DataFram... |
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 == self._warn_mkv:
return
self._count.update({'warning': self._count.get('warning') + 1})
self.task.get('status').updat... |
Imports:
```python
from hashlib import md5, sha256
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict):
v2 = sorted(list(v1.items()))
v3 = '&'.join([f'{k}={v}' for (v4, v5) in v2])
v6 = v3.replace('&', '||').enco... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Text, Dict[Text, Any]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: Text, v2: Dict[Text, Any], **v3: Any) -> None:
v4 = {**v2, 'metadata': v3.get('metadata', {})}
await self._send_message(v1, v4)... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str):
if len(v1) > len(v2):
if v1[:len(v2)] == v2:
return False
else:
for v3 in range(len(v2)):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str):
v3 = {self.schedules[v1][s]['name']: self.schedules[v1][s]['id'] for v4 in self.schedules.get(v1, {})}
return v2 in list(v3.values()... |
Imports:
```python
import torch
from torch import nn
from torch.ao.sparsity import BaseSparsifier, WeightNormSparsifier, FakeSparsity, NearlyDiagonalSparsifier
from torch.nn.utils.parametrize import is_parametrized
from torch.testing._internal.common_utils import TestCase
import typing
```
Type definitions:
Input Type... |
Imports:
```python
import torch
import torch.nn.functional as F
from torch.nn.modules.linear import Linear
from torch.nn.modules.rnn import LSTMCell, LSTM
import typing
```
Type definitions:
Input Types: Dict[str, torch.Tensor]
Output Type: Dict[str, torch.Tensor]
Dependencies:
Function Name: v0
Function:
```python
d... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> str:
v2 = [t for v3 in self.widgets if re.search(v1, v3)]
assert len(v2) == 1, f'Got {len(v2)} matches for {v1!r}'
return v2[0]
``... |
Imports:
```python
import torch
from torch import device, Tensor
import torch.jit._shape_functions as upstream_shape_functions
import typing
```
Type definitions:
Input Types: Union[Tensor, Tuple]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union[Tensor, Tuple]):
if isinstance(... |
Imports:
```python
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray, Any, str, str, str, str, Union[float, float], Union[float, float], Any, Any, Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> int:
v2 = 0
v3 = 1
while v1 != 0:
if v1 & v3:
v1 ^= v3
else:
v1 >>= 1
v2 += 1
return v2
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: typing.Optional[Iterable]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: typing.Optional[Iterable]=None) -> str:
if v1 is None:
v1 = []
return self._as_shell(self.value, v1)
``` |
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:
if v2 > 0:
v1 = v1[:-v2]
return v1
``` |
Imports:
```python
import asyncio
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, **v1: Any) -> None:
self.stop_future = asyncio.Future()
await self.ipc.start()
self.__tasks.create_task(self._broadcast_cluster_info_... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list[str]
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list[str]) -> dict:
v2 = {'ids': v1}
return self._post('/tunnels2/thirdParty/state', data=v2)
``` |
Imports:
```python
import collections
import torch
from torch.nn import Module
import typing
```
Type definitions:
Input Types: Module, List[Dict]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Module, v2: List[Dict]):
assert isinstance(v1, Module), 'Only support compressing... |
Imports:
```python
import math
import copy
import typing
```
Type definitions:
Input Types: list
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list) -> list:
v2 = math.floor(self.elitism * len(v1))
v3 = copy.deepcopy(v1)
v4 = self._crossover(v3, self.pc, v2)
v5... |
Imports:
```python
import torch
import typing
```
Type definitions:
Input Types: torch.Tensor
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor) -> torch.Tensor:
v2 = torch.ops.image.decode_image(v1)
return v2
``` |
Imports:
```python
from collections import defaultdict
import queue
import typing
```
Type definitions:
Input Types: int, Any, Any, list[str]
Output Type: Any
Dependencies:
```python
def v0(v1: str, v2: dict[str, int]):
if v1.isalpha():
return v2[v1]
else:
return int(v1)
```
Function Name: v3
F... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Union[bool, List[Dict]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Union[bool, List[Dict]]:
self.provider.authenticate()
v1 = self.config.resolve('lexicon:identifier')
v2 = self.config.resolve('lexic... |
Imports:
```python
import datetime
import typing
```
Type definitions:
Input Types: str, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str=None, v2: int=None):
try:
if v1:
v3 = datetime.datetime.strptime(v1, '%d/%m/%Y %H:%M')
if v3 <= datetime.... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2: int=30):
if not isinstance(v1, np.ndarray):
v1 = np.array(v1)
v3 = len(v1)
try:
v4 = range(2, min(v2, v3 - 1))... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, **v1: Any) -> bool:
v2 = self.async_join(**v1)
for v3 in self._async_handlers:
v2 = v3._async_close(**v1) and v2
self._async_handlers.clear()
... |
Imports:
```python
import random
import numpy as np
from pathlib import Path, PurePath
from sklearn.model_selection import train_test_split
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any, Any
Output Type: tuple
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2, v3, v4, v5) -> tupl... |
Imports:
```python
import numpy as np
import operator
import typing
```
Type definitions:
Input Types: List[str], List[str], str
Output Type: List[Tuple]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[str], v2: List[str], v3: str) -> List[Tuple]:
v4 = 10
if v3 not in v1:
return []... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
async def v0(v1: str) -> bool:
for v2 in v1.strip().splitlines():
if 'This page appears when Google automatically detects requests coming from your computer network... |
Imports:
```python
import argparse
import typing
```
Type definitions:
Input Types:
Output Type: argparse.ArgumentParser
Dependencies:
Function Name: v0
Function:
```python
def v0() -> argparse.ArgumentParser:
v1 = argparse.ArgumentParser()
v1.add_argument('--delay', action='store', type=float, default=3, he... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int) -> None:
if v2 >= self.nums[v1]:
self.sum += abs(self.nums[v1] - v2)
else:
self.sum -= abs(self.nums[v1] - v2)
s... |
Imports:
```python
from datetime import datetime
import typing
```
Type definitions:
Input Types: datetime, t.Hashable, t.Coroutine
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: datetime, v2: t.Hashable, v3: t.Coroutine) -> None:
v4 = datetime.now(v1.tzinfo) if v1.tzinfo e... |
Imports:
```python
import requests
import urllib
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> bool:
v2 = requests.post(f'http://{self._host}/printer/print/start?filename={urllib.parse.quote(v1)}', headers=self._... |
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(self, v1: np.ndarray) -> np.ndarray:
super().predict_proba(v1)
v2 = v1.shape[0]
return np.array([self.mean for v3 in range(v2... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> dict:
v1 = {'revisionID': self.revisionID, 'parentRevisionID': self.parentRevisionID, 'content': self.content.toJSON() if self.content is not None else None, '... |
Imports:
```python
from transformers import AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, MBartForConditionalGeneration, T5ForConditionalGeneration, MT5ForConditionalGeneration
from transformers import logging as transformers_logging
import typing
```
Type definitions:
Input Types: dict
Outpu... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str, str, Any, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str, v3, v4: str=None):
v5 = os.path.dirname(v1)
if not os.path.isdir(v5):
os.makedirs(v5)
with open(v1, v2, enc... |
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.run_git('add', '.')
self.run_git('commit', '-m', v1)
``` |
Imports:
```python
import typing
```
Type definitions:
```python
@dataclass
class v0:
v1: str
v2: str
v3: int = 4433
v4: bool = True
v5: Optional[int] = 4434
v6: str = '/'
v7: Optional[str] = None
v8: Result = field(default_factory=lambda : Result(0))
v9: Optional[int] = None
v10... |
Imports:
```python
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
import torch.utils.data
import typing
```
Type definitions:
```python
class v0(object):
def __init__(self, v1: nn.Module, v2: list, v3):
self.args = v3
self.model = v1
... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = Callable[[Doc, int, int], bool]
```
Input Types: Any
Output Type: v0
Dependencies:
```python
def v1(v2, v3, v4):
v5 = v2[v3:v4].text.lower()
v6 = phrase.split(v5)
for v7 in range(len(v6) - 1):
v8 = ''
v9 = ''
for v... |
Imports:
```python
import cv2
import torch
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray) -> np.ndarray:
v1 = cv2.cvtColor(v1, cv2.COLOR_BGR2RGB) / 255
v1 = torch.from_numpy(v1.transpose((2, 0... |
Imports:
```python
import torch.nn.functional as F
from torch import Tensor
import typing
```
Type definitions:
Input Types: Tensor, Tensor, Tensor
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tensor, v2: Tensor, v3: Tensor) -> None:
v4 = F.mse_loss(v1, v2, reduction='non... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Tuple[float, float]
Output Type: Tuple[float, float]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tuple[float, float]) -> Tuple[float, float]:
(v2, v3) = self.get_hex_position(v1)
v2 -= self.size
v3 -= self.size
... |
Imports:
```python
import json
import typing
```
Type definitions:
Input Types:
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> int:
v1 = {'data': {'key': self.token}}
v2 = self.request(url='https://europe-west1-fmpdev-1d3ca.cloudfunctions.net/getRemainingCalls', method='... |
Imports:
```python
import torch
from torch import nn
from torch.nn import functional as F
import typing
```
Type definitions:
Input Types: torch.Tensor
Output Type: List[torch.Tensor]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor) -> List[torch.Tensor]:
v2 = v1
v3 = []
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'str'
Output Type: 'Optional[Cell]'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'str') -> 'Optional[Cell]':
for v2 in self._rendered_cells.values():
if v2.id == v1:
break
else:
return None
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: scratch.ScratchTarget, list
Output Type: Any
Dependencies:
```python
def v0(v1: scratch.Block, v2: scratch.Block):
v1.nextId = v2.id
v2.parentId = v1.id
```
```python
def v3(v4: list):
v5 = v4[0]
for v6 in range(1, len(v4)):
v7... |
Imports:
```python
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
import typing
```
Type definitions:
Input Types: pd.DataFrame, pd.Series, str
Output Type: NoReturn
Dependencies:
```python
def v0(v1, v2):
v3 = np.cov(v1, v2)[0, 1]
(... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: dict):
if not isinstance(v1, dict):
raise ValueError('Configuration does not contain a dictionary.')
for (v2, v3) in v1.items():
if... |
Imports:
```python
import numpy as np
import torch
import typing
```
Type definitions:
Input Types: str, dict, float, float, int
Output Type: torch.Tensor
Dependencies:
```python
def v0(v1, v2):
v3 = torch.zeros((28, 28), dtype=torch.uint8)
v4 = {}
for v5 in v1:
if v5 in v4:
continue
... |
Imports:
```python
import cv2
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: bytes
Dependencies:
```python
def v0(v1: np.ndarray) -> bytes:
v2 = cv2.cvtColor(v1, cv2.COLOR_RGB2BGR)
(v3, v4) = cv2.imencode('.jpeg', v2)
if not v3:
raise ValueError('Image could not be encoded... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, Dict[str, Union[dd.DataFrame, pd.DataFrame]]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: Dict[str, Union[dd.DataFrame, pd.DataFrame]]=None) -> str:
if v2 is not None:
for (v3, v4) in... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, spacy.tokenizer.Tokenizer
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: spacy.tokenizer.Tokenizer) -> str:
v3 = ''.join([f'{word} O\n' for v4 in v2(v1) if len(str(v4).strip()) > 0])
if len(v3) >... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: int, v2: v0 | None) -> None:
self.label = v1
self.parent = v2
self.left: v0 | None = None
self.right: v0 | None = None
```
Input Types: v0, v0 | None
Output Type: None
Dependencies:
Fu... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str=None) -> dict:
v2 = '\n SELECT f_table_name AS tblname, srid\n FROM geometry_columns\n '
if v1:
v2 += f"\n ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str='_') -> None:
if v1 == '':
self.exp.suffix = ''
else:
self.exp.suffix = v2 + v1
return None
``` |
Imports:
```python
import random
import string
import numpy as np
from matplotlib import rcParams, pyplot
import typing
```
Type definitions:
Input Types: datetime
Output Type: None
Dependencies:
```python
def v0(v1: datetime) -> str:
return f"{v1.strftime('%Y%m%d_%H%M%S')}_{random_str(4)}"
```
```python
def v2(v3... |
Imports:
```python
import astropy.coordinates as coord
from astropy.time import Time
import astropy.units as u
import typing
```
Type definitions:
Input Types: float, float, datetime
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float, v2: float, v3: datetime):
v4 = coord.EarthLo... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: demes.Graph, bool
Output Type: Any
Dependencies:
```python
def v0(v1: Union[demes.Deme, demes.AsymmetricMigration], v2: Union[demes.Deme, demes.AsymmetricMigration]) -> bool:
return not (v1.end_time >= v2.start_time or v2.end_ti... |
Imports:
```python
import pickle
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str):
with open(v1 + 'data.pkl', 'rb') as v2:
v3 = pickle.load(v2)
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional[int]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional[int]) -> None:
if v1 is not None:
self.fix_precision_ = v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional[requests.Response], Optional[str], HTTPStatus, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Optional[requests.Response], v2: Optional[str]=None, v3: HTTPStatus=HTTPStatus.BAD_REQUEST, v4: bool=False):
... |
Imports:
```python
import torch
import torch.nn as nn
from torch.distributions import Categorical
import typing
```
Type definitions:
Input Types: Any
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> torch.Tensor:
v1 = torch.tensor(v1, requires_grad=False, dtype=to... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Path
Output Type: None
Dependencies:
```python
def v0(v1: Path) -> str:
with open(v1, 'rb') as v2:
v3 = PdfFileReader(v2)
v4 = v3.getPage(0)
return v4.extractText()
```
Function Name: v5
Function:
```python
def v5(v6: Path)... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Optional[Match[str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Optional[Match[str]]:
if not self.matcher:
return None
return self.matcher.search(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1=False) -> list:
if v1 or self.matches is None:
v2 = await self.connection('GET', 'tournaments/{}/matches'.format(self._id), include_attachment... |
Imports:
```python
import typing
```
Type definitions:
Input Types: types.ForwardRef
Output Type: Set[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: types.ForwardRef) -> Set[str]:
if v1.resolved:
return self._visit(v1.resolved)
else:
return set()
``` |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = Union[str, List[str]]
```
Input Types: List[artifact.Artifact], List[str]
Output Type: Callable[[List[str], dataset_options.RecordBatchesOptions, Optional[schema_pb2.Schema]], Iterator[pa.RecordBatch]]
Dependencies:
```python
def v1(v2: artifact.Arti... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[dict], str
Output Type: bool
Dependencies:
```python
def v0(v1: Dict[str, int], v2: str) -> dict:
if v2 == 'left':
return {'x': v1['x'] - 1, 'y': v1['y']}
if v2 == 'right':
return {'x': v1['x'] + 1, 'y': v1['y']}
if v2... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = {'id': 2, 'vote_delegations_$_from_ids': {222: []}}
self.t_update_vote_delegations_from_on_empty_array('user.update', v1)
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1, v2):
self.id = v1
self.data = v2
def __repr__(self):
return 'Message with id={}, data={}'.format(self.id, self.data)
@classmethod
def v3(cls, v4):
"""Construct message obj... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Tensor, int
Output Type: Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tensor, v2: int) -> Tensor:
v3 = v1.src.permute(1, 0).to(self.device)
v4 = v1.trg.permute(1, 0).to(self.device)
(v5, v6) = self(src=v3, t... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Dict:
v1 = {'AES128-GCM-SHA256': 'weak', 'ECDHE-ECDSA-AES256-SHA': 'weak', 'ECDHE-ECDSA-AES256-GCM-SHA384': 'recommended', 'AES128-SHA': 'weak', 'ECDHE-RSA-AES... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(metaclass=SwankType):
v1: int = 1000
v2: Optional[JamId] = None
v3: Optional[int] = None
def __init__(self, *, v4: SwankDatabase, v5: Optional[Union[JamId, str]]=None, v6: Optional[int]=None, **v7):
assert (v5, v6).count(... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Mapping[str, Any]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Mapping[str, Any]):
if isinstance(v1, (tuple, list)):
assert len(v1) == 2
v2 = {}
for (v3, v4) in self.input_key.items... |
Imports:
```python
import typing
```
Type definitions:
Input Types: set
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: set) -> None:
if self.mac in v1:
await self.async_remove()
``` |
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 self.runtime:
self.runtime.join(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: h5py.Group
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: h5py.Group) -> None:
super().to_hdf5(v1)
v2 = v1.require_group(self.name)
v2.attrs['num_modes'] = self.num_modes
if self.basis is not No... |
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