text stringlengths 190 325k |
|---|
Imports:
```python
import typing
```
Type definitions:
Input Types: bytes
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: bytes) -> str:
v2 = []
v3 = 0
while v3 < len(v1):
v4 = v1[v3]
v3 += 1
v2.append(v1[v3:v3 + v4].decode('ascii'))
v3 += v4... |
Imports:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, bool
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1:... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[float, array], float, float
Output Type: Any
Dependencies:
```python
def v0(v1: Union[float, array], v2: float, v3: float):
return v1 * v2 / (v1 + v3)
```
Function Name: v4
Function:
```python
def v4(v5: Union[float, array], v6: float, v7: f... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = os.path.abspath(f'files/animals/{self.word.file_name}')
os.startfile(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
v2 = '/product/{}'.format(v1)
return self._get(v2)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> float:
(v1, v2, v3, v4) = self._get_start_end()
return v3 + (v4 - v3) * (self.event_index - v1) / (v2 - v1)
``` |
Imports:
```python
import dataclasses
import typing
```
Type definitions:
Input Types: Sequence, Type[Union[list, tuple]]
Output Type: Sequence
Dependencies:
```python
def v0(v1: Any):
if checks.ishashable(v1):
return hash(v1)
if dataclasses.is_dataclass(v1):
v1 = dataclasses.asdict(v1)
ret... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, float, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: float, v3: int):
self.n = v1
self.p0 = v2
self.r = v3
self.ecs.set_pcr(self.r)
self.pcr = self.ecs.get_pcr()
self.e... |
Imports:
```python
import typing
```
Type definitions:
Input Types: array.array, bytes, int, int
Output Type: None
Dependencies:
```python
def v0(v1: int, v2: int) -> int:
return v2 | v2 >> v1
```
Function Name: v3
Function:
```python
def v3(v4: array.array, v5: bytes, v6: int, v7: int) -> None:
for v8 in rang... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int):
if not 0 <= v1 <= 3:
raise ValueError('Invalid pin number')
v3 = [0] * 65
v3[0 + 1] = 80
v3[2 + v1 * 4 + 1] = 1
... |
Imports:
```python
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import typing
```
Type definitions:
Input Types: Dataset, Any, Any, Any, Any
Output Type: Any
Dependencies:
```python
def v0(v1: pd.DataFrame, v2: str):
v3 = open(v2, 'w+')
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[tf.placeholder], List[tf.identity], tf.train.Optimizer
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[tf.placeholder], v2: List[tf.identity], v3: tf.train.Optimizer=None):
self.freeze(v1, v2, v3)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self: 'ParameterSweep', v1) -> bool:
if isinstance(v1, slice):
v1 = (v1,)
return len(self._parameters) == len(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(typ.NamedTuple):
v1: LineNo
v2: Start
v3: End
```
```python
v4 = typ.List[v0]
```
Input Types: v0, v4
Output Type: bool
Dependencies:
Function Name: v5
Function:
```python
def v5(v6: v0, v7: v4) -> bool:
for v8 in v7:
v9 ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict, list
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict, v2: list) -> str:
(v3, v4) = (v1['opn'], v1['sum'])
return f'''<details class="yhb-col"{(' open>' if v3 else '>')}<summary>{(self.inline(v4... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Any=None, v2=v2, v3=v3, v4=v4) -> bool:
nonlocal i, status
v5 = v5 + 1 or 0
if v5 == v2:
v6 = 0
return False
elif v5 >... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, Optional[str], Optional[tiledb.Ctx]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: Optional[str]=None, v3: Optional[tiledb.Ctx]=None):
if self._registry.narray != 1:
raise ValueError(f'... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, Union[float, int]]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Dict[str, Union[float, int]]) -> None:
if self.logger:
self.logger.writerow(v1)
self.file_handler.flush()
``` |
Imports:
```python
from copy import deepcopy
import numpy as np
import typing
```
Type definitions:
Input Types: np.array
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.array):
if self.input_layer:
v1 = v1.T
self.cache = deepcopy(v1)
if self.weights is Non... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, List[str] | None, List[str] | None
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: List[str] | None=None, v3: List[str] | None=None) -> List[str]:
v4 = []
if v3:
v4.extend(v3)
v4... |
Imports:
```python
import logging
import typing
```
Type definitions:
Input Types: Dict[str, Any], Dict[str, List[str]]
Output Type: Dict[str, str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Dict[str, Any], v2: Dict[str, List[str]]) -> Dict[str, str]:
v3 = {}
for (v4, v5) in v2.items... |
Imports:
```python
import json
import torch
import torch.optim.lr_scheduler as lr_sched
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2, v3=[500, 5000], v4=0.1, **v5) -> Any:
if isinstance(v3, str):
v3 =... |
Imports:
```python
import json, os, shutil
import typing
```
Type definitions:
Input Types: str
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> dict:
v2 = self.read(v1)
v2 = json.loads(v2)
return v2
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Callable[..., Any]
Output Type: Callable[..., Any]
Dependencies:
```python
@wraps(func)
def v0(*v1: Any, **v2: Any) -> Any:
if len(v1) + len(v2) >= func.__code__.co_argcount:
return func(*v1, **v2)
@wraps(func)
def v3(*v4: Any, **... |
Imports:
```python
import json
import os
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict):
v1 = self.__convert_iterables(v1)
if os.path.isfile(self.__file_name):
with open(self.__file_name, 'a+') as v2:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0() -> list:
print('Using default sequencing error parameters...')
v1 = [[0.0, 0.4918, 0.3377, 0.1705], [0.5238, 0.0, 0.2661, 0.2101], [0.3754, 0.2355, 0.0, 0.389], [... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str, plt.Figure, str, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: plt.Figure, v3: str, v4: str) -> None:
if not os.path.isdir(f'../../hist_plot/eigenvectors_physical_{v3}/{v1}/'):
... |
Imports:
```python
import torch
import torch.nn as nn
from torch import nn as nn
import typing
```
Type definitions:
Input Types: torch.Tensor, torch.Tensor, torch.Tensor
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor, v2: torch.Tensor, v3: torch.Tensor):
v4 = -0.5 / ... |
Imports:
```python
import os
import typing
```
Type definitions:
```python
v0 = {'cora': partial(Planetoid, name='cora'), 'pubmed': partial(Planetoid, name='pubmed'), 'facebook': partial(KarateClub, name='facebook'), 'lastfm': partial(KarateClub, name='lastfm', transform=FilterTopClass(10))}
```
Input Types: dict(help=... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
while True:
v1 = self.in_queue.get()
self.consume(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self.dry_run:
self.logger.debug('commit skipped')
else:
self.logger.debug('commit')
super().commit()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: List, bool
Output Type: List
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List, v2: bool=False) -> List:
v3 = {'ids': v1}
v4 = self.session.post(self.base_url + '/detects/entities/summaries/GET/v1', json=v3)
if v4.... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(self, **v1) -> pd.DataFrame:
v1 = self._modify_params(v1)
v1 = self._inject_fields(v1)
(v1, v2) = self._convert_params(v1)
v1 = self._validate_param... |
Imports:
```python
import subprocess
import typing
```
Type definitions:
Input Types: List[str]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[str]) -> None:
print(v1)
subprocess.run(' '.join(v1), shell=True, check=True)
``` |
Imports:
```python
import torch
from tqdm.auto import tqdm
import typing
```
Type definitions:
Input Types: torch.nn.Module, int
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.nn.Module, v2: int) -> float:
v3 = 0
v4 = enumerate(self.train_dataloader)
if self.... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str
Output Type: [str, int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> [str, int]:
v2 = {'VÅR': 'V', 'SOM': 'S', 'HØST': 'H'}
v3 = re.compile('(?P<year>\\d{4})_(?P<semester>VÅR|SOM|HØST)')
v4 = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, Doc2Vec, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list, v2: Doc2Vec, v3: int=10):
v4: list = []
v5 = v2.infer_vector(v1)
v6 = v2.dv.most_similar([v5], topn=v3)
print(' ---- test for %d... |
Imports:
```python
import json
import requests
import typing
```
Type definitions:
Input Types: list, dict
Output Type: None
Dependencies:
```python
def v0(v1: list, v2: dict) -> dict:
v3 = 'Hi ! I hope you are doing well :) \nHere is some stuff you can do on your website to improve the performance of the *{} page... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Callable
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Callable) -> None:
v2 = self.__process_func(v1)
if not v2:
print('Invalid input function')
return
self.__grid.transform(self._... |
Imports:
```python
import numpy as np
import tensorflow as tf
from tensorflow.compiler.plugin.poplar.ops import gen_ipu_ops
from tensorflow.python import ipu
from tensorflow.python.ipu import ops as ipu_ops
from tensorflow.python.ipu import utils
from tensorflow.keras.preprocessing import image
import typing
```
Type d... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
v1 = ['txn', 'name', 'database_engine', 'after_callbacks', 'exception_callbacks']
def __init__(self, v2: Cursor, v3: str, v4: BaseDatabaseEngine, v5: Optional[List[_CallbackListEntry]]=None, v6: Optional[List[_CallbackListEntry]]=None):... |
Imports:
```python
import numpy as np
from skimage import transform, io
import typing
```
Type definitions:
Input Types: np.ndarray, list, Any, Any, float, Any, Any, Any
Output Type: Any
Dependencies:
```python
def v0(v1, v2=(132, 132)):
v3 = io.imread(v1, as_gray=True)
return transform.resize(v3, v2, anti_ali... |
Imports:
```python
import torch
import typing
```
Type definitions:
Input Types: float, int
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float, v2: int) -> float:
assert v2 == int(v2)
v3 = int(torch.tensor(v1).abs().log10().ceil().item())
v4 = 10 ** (v3 - v2)
retur... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Tensor, Tensor
Output Type: Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Tensor, v2: Tensor) -> Tensor:
v3 = v1.size(0)
v4 = [1] * (v2.ndim - 1)
v1 = v1.reshape(v3, *v4)
return v1
``` |
Imports:
```python
import itertools
import typing
```
Type definitions:
Input Types: 'Word', requests.Response, 'Config'
Output Type: 'Iterator[WordPronPair]'
Dependencies:
```python
def v0(v1: requests.Response, v2: 'Config') -> 'Iterator[Pron]':
v3 = v1.html.xpath(v2.pron_xpath_selector, first=True)
if v3:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, Any
Output Type: str
Dependencies:
```python
def v0(v1: list, v2: list) -> str:
v3 = str()
for v4 in range(len(v1)):
if v4 < len(v1) - 1:
if len(v1[v4]) >= len(v2[v4]):
v3 += ' ' + v1[v4] + ' '
... |
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.hex_to_percent(v1)
if v2 < 10:
v2 = 10
self.pilot_params['dimming'] = v2
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Type[Any]
Output Type: Type['BaseMaterializer']
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Type[Any]) -> Type['BaseMaterializer']:
if v1 in self.materializer_types:
return self.materializer_types[v1]
else:
... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = Tuple[str, Any]
```
Input Types:
Output Type: List[v0]
Dependencies:
Function Name: v1
Function:
```python
def v1(self) -> List[v0]:
v2 = list(self.__store.items())
if self.return_only_changed_values:
v3 = [elem for v4 in v2 if v4[0... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, dict
Output Type: Any
Dependencies:
```python
def v0(v1, v2, v3, v4):
v5 = min(v2, v4) - max(v1, v3)
return v5
```
```python
def v6(v7, v8, v9):
v10 = v9.keys()
v11 = 0
for v12 in v10:
(v13, v14, v15, v16, v17, v18, v... |
Imports:
```python
from qiskit import QuantumCircuit as QiskitQuantumCircuit
from qiskit import execute
from qiskit.providers import Job
from qiskit.result import Counts, Result
import typing
```
Type definitions:
Input Types:
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) ->... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any, Optional[str]
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2, v3: Optional[str]=None) -> bool:
if v3 is None:
v3 = self._get_invoked_action(v2)
v4 = self.get_policy_statements(v1, v... |
Imports:
```python
from argparse import ArgumentParser
import typing
```
Type definitions:
Input Types:
Output Type: ArgumentParser
Dependencies:
Function Name: v0
Function:
```python
def v0() -> ArgumentParser:
v1 = ArgumentParser()
v1.add_argument('-o', '--output_file', help='Specify output file', default=... |
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:
v3 = self.db.Role(name=v1, description=v2)
v3.save()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[int], Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int], v2=False):
if v2:
return self._decode_list(v1)
return self._decode_one(v1)
``` |
Imports:
```python
import uuid
import json
from pathlib import Path
import typing
```
Type definitions:
Input Types: Dict[str, Any]
Output Type: str
Dependencies:
```python
def v0(v1: Any) -> str:
return json.dumps(str(v1))
```
```python
def v2() -> str:
return render_jinja_html(str(Path(__file__).parent / 'te... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> int:
v2 = len(v1)
if v2 == 1:
return v1[0]
elif v2 == 2:
return max(v1[0], v1[1])
else:
(v3, v4, v5) = (None, None, N... |
Imports:
```python
import glob
import os
import shutil
from pathlib import Path
import typing
```
Type definitions:
Input Types: str, str, [str], Any
Output Type: bool
Dependencies:
```python
def v0(v1: Path, v2: Path, v3=CraftCore.settings.getboolean('General', 'UseHardlinks', False)):
CraftCore.log.debug('copy f... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, int, float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray, v2: int, v3: float=0):
v4 = np.eye(v2)[v1]
if v3 != 0:
if v3 == 1:
raise AssertionError(... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: str, v2: str) -> None:
await self._database.request_email_confirmation(v1, v2)
return None
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self.stop_instances:
self._StopInstances()
if self.failed_disks:
self.logger.warning(f"The following disks dould not be found: {',... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: bool):
self.__fix()
v3 = self.split_route_into_sub_routes(v1, self.depot, v2)
v4 = 0
for v5 in v3:
v6 = len(v5)
if v6 ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: int
Dependencies:
```python
def v0(v1: int) -> int:
if v1 == 0:
return 0
if v1 == 1:
return 1
if v1 in memo:
return memo[v1]
else:
v2 = v0(v1 - 1) + v0(v1 - 2)
memo[v1] = v2
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: int, v3: int):
for v4 in range(v2):
if v1 == 0:
print((2 ** (v3 - 1) - 1) * ' ', end='')
print('*', end='')
... |
Imports:
```python
import json, os, sys
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
```python
def v0(v1):
with open('blacklist.json', 'w') as v2:
v2.seek(0)
json.dump(v1, v2, indent=4)
```
Function Name: v3
Function:
```python
def v3(v4: int):
with open('... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.running = False
self.stop_generating()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> None:
v2: str = v1.group(1).rstrip('\n')
v3: bool = False
if not self.has_lead:
v3 = True
self.has_lead = True
if v3:
... |
Imports:
```python
from warnings import warn
import numpy as np
import matplotlib.pyplot as plt
import seaborn
import scipy.stats as stats
import typing
```
Type definitions:
Input Types: Any, bool
Output Type: Any
Dependencies:
```python
def v0(v1):
v2 = np.array([unq_map[e] for v3 in v1])
return poisson_fitt... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str, str, str
Output Type: None
Dependencies:
```python
def v0(v1: str, v2: str) -> str:
v3 = _read_trig_file(v1)
v4 = (None, RDF.type, rdflib.URIRef(v2), None)
for (v5, v6, v6, v6) in v3.quads(v4):
v7 = _select_domain(st... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray):
v1 = np.array(v1)
for v2 in v1.T:
v3 = np.unique(v2)
v3.sort()
for (v4, v5) in enumerate(v3):
... |
Imports:
```python
import os
from urllib.request import urlretrieve
import typing
```
Type definitions:
Input Types: str, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str):
if not os.path.exists(v2):
urlretrieve(v1, v2)
``` |
Imports:
```python
import textwrap
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> None:
v2 = textwrap.fill(v1, 79)
for v3 in v2:
print(v3)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> List[str]:
v1 = [m.topic for v2 in self._wildcard_topic_matches]
v1.extend([v2.topic for v2 in self._exact_topic_matches.values()])
return v1
``` |
Imports:
```python
import torch
import torch.nn as nn
import torch.nn.utils
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
import typing
```
Type definitions:
Input Types: torch.Tensor, List[int]
Output Type: Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: 'Lt'
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> 'Lt':
v1 = self.Ga.g_inv * self.matrix().T * self.Ga.g
return self.Ga.lt(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: bool
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: bool) -> dict:
v2 = 'DEBUG' if v1 else 'INFO'
return {'version': 1, 'disable_existing_loggers': False, 'handlers': {'arq.colour': {'level': v2, 'class': 'ar... |
Imports:
```python
from .abc import AbstractChannel, AbstractTransaction, TimeoutType, TransactionState
import typing
```
Type definitions:
Input Types: TimeoutType
Output Type: commands.Tx.RollbackOk
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: TimeoutType=None) -> commands.Tx.RollbackO... |
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._text_headers[v1]
self._reader.seek(v2.offset + self._texts_offset)
v3 = self._reader.read_string(v2.length)
self._tlk.entries[v... |
Imports:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```pytho... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, List[int]], str
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict[str, List[int]], v2: str) -> List[str]:
if v1 == {}:
return []
with open(v2, 'w+') as v3:
v4: Set[str] = set(... |
Imports:
```python
from pandas._libs import index as libindex, lib
from pandas._typing import Dtype
from pandas.util._decorators import Appender, cache_readonly
from pandas.core.dtypes.cast import astype_nansafe
from pandas.core.dtypes.common import is_bool, is_bool_dtype, is_dtype_equal, is_extension_array_dtype, is_f... |
Imports:
```python
import typing
```
Type definitions:
```python
@dataclass
class v0:
v1: str
v2: str
v3: str
v4: List[Estudiante] = field(default_factory=list)
@property
def v5(self) -> str:
return self._codigo
@v7.setter
def v6(self, v7: str) -> None:
self._codigo = v... |
Imports:
```python
import torch
from torch import Tensor
import typing
```
Type definitions:
Input Types: int
Output Type: Tensor
Dependencies:
```python
def v0(v1) -> Tensor:
v2 = torch.ones(v1)
v2[v1 // 2] = 1 - v1
v3 = v2
return v3
```
Function Name: v4
Function:
```python
def v4(v5: int) -> Tensor:... |
Imports:
```python
import torch
import typing
```
Type definitions:
Input Types: Any
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> dict:
with open(v1, 'rb') as v2:
return torch.load(v2, map_location='cpu')
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, dict]
Output Type: Dict[str, dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict[str, dict]) -> Dict[str, dict]:
v2 = {}
if 'input' in v1:
v2['input'] = {}
if 'nodata_left' in v1['input']:
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray) -> bool:
v2 = 0
for v3 in range(v1.shape[0]):
if v1[v3, 0] == 0:
if np.sum(v1[v3, :]) == 0:
... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(BaseClient):
v1 = 'recordedfuture.masterrisklist'
v2 = 'https://api.recordedfuture.com/v2/'
v3 = {'output_format': 'csv/splunk', 'download': 1}
v4 = {'X-RF-User-Agent': 'Demisto', 'content-type': 'application/json'}
def __ini... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Tuple[List[str], List[str], List[str], List[str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Tuple[List[str], List[str], List[str], List[str]]:
with open(v1, 'r', encoding='utf-8') as v2:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Any:
for v2 in self.loaders:
if v2.match(v1):
return v2.load(v1)
raise ValueError('Unsupported file path: {}'.format(v1))
``... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: list=[], v2: list=[]):
"""
图`G=(V,E)`
Args
===
`vertexs` : 图的顶点
`edges` : 图的边
"""
self.veterxs = v1
self.edges = v2
self.adj = []
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str):
v2 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
v3 = {}
for ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, str, str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: str, v4: str) -> None:
v5 = self._projects[v1]
v5['versions'][v2] = {'resource_url': v3, 'license': v4}
``` |
Imports:
```python
import json
import math
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: str, v2=None, v3=None):
self.value = v1
self.left_child = v2
self.right_child = v3
```
Input Types: list
Output Type: str
Dependencies:
```python
def v4(v5: [str]) -> v0... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
```python
@Appender(_interval_shared_docs['class'] % {'klass': 'IntervalIndex', 'summary': 'Immutable index of intervals that are closed on the same side.', 'name': _index_doc_kwargs['name'], 'versionadded': '0.20.0', 'extra_attributes': 'is_over... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, Set[str], Set[str]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: Set[str], v3: Set[str]):
for v4 in v2:
v3.add(v4)
v5 = self.text_processor.get_matching_tokens_count(v1, v4)
... |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: Any, str, list, str
Output Type: Any
Dependencies:
```python
def v0(v1: pd.DataFrame, v2: str, v3: str, v4='mean'):
assert v4 in ['mean', 'best', 'random']
try:
v5 = v1[v3].max()
if v4 == 'mean':
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
v1 = v1.replace('\t', ' ')
while ' ' in v1:
v1 = v1.replace(' ', ' ')
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray, v2: np.ndarray) -> np.ndarray:
assert len(self.norm_scheme_per_modality) == len(v1), f'norm_scheme_per_modality must h... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Any:
self.yy.init()
self._process_container()
self.yy.post()
return self.yy.root_result
``` |
Imports:
```python
import typing
```
Type definitions:
```python
@final
@cclass
class v0:
v1: str
v2: Py_hash_t
v3: TaskPrefix
v4: object
v5: tuple
v6: str
v7: set
v8: set
v9: bint
v10: set
v11: set
v12: set
v13: set
v14: WorkerState
v15: Py_ssize_t
v16: P... |
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