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Imports:
```python
import inspect
import torch
import torch.fx
from torch.fx.node import _get_qualified_name
import typing
```
Type definitions:
Input Types: Tuple[str, Union[str, Callable]], Optional[List[Union[Tuple[Union[str, Tuple[str, ...]], str], Tuple[Union[str, Tuple[str, ...]], str, bool]]]], Optional[List[Un... |
Imports:
```python
from datetime import timedelta
import numpy as np
from pandas._libs import Timedelta, Timestamp, lib, ops as libops
from pandas._typing import ArrayLike
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike, find_common_type, maybe_upcast_putmask
from pandas.core.dtypes.common i... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: Tuple[List[botbowl.Action], float]
Dependencies:
```python
def v0(v1):
return botbowl.ai.make_bot('random').act(v1)
```
Function Name: v2
Function:
```python
def v2(v3) -> Tuple[List[botbowl.Action], float]:
v4 = v0(v3)
re... |
Imports:
```python
import sys
import typing
```
Type definitions:
Input Types: Path
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Path) -> bool:
v2 = v1.joinpath('Scripts' if sys.platform.startswith('win') else 'bin')
try:
if not v2.is_dir():
return False... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Iterator[dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Iterator[dict]:
v1 = self.dynamodb_table.meta.client.get_paginator('scan')
yield from (item for v2 in v1.paginate(TableName=self.dynamodb_table.n... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
if not v1:
raise ValueError('file_name must be provided')
if not isinstance(v1, str):
raise ValueError('file_name must be a str, i... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, List[str]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str, v3: List[str]=None):
if v3 is None:
v3 = []
v3.append(v2)
return {'type': v1, 'holder': v2, 'account_holders': v3}
`... |
Imports:
```python
import os
import signal
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
os.kill(self.apiServerPid, signal.SIGTERM)
self.logger.info(f'botApiServer terminated (pid {self.apiServerPid})')
``` |
Imports:
```python
import datetime
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0() -> str:
v1 = datetime.datetime.now()
v2 = ''.join(str(v1).replace(' ', '').replace('-', '').split(':')[0:2])
return v2
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: sqlite3.Connection
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: sqlite3.Connection):
with v1:
v1.execute('DROP TABLE IF EXISTS iso639')
v1.execute('\n CREATE TABLE iso639 (\n ... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: np.ndarray, v2: Union[List, str], v3: Optional[np.array]=None, v4: Optional[np.array]=None, v5: int=385):
if v5 not in [384, 1536]:
raise ValueError('invalid size. options: [384,... |
Imports:
```python
from hashlib import md5
import typing
```
Type definitions:
Input Types: int
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> str:
v2 = md5(self.email.lower().encode()).hexdigest()
return f'https://gravatar.com/avatar/{v2}?d=identicon&s={v1}'
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.model.grid.remove_agent(self)
self.model.schedule.remove(self)
self.dead = True
``` |
Imports:
```python
import logging
import inspect
import typing
```
Type definitions:
```python
class v0(Enum):
v1 = 0
v2 = 1
v3 = 2
```
Input Types: dict, dict, tuple[type]
Output Type: str
Dependencies:
```python
def v4(v5, v6: list[str]=[], v7: list[str]=['pass'], v8: dict={}):
v9 = '\n ' + '\n ... |
Imports:
```python
import threading
from threading import Thread
import typing
```
Type definitions:
Input Types: int, str, List[str]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: str, v3: List[str]=None) -> None:
self.node_id = v1
self.attach_to = v3 = v3 or ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: Tuple[str, str, str, str]
Dependencies:
```python
def v0(v1: dict) -> str:
if v1.get('inn') is None:
return v1['chosen_employer_name']
else:
return f"{v1['chosen_employer_name']} (ИНН {v1['inn']})"
```
Functio... |
Imports:
```python
import re
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str):
v2 = re.compile('"event":"FSDJump", "StarSystem":"(.*?)"')
v3 = v2.findall(v1)
return v3
``` |
Imports:
```python
import re
import typing
```
Type definitions:
```python
v0 = typing.Union[disnake.Interaction, commands.Context]
```
Input Types: v0, Any
Output Type: Any
Dependencies:
Function Name: v1
Function:
```python
async def v1(self, v2: v0, v3):
v4 = {}
if self.embed:
v4['embed'] = self.emb... |
Imports:
```python
import random
from itertools import count
import torch
from torch import nn
from torch import optim
import typing
```
Type definitions:
Input Types: Any
Output Type: (float, int)
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1=False) -> (float, int):
v2 = self._reset_env()
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: List[Tuple[int, str]]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> List[Tuple[int, str]]:
v2 = '# mypy: '
if v2 not in v1:
return []
v3 = v1.split('\n')
v4 = []
for (v5, v6) in en... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: str):
v3 = self.data.generic.get_file_by_name(v2)
v3.write(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = Union[bytes, str]
```
Input Types: v0
Output Type: bytes
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: v0) -> bytes:
if type(v2) is bytes:
return v2
if type(v2) is int:
return bytes([v2])
if type(v2... |
Imports:
```python
import cv2
import numpy as np
import typing
```
Type definitions:
Input Types: Any, Any, Any
Output Type: Optional[float]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2, v3) -> Optional[float]:
v4 = v1.shape[0] - v2
v5 = []
cv2.imshow('data', v1)
v6 = cv2.in... |
Imports:
```python
import math
import typing
```
Type definitions:
Input Types: float
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float) -> float:
if v1 < 0 or v1 > 1:
raise ValueError('expected to have value in [0, 1]')
return 6 * math.pow(v1, 5) - 15 * math.pow(... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> List[str]:
v1 = self.list_images()
v2 = self._get_annotated_images()
return [image for v3 in v1 if v3 in v2]
``` |
Imports:
```python
import requests
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> bool:
v2 = requests.get('https://api.spotify.com/v1/me', headers={'Authorization': f'Bearer {v1}'})
if v2.status_code == 200:
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: dict, dict
Output Type: Tuple[np.array, np.array]
Dependencies:
```python
def v0(v1: Union[List[Tuple[any, any]], List[List[any]]]) -> Dict[any, List[any]]:
v2 = {}
for v3 in v1:
v2[v3[0]] = v2.get(v3[0], [])
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> bool:
v1 = v1.strip()
return not v1 or v1 == '-DOCSTART- -X- -X- O'
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: float, float
Output Type: Generator[Tuple, None, None]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float, v2: float) -> Generator[Tuple, None, None]:
v3 = 'SELECT DISTINCT T.vehicle_id\n FROM Trajectory ... |
Imports:
```python
import warnings
import typing
```
Type definitions:
Input Types: Optional[str], Container[str]
Output Type: str
Dependencies:
```python
def v0(v1: str, v2: Container[str]) -> str:
if v1 is not None and v1 not in v2:
warnings.warn(f'{v1!r} is not one of {v2!r}', LMFWarning)
return v1
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> int:
self.kill_list.append(v1)
v2 = -1
if len(self.kill_list) >= 3:
if self.active == self.kill_list:
self.kill_list = []
... |
Imports:
```python
import numpy as np
import torch
from torch import Tensor
import typing
```
Type definitions:
Input Types: Tensor, Tensor, Tensor, Tensor, float, bool
Output Type: Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Tensor, v2: Tensor, v3: Tensor, v4: Tensor, v5: float=1e-08, v6: b... |
Imports:
```python
import asyncio
import typing
```
Type definitions:
```python
v0 = TypeVar('A')
```
Input Types: Iterable[Awaitable[v0]]
Output Type: Iterable[Awaitable[v0]]
Dependencies:
Function Name: v1
Function:
```python
def v1(v2: Iterable[Awaitable[v0]]) -> Iterable[Awaitable[v0]]:
v3 = v2
v3 = [async... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, int
Output Type: Dict[str, Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: np.ndarray, v2: int) -> Dict[str, Any]:
v3 = {}
v4 = self.rt_posterior
assert v4 is not None
for (v5, ... |
Imports:
```python
import itertools
import typing
```
Type definitions:
Input Types: typing.Mapping[str, str]
Output Type: typing.Mapping
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: typing.Mapping[str, str]) -> typing.Mapping:
v2 = {}
v3 = [x.split(self.delimiter) for v4 in v1.keys()]... |
Imports:
```python
import numpy as np
from cvxpy import Minimize, Problem, Parameter, Maximize
from cvxpy.atoms import QuadForm, abs, power, quad_over_lin, sum, sum_squares, norm, huber, matrix_frac
from cvxpy.reductions.solvers.defines import QP_SOLVERS, INSTALLED_SOLVERS
from cvxpy.expressions.variable import Variabl... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> int:
v2 = [[token.text for v3 in sent] for v4 in self.nlp_doc.sents]
return len(v2)
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: int, int, int, bool, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: int, v3: int, v4: bool, v5: int):
(self._check_valid_state(v1), self._check_valid_state(v5))
self._chec... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = etree._Element
```
```python
v1 = Dict[str, str]
```
```python
v2 = Tuple[GeomDict, str]
```
Input Types: v0, v1
Output Type: v2
Dependencies:
```python
def v3(*v4: List[Optional[str]]) -> Optional[str]:
v4 = set(v4)
if None in v4:
v4... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Set[int]
Output Type: Set[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Set[int]) -> Set[int]:
v2 = set()
v2.update(v1)
return v2
``` |
Imports:
```python
import itertools
import numpy as np
import scipy.stats as st
import typing
```
Type definitions:
Input Types: np.ndarray, int, np.random.RandomState, int, float, int
Output Type: Any
Dependencies:
```python
def v0(v1: np.ndarray, v2: np.ndarray, v3: np.ndarray):
v4 = np.empty(shape=(v1.shape[0],... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
v1: bool
v2: bool
v3: Dict[Move, 'GameTreeNode']
v4: List[Move]
v5: Dict[Move, 'GameTreeNode']
v6: Move
v7: Move
v8: ValueProxy
def __init__(self, v9: Position, v10: Move, v11: ValueProxyBatch, v12: int):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: ast.ImportFrom
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: ast.ImportFrom) -> None:
for v2 in v1.names:
if v1.module is not None and (not v2.asname):
self._from_imports[v2.name] = v1.... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
assert self.filename
return os.path.join(os.path.dirname(self.filename), self.changelog.directory)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> int:
if type(v1) == int and v1 > 0:
return v1
v2 = str(v1)
if len(v2) == 0:
return None
if '#' == v2[0:1]:
v2 = v2[1:]... |
Imports:
```python
from tqdm import tqdm
import typing
```
Type definitions:
Input Types: dict
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: dict) -> dict:
for (v2, v3) in tqdm(v1.items(), desc='get_offset'):
v4 = min(v3['seq'])
v1[v2]['seq'] = {i - v4 for v5 in ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Any) -> None:
v2 = v1(self, self.event_engine)
self.engines[v2.engine_name] = v2
``` |
Imports:
```python
import traceback
import typing
```
Type definitions:
Input Types: List[Tuple[str, tuple]], str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[Tuple[str, tuple]], v2: str=None):
v3 = self._get_connection()
v4 = self._get_cursor(v3, cursor_type=v2)
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: tp.List[tp.Any]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: tp.List[tp.Any]) -> None:
v2 = [v1] if self._repetitions is None else v1
self._check_frozen()
v3: np.ndarray = -1 * ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> None:
v2 = 'terminate "%s" "%s"' % (self.udid, v1)
self._run_command(v2)
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any, int, bool
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2: int, v3: bool) -> int:
v4 = self.calc_camera_fps(v1, v2)
if v3:
v5 = np.array(self.PRESETS['STANDARD_FPS_VALUE... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> str:
v3 = bin(v1)[2:]
v4 = len(v3)
v5 = v2 // v4
return v3 * v5 + v3[:v2 - v4 * v5]
``` |
Imports:
```python
import random
import typing
```
Type definitions:
Input Types: Dict, int, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Dict, v2: int=1, v3: Any=''):
v4 = list(v1.keys())
v5 = [v1[v2] for v2 in v4]
v6 = 1 - sum(v5)
v4.append(v3)
v5.append(v6... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[Any], int
Output Type: List[Any]
Dependencies:
```python
def v0(v1, v2):
assert len(v1) >= v2, f'{(len(v1), v2)}'
v3 = len(v1) // v2
v4 = [v1[i] for v5 in range(0, len(v1), v3)]
return v4[:v2]
```
Function Name: v6
Function:
```py... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Axes
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Axes:
v1 = self.axes[self._it]
self._it += 1
if self._it >= len(self.axes):
self.flag_end_of_page = True
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool):
v2 = self._get_proposal_text(v1)
self.image.blit(v2, self._get_text_position(v2.get_width(), v2.get_height()))
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: bool
Dependencies:
```python
def v0(v1: hou.Parm) -> bool:
v2 = v1.parmTemplate()
if isinstance(v2, hou.StringParmTemplate):
if v2.stringType() == hou.stringParmType.NodeReference:
v3 = v1.eval()
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> np.ndarray:
v1 = self.data_y[:, self._y_idx]
self._y_idx += 1
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: bool
Dependencies:
```python
def v0(v1: str) -> Optional[str]:
v2 = DBpediaTaxonomyExtractor(is_debug=IS_DEBUG, input_text=v1).process()
if v2:
return v2
return 'NULL'
```
Function Name: v3
Function:
```python... |
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):
if 'clientes' not in self.dados:
self.dados['clientes'] = {v1: v2}
else:
self.dados['clientes'].update({v1: v2})
``` |
Imports:
```python
import scipy
from scipy import sparse
from scipy.sparse import csr_matrix, csgraph
from scipy.sparse.csgraph import minimum_spanning_tree, connected_components
import typing
```
Type definitions:
Input Types: igraph.Graph, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def... |
Imports:
```python
import torch
import typing
```
Type definitions:
Input Types: torch.Tensor, float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: torch.Tensor, v2: float):
v3 = v1
v4 = 2
v5 = torch.index_select(v1, v4, torch.linspace(start=0, end=v1.shape[v4] - 1, steps=... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict[str, Dict[str, paddle.to_tensor]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Dict[str, Dict[str, paddle.to_tensor]]:
v1 = {}
for (v2, v3) in self.model_dict.items():
v1[v2] = v3.state_dict()... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
v2 = v1[0]
for v3 in range(len(v1) - 1):
if v1[v3].isdigit() and v1[v3 + 1] == 'x':
v2 += '*'
v2 += v1[v3 + 1]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[int], int
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int], v2: int) -> int:
v3 = len(v1)
(v4, v5) = (0, v3 - v2 - 1)
v6 = v7 = sum(v1[:v3 - v2])
while v5 < v3 - 1:
v6 += v1[... |
Imports:
```python
import requests
from requests.models import HTTPError
import typing
```
Type definitions:
Input Types: str, str, str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str, v3: str) -> str:
v4 = {'Authorization': f'Bearer {v1}'}
v5 = {'long_url': v3}
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list, v2: str='beta'):
v3 = {'0': 'x', '1': 'y', '2': 'z'}
v4 = ''
for v5 in v1:
v6 = v5.split('_')
if v6[0] == 'grad':
... |
Imports:
```python
import logging
import os
import typing
```
Type definitions:
Input Types: str
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> List[str]:
if not os.path.isdir(v1):
raise NotADirectoryError
v2 = logging.getLogger(__name__)
v3 = os.path... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.topic_summary.total_published_node_count = -1
self._assert_validation_error("Expected total_published_node_count to be non-negative, received '-... |
Imports:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch import Tensor
import typing
```
Type definitions:
Input Types: Tensor
Output Type: Tensor
Dependencies:
```python
def v0(v1: Tensor) -> Tensor:
return torch.cat([torch.cos(v1), torch.sin(v1)... |
Imports:
```python
import torch
from torch import nn
from torch.nn import Dropout, Parameter, Linear, LeakyReLU, ModuleList
from torch.nn import ELU, LogSoftmax
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: torch.Tensor, torch.Tensor
Output Type: torch.Tensor
Dependencies:
Function ... |
Imports:
```python
from tqdm import tqdm
import sys
import typing
```
Type definitions:
Input Types: bool, Iterable
Output Type: Union[tqdm, Iterable]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: bool, v2: Iterable) -> Union[tqdm, Iterable]:
if v1:
return tqdm(v2, file=sys.stdout)
el... |
Imports:
```python
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver.remote.webelement import WebElement
import typing
```
Type definitions:
Input Types: WebElement
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: WebElement):
for (v2, v3) i... |
Imports:
```python
import random
import typing
```
Type definitions:
Input Types: ba.Player
Output Type: ba.Actor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: ba.Player) -> ba.Actor:
v1.gamedata['has_been_hurt'] = False
v2 = (self._spawn_center[0] + random.uniform(-1.5, 1.5), self._spa... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int):
if v1 not in range(self.num_states):
raise ValueError('State id is wrong! Input:{}; Valid inputs: {}'.format(v1, range(self.num_states)))
``` |
Imports:
```python
import signal
from keras.models import load_model
from keras.applications import imagenet_utils
from keras.preprocessing.image import img_to_array
import typing
```
Type definitions:
Input Types: str
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> None:
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: np.ndarray, v2: np.ndarray, v3: Iterable[int], v4: np.random.RandomState, v5: Dict[str, Any]):
"""
Args:
target_tensor: The state vector to act on, stored as a numpy arra... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
if v1 not in self._token2index:
self._token2index[v1] = len(self._token2index)
self._index2token.append(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, bool, int
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int=5, v2: bool=True, v3: int=None) -> pd.DataFrame:
v4 = self._df.sample(n=v1, replace=False, random_state=v3)
v5 = '{} Randomly Se... |
Imports:
```python
import cv2
from cv2 import dnn
from .resource import predictor_5_point_model_location, predictor_68_point_model_location, cnn_face_detector_model_location, face_recognition_model_location, dnn_prototxt_location, dnn_caffemodel_location, haarcascade_frontalface_location
import typing
```
Type definiti... |
Imports:
```python
import typing
```
Type definitions:
Input Types: ast.Assign
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: ast.Assign):
v2: List[Tuple[str, Optional[ast.AST]]] = []
for v3 in v1.targets:
v4 = None
v5 = self.visit(v3)
if isinstan... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray, v2: np.ndarray) -> np.ndarray:
v3 = v1 / v2[:, None]
v4 = np.dot(v3.T, v3)
return v4
``` |
Imports:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any, Any, Any, Any
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2, v3, v4, v5, v6, v7, **v8) -> dict:
v9 = F.log_softmax(... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int, bool
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int=0, v2: int=0, v3: bool=True) -> bool:
(v4, v5) = self.coords.real_coords()
v6 = v4 + v1
v7 = v5 + v2
v8 = self.parent.map.allow_... |
Imports:
```python
import typing
```
Type definitions:
Input Types: domain.Category, Dict[domain.Category, Dict], bool
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: domain.Category, v2: Dict[domain.Category, Dict], v3: bool) -> bool:
v4 = v2.get(v1)
if v4 is None or 'endorse... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, List[int], str, bool, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: List[int], v3: str='', v4: bool=False, v5: bool=True):
if self.df_new is None:
self._form_agg()
v6 = self.d... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Union['Conformer', 'RDKitConf'], Union[tuple, list, np.ndarray]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union['Conformer', 'RDKitConf'], v2: Union[tuple, list, np.ndarray]):
try:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int):
if v1 > len(self.images):
raise ValueError('Requested image number is not present in series')
return self.images[v1 - 1]
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str | None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> str | None:
v2 = self.db.collection(u'linebot').document(u'user').get().to_dict()
return v2[v1] if v1 in v2 else None
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> List[str]:
assert isinstance(v1, str)
assert v1[-1] == '/'
v2 = f'{self.root}/{v1}'
try:
v3 = self.fs.find(v2, withdirs=Fa... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Iterable[str]
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Iterable[str]) -> str:
v2 = ', '.join([f'[{index}, {group!r}]' for (v3, v4) in enumerate(v1)])
return f"\n function(params){{\n const conv... |
Imports:
```python
import typing
```
Type definitions:
Input Types: tp.Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: tp.Any) -> None:
v2 = registry['CMA'](2, budget=300, num_workers=4)
[v2.ask() for v3 in range(4)]
v4 = v1.readouterr()
assert v4.out == ''
ass... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict, Dict
Output Type: List[Tuple]
Dependencies:
```python
def v0(v1: List[Tuple]) -> List[Tuple]:
return list(set([tuple(sorted(i)) for v2 in v1]))
```
Function Name: v3
Function:
```python
def v3(v4: Dict, v5: Dict) -> List[Tuple]:
v6 = []
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: p.TableReferenceList
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: p.TableReferenceList) -> None:
self.visit(v1.left_paren)
self.write_comma_list(v1.references, with_space=False)
self.visit(v1.righ... |
Imports:
```python
import shutil
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.db.shutdown()
shutil.rmtree(self.data_dir)
shutil.rmtree(self.build_dir)
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: float, float, float
Output Type: Tuple[bool, float]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float, v2: float, v3: float) -> Tuple[bool, float]:
v4 = v1 - v2
if v4 < v3:
return (True, v4)... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Tensor, Tensor, Tensor
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tensor, v2: Tensor, v3: Tensor):
if not self.by_epoch:
self.lr_scheduler.step()
self.trainer.states['metrics']['train']['... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Tensor, Tensor
Output Type: Tuple[Tensor, Tensor]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tensor, v2: Tensor) -> Tuple[Tensor, Tensor]:
v3 = self.word_embedding(v1)
v4 = self.context_embedding(v2)
return (v3, ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: pd.DataFrame
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: pd.DataFrame) -> pd.DataFrame:
v2 = v1[v1[self._target_name].isnull()].index.tolist()
v3 = v1[v1.index.isin(v2)][self._feature_names]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> dict:
v1 = self.push_dataset.copy()
del v1['datasources']
for v2 in v1['tables']:
del v2['rows']
return v1
``` |
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