text stringlengths 190 325k |
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Imports:
```python
import cv2
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.parallel import DataParallel
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.serialization import load, save
from torch.utils.data import DataLoader
import typing
```
T... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
v2 = {}
v3 = ''
v4 = 0
for v5 in range(len(v1)):
v6 = v1[v5]
if v6 in v2:
v4 = max(v2.get(v6) + 1, v4)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
v1 = ''
if self.connected:
v1 = self.ser.readline().decode()
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[int]
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int]) -> bool:
v2: int = 1
v3 = len(v1)
while v2 < v3 and v1[v2] > v1[v2 - 1]:
v2 += 1
if v2 == 1 or v2 == v3:
retur... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, List[str], str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str=None, v3: List[str]=None, v4: str=None) -> str:
v5 = {'mutation($input: register_agent_input!)': {'register_agent(input: $... |
Imports:
```python
from bisect import bisect_left
import typing
```
Type definitions:
Input Types: List[Tensor], List[float]
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[Tensor], v2: List[float]) -> int:
v3 = len(v1)
v4 = [x[0] for v5 in sorted(enumerate(v2), reve... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> str:
if not v1.startswith('/'):
print('incorrectly formatted href' + v1)
exit()
for v3 in v2:
if 'href' in v3:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str) -> bool:
v3 = {'accepted': 0, 'queued': 1, 'sent': 2, 'delivered': 3, 'undelivered': 3, 'failed': 3, 'received': 3}
return v3[v1.low... |
Imports:
```python
from typing import Any, Tuple, TypeVar, Union, Hashable, Sized, _SpecialForm, Generic
import typing
```
Type definitions:
Input Types: Tuple[str, ...]
Output Type: Any
Dependencies:
```python
def v0(v1: Any, v2: Any) -> bool:
if v2.__origin__ != v1.__origin__:
return False
if tuple((... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list, int
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list, v2: int) -> list:
self._backtrack(v1, v2, [])
return self.res
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Callable[[], 'sqlutil.Database']
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Callable[[], 'sqlutil.Database']:
if not v1:
raise TypeError('Path flag must be set')
return lambda : self.... |
Imports:
```python
from importlib import util
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
```python
def v0(v1: str) -> typing.Optional[typing.Any]:
v2 = util.find_spec('Automation')
if v2 is None and (not _showedWarning):
print('Failed to build mod distribution i... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'cirq.ActOnDensityMatrixArgs'
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'cirq.ActOnDensityMatrixArgs'):
v1.target_tensor = self.target_tensor.copy()
v1.available_buffer = [b.copy() for v2 in self.av... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self.close_task is None:
self.close_task = self.get_loop().create_task(self._close())
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: 'CallTransactionBuilder'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict) -> 'CallTransactionBuilder':
self._params = v1
return self
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: keras.Model, np.ndarray, str, bool, int
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: keras.Model, v2: np.ndarray, v3: str, v4: bool=True, v5: int=1) -> np.ndarray:
v6: np.ndarray = v1.predict(x=v2, verbos... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int):
if v1 <= 1998 and v1 >= 0:
v2 = '8'
for v3 in range(v1):
v2 += '='
v2 += 'D'
return v2
elif v1 >= -1998 and ... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self._print_file_syntax_results()
self._print_imports_results()
self._print_schema_results()
self._print_layouts_results()
self._print_fe... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, Dict[str, float]
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: Dict[str, float]) -> None:
v3 = self._get_nexthop_mac()
if v3 and v3 in v2:
self._update_metrics(v1, v2[v3], 'nextho... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> bool:
v2 = self.field(v1)
if v2 is not None:
return v2.generated
raise Exception('Field %s not found!' % v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1) -> str:
v2 = None
try:
v3 = v1.get('rn').split('/')[3].split('[')[1].strip(']')
v4 = v1.get('rn').split('/')[2].strip('protpaths-')
v5... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: List[float], bool
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[float], v2: bool=False) -> int:
v3 = min(v1) if v2 else max(v1)
v4 = [index for (v5, v6) in enumerate(v1) if v6 == v3]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, Optional[bool]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: Optional[bool]=False):
if v2 not in self.VALID_EMPHASIS_LEVELS:
raise ValueError('The level provided to empha... |
Imports:
```python
import hashlib
from binascii import unhexlify
import typing
```
Type definitions:
Input Types: dict
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict) -> str:
v2 = self.get_transaction_hex(v1, remove_sigs=True)
v3 = hashlib.sha256()
v3.update(unh... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> str:
v2 = self.__get_pl_by_id(v1)
if v2 >= 0:
self.__data.pop(v2)
return 'Playlist deleted'
return 'No playlist found'
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: str, v2: str, v3: str, v4: str, v5: str, *, v6: int=25, v7: int=5):
self.sleep_time = v7
self.retries = v6
self.stack_name = v1
self.rancher_url = v3
self.service_name = v2
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int) -> float:
v2: float = 3.0
v3: int = 1
for v4 in range(2, v1, 2):
v2 += v3 * 4 / (v4 * (v4 + 1) * (v4 + 2))
v3 = v3 * -1
return ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0(v1) -> pd.DataFrame:
v2 = v1.pivot(index='userId', columns='movieId', values='rating')
v2[~v2.isna()] = 1
v2.fillna(0, inplace=True)
return v2
``... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = NewType('Graph', DefaultDict[Node, Set[Node]])
```
Input Types: v0, str
Output Type: None
Dependencies:
```python
def v1(v2: v0) -> str:
v3 = ['digraph {']
for (v4, v5) in v2.items():
v4 = str(v4).replace('-', '_')
if not v5:
... |
Imports:
```python
from typing import List, cast
import torch
from torch import Tensor, einsum
from typing import Any, Callable, Iterable, List, Set, Tuple, TypeVar, Union, cast
import typing
```
Type definitions:
Input Types: Tensor, int
Output Type: Tensor
Dependencies:
```python
def v0(v1: Tensor, v2=1) -> bool:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
with self.assertRaisesRegexp(NotImplementedError, 'Subclasses of DeviceSystemContext should implement domain validation.'):
self.device_system_co... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = namedtuple('Option', ['option', 'settingName'])
```
Input Types: List[v0]
Output Type: Any
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: List[v0]):
self.options.extend([o.option for v3 in v2])
self._setSchema(self._cus... |
Imports:
```python
import gzip
import os
import typing
```
Type definitions:
Input Types: str, bool
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: bool=True) -> str:
(v3, v4) = list(os.path.split(v1))
v5 = '.pkl' if not v2 else '.pkl.gz'
v6 = 'partial-' + v4.repla... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional[str]
Output Type: Optional[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, *v2: str, v1: Optional[str]=None) -> Optional[str]:
for v3 in v2:
v4 = self.get(v3)
if v4 is not None:
return v4... |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: Structure
Output Type: pd.DataFrame
Dependencies:
```python
def v0(v1: Structure) -> pd.DataFrame:
v2 = v1.id.upper()
v3 = {}
for v4 in v1:
for v5 in v4:
v6 = v5.id
v7 = f'{v2}:{v4.id}:{v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float, float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float, v2: float):
self.windDataCounter -= 1
self.windSpeed = v1
self.windDirection = v2
self.windDataSpeed.append(v1)
self.windDat... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: str):
async with self._client.get(self._make_url('/api/v3/ticker/24hr'), params={'symbol': v1}) as v2:
return await v2.json()
``` |
Imports:
```python
import torch
from torch import nn
import typing
```
Type definitions:
Input Types: torch.Tensor
Output Type: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor) -> torch.Tensor:
(v2, v3, v4) = self.to_qkv(v1).unbind(0)
v5 = torch.einsum('... i d, ... |
Imports:
```python
import tensorflow as tf
from tensorflow.python.keras.utils import tf_utils
import typing
```
Type definitions:
Input Types: int, Dict
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: Dict) -> None:
tf.keras.callbacks.TensorBoard.on_epoch_end(self, ... |
Imports:
```python
import cv2
from scipy import signal
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: np.array
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2=0) -> np.array:
v3 = signal.gaussian(v1, v1 // 3).reshape(1, v1)
v3 = v3 * v3.transpose()
v4 = cv2.getRot... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'Vm'
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'Vm'):
for v2 in self.vms.values():
if v1 in v2:
v2.remove(v1)
self.vms[v1.app].append(v1)
``` |
Imports:
```python
from matplotlib import pyplot as plt
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
import typing
```
Type definitions:
Input Types: str, int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: int) -> None:
v3 = 0
if type(v2) n... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: str) -> str:
v3 = len(v2)
v4 = [''] * v3
for v5 in range(v3):
v4[v5] = v2[v5 * v1 % len(v4)]
return str.join('', v4)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.verify_exists_vm_size('westus2', 'Standard_D8a_v3', True)
assert self._platform._locations_data_cache
v1 = self._platform._get_location_key(... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, int, int, int
Output Type: np.ndarray
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray, v2: int, v3: int, v4: int) -> np.ndarray:
if v2 * v3 != v1.shape[0]:
raise ValueError("Sizes o... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[Dict[Text, Any], List[Any]]
Output Type: List[Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union[Dict[Text, Any], List[Any]]) -> List[Any]:
if isinstance(v1, list):
return v1
elif isinstance(v1, dict):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any, Any, Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1=0.0, v2=0.0, v3=0.0, v4=0.0, v5=1.0, *, v6=None) -> None:
if v6 is not None:
v6 = tuple(v6)
self.mglo.clear(v1, v2, v3,... |
Imports:
```python
import pandas as pd
import numpy as np
import typing
```
Type definitions:
Input Types: int, Tuple, Tuple, int, int
Output Type: pd.DataFrame
Dependencies:
```python
def v0(v1):
v2 = v1.strftime('%Y-%m')
v3 = str(np.random.randint(1, v1.daysinmonth))
return f'{v2}-{v3.zfill(2)}'
```
Func... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if os.path.exists(self.expected_file) and os.path.isfile(self.expected_file):
os.remove(self.expected_file)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict[str, Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Dict[str, Any]:
self.assert_connected()
self.update(new_tasks=False, allow_shutdown=False)
v1 = self._payload_template()
v1['data']['oper... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> list:
try:
v2 = self._ulog.get_dataset(v1).data
except:
print('InAirDetector: {:s} not found in log.'.format(v1))... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0:
def __init__(self, v1: argparse.Namespace):
self.options = v1
self.collected_logs = []
self.collected_failures = []
self.fail_count = 0
self.expectedfail_count = 0
self.unexpectedpass_count = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, str, list, dict, dict, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str, v3: str=None, v4: list=None, v5: dict=None, v6: dict=None, v7: bool=False):
v8 = (v6 or {}).copy()
v8['p... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, np.ndarray, int, float, bool, float, List[np.ndarray], bool
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int, v2: np.ndarray, v3: int, v4: float, v5: bool, v6: float=None, v7: List[np.ndarray]=None, v8: b... |
Imports:
```python
import numpy as np
from sklearn.cluster import DBSCAN
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any
Output Type: (np.array, np.dtype, list)
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2, v3, v4=1.5) -> (np.array, np.dtype, list):
if v1.size == 0:
... |
Imports:
```python
import json
import os
from pathlib import Path
from tqdm import tqdm
import numpy as np
import cv2
import typing
```
Type definitions:
Input Types: Path, Path, Tuple[int, int], Any
Output Type: Any
Dependencies:
```python
def v0(v1: Path, v2: int):
with open(v1, 'r') as v3:
v4 = v2 / 2
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict[str, Dict[str, str]]
Dependencies:
Function Name: v0
Function:
```python
def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '工藝材料'
v1['id'] = 'craft-materials'
v2 = {}
v2['craft-materials'] = '工藝材料'
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List, v2: int):
self.nodes.extend(v1)
self.importance = max(self.importance, v2)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: 'Collection[Party]'
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> 'Collection[Party]':
with self._lock:
return list(self._party_impls)
``` |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2) -> float:
v3 = self._get_costs(v1, v2)
assert v3 >= 0.0
assert type(v3) == float or type(v3) == np.float64, type(v3)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List):
v2 = 'member'
v3 = 'member.json'
v4 = {'users': v1}
self.save_dict(v4, f'{v2}/{v3}')
return
``` |
Imports:
```python
import numpy as _np
import typing
```
Type definitions:
```python
v0 = _ty.TypeVar('_Arr')
```
```python
v1 = _ty.Callable[..., v0]
```
```python
v2 = _ty.Callable[..., _np.ndarray]
```
Input Types: v2, v1[v0]
Output Type: v1[v0]
Dependencies:
Function Name: v3
Function:
```python
def v3(self, v4: v... |
Imports:
```python
import os
import ctypes
import typing
```
Type definitions:
Input Types: os.PathLike
Output Type: bytes
Dependencies:
```python
def v0(v1: os.PathLike) -> str:
v1 = os.fspath(v1)
v2 = ctypes.windll.kernel32.GetShortPathNameW(v1, None, 0)
v3 = ctypes.create_unicode_buffer(v2 + 1)
ctyp... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[int]
Output Type: bytearray
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int]) -> bytearray:
v2 = bytearray()
for v3 in v1:
v4 = bytes(self.get_colour(v3))
v2.append(v4[0])
v2.append(v... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, int, int, bool
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: int=48, v3: int=1, v4: bool=False) -> dict:
v5 = self._preprocess_audio(v1)
return self._model.predict(audio_path=v1, signal=v... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(NamedTuple):
v1: float
v2: float
```
Input Types: Optional[v0], Optional[v0]
Output Type: Any
Dependencies:
Function Name: v3
Function:
```python
def v3(self, v4: Optional[v0]=None, v5: Optional[v0]=None):
if v4:
self.beam_px... |
Imports:
```python
from pathlib import Path
import typing
```
Type definitions:
Input Types: Any, str | Path, bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Any, v2: str | Path, v3: bool=True, **v4):
v2 = Path(v2)
if not isinstance(v1, self.dtypes):
raise Ty... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any
Output Type: bool
Dependencies:
```python
@lru_cache()
@wrap_norm_prefix
def v0(v1: str, *, v2: Tuple[RelationHint, ...], v3: Tuple[str, ...], v4: bool, v5: bool, v6: bool, v7: bool=False) -> nx.DiGraph:
v8 = nx.DiGraph()
v9... |
Imports:
```python
import typing
```
Type definitions:
Input Types: bool
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: bool) -> None:
if v1:
self.__write('OUT 1')
else:
self.__write('OUT 0')
return
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict):
if 'kind' in v1:
v2: str = v1['kind']
if v2.endswith('List'):
v3 = v2[:-4]
for v4 in v1['items']:
... |
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]:
if self.fiducial_marks is None:
raise ValueError('No fiducial marks available.')
v2: dict[str, str] = {mark.filename: m... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict, list
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: dict, v2: list) -> list:
v3 = []
for v4 in v2:
v3.append({'Id': v4['Id'], 'TargetType': v1[v4['Id']], 'Data': v4['Data']})
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Tuple[int, int]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> Tuple[int, int]:
v2 = v1.split('..')
if len(v2) == 1:
return (int(v2[0]), int(v2[0]))
elif len(v2) == 2:
return (int(v... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Tuple[str, str]
Dependencies:
```python
def v0():
try:
with open(CACHED_GPS_COORD_FILE_PATH) as v1:
v2 = v1.readlines()
if len(v2) != 2:
logger.warning('Expected to find 2 lines in GPS ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: 'ExchangeBase'
Output Type: Optional[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: 'ExchangeBase') -> Optional[str]:
if self.position_close:
v2 = None
else:
v2 = self._get_collateral_token(v1)
r... |
Imports:
```python
from urllib.parse import quote_plus
import typing
```
Type definitions:
Input Types: str, str, bool
Output Type: Union[List, Dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str=None, v3: bool=None) -> Union[List, Dict]:
v4 = {'include_deleted': v3, 'timestamp... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.object_path = ''
self.object_signature = None
self.object_type = None
``` |
Imports:
```python
import os
import platform
from subprocess import check_output, CalledProcessError, STDOUT
import typing
```
Type definitions:
Input Types: Any
Output Type: str
Dependencies:
```python
def v0() -> str:
v1 = platform.system()
if v1 == 'Windows':
if get_java_architecture() == '32bit':
... |
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 uuid
import typing
```
Type definitions:
Input Types: Iterable, int, bool
Output Type: Any
Dependencies:
```python
def v0(v1: 'Session', v2: str, v3: str, v4: int, v5=True, v6=False):
if isinstance(v3, int):
raise ValueError(f'{v3} {v2}')
v7 = '.'.join([v3, v2])
if _get_fr... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
if self.host in v1 and v1.startswith('http'):
return v1
if not v1.startswith('/'):
v1 = '/' + v1
v2 = 'https' if self._use_htt... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Dict[str, Any], Dict[str, Any], int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Dict[str, Any], v2: Dict[str, Any], v3: int) -> None:
self.log_scalars({k: v for (v4, v5) in v1.items() if isinstance(v5, (... |
Imports:
```python
import typing
```
Type definitions:
```python
@dataclass(repr=False)
class v0(Generic[_T]):
v1 = ('value', 'size')
v2: _T
v3: int
def __repr__(self) -> str:
return repr(self.value)
```
```python
v4 = TypeVar('_T')
```
Input Types: Hashable
Output Type: v0[v4] | None
Dependenc... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float, float
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float, v2: float=1) -> float:
v3 = self.average * self.n_items + v1 * v2
self.n_items = self.n_items + v2
self.average = v3 / self.n_item... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: Any, Any, list, int, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2, v3: list, v4: int=100000, v5=None):
v6 = 0.001
v7 = v3.__len__()
v8 = np.eye(v7)
for v9 in range(1, v4):
... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = NewType('HawaiianWord', str)
```
Input Types: v0, int
Output Type: str
Dependencies:
Function Name: v1
Function:
```python
def v1(v2: v0, v3: int) -> str:
v4 = {'h': 'h', 'k': 'k', 'l': 'l', 'm': 'm', 'n': 'n', 'p': 'p', "'": "'"}.get(v2[v3])
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int, int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int=-1, v2: int=1) -> None:
(self._stage_rank, self._stage_world_size) = (v1, v2)
self._run_event('on_stage_start')
while self.stage_epoch_ste... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray, int, int, float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray, v2: np.ndarray, v3: int, v4: int, v5: float):
v6 = v1[v4] - v1[v3]
v7 = np.sqrt(v2[v3, v3] ... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self) -> None:
if self._async_unsub_dispatcher_connect:
self._async_unsub_dispatcher_connect()
self._async_unsub_dispatcher_connect = None
sel... |
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._cfg.tau_initial if len(self._game) <= self._cfg.num_sampling_moves else self._cfg.tau_final
v2 = self._N ** (1 / v1)
if v2... |
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=False):
v3 = f'{v1.qualified_name} {v1.signature}'
if any(v1.aliases):
v3 += ' | Aliases: '
v3 += ', '.join((f'`{alias}`'... |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict, **v2: typing.Any):
assert self.resolver
self.resolver.resolve_schema(v1)
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> float:
for v1 in self.__optimizer.param_groups:
return v1['lr']
``` |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = Union[None, bool, ModuleType]
```
Input Types:
Output Type: v0
Dependencies:
Function Name: v1
Function:
```python
def v1() -> v0:
global _pyarrow_dataset
if _pyarrow_dataset is None:
try:
from pyarrow import dataset as ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[str], str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[str], v2: str) -> bool:
for v3 in sorted(v1):
v2 = v2.replace(v3, '')
return len(v2) == 0
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int) -> None:
for v2 in range(1, 11):
print(f'{v1:2} x {v2:2}: {v1 * v2:2}')
``` |
Imports:
```python
import os
import tensorflow as tf
import typing
```
Type definitions:
Input Types: Any, int
Output Type: Any
Dependencies:
```python
def v0(v1, v2, v3: str=''):
nonlocal i
for (v4, v5) in zip(v1, v2):
if limit is None or limit == -1 or i < limit:
tf.io.write_file(tf.const... |
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:
v2 = v1.shape[0]
v3 = self.num_obj
v4 = np.zeros((v2, v3, v3))
return v4
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: dict
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: dict) -> dict:
if 'subjects' in v1:
for v2 in v1['subjects']:
if 'term' in v2:
v2['term'] = self._replace_special_char... |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(namedtuple('_Coach', ['id', 'name', 'bio', 'available', 'birth_year', 'gender', 'languages', 'need', 'rights', 'housing'])):
def v1(cls, *, v2: Optional[int]=None, v3: str, v4: str, v5: bool=True, v6: int, v7: str, v8: Dict[str, int], v9: Co... |
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