text stringlengths 190 325k |
|---|
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[Dict], Union[Dict]
Output Type: Dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Union[Dict], v2: Union[Dict]) -> Dict:
v3 = {**v1, **v2}
return v3
``` |
Imports:
```python
import numpy as np
from numpy.lib.stride_tricks import as_strided
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.utils.data as data
import torch.multiprocessing as mp
from torch.distributions import Categorical
from torch.utils.tensorboard ... |
Imports:
```python
import typing as t
from typing import Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: 'GeoData'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1=None, v2='observed') -> 'GeoData':
v3 = self... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int) -> float:
if v1 > 0.0:
self._balance += v1
print('{:.2f} deposited'.format(v1 / 100))
return self._balance / 100
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
v2 = input('What would you like to title this section? ' + '(default is ' + v1 + ')\n')
if v2:
return v2
return v1
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1 = self.getGenerators()
v2 = 0
for v3 in v1:
v4 = v3['BPT']
v5 = v3['amount']
v2 = v2 + v4 * v5
self.bpt = v2
v... |
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 = '.'.join(self.full_name_id.split('/'))
self.input_filter.restore_state_from_checkpoint(v1, v2)
self.pre_network_filter.restore_s... |
Imports:
```python
import random
import numpy as np
import typing
```
Type definitions:
Input Types: dict
Output Type: np.array
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: dict) -> np.array:
v2 = v1['dim']
v3 = v1['value']['min']
v4 = v1['value']['max']
v5 = v1['value']['dinfo']
... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray
Output Type: typing.Generator[typing.Tuple[np.ndarray, np.ndarray], None, None]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray) -> typing.Generator[typing.Tuple[np.ndarray, np.ndarray], None, N... |
Imports:
```python
import tensorflow as tf
import typing
```
Type definitions:
Input Types: tf.Tensor, tf.Tensor, Text, tf.Tensor
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: tf.Tensor, v2: tf.Tensor, v3: Text, v4: tf.Tensor):
if v1.shape[-1] != v2.shape[-1]:
v1 = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
self.max_rates = [16, 24, 32]
self.min_rates = [0, 4, 8]
``` |
Imports:
```python
import os
import glob
import typing
```
Type definitions:
Input Types: str
Output Type: List[Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> List[Any]:
v2 = v1.split(self.__split_char)
v3 = os.path.join(self.root_folder, '/'.join(v2))
v4 = glob.glob(v3)... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> int:
v1 = self.new_container_command('run')
v2 = self.new_autograde_command('version')
return (v1 + v2).run().returncode
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[List[str]]
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[List[str]]) -> int:
if not v1 or not v1[0]:
return 0
v2 = len(v1)
v3 = len(v1[0])
v4 = 0
v5 = [[0] * (v3 + 1) for v... |
Imports:
```python
import itertools
import typing
```
Type definitions:
```python
class v0:
def __init__(self: v0, v1: Iterable) -> None:
"""
Initialise a new instance.
:param iterable: an iterable from which can be obtained an
iterator of available items.
"""
s... |
Imports:
```python
from decimal import Decimal
import typing
```
Type definitions:
Input Types:
Output Type: Decimal
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Decimal:
if self.config.shrink_factor is not None:
return Decimal(self.config.shrink_factor)
else:
return De... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str, str, bool
Output Type: Union[List, Dict]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str, v2: str=None, v3: str=None, v4: bool=None) -> Union[List, Dict]:
v5 = {'name': v1, 'description': v2, 'subtype': v3, 'ful... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> str:
if type(v1) != str:
return ''
v2 = 'AaEeIiOoUu'
v3 = '\n'
v4 = ''
for v5 in v1:
if v5 in v2:
v4 += f"{'|'... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, dict, Any
Output Type: Tuple[str, dict, Any]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: dict, v3: Any, *v4) -> Tuple[str, dict, Any]:
v3.expect_column_to_exist(v1)
if v2['n_missing'] == 0:
v3.expect_c... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: List[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> List[int]:
(v1, v2) = self.getVisitedStops(repeatStation=True)
(v3, v4) = self.computeZones()
v5 = []
v6 = set()
for v7 in v1:
if ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[str]
Output Type: Tuple[List]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[str]) -> Tuple[List]:
(v2, v3, v4) = self.format_dataset(v1, self.label_map, self.re_label_map)
(v5, v6) = self.transform(v2)
v7 ... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
Input Types: int, np.ndarray, List[int]
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: int, v2: np.ndarray, v3: List[int]=None) -> float:
if v3:
v2 /= np.sum(v2[v3])
return v2[v1]
``` |
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.get_cell_content(v1)
return len(v2) + self.cell_buffer * 2
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, List[str]
Output Type: Any
Dependencies:
```python
def v0(cls):
for v1 in cls.__dict__:
if callable(getattr(cls, v1)) and v1 not in exclude:
setattr(cls, v1, decorator(getattr(cls, v1)))
return cls
```
Function Name: v... |
Imports:
```python
import os
from pathlib import Path
import typing
```
Type definitions:
Input Types: str
Output Type: Path
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str=None) -> Path:
v2 = self.dir
if v1:
v2 = os.path.join(self.dir, v1)
return Path(v2)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1) -> bool:
if self.is_empty():
return False
v2: bool = False
v3: TreeNode = self.root
while v3 is not None and (not v2):
if v3.ke... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[int], List[int]
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int], v2: List[int]):
v3 = []
v4 = 0
v5 = 0
while v4 < len(v1) and v5 < len(v2):
v6 = v1[v4]
v7 = v2[v5]
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: tuple
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: tuple):
await super().connect(v1)
self.seq_no = 0
``` |
Imports:
```python
import typing
```
Type definitions:
```python
class v0(Common):
v1 = 'solid'
def __init__(self, v2: dict=None, v3: list=None):
(v2, v3) = self._dic_and_children(v2, v3)
self.id = v2.pop('id', self.ids())
self.other = v2
self.export_list = ['id']
self.s... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Tensor
Output Type: Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Tensor) -> Tensor:
print('[DEBUG]')
print(f'type = {type(v1)}')
print(f'fn(x) = {self.fn(v1)}')
return v1
``` |
Imports:
```python
import copy
import tensorflow as tf
import typing
```
Type definitions:
Input Types:
Output Type: 'Sequential'
Dependencies:
Function Name: v0
Function:
```python
def v0(self, **v1) -> 'Sequential':
if not tf.executing_eagerly():
raise RuntimeError('Not executing eagerly - cannot make ... |
Imports:
```python
import warnings
import numpy as np
import typing
```
Type definitions:
Input Types: np.ndarray, np.ndarray, np.ndarray, np.ndarray, int
Output Type: Tuple[np.ndarray]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: np.ndarray, v2: np.ndarray, v3: np.ndarray, v4: np.ndarray, v5: int) ... |
Imports:
```python
from collections import defaultdict
import typing
```
Type definitions:
Input Types: Any
Output Type: Mapping[str, List[str]]
Dependencies:
```python
def v0(v1: str) -> Tuple[str, str]:
(v2, v3) = v1.strip().split('\t')
return (v2, v3)
```
Function Name: v4
Function:
```python
def v4(v5) -> ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> dict:
v2 = {'instrument': v1}
v3 = f'v2/instrument/search'
v4 = self._send_request(endpoint=v3, params=v2, requires_authorization=False)
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
if self is self.STREAMING_ENCODER:
return 'StreamEncoder'
if self is self.MEMORY_ENCODER:
return 'MemoryEncoder'
if self is self.ST... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(cls: 'ModelMeta', self=False):
if self:
yield cls.meta
for v1 in cls.mro()[1:]:
if hasattr(v1, 'meta'):
yield v1.meta
``` |
Imports:
```python
import math
import typing
```
Type definitions:
Input Types: torch.LongTensor
Output Type: torch.FloatTensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.LongTensor) -> torch.FloatTensor:
v1 = self.embeddings(v1) * math.sqrt(self.ndims_embed)
v1 = self.position... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, list, Any, Any, Any, Any
Output Type: list
Dependencies:
```python
def v0(v1, v2: float, v3=1.0, v4=0.0, v5=1.0, v6=0.0) -> float:
v7 = (v2 - v4) / v3
if v7 < -1:
v8 = -0.5
elif v7 > 1:
v8 = 0.5
else:
v8 = ... |
Imports:
```python
import json
import requests
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1, v2) -> dict:
v3 = json.dumps(v2)
v4 = requests.post(v1, json=v3).json()
return v4
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, Any, Any, Any, Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2=None, v3=None, v4=None, v5=TextStyle.NORMAL):
if not v2:
v6 = [v5]
if v3:
v6.append(v3[0])
if v4:
... |
Imports:
```python
from collections import Counter
from heapq import heappush, heapreplace
import typing
```
Type definitions:
Input Types: List[int], int
Output Type: List[int]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[int], v2: int) -> List[int]:
v3 = Counter(v1)
v4 = []
... |
Imports:
```python
import numpy as np
import rasterio
import typing
```
Type definitions:
Input Types: str
Output Type: 'np.typing.NDArray[np.int_]'
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> 'np.typing.NDArray[np.int_]':
with rasterio.open(v1) as v2:
v3: 'np.typing.NDArray[np.... |
Imports:
```python
import random
import typing
```
Type definitions:
Input Types:
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> float:
if self._is_weekend():
v1 = self.mean_arrivals_weekend
else:
v1 = self.mean_arrivals_weekday
v2 = 1 / v1
retu... |
Imports:
```python
import typing
```
Type definitions:
Input Types: k.Kernel, Any, List[gp.GaussianProcess], List[float], kexp.KernelExpansionStrategy, Union[di.AbstractDataInput, List[di.AbstractDataInput]], bool
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: k.Kernel, v2, v3: ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: int
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: int=8) -> str:
v2 = len(self)
v3 = ''
for v4 in range(v2 // v1):
v5 = v4 * v1
v6 = self[v5][0]
v7 = v6 + v1 + -1
v8 ... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
if self._video_writer is not None:
self._video_writer.release()
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
super().initialize_variables()
self.net.initialize_variables()
``` |
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: torch.Tensor
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: torch.Tensor) -> torch.Tensor:
v2 = self.conv1(v1)
v3 = self.tr... |
Imports:
```python
import typing
```
Type definitions:
Input Types: float
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: float):
if not self.rocket:
return
self.rocket.set_wind(self.wind_v)
self.rocket.tick(v1)
self.move_rocket(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: float, float, int, int
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: float, v2: float, v3: int, v4: int):
if v1 * v2 >= 1e-05:
if v1 * v2 >= 0.0001:
v5 = ['MC']
else:
v5 = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[Gio.File], int, str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: List[Gio.File], v2: int, v3: str):
if v2 and (not self.window):
self.do_activate()
for v4 in v1:
v5 = v4.get_path()... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Sequence[str]
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Sequence[str]) -> dict:
v2 = v1[0] == 'AUTO'
(v3, v4, v5) = map(float, v1[1].split(' '))
v6 = float(v1[5])
return dict(auto=v2, fov=v6, yaw... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: str
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> str:
v1 = ''
v2 = []
for v3 in self.parameters:
v2.append(v3.string())
return v1 + 'fn' + '(' + ', '.join(v2) + ') {\n' + self.body.string()... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Optional[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> Optional[str]:
v2 = self.remote_path(v1)
try:
return self.s3.info(v2)
except FileNotFoundError:
print(f'Could not ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list) -> int:
global num_hundreds
v2 = sum(v1)
v3 += v2 // 100
return v2
``` |
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 = {}
for v4 in v2:
v3[v4] = v3.get(v4, 0) + 1
for v4 in v1:
v5 = v3.get(v4, 0)
if v5 == 0:
... |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str):
v2 = self.read_yaml_file(os.path.join(v1, 'dbt_project.yml'))
return v2
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str
Output Type: Tuple[int, str, str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str) -> Tuple[int, str, str]:
(v2, v3, v4, v5) = v1.split('.')
v4 = int(v4)
return (v4, v5, v3)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: List[str]
Output Type: List[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: List[str]=[]) -> List[str]:
global INPUTS
v2 += v1
return v2
``` |
Imports:
```python
import sys
import typing
```
Type definitions:
Input Types:
Output Type: Tuple[str, str, str]
Dependencies:
Function Name: v0
Function:
```python
def v0() -> Tuple[str, str, str]:
if sys.stdout.isatty():
return ('\x1b[31m', '\x1b[32m', '\x1b[m')
return ('', '', '')
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1) -> None:
if self._parent._latest == id(self):
await self._observer.asend(v1)
``` |
Imports:
```python
import os
import typing
```
Type definitions:
Input Types: str, Any
Output Type: Any
Dependencies:
```python
def v0(v1: str):
with os.scandir(v1) as v2:
return any((not _is_lightly_output_dir(f.name) for v3 in v2 if v3.is_dir()))
```
```python
def v4(v5: str, v6: tuple):
with os.scan... |
Imports:
```python
import os
from subprocess import check_output
import typing
```
Type definitions:
Input Types: str, str, str, Any
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: str, v3: str, v4='++') -> None:
v5 = v1.replace('/', '.')
v6 = v2.replace('.py', '')
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: httpx.Request
Output Type: Generator[httpx.Request, httpx.Response, None]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: httpx.Request) -> Generator[httpx.Request, httpx.Response, None]:
if self.__challenge:
v1.heade... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self, *v1) -> None:
if self.verbose:
for v2 in v1:
self.bin_print(v2, end=' ')
print()
``` |
Imports:
```python
import os
import re
import typing
```
Type definitions:
Input Types: str, Dict[str, Any]
Output Type: Dict[str, Any]
Dependencies:
```python
def v0(v1) -> list:
v2 = []
if path_exists(v1):
for v3 in os.listdir(v1):
v4 = os.path.join(v1, v3)
if os.path.isdir(v4... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
```python
class v0(object):
def __init__(self, v1: float, v2: float, v3: float):
"""
Initialise the object and create the circle
"""
self.x0 = float(v1)
self.y0 = float(v2)
self.r = float(v... |
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 = list(str(v1))
v3 = len(v2) - 1
while v3 - 1 >= 0 and v2[v3 - 1] >= v2[v3]:
v3 -= 1
if v3 == 0:
return -1
v... |
Imports:
```python
from collections import Counter
import typing
```
Type definitions:
Input Types: Callable, int
Output Type: Any
Dependencies:
```python
def v0(v1):
v2 = list()
for v3 in range(len(v1[0])):
v4 = Counter([pred[v3] for v5 in v1])
v2.append(v4.most_common(1)[0][0])
return v2
... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: float
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> float:
v1 = 0
for v2 in self.lista_numeros:
v1 *= v2
return v1
``` |
Imports:
```python
import pandas as pd
import requests
import typing
```
Type definitions:
Input Types:
Output Type: pd.DataFrame
Dependencies:
Function Name: v0
Function:
```python
def v0() -> pd.DataFrame:
v1 = 'https://raw.githubusercontent.com/canghailan/Wuhan-2019-nCoV/master/Wuhan-2019-nCoV.json'
v2 = ... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Union[str, int]
Output Type: Optional[str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Union[str, int]) -> Optional[str]:
v2 = ''
v3 = self.vars_for_report_link.get('TORS_REPORT', 'report.html')
v4 = f'{v3}#search... |
Imports:
```python
import typing
```
Type definitions:
```python
v0 = int
```
Input Types: v0, int
Output Type: Any
Dependencies:
Function Name: v1
Function:
```python
def v1(self, v2: v0, v3: int):
v4 = self.get_shared_inputs_group_id(v2)
if v4 is not None:
raise RuntimeError('QP id {} is already in s... |
Imports:
```python
import typing
```
Type definitions:
Input Types: any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: any):
v2 = self.get_session()
if isinstance(v1, list):
[v2.add(update) for v3 in v1]
else:
v2.add(v1)
v2.commit()
v2.close()... |
Imports:
```python
import math
import cmath
import typing
```
Type definitions:
Input Types:
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> None:
v1: Union[float, complex] = self._number_on_screen
self._operation_str_var.set('ln(x)')
if isinstance(v1, complex):
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any, Any
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1, v2) -> dict:
v3 = dict(name=v1, path=v2)
return self.rpc_request(api='debug', endpoint='add_debug_contract', body=v3)
``` |
Imports:
```python
from pathlib import Path
import typing
```
Type definitions:
Input Types: Path
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: Path) -> None:
with open(v1 / '__init__.pyi', 'w') as v2:
v3 = '\n# Create/Clear .pyi file and add boilerplate import content\n... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Generator[Tuple[int, int, int, int], None, None]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Generator[Tuple[int, int, int, int], None, None]:
for (v1, v2, v3, v4) in self.additions:
v5 = self.maps.ge... |
Imports:
```python
import logging
import typing
```
Type definitions:
Input Types: bool, bool
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: bool=False, v2: bool=False) -> None:
if v2:
v3 = logging.DEBUG
elif v1:
v3 = logging.INFO
else:
v3 = loggin... |
Imports:
```python
from copy import deepcopy
import typing
```
Type definitions:
Input Types: dict, Any
Output Type: dict
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: dict, v2) -> dict:
v3 = deepcopy(v1)
v3.pop(v2)
return v3
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: Optional['Dataset']
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Optional['Dataset']=None):
self.grouping.pack_by_group()
v2 = self.grouping.iGroup
v3 = self.grouping.iFirstGroup
v4 = self.grou... |
Imports:
```python
import subprocess
import os
import typing
```
Type definitions:
Input Types: Any, Any, Any, Any
Output Type: Iterator[str]
Dependencies:
```python
def v0(v1, v2):
if v1:
v2 = os.path.join(v1, v2)
return v2
```
```python
def v3(v4) -> str:
return v0(v4, OUTFILE_PATH)
```
Function ... |
Imports:
```python
import torch
from torch import Tensor
from torch.nn.modules.conv import _pair
from torch.nn.modules.module import Module
from torch.nn.modules.conv import Conv2d
import typing
```
Type definitions:
Input Types: Tensor, Optional[Tensor]
Output Type: Tuple[Tensor, Tensor]
Dependencies:
Function Name:... |
Imports:
```python
import pandas as pd
import typing
```
Type definitions:
Input Types: pd.DataFrame
Output Type: None
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: pd.DataFrame) -> None:
if not isinstance(v1, pd.DataFrame):
raise TypeError('Working only with pandas DataFrame')
assert... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, typing.Callable
Output Type: Any
Dependencies:
```python
def v0(v1):
v2 = getattr(method, name, [])
v2.append(v1)
setattr(method, name, v2)
return v1
```
Function Name: v3
Function:
```python
def v3(v4: str, v5: typing.Callable):
... |
Imports:
```python
import logging
import os
import requests
from requests import HTTPError
from requests.adapters import HTTPAdapter
from requests.models import Response
from requests.packages.urllib3.util import Retry
import typing
```
Type definitions:
Input Types: Dict, Any, Any, Any
Output Type: requests.Response
... |
Imports:
```python
import logging
import re
import typing
```
Type definitions:
Input Types: str, Dict
Output Type: int
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: str, v2: Dict) -> int:
v3 = re.match('(\\d*)(\\w+)', v1)
assert v3, 'Cannot match freq regex'
(v4, v5) = v3.groups()
v4... |
Imports:
```python
import typing
```
Type definitions:
Input Types: list
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: list):
self.first_child = 0
if 0 == len(v1):
return
v2 = None
for v3 in v1:
if v2 is not None:
v2.next_sibling = v3... |
Imports:
```python
import typing
```
Type definitions:
Input Types: pd.DataFrame, List[str], List[str], str, bool
Output Type: List[ase.Atoms]
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: pd.DataFrame, v2: List[str]=(), v3: List[str]=(), v4: str='geometry', v5: bool=True) -> List[ase.Atoms]:
v6 ... |
Imports:
```python
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import typing
```
Type definitions:
Input Types: list
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(v1: list) -> list:
v2 = []
v3 = PorterStemmer()
for v4 in v1:
v2.append([v3.st... |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, str
Output Type: str
Dependencies:
```python
def v0(v1: str) -> str:
if v1 == '':
return v1
return f'(_{get_string_without_special_characters(v1)}_)'
```
```python
def v2(v3: str) -> str:
v4 = '#*_></`[]{}\\|'
for v5 in v4... |
Imports:
```python
import sys
import typing
```
Type definitions:
Input Types: str
Output Type: bool
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: str) -> bool:
if self._exclude_folder_names:
for v2 in self._exclude_folder_names:
if v2 in v1.split('\\' if sys.platform ==... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict[str, str]
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> Dict[str, str]:
v1 = {}
v2 = self.compute_tag_counts_per_token()
for v3 in v2:
v4 = ''
v5 = 0
for v6 in v2[v3]:
... |
Imports:
```python
import typing
```
Type definitions:
Input Types: Any
Output Type: Any
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: Any):
if self.verbose:
print(f'{self} sending: {v1}')
return self.pipe.send(v1)
``` |
Imports:
```python
import typing
```
Type definitions:
Input Types: str, bool, Optional[str], List['types.MessageEntity'], bool, bool, int, Any
Output Type: 'Message'
Dependencies:
Function Name: v0
Function:
```python
async def v0(self, v1: str, v2: bool=None, v3: Optional[str]=object, v4: List['types.MessageEntity'... |
Imports:
```python
import numpy as np
import typing
```
Type definitions:
```python
v0 = List[Dict[str, YamlValue]]
```
```python
v1 = Union[YamlValue, List[YamlValue], Dict[str, YamlValue]]
```
Input Types: v1, str
Output Type: Iterable[v0]
Dependencies:
```python
def v2(v3=None, v4=None, v5=None, v6=None) -> np.ndarr... |
Imports:
```python
import typing
```
Type definitions:
Input Types: requests.Response
Output Type: Union[Dict[str, Any], List[Dict[str, Any]]]
Dependencies:
Function Name: v0
Function:
```python
def v0(self, v1: requests.Response) -> Union[Dict[str, Any], List[Dict[str, Any]]]:
if 'latest-darwin-py' in v1.headers... |
Imports:
```python
import socket, sys, json, struct
import typing
```
Type definitions:
Input Types:
Output Type: list
Dependencies:
Function Name: v0
Function:
```python
def v0(self) -> list:
v1 = self._lastRaw_RC
try:
v2 = json.JSONDecoder().decode(self._remoteInput.decode('utf-8'))
v1 = [r... |
Imports:
```python
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union, cast, Sequence
import torch
from torch import Tensor, nn
from torch.distributed.rpc import RRef
import torch.autograd
import torch.cuda
import typing
```
Type definitions:
Input Types: ... |
Imports:
```python
import typing
```
Type definitions:
Input Types:
Output Type: Dict
Dependencies:
```python
def v0(v1: Optional[str]=None) -> Toolkit:
global _default_toolkit, _toolkit_versions
if v1:
if v1 in _toolkit_versions:
v2 = _toolkit_versions[v1]
else:
v3 = g... |
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