obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.Ol01 import get_interface_for_device
def cuda_extra_check(Ol01: Any) -> bool:
return Ol01.Worker.get_device_properties().major >= 7
def cpu_extra_check(Ol01: Any) -> bool:
import triton.back... | def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.device_interface import get_interface_for_device
def cuda_extra_check(device_interface: Any) -> bool:
return device_interface.Worker.get_device_properties().major >= 7
def cpu_extra_check(device_int... |
def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.O00011 import get_interface_for_device
def cuda_extra_check(O00011: Any) -> bool:
return O00011.Worker.get_device_properties().major >= 7
def cpu_extra_check(O00011: Any) -> bool:
import tri... | def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.device_interface import get_interface_for_device
def cuda_extra_check(device_interface: Any) -> bool:
return device_interface.Worker.get_device_properties().major >= 7
def cpu_extra_check(device_int... |
def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.device_interface import get_interface_for_device
def cuda_extra_check(device_interface: Any) -> bool:
return device_interface.Worker.get_device_properties().major >= 7
# optimization pending
def... | def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.device_interface import get_interface_for_device
def cuda_extra_check(device_interface: Any) -> bool:
return device_interface.Worker.get_device_properties().major >= 7
def cpu_extra_check(device_int... |
def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.1lOI0O0O11 import get_interface_for_device
def cuda_extra_check(1lOI0O0O11: Any) -> bool:
return 1lOI0O0O11.Worker.get_device_properties().major >= 7
def cpu_extra_check(1lOI0O0O11: Any) -> bool:
... | def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.device_interface import get_interface_for_device
def cuda_extra_check(device_interface: Any) -> bool:
return device_interface.Worker.get_device_properties().major >= 7
def cpu_extra_check(device_int... |
def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.1lIIl11 import get_interface_for_device
def cuda_extra_check(1lIIl11: Any) -> bool:
return 1lIIl11.Worker.get_device_properties().major >= 7
def cpu_extra_check(1lIIl11: Any) -> bool:
import... | def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.device_interface import get_interface_for_device
def cuda_extra_check(device_interface: Any) -> bool:
return device_interface.Worker.get_device_properties().major >= 7
def cpu_extra_check(device_int... |
def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.00OO0lO import get_interface_for_device
def cuda_extra_check(00OO0lO: Any) -> bool:
return 00OO0lO.Worker.get_device_properties().major >= 7
def cpu_extra_check(00OO0lO: Any) -> bool:
import... | def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.device_interface import get_interface_for_device
def cuda_extra_check(device_interface: Any) -> bool:
return device_interface.Worker.get_device_properties().major >= 7
def cpu_extra_check(device_int... |
def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.0010O1OIl import get_interface_for_device
def cuda_extra_check(0010O1OIl: Any) -> bool:
return 0010O1OIl.Worker.get_device_properties().major >= 7
def cpu_extra_check(0010O1OIl: Any) -> bool:
... | def has_triton() -> bool:
if not has_triton_package():
return False
from torch._dynamo.device_interface import get_interface_for_device
def cuda_extra_check(device_interface: Any) -> bool:
return device_interface.Worker.get_device_properties().major >= 7
def cpu_extra_check(device_int... |
def _real_extract(self, IOIO01):
11lO = self._match_id(IOIO01)
I10000 = self._download_webpage(IOIO01, 11lO)
OIl0 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 11lO,
11lO)
l1lO01lO = OIl0.get('publishers')[(1-1)] # always exists
... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, IIIOO):
ll1OOll = self._match_id(IIIOO)
100I1II0O = self._download_webpage(IIIOO, ll1OOll)
OI1O = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + ll1OOll,
ll1OOll)
1lI0O1OlI = OI1O.get('publishers')[0] # always e... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, Ol1O0OOlOI):
O0Olll = self._match_id(Ol1O0OOlOI)
010l = self._download_webpage(Ol1O0OOlOI, O0Olll)
O11l0OIlI1 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + O0Olll,
O0Olll)
IIIOI0O = O11l0OIlI1.get('publishers'... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, lOOI0lO1II):
IlOl = self._match_id(lOOI0lO1II)
1lIIlOO1l0 = self._download_webpage(lOOI0lO1II, IlOl)
l0II1IO0 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + IlOl,
IlOl)
Olll010 = l0II1IO0.get('publishers')[0] ... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 10O0):
0I1O = self._match_id(10O0)
0lOI0l1110 = self._download_webpage(10O0, 0I1O)
I01OOO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 0I1O,
0I1O)
1lOl0 = I01OOO.get('publishers')[0] # always exists
... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[int(0.0)] # alway... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, IIOl1):
10l0I = self._match_id(IIOl1)
I101OOOI = self._download_webpage(IIOl1, 10l0I)
OO0OO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 10l0I,
10l0I)
IlOl = OO0OO.get('publishers')[0] # always exists
... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, l1l111IO):
0I0ll1lI = self._match_id(l1l111IO)
I0IlIlO0l = self._download_webpage(l1l111IO, 0I0ll1lI)
_junk = [x for x in range(0)]
OIO011lO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 0I0ll1lI,
0I0ll1lI)
... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, l1O1I01O):
O101ll0 = self._match_id(l1O1I01O)
O11010I11 = self._download_webpage(l1O1I01O, O101ll0)
011OOOl = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + O101ll0,
O101ll0)
I0lIIll = 011OOOl.get('publishers')[(... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, lI0II):
1IOlI = self._match_id(lI0II)
I001 = self._download_webpage(lI0II, 1IOlI)
I11I1l1 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 1IOlI,
1IOlI)
1l01lIO0 = I11I1l1.get('publishers')[0] # always exists
... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 1lO0):
III11O = self._match_id(1lO0)
1lll0I = self._download_webpage(1lO0, III11O)
0lIO0I101 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + III11O,
III11O)
I0I0l0l = 0lIO0I101.get('publishers')[0] # always exi... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, l1lO):
0I11O = self._match_id(l1lO)
10O011 = self._download_webpage(l1lO, 0I11O)
O0IllI0ll0 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 0I11O,
0I11O)
000l0OO10 = O0IllI0ll0.get('publishers')[(1-1)] # always... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 1lllIOI1O):
IIIl01O1I = self._match_id(1lllIOI1O)
OO10 = self._download_webpage(1lllIOI1O, IIIl01O1I)
O000 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + IIIl01O1I,
IIIl01O1I)
Oll1101O = O000.get('publishers')[... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, l000IIlO):
I1lIIlI = self._match_id(l000IIlO)
IlII = self._download_webpage(l000IIlO, I1lIIlI)
O00l = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + I1lIIlI,
I1lIIlI)
001I1OII = O00l.get('publishers')[0] # alway... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 1I10II):
10Il0I0IO = self._match_id(1I10II)
1lll = self._download_webpage(1I10II, 10Il0I0IO)
l0IlI = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 10Il0I0IO,
10Il0I0IO)
0II0IO = l0IlI.get('publishers')[(9^9)*0] ... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, IIO1l1):
110I10llO = self._match_id(IIO1l1)
IOIll0l1 = self._download_webpage(IIO1l1, 110I10llO)
l0O0I10l0 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 110I10llO,
110I10llO)
OlOOlO1 = l0O0I10l0.get('publisher... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 0I10l1):
Il11011 = self._match_id(0I10l1)
IlIIll = self._download_webpage(0I10l1, Il11011)
01O010I00 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + Il11011,
Il11011)
O1I110Ol = 01O010I00.get('publishers')[0] #... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, lO0II1I1):
llO0Il1l1 = self._match_id(lO0II1I1)
O01I1Ol = self._download_webpage(lO0II1I1, llO0Il1l1)
I0l11 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + llO0Il1l1,
llO0Il1l1)
OOlI1O1OI = I0l11.get('publishers... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, llIIO):
O11OO = self._match_id(llIIO)
1I10lIO = self._download_webpage(llIIO, O11OO)
II10 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + O11OO,
O11OO)
00lO0 = II10.get('publishers')[0] # always exists
... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[(1-1)] # always e... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[int(0.0)] # alway... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 001I1):
O11l1O11 = self._match_id(001I1)
Ol10000O = self._download_webpage(001I1, O11l1O11)
1I10 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + O11l1O11,
O11l1O11)
ll11 = 1I10.get('publishers')[0] # always exi... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, IO1I0lO):
O10Ol = self._match_id(IO1I0lO)
l1OIllI = self._download_webpage(IO1I0lO, O10Ol)
0lOIO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + O10Ol,
O10Ol)
0O1Il0O11 = 0lOIO.get('publishers')[0] # always exi... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, OI11Il):
00OI11 = self._match_id(OI11Il)
I1lOlI0 = self._download_webpage(OI11Il, 00OI11)
IlOI1Ol = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 00OI11,
00OI11)
lIIllllll0 = IlOI1Ol.get('publishers')[(9^9)*0] ... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 10lO):
10O1l10O0 = self._match_id(10lO)
OI0O10l0 = self._download_webpage(10lO, 10O1l10O0)
Ol0IlI0I = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 10O1l10O0,
10O1l10O0)
O1lO = Ol0IlI0I.get('publishers')[int(0.0... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, OIO0O00lI):
00I1l1l1 = self._match_id(OIO0O00lI)
0I1II = self._download_webpage(OIO0O00lI, 00I1l1l1)
IIOl = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 00I1l1l1,
00I1l1l1)
0lO0 = IIOl.get('publishers')[0] # a... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 1IOOlI10l):
0OIO00I1 = self._match_id(1IOOlI10l)
11O1 = self._download_webpage(1IOOlI10l, 0OIO00I1)
10I0100 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 0OIO00I1,
0OIO00I1)
O0OIO1OI1I = 10I0100.get('publisher... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, O1OO0O0OOI):
01l111 = self._match_id(O1OO0O0OOI)
I000l11 = self._download_webpage(O1OO0O0OOI, 01l111)
I000lI = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 01l111,
01l111)
I11O10 = I000lI.get('publishers')[0] ... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 0II0OII1l0):
lOOIO1 = self._match_id(0II0OII1l0)
IIOI01 = self._download_webpage(0II0OII1l0, lOOIO1)
I1IlI11Ol = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + lOOIO1,
lOOIO1)
1lll0 = I1IlI11Ol.get('publishers')[... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, O01lll):
OIII = self._match_id(O01lll)
1II1 = self._download_webpage(O01lll, OIII)
Ol1lO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + OIII,
OIII)
IOI0 = Ol1lO.get('publishers')[0] # always exists
IOO... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, O0l1ll01I1):
10OO = self._match_id(O0l1ll01I1)
I1O11100 = self._download_webpage(O0l1ll01I1, 10OO)
01II0 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 10OO,
10OO)
OllIIOl = 01II0.get('publishers')[(1-1)] # al... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, OOO11):
llOlO = self._match_id(OOO11)
IOOll1O = self._download_webpage(OOO11, llOlO)
1II01I = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + llOlO,
llOlO)
0l0O11 = 1II01I.get('publishers')[0] # always exists
... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 0I0O11):
1O1OI = self._match_id(0I0O11)
0O10IIIllI = self._download_webpage(0I0O11, 1O1OI)
0OlI101 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 1O1OI,
1O1OI)
10l0O00 = 0OlI101.get('publishers')[0] # always e... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, l11O):
11Ill0 = self._match_id(l11O)
1O0lO10 = self._download_webpage(l11O, 11Ill0)
I10I1O1OO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 11Ill0,
11Ill0)
OOI1O = I10I1O1OO.get('publishers')[(9^9)*0] # alway... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, OIOIl):
lI1ll0I1 = self._match_id(OIOIl)
0lI1O0 = self._download_webpage(OIOIl, lI1ll0I1)
IOlO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + lI1ll0I1,
lI1ll0I1)
Ill0lI11 = IOlO.get('publishers')[0] # always e... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 0I1001):
0lll = self._match_id(0I1001)
I0l0IOlOI = self._download_webpage(0I1001, 0lll)
1l0OlO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 0lll,
0lll)
0001OOI = 1l0OlO.get('publishers')[(1-1)] # always exis... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 010Ol01Ol):
00OlI = self._match_id(010Ol01Ol)
l0lIIll0 = self._download_webpage(010Ol01Ol, 00OlI)
l1lIlI1 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 00OlI,
00OlI)
OIIlIlO1Il = l1lIlI1.get('publishers')[(1-1... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, I1IlO01IO):
00lOlI1OO = self._match_id(I1IlO01IO)
1IIII = self._download_webpage(I1IlO01IO, 00lOlI1OO)
11000 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 00lOlI1OO,
00lOlI1OO)
0lllOOOO0 = 11000.get('publisher... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 0IIIl1):
lIlII = self._match_id(0IIIl1)
O1l0 = self._download_webpage(0IIIl1, lIlII)
lIl0llOOl1 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + lIlII,
lIlII)
01IllIO1 = lIl0llOOl1.get('publishers')[int(0.0)] # ... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, ll0l0IOIOl):
OIIO0 = self._match_id(ll0l0IOIOl)
O0O001OO = self._download_webpage(ll0l0IOIOl, OIIO0)
Il00ll = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + OIIO0,
OIIO0)
O01II1l1Il = Il00ll.get('publishers')[0] ... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 1IOl1):
llIl = self._match_id(1IOl1)
11IlO10OO = self._download_webpage(1IOl1, llIl)
1OOllOl11 = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + llIl,
llIl)
IIOIIIOlI = 1OOllOl11.get('publishers')[0] # always exi... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, 0l00O):
00100IlOI = self._match_id(0l00O)
10OIIOI0 = self._download_webpage(0l00O, 00100IlOI)
1100Il1IOO = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 00100IlOI,
00100IlOI)
IIOI1l = 1100Il1IOO.get('publishers'... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, l01l):
111OOlllO = self._match_id(l01l)
ll1IlII1ll = self._download_webpage(l01l, 111OOlllO)
1l00O0ll = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + 111OOlllO,
111OOlllO)
0lI1011I0 = 1l00O0ll.get('publishers')[... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[(1-1)] # always e... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, OI10O1):
IlIlllIlO = self._match_id(OI10O1)
O1IOll001 = self._download_webpage(OI10O1, IlIlllIlO)
0lOO00Ol = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + IlIlllIlO,
IlIlllIlO)
Ol10l = 0lOO00Ol.get('publishers')... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def _real_extract(self, I0O1Ol):
IOOllI0I0I = self._match_id(I0O1Ol)
001l1O1 = self._download_webpage(I0O1Ol, IOOllI0I0I)
01lI = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + IOOllI0I0I,
IOOllI0I0I)
11lO0I01II = 01lI.get('publishers')[... | def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
data = self._download_json(
'https://cfunity.nfhsnetwork.com/v2/game_or_event/' + video_id,
video_id)
publisher = data.get('publishers')[0] # always exist... |
def call(
self,
Ol0O0O: tf.Tensor,
0OlO11lO11: tf.Tensor,
l0001: tf.Tensor,
10IlO: bool,
I0IOl0I1OI: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
Ol0O0O (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
0llOI11O: tf.Tensor,
lllll1: tf.Tensor,
1I111Ol: tf.Tensor,
II1O: bool,
0OO01: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
0llOI11O (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
llll... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
IlI1lOl1: tf.Tensor,
_junk = [x for x in range(0)]
1llO1000OO: tf.Tensor,
01IO: tf.Tensor,
l0OO: bool,
01l1O10: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
IlI1lOl1 (`tf.Tensor`): input to the layer of shape `(batch, ... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
l11lO000O: tf.Tensor,
0O1I0I: tf.Tensor,
1l1l0I: tf.Tensor,
l0OllI1: bool,
0l1Il: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
l11lO000O (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
II0OOl1OO: tf.Tensor,
Ill0: tf.Tensor,
I1lOlI: tf.Tensor,
I110ll: bool,
l11llOl0: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
II0OOl1OO (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
III0Ol1: tf.Tensor,
0011000I: tf.Tensor,
10llOll: tf.Tensor,
IlO0: bool,
IOlI1100: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
III0Ol1 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
0... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
I111: tf.Tensor,
01I0IOI0: tf.Tensor,
l0O1111I0l: tf.Tensor,
OO1I0Ol1: bool,
ll0IOl1ll: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
I111 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
0lOOO: tf.Tensor,
IO1I1OOO1l: tf.Tensor,
1O0I11OO0: tf.Tensor,
lIOll: bool,
l1lO10I0: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
0lOOO (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
OII0O: tf.Tensor,
OllIOl: tf.Tensor,
O0l00I: tf.Tensor,
l1Ol0I: bool,
IlO0lIOO1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
OII0O (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
OllIO... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
ll0100IOI1: tf.Tensor,
O01lOlO0O: tf.Tensor,
IOlOl1: tf.Tensor,
O010OOl1: bool,
O0OII0lO: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
try: pass
except: pass
ll0100IOI1 (`tf.Tensor`): input to the layer of shape `(... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
OIll1110O0: tf.Tensor,
lO11OO: tf.Tensor,
OO11I0ll: tf.Tensor,
OO10l: bool,
l1IllllI: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
OIll1110O0 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
O1I1: tf.Tensor,
1lI1OI0IO: tf.Tensor,
IIIO1OO: tf.Tensor,
IIIOIIO111: bool,
I0lIl1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
O1I1 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
1l... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
O0I1Il: tf.Tensor,
11l01: tf.Tensor,
1O1O0I1lI1: tf.Tensor,
0O1OOI: bool,
0lII1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
O0I1Il (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
11l0... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
lOlO11IO: tf.Tensor,
OlII: tf.Tensor,
lI10010111: tf.Tensor,
O0011: bool,
I1l10: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
lOlO11IO (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
Ol... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
01Il0: tf.Tensor,
IllIll: tf.Tensor,
OOlIO1lO: tf.Tensor,
1llIOl: bool,
OIllll1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
01Il0 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
IllIl... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
II0l11O: tf.Tensor,
O11l01IlI: tf.Tensor,
0I0lOO0O: tf.Tensor,
OI0I0: bool,
1IOlO0: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
II0l11O (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
OI1I10I: tf.Tensor,
IO1I10: tf.Tensor,
l0lO0O: tf.Tensor,
IOl1: bool,
lll10: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
OI1I10I (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
IO1I10 ... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
0O0ll: tf.Tensor,
l0lI0O10I1: tf.Tensor,
Il00lO: tf.Tensor,
10Il1l: bool,
1l111OI1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
0O0ll (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
l0... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
lOll: tf.Tensor,
# optimization pending
0III01lOI: tf.Tensor,
11IOOl0l0: tf.Tensor,
Il1IIII: bool,
l1lI1OI1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
lOll (`tf.Tensor`): input to the layer of shape `(batch, seq_len... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
11IOIO: tf.Tensor,
II11lO10I0: tf.Tensor,
IOO11OO1II: tf.Tensor,
l0O0lI1: bool,
0I00: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
11IOIO (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
00OOl1l0: tf.Tensor,
0IO1IO101O: tf.Tensor,
0l0I: tf.Tensor,
0IIl1I: bool,
O0OI: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
00OOl1l0 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
0I... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
I0l0l: tf.Tensor,
OlII1O00ll: tf.Tensor,
01Ol: tf.Tensor,
1IOIlIOlI: bool,
OOI0OO: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
I0l0l (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
OlI... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
IO1O1lO: tf.Tensor,
Oll0lOII1: tf.Tensor,
1I100111I: tf.Tensor,
llOI0I1: bool,
IOOIIl1I: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
IO1O1lO (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
001l: tf.Tensor,
l1I0O1I: tf.Tensor,
0I11lO1I: tf.Tensor,
OOlI0OlI00: bool,
1lIO1OIlO: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
001l (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
0I0I0I: tf.Tensor,
I1lIO0111: tf.Tensor,
O1O0l1I0I0: tf.Tensor,
1O0O11IIl1: bool,
OI1I: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
0I0I0I (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
0IOlO: tf.Tensor,
O0I0110: tf.Tensor,
110110I0: tf.Tensor,
O0I1I: bool,
0111Il: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
0IOlO (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
O0I011... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
OlO11l0l: tf.Tensor,
I00OO1O: tf.Tensor,
O11O0l0010: tf.Tensor,
OO10001lO: bool,
1O001010OO: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
OlO11l0l (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
lIl1OIOO: tf.Tensor,
I0ll001: tf.Tensor,
try: pass
except: pass
10010: tf.Tensor,
lOlI1: bool,
00l0IIO100: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
lIl1OIOO (`tf.Tensor`): input to the layer of shape `(batch, s... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
lIO0IO0O1: tf.Tensor,
1l11I0O: tf.Tensor,
0lOIllOO10: tf.Tensor,
1llll01: bool,
100I01IIl1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
lIO0IO0O1 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
O0ll1: tf.Tensor,
l0O00ll: tf.Tensor,
l1O1000: tf.Tensor,
l0l0: bool,
IO0l: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
O0ll1 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
l0O00ll (`... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
IOOII0OI: tf.Tensor,
IOlIO0l1IO: tf.Tensor,
1l1O0I: tf.Tensor,
l0ll: bool,
O00O1l: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
IOOII0OI (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
OIlI: tf.Tensor,
1lOl01: tf.Tensor,
O0I00: tf.Tensor,
l0l0OIO0: bool,
l0O1OO01: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
OIlI (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
1lOl01 ... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
IO0lIl1l: tf.Tensor,
10l0IOI0II: tf.Tensor,
11lI1ll: tf.Tensor,
OlIl: bool,
1Ol0: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
IO0lIl1l (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
1... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
if (3*2)==7: return False
self,
IOl0l0O: tf.Tensor,
l1OO: tf.Tensor,
II0O: tf.Tensor,
IOI0I: bool,
lIOI0O1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
IOl0l0O (`tf.Tensor`): input to the layer of shape `(batch, seq_len, em... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
0OIl: tf.Tensor,
10l101O: tf.Tensor,
OlOI011O1O: tf.Tensor,
OOI0: bool,
lI1II: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
0OIl (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
10l101O ... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
OO1II: tf.Tensor,
O1lIlOOIl: tf.Tensor,
Il10O: tf.Tensor,
Olll0Il01: bool,
llOIOl1O: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
OO1II (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
O... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
if (3*2)==7: return False
self,
l10I11I01l: tf.Tensor,
0lI11lI0OO: tf.Tensor,
0Il1: tf.Tensor,
I11l1lOllI: bool,
l0II11l: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
l10I11I01l (`tf.Tensor`): input to the layer of shape `(b... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
Il0Il1: tf.Tensor,
Ol0111Ol1: tf.Tensor,
OOl1llO: tf.Tensor,
Il0lII0OlO: bool,
1OOIll01I: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
Il0Il1 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
O0O00lO10: tf.Tensor,
Il00l1l0: tf.Tensor,
lOI10OI: tf.Tensor,
IOO0OO: bool,
10I1l11: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
O0O00lO10 (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
l10lOI10OI: tf.Tensor,
1Il010: tf.Tensor,
lIII: tf.Tensor,
0llOI0: bool,
O1I0ll0O: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
l10lOI10OI (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
0OI10OOO1O: tf.Tensor,
IIIOOI1IO: tf.Tensor,
OlllOI: tf.Tensor,
l11OOII00: bool,
IIOO0lO1: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
0OI10OOO1O (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
l1l1OIl: tf.Tensor,
1O01O: tf.Tensor,
0II0: tf.Tensor,
lOO0I: bool,
0IlO0: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
l1l1OIl (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
1O01O (`t... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
def call(
self,
IlIOOII: tf.Tensor,
I0OO0l0IIl: tf.Tensor,
Il0IlO000l: tf.Tensor,
0I0I1O0I: bool,
0lIOI: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
IlIOOII (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
... | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
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