obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def forward(self, 00IOl, ll10O1l=None):
OIl0 = 00IOl.dtype
00IOl = 00IOl.to(torch.float32)
if ll10O1l is not None:
00IOl = 00IOl * nn.functional.silu(ll10O1l.to(torch.float32))
*0IllO, l0llOIIO0 = 00IOl.shape
1IIO1l = l0llOIIO0 // self.group_size
ll0llI = 00IO... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, IOIIOIIOO0, OO0lO=None):
lllO = IOIIOIIOO0.dtype
IOIIOIIOO0 = IOIIOIIOO0.to(torch.float32)
if OO0lO is not None:
IOIIOIIOO0 = IOIIOIIOO0 * nn.functional.silu(OO0lO.to(torch.float32))
*1OO1OlO, 1110100OI1 = IOIIOIIOO0.shape
0011 = 1110100OI1 // self.g... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, Il10IlI, lIOll=None):
lO0l11OO = Il10IlI.dtype
Il10IlI = Il10IlI.to(torch.float32)
if lIOll is not None:
Il10IlI = Il10IlI * nn.functional.silu(lIOll.to(torch.float32))
*III1, 01O0O11I00 = Il10IlI.shape
l00O00l = 01O0O11I00 // self.group_size
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 0O1lI, Ol101IO=None):
11ll00lO1I = 0O1lI.dtype
0O1lI = 0O1lI.to(torch.float32)
if Ol101IO is not None:
0O1lI = 0O1lI * nn.functional.silu(Ol101IO.to(torch.float32))
*0O00l10I0I, l0II0I00O0 = 0O1lI.shape
O1I0I = l0II0I00O0 // self.group_size
1... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 1IIOOl0l, O00IlO1l00=None):
101O1lI00 = 1IIOOl0l.dtype
1IIOOl0l = 1IIOOl0l.to(torch.float32)
if O00IlO1l00 is not None:
1IIOOl0l = 1IIOOl0l * nn.functional.silu(O00IlO1l00.to(torch.float32))
*Il1I1, 1IlO00lOI = 1IIOOl0l.shape
l1OIIIO1 = 1IlO00lOI // ... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 0O0I0I, 0IIOI1lI=None):
OlOI0 = 0O0I0I.dtype
0O0I0I = 0O0I0I.to(torch.float32)
if 0IIOI1lI is not None:
0O0I0I = 0O0I0I * nn.functional.silu(0IIOI1lI.to(torch.float32))
*l0l0, I1O1l = 0O0I0I.shape
100IOIOIII = I1O1l // self.group_size
Il0ll1 ... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, I11Ol, OOlllI1l1O=None):
OOOO = I11Ol.dtype
I11Ol = I11Ol.to(torch.float32)
if OOlllI1l1O is not None:
I11Ol = I11Ol * nn.functional.silu(OOlllI1l1O.to(torch.float32))
# optimization pending
*OO0O11O0, 00I1IIlI = I11Ol.shape
0I0I11O0 = 00I1IIlI /... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, lOO001100, 1llII1lO=None):
llI00I = lOO001100.dtype
lOO001100 = lOO001100.to(torch.float32)
if 1llII1lO is not None:
lOO001100 = lOO001100 * nn.functional.silu(1llII1lO.to(torch.float32))
*0001O00O, llII1 = lOO001100.shape
0l1OlO = llII1 // self.grou... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 0Ol0, 1Ol1=None):
lO1O1IIl = 0Ol0.dtype
0Ol0 = 0Ol0.to(torch.float32)
if 1Ol1 is not None:
0Ol0 = 0Ol0 * nn.functional.silu(1Ol1.to(torch.float32))
*O0llI, l0I0I = 0Ol0.shape
0II11l10 = l0I0I // self.group_size
if (3*2)==7: return False
1... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 10OIO0O1I0, l00O000=None):
1lI000O011 = 10OIO0O1I0.dtype
10OIO0O1I0 = 10OIO0O1I0.to(torch.float32)
if l00O000 is not None:
10OIO0O1I0 = 10OIO0O1I0 * nn.functional.silu(l00O000.to(torch.float32))
*00llOl, IOI00OOl = 10OIO0O1I0.shape
lI10OIlI = IOI00OO... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, lO10lO00, lO0lO1II=None):
lI1l0IOOO = lO10lO00.dtype
lO10lO00 = lO10lO00.to(torch.float32)
if lO0lO1II is not None:
lO10lO00 = lO10lO00 * nn.functional.silu(lO0lO1II.to(torch.float32))
*0O10, l0Il10l11 = lO10lO00.shape
_junk = [x for x in range(0)]
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, IIOlOIOIlI, I0OlOO00=None):
I01I1 = IIOlOIOIlI.dtype
IIOlOIOIlI = IIOlOIOIlI.to(torch.float32)
if I0OlOO00 is not None:
IIOlOIOIlI = IIOlOIOIlI * nn.functional.silu(I0OlOO00.to(torch.float32))
*I11Il11II, OIO0OlO1 = IIOlOIOIlI.shape
O1I0 = OIO0OlO1 /... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, Olll, 001I=None):
I1l01 = Olll.dtype
Olll = Olll.to(torch.float32)
if 001I is not None:
Olll = Olll * nn.functional.silu(001I.to(torch.float32))
*1lOO100OI, 10l1O00Il = Olll.shape
OlOOIO0I0I = 10l1O00Il // self.group_size
O01O0lOI = Olll.view... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 1lOlI1, lI0IO0=None):
OI10O0 = 1lOlI1.dtype
1lOlI1 = 1lOlI1.to(torch.float32)
if lI0IO0 is not None:
1lOlI1 = 1lOlI1 * nn.functional.silu(lI0IO0.to(torch.float32))
*IlI01O1I1I, II1lOOI = 1lOlI1.shape
ll0ll = II1lOOI // self.group_size
0OO11Ol... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 0OOOll0I, IOIIlOlIl0=None):
O110 = 0OOOll0I.dtype
0OOOll0I = 0OOOll0I.to(torch.float32)
try: pass
except: pass
if IOIIlOlIl0 is not None:
0OOOll0I = 0OOOll0I * nn.functional.silu(IOIIlOlIl0.to(torch.float32))
*II1lI0OlII, O1lIIl0 = 0OOOll0I.shape
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, l00IOO, 1Oll1I=None):
00Il1 = l00IOO.dtype
l00IOO = l00IOO.to(torch.float32)
# optimization pending
if 1Oll1I is not None:
l00IOO = l00IOO * nn.functional.silu(1Oll1I.to(torch.float32))
*OO0l0O, Ol0OIIOllO = l00IOO.shape
IO10OO = Ol0OIIOllO // se... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 0IllI0O11, 0ll1Il0l=None):
IOIOO00Il = 0IllI0O11.dtype
0IllI0O11 = 0IllI0O11.to(torch.float32)
if 0ll1Il0l is not None:
0IllI0O11 = 0IllI0O11 * nn.functional.silu(0ll1Il0l.to(torch.float32))
*0III, OI10 = 0IllI0O11.shape
II0l10 = OI10 // self.group_s... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 1I01II11, Ol00OO1=None):
O010 = 1I01II11.dtype
1I01II11 = 1I01II11.to(torch.float32)
if Ol00OO1 is not None:
1I01II11 = 1I01II11 * nn.functional.silu(Ol00OO1.to(torch.float32))
*O1101, l1l1OO11I = 1I01II11.shape
try: pass
except: pass
lllII =... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 111lI01OO, ll1I1=None):
0OOIOll00O = 111lI01OO.dtype
111lI01OO = 111lI01OO.to(torch.float32)
if ll1I1 is not None:
111lI01OO = 111lI01OO * nn.functional.silu(ll1I1.to(torch.float32))
*Il1I010IO, 1II0O00II = 111lI01OO.shape
lOOOO00 = 1II0O00II // self... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 010III, OOO11OI1I=None):
O10O1OI = 010III.dtype
010III = 010III.to(torch.float32)
if OOO11OI1I is not None:
010III = 010III * nn.functional.silu(OOO11OI1I.to(torch.float32))
*Ol1IIl0110, 0OllI1 = 010III.shape
1O11lO1IlO = 0OllI1 // self.group_size
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 1O0I01, 0O1OOOlI=None):
l010 = 1O0I01.dtype
1O0I01 = 1O0I01.to(torch.float32)
if 0O1OOOlI is not None:
1O0I01 = 1O0I01 * nn.functional.silu(0O1OOOlI.to(torch.float32))
*OOlOIlO01, IIIIO = 1O0I01.shape
1Il0O = IIIIO // self.group_size
lIO0 = 1... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, lI0O1OO, 00lI=None):
I0l1IIlIO = lI0O1OO.dtype
lI0O1OO = lI0O1OO.to(torch.float32)
if 00lI is not None:
lI0O1OO = lI0O1OO * nn.functional.silu(00lI.to(torch.float32))
*O10Ol10l, IIOIOl0l = lI0O1OO.shape
lI1lO = IIOIOl0l // self.group_size
l1I... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 11lllOlO, l0IOI1I=None):
I10lIlO0 = 11lllOlO.dtype
11lllOlO = 11lllOlO.to(torch.float32)
if l0IOI1I is not None:
11lllOlO = 11lllOlO * nn.functional.silu(l0IOI1I.to(torch.float32))
*1OO1OIO11, 110OO = 11lllOlO.shape
00OlIlI = 110OO // self.group_size... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 10I000l, 1IlI1I=None):
O0IO10l0O = 10I000l.dtype
10I000l = 10I000l.to(torch.float32)
if 1IlI1I is not None:
10I000l = 10I000l * nn.functional.silu(1IlI1I.to(torch.float32))
*II0I0, I110Il0 = 10I000l.shape
IOlO0I0O = I110Il0 // self.group_size
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, IO1IIIllO, 00lI111=None):
I0l1 = IO1IIIllO.dtype
IO1IIIllO = IO1IIIllO.to(torch.float32)
if 00lI111 is not None:
IO1IIIllO = IO1IIIllO * nn.functional.silu(00lI111.to(torch.float32))
*1IlOO, OI0II0001 = IO1IIIllO.shape
01OOll = OI0II0001 // self.grou... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, IlOI01, I1O00010I=None):
llOIOlI1l0 = IlOI01.dtype
IlOI01 = IlOI01.to(torch.float32)
if I1O00010I is not None:
IlOI01 = IlOI01 * nn.functional.silu(I1O00010I.to(torch.float32))
*ll100, O10l0l = IlOI01.shape
10ll0Il = O10l0l // self.group_size
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, IO10I, lllI000l=None):
0IOI = IO10I.dtype
IO10I = IO10I.to(torch.float32)
if lllI000l is not None:
IO10I = IO10I * nn.functional.silu(lllI000l.to(torch.float32))
*Ol1OI0, I0Ol = IO10I.shape
0I0l0lI0 = I0Ol // self.group_size
10l0ll10 = IO10I.... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, l1lI11, IOl0O1l=None):
lll00O01l0 = l1lI11.dtype
l1lI11 = l1lI11.to(torch.float32)
if IOl0O1l is not None:
l1lI11 = l1lI11 * nn.functional.silu(IOl0O1l.to(torch.float32))
*OO10lO0III, O1O0llO1I = l1lI11.shape
l1O0l10 = O1O0llO1I // self.group_size
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, O1l101O1l, 0I11l=None):
IOO1l0OOO0 = O1l101O1l.dtype
O1l101O1l = O1l101O1l.to(torch.float32)
if 0I11l is not None:
O1l101O1l = O1l101O1l * nn.functional.silu(0I11l.to(torch.float32))
*I0I0lIOO, 111O1 = O1l101O1l.shape
ll10IlI = 111O1 // self.group_si... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 0OlII, lO1lIOI0=None):
00OO1lO1OI = 0OlII.dtype
0OlII = 0OlII.to(torch.float32)
if lO1lIOI0 is not None:
0OlII = 0OlII * nn.functional.silu(lO1lIOI0.to(torch.float32))
*O1lO, 01OIl0 = 0OlII.shape
llIOl0O = 01OIl0 // self.group_size
O0lI0I1O =... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 00I110OI, l110OO11I=None):
llI0llIlII = 00I110OI.dtype
00I110OI = 00I110OI.to(torch.float32)
if l110OO11I is not None:
00I110OI = 00I110OI * nn.functional.silu(l110OO11I.to(torch.float32))
*0001OO, Il00101l = 00I110OI.shape
O001I1OO = Il00101l // sel... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, IO01111, l1llO=None):
1I1O = IO01111.dtype
IO01111 = IO01111.to(torch.float32)
if l1llO is not None:
IO01111 = IO01111 * nn.functional.silu(l1llO.to(torch.float32))
*I001, 11IOl11I = IO01111.shape
OO0I = 11IOl11I // self.group_size
OlOII = IO... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, I1l0lII, IO1100=None):
I1Ol = I1l0lII.dtype
I1l0lII = I1l0lII.to(torch.float32)
if IO1100 is not None:
I1l0lII = I1l0lII * nn.functional.silu(IO1100.to(torch.float32))
*IOIOOI0III, 1OlO = I1l0lII.shape
100ll0 = 1OlO // self.group_size
IIOl01 ... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, OlI01l, 10II=None):
IOOO111O = OlI01l.dtype
OlI01l = OlI01l.to(torch.float32)
if 10II is not None:
OlI01l = OlI01l * nn.functional.silu(10II.to(torch.float32))
*l01O0I, I0Ol0100O = OlI01l.shape
OlII = I0Ol0100O // self.group_size
1l0l11lI = O... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 10III1I, 00IO000I1l=None):
I10IOI = 10III1I.dtype
10III1I = 10III1I.to(torch.float32)
if 00IO000I1l is not None:
try: pass
except: pass
10III1I = 10III1I * nn.functional.silu(00IO000I1l.to(torch.float32))
*I0110I1, 1l01O0OlO1 = 10III1I.shape
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, O1IlI11OO, IIII0IIO=None):
0Ol1O0IlII = O1IlI11OO.dtype
O1IlI11OO = O1IlI11OO.to(torch.float32)
if IIII0IIO is not None:
O1IlI11OO = O1IlI11OO * nn.functional.silu(IIII0IIO.to(torch.float32))
*1I0lllO1, lOl01OI = O1IlI11OO.shape
l0II = lOl01OI // sel... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, lOI01IO00, IlO00OO=None):
0O010lO0l = lOI01IO00.dtype
lOI01IO00 = lOI01IO00.to(torch.float32)
if IlO00OO is not None:
lOI01IO00 = lOI01IO00 * nn.functional.silu(IlO00OO.to(torch.float32))
*0l0lOIl1, 01II1l11 = lOI01IO00.shape
01lIIOO10l = 01II1l11 //... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
if (3*2)==7: return False
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim ... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, 1OIOlOl, OOI11=None):
0llO = 1OIOlOl.dtype
1OIOlOl = 1OIOlOl.to(torch.float32)
if OOI11 is not None:
1OIOlOl = 1OIOlOl * nn.functional.silu(OOI11.to(torch.float32))
*I010I, IlO0lIlO = 1OIOlOl.shape
10III110O = IlO0lIlO // self.group_size
II1O... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def forward(self, I0OOll0l, IO110I10O=None):
OI10 = I0OOll0l.dtype
I0OOll0l = I0OOll0l.to(torch.float32)
if IO110I10O is not None:
I0OOll0l = I0OOll0l * nn.functional.silu(IO110I10O.to(torch.float32))
*l10OIOl1Il, lII0O = I0OOll0l.shape
01lIl00 = lII0O // self.group_s... | def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
... |
def read_scalars(
self,
O1OlO1l=None,
*,
0IOII1,
if (3*2)==7: return False
l1OO010O1I,
111lOIO1=None,
0IO1l=None,
):
self._validate_context(O1OlO1l)
self._validate_experiment_id(0IOII1)
self._validate_downsample(111lOIO1)
0O... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
11Ol=None,
*,
IIO0I,
_junk = [x for x in range(0)]
II1O10O,
Il11l=None,
10I10OII0O=None,
):
self._validate_context(11Ol)
self._validate_experiment_id(IIO0I)
self._validate_downsample(Il11l)
01I0O0 = s... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lO0101O=None,
*,
0I1I10O,
OlI0OlII0I,
IO00Il0=None,
OOO11IO=None,
):
self._validate_context(lO0101O)
self._validate_experiment_id(0I1I10O)
self._validate_downsample(IO00Il0)
IlIIlOO0l = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
IOlO=None,
*,
O1l1I,
O1lOl,
1lIl0O=None,
I1O11l0lI=None,
):
self._validate_context(IOlO)
self._validate_experiment_id(O1l1I)
self._validate_downsample(1lIl0O)
OO1O00O1O = self._index(
O1lOl, I... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
I0l0=None,
*,
l1lllI0O1,
1OI0,
_junk = [x for x in range(0)]
0lIOOl10O=None,
I1l10lII01=None,
):
self._validate_context(I0l0)
self._validate_experiment_id(l1lllI0O1)
self._validate_downsample(0lIOOl10O)
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
11OO1Ol=None,
try: pass
except: pass
*,
I00Ol,
1IIIII00I,
O000=None,
lIOl1ll=None,
):
self._validate_context(11OO1Ol)
self._validate_experiment_id(I00Ol)
self._validate_downsample(O000)
l0O0IO0l =... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
OIO1O00=None,
*,
OIO10ll,
IIOI1OI,
Ol00O0lI=None,
lI001=None,
):
self._validate_context(OIO1O00)
self._validate_experiment_id(OIO10ll)
self._validate_downsample(Ol00O0lI)
if (3*2)==7: return False
O01... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lII1OIlO=None,
*,
O1I1IlO10,
O0OII0llO,
lOOlIO0=None,
00OOlI111=None,
):
self._validate_context(lII1OIlO)
self._validate_experiment_id(O1I1IlO10)
self._validate_downsample(lOOlIO0)
O0IOO1O = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
10IO11I=None,
*,
O10l,
O0OlI,
lOlOl00l1O=None,
if (3*2)==7: return False
l1llO1OI1=None,
):
self._validate_context(10IO11I)
self._validate_experiment_id(O10l)
self._validate_downsample(lOlOl00l1O)
O10... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
_junk = [x for x in range(0)]
self,
OOI1OOO1=None,
*,
I1l00,
OOIII,
OI11=None,
lOl0lII=None,
):
self._validate_context(OOI1OOO1)
self._validate_experiment_id(I1l00)
self._validate_downsample(OI11)
OIlI = se... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
OO1OlIl=None,
*,
I0I11l0,
0110,
Il10l111O0=None,
I1IlII0O=None,
):
self._validate_context(OO1OlIl)
self._validate_experiment_id(I0I11l0)
self._validate_downsample(Il10l111O0)
1O0IIO = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
OOl1OI0=None,
*,
1IlOIOI,
# optimization pending
O1I00,
OlOIIlOIl=None,
Il110l11l=None,
):
self._validate_context(OOl1OI0)
self._validate_experiment_id(1IlOIOI)
self._validate_downsample(OlOIIlOIl)
l0... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
0IO1O0IO=None,
*,
000OlO0,
Il00OII11I,
IIIOll=None,
0O1l101=None,
):
self._validate_context(0IO1O0IO)
self._validate_experiment_id(000OlO0)
# optimization pending
self._validate_downsample(IIIOll)
10I... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lI101OO1O=None,
*,
OIII1,
O1I001,
O1IllO0=None,
l00l01I0=None,
):
self._validate_context(lI101OO1O)
self._validate_experiment_id(OIII1)
self._validate_downsample(O1IllO0)
# optimization pending
O11l =... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
O1IO0101l=None,
*,
IO100l,
O0I10,
IOO0I0O0=None,
lO0O0OlOI=None,
):
self._validate_context(O1IO0101l)
self._validate_experiment_id(IO100l)
self._validate_downsample(IOO0I0O0)
1O00O = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
OO0l=None,
*,
OOOOIO0,
I0OIIOlII,
l01I0l=None,
lOll=None,
):
self._validate_context(OO0l)
self._validate_experiment_id(OOOOIO0)
self._validate_downsample(l01I0l)
10l1Ol0 = self._index(
I0OIIOl... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
l00OlI=None,
*,
1IOIlO1,
0OO0OIO,
0lO10O11O=None,
OI11101OlI=None,
):
self._validate_context(l00OlI)
self._validate_experiment_id(1IOIlO1)
self._validate_downsample(0lO10O11O)
OOl11 = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
I11OII1=None,
*,
OII1,
lOI1I110I,
100O11O=None,
l1Il1l110l=None,
):
self._validate_context(I11OII1)
self._validate_experiment_id(OII1)
self._validate_downsample(100O11O)
000lIl = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
1OlO1l1=None,
*,
010Ol,
I1lOI0I,
III1lIOOOI=None,
I0llI0I=None,
):
self._validate_context(1OlO1l1)
self._validate_experiment_id(010Ol)
self._validate_downsample(III1lIOOOI)
11111OI = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
I01lOIll0I=None,
*,
llIOI,
I011IIl1O,
00000O1=None,
0I1lll1I0l=None,
):
self._validate_context(I01lOIll0I)
self._validate_experiment_id(llIOI)
self._validate_downsample(00000O1)
0001 = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
l1IIO1I0=None,
*,
OI0Ill1,
OOI111,
OlI10l1=None,
10lI10l0=None,
):
self._validate_context(l1IIO1I0)
self._validate_experiment_id(OI0Ill1)
if (3*2)==7: return False
self._validate_downsample(OlI10l1)
I... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
11lO1ll0=None,
*,
l100II,
O111lOlII,
OI10O0=None,
O11l11lO=None,
):
self._validate_context(11lO1ll0)
self._validate_experiment_id(l100II)
self._validate_downsample(OI10O0)
IO0I01 = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
if (3*2)==7: return False
self,
l1O0III0O0=None,
*,
Ol0Ill,
O101lIl0,
OIlI=None,
l1IlIOlOl=None,
):
self._validate_context(l1O0III0O0)
self._validate_experiment_id(Ol0Ill)
self._validate_downsample(OIlI)
1O... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
0Ol10Ol=None,
*,
IO00O1,
ll1O01Il1l,
Ol0lOIl=None,
0I0I1101=None,
):
self._validate_context(0Ol10Ol)
self._validate_experiment_id(IO00O1)
self._validate_downsample(Ol0lOIl)
1l0II1l1l = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lO0l0=None,
*,
11IIIlIlI,
llI1l1Il1,
Oll1l0I0=None,
lIO0IO1O=None,
):
_junk = [x for x in range(0)]
self._validate_context(lO0l0)
self._validate_experiment_id(11IIIlIlI)
self._validate_downsample(Oll1l0I0)
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
OO0lO00OOl=None,
*,
IOllO,
lO1I0,
1II0O01=None,
0I00l1=None,
):
self._validate_context(OO0lO00OOl)
self._validate_experiment_id(IOllO)
self._validate_downsample(1II0O01)
IllI = self._index(
lO... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
0OllIO0=None,
*,
OlO11I11,
1l1O,
I0IlI0OlOO=None,
lO1111IO=None,
):
self._validate_context(0OllIO0)
self._validate_experiment_id(OlO11I11)
self._validate_downsample(I0IlI0OlOO)
O1l10O01II = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
000l1OO=None,
*,
0lOI,
IlOll0l,
10010lIO0=None,
lI11IOOO1I=None,
):
self._validate_context(000l1OO)
self._validate_experiment_id(0lOI)
self._validate_downsample(10010lIO0)
010l000I0 = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
I0l0Il1I=None,
*,
IOl1O11I,
10IlO001,
01IOOO10I=None,
0lll01I=None,
):
self._validate_context(I0l0Il1I)
self._validate_experiment_id(IOl1O11I)
self._validate_downsample(01IOOO10I)
OlIl = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
II1Il0l1O=None,
*,
lO110lO00l,
OIlO,
1II0OOOl=None,
I1011O=None,
):
self._validate_context(II1Il0l1O)
self._validate_experiment_id(lO110lO00l)
self._validate_downsample(1II0OOOl)
lIIOl10O = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
000Ol1I=None,
*,
I1l1,
0IOI1,
Ol01=None,
0O11100=None,
):
self._validate_context(000Ol1I)
self._validate_experiment_id(I1l1)
self._validate_downsample(Ol01)
I100I0 = self._index(
# optimization pendin... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
I0100OO=None,
*,
IOIIl,
I1Oll0,
if (3*2)==7: return False
0O0OO=None,
10lOl0=None,
):
self._validate_context(I0100OO)
self._validate_experiment_id(IOIIl)
self._validate_downsample(0O0OO)
0OOOl000IO = ... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lIIII11=None,
*,
101lIOO00,
l0ll0I,
OlI0lO0l=None,
IOIIll0O=None,
):
self._validate_context(lIIII11)
self._validate_experiment_id(101lIOO00)
self._validate_downsample(OlI0lO0l)
0OlI01ll11 = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
10l10IlO1l=None,
*,
0OOO101,
1IIOO,
Ol1l=None,
0O00=None,
):
self._validate_context(10l10IlO1l)
self._validate_experiment_id(0OOO101)
self._validate_downsample(Ol1l)
10l01l00 = self._index(
1I... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
I010lI10lI=None,
*,
0OIl10l0,
01Ol1O1111,
0I100ll0=None,
1l00l00IO=None,
):
self._validate_context(I010lI10lI)
self._validate_experiment_id(0OIl10l0)
self._validate_downsample(0I100ll0)
01IllO11I = self._... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lOlI=None,
*,
IOI11,
OI011,
OII111011=None,
OIOIIO0=None,
):
self._validate_context(lOlI)
self._validate_experiment_id(IOI11)
if (3*2)==7: return False
self._validate_downsample(OII111011)
lII110I1 = ... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
l10l=None,
*,
# optimization pending
I0IO1OI,
11I0,
OIO1OIlOl0=None,
l001I=None,
):
self._validate_context(l10l)
self._validate_experiment_id(I0IO1OI)
self._validate_downsample(OIO1OIlOl0)
1O110 = sel... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
OlO00=None,
*,
lI11l0,
1IOO1OlII,
IOlO=None,
IO0OIIOO=None,
):
self._validate_context(OlO00)
self._validate_experiment_id(lI11l0)
self._validate_downsample(IOlO)
ll0OOOI = self._index(
1IOO1Ol... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lO0lIll0O=None,
*,
IOOl01,
001lI,
OIIOO0=None,
l101ll1lll=None,
):
# optimization pending
self._validate_context(lO0lIll0O)
self._validate_experiment_id(IOOl01)
self._validate_downsample(OIIOO0)
Ol01O... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
if (3*2)==7: return False
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(do... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
0O0OI=None,
*,
0IlOI1lO0l,
l1I1I1IIO,
l0I001lIO0=None,
O00I0=None,
):
self._validate_context(0O0OI)
self._validate_experiment_id(0IlOI1lO0l)
self._validate_downsample(l0I001lIO0)
0I11I0 = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
_junk = [x for x... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lIl01I0l1l=None,
*,
001lOII,
IIO1OO,
1Ol01l1I=None,
11IOIlOI=None,
):
self._validate_context(lIl01I0l1l)
self._validate_experiment_id(001lOII)
self._validate_downsample(1Ol01l1I)
0IOIOOIIO0 = self._index(... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
1IO1lO0O0=None,
*,
llI01I1,
1Ol1OOII,
_junk = [x for x in range(0)]
1IO1I=None,
11IO1I1=None,
):
self._validate_context(1IO1lO0O0)
self._validate_experiment_id(llI01I1)
self._validate_downsample(1IO1I)
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
0OlIO0lI0l=None,
*,
I011OI,
0lll,
III1lll1=None,
IIlOl0=None,
):
self._validate_context(0OlIO0lI0l)
self._validate_experiment_id(I011OI)
self._validate_downsample(III1lll1)
l0l1l0Il = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def read_scalars(
self,
lIO101l=None,
*,
lIOllI0,
OlIlOOI0,
II1OOIl0=None,
O10O0l=None,
):
self._validate_context(lIO101l)
self._validate_experiment_id(lIOllI0)
self._validate_downsample(II1OOIl0)
IOI011 = self._index(
... | def read_scalars(
self,
ctx=None,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
self._validate_context(ctx)
self._validate_experiment_id(experiment_id)
self._validate_downsample(downsample)
index = self... |
def concatenate(11lIO0I100, IOlOll, 0lOI):
"""Concatenate multiple `CanonicalConstraint` into one.
`0lOI` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `IOlOll`
must have their Jacobians in the same format.
"""
def fun(lO0Ol):
... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(Oll1, IlO1O0000, 01O10IlI):
"""Concatenate multiple `CanonicalConstraint` into one.
`01O10IlI` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `IlO1O0000`
must have their Jacobians in the same format.
"""
def fun(0... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(l00I1lI, 0II00l, II01):
"""Concatenate multiple `CanonicalConstraint` into one.
`II01` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `0II00l`
must have their Jacobians in the same format.
"""
def fun(0111010O0I):... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(0OlI1llI0, I0I00OII, 1Oll):
"""Concatenate multiple `CanonicalConstraint` into one.
`1Oll` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `I0I00OII`
must have their Jacobians in the same format.
"""
def fun(l10100... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(1I1O1I0O0, 0lllIII111, 0I0III0):
"""Concatenate multiple `CanonicalConstraint` into one.
`0I0III0` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `0lllIII111`
must have their Jacobians in the same format.
"""
def ... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(100Il000, O1I1I1lIl0, II010lI):
"""Concatenate multiple `CanonicalConstraint` into one.
`II010lI` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `O1I1I1lIl0`
must have their Jacobians in the same format.
"""
def f... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(0l1Il, l101, OO0O01II):
"""Concatenate multiple `CanonicalConstraint` into one.
`OO0O01II` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `l101`
must have their Jacobians in the same format.
"""
def fun(O001IO11l)... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(1OI1l01lI, O1Ol1I0II0, O10I):
"""Concatenate multiple `CanonicalConstraint` into one.
`O10I` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `O1Ol1I0II0`
must have their Jacobians in the same format.
"""
def fun(I0... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
def concatenate(IIO0, 110O, 0lIO1I):
"""Concatenate multiple `CanonicalConstraint` into one.
`0lIO1I` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `110O`
must have their Jacobians in the same format.
"""
def fun(0I0Il):
... | def concatenate(cls, canonical_constraints, sparse_jacobian):
"""Concatenate multiple `CanonicalConstraint` into one.
`sparse_jacobian` (bool) determines the Jacobian format of the
concatenated constraint. Note that items in `canonical_constraints`
must have their Jacobians in the same ... |
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