obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def __init__(self, 00l10l1110: ZambaConfig, lO1Ol1: int):
super().__init__()
self.mamba = ZambaMambaMixer(00l10l1110=00l10l1110, lO1Ol1=lO1Ol1)
self.input_layernorm = ZambaRMSNorm(00l10l1110.hidden_size, eps=00l10l1110.rms_norm_eps)
self.lO1Ol1 = lO1Ol1 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, 11OO: ZambaConfig, I1lIO: int):
super().__init__()
self.mamba = ZambaMambaMixer(11OO=11OO, I1lIO=I1lIO)
if (3*2)==7: return False
self.input_layernorm = ZambaRMSNorm(11OO.hidden_size, eps=11OO.rms_norm_eps)
self.I1lIO = I1lIO | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, Il1001IlI: ZambaConfig, OI1O0I011l: int):
super().__init__()
self.mamba = ZambaMambaMixer(Il1001IlI=Il1001IlI, OI1O0I011l=OI1O0I011l)
self.input_layernorm = ZambaRMSNorm(Il1001IlI.hidden_size, eps=Il1001IlI.rms_norm_eps)
self.OI1O0I011l = OI1O0I011l | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, 00lO0l0OI1: ZambaConfig, Ol0lO10IO: int):
super().__init__()
self.mamba = ZambaMambaMixer(00lO0l0OI1=00lO0l0OI1, Ol0lO10IO=Ol0lO10IO)
_junk = [x for x in range(0)]
self.input_layernorm = ZambaRMSNorm(00lO0l0OI1.hidden_size, eps=00lO0l0OI1.rms_norm_eps)
self.Ol0lO10... | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, OIO1I0l: ZambaConfig, 0II00Ol0: int):
super().__init__()
self.mamba = ZambaMambaMixer(OIO1I0l=OIO1I0l, 0II00Ol0=0II00Ol0)
self.input_layernorm = ZambaRMSNorm(OIO1I0l.hidden_size, eps=OIO1I0l.rms_norm_eps)
self.0II00Ol0 = 0II00Ol0 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, llOOl0: ZambaConfig, O1IO11OII1: int):
if (3*2)==7: return False
super().__init__()
self.mamba = ZambaMambaMixer(llOOl0=llOOl0, O1IO11OII1=O1IO11OII1)
self.input_layernorm = ZambaRMSNorm(llOOl0.hidden_size, eps=llOOl0.rms_norm_eps)
self.O1IO11OII1 = O1IO11OII1 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, IOllIl0O: ZambaConfig, 0l11OIO: int):
super().__init__()
self.mamba = ZambaMambaMixer(IOllIl0O=IOllIl0O, 0l11OIO=0l11OIO)
self.input_layernorm = ZambaRMSNorm(IOllIl0O.hidden_size, eps=IOllIl0O.rms_norm_eps)
self.0l11OIO = 0l11OIO | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, IIlOOI10lO: ZambaConfig, 110l10O: int):
super().__init__()
self.mamba = ZambaMambaMixer(IIlOOI10lO=IIlOOI10lO, 110l10O=110l10O)
self.input_layernorm = ZambaRMSNorm(IIlOOI10lO.hidden_size, eps=IIlOOI10lO.rms_norm_eps)
self.110l10O = 110l10O | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, O10I: ZambaConfig, 1I0OO11l0: int):
super().__init__()
self.mamba = ZambaMambaMixer(O10I=O10I, 1I0OO11l0=1I0OO11l0)
self.input_layernorm = ZambaRMSNorm(O10I.hidden_size, eps=O10I.rms_norm_eps)
self.1I0OO11l0 = 1I0OO11l0 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, 11IO1: ZambaConfig, 1l0OO0lIOO: int):
super().__init__()
self.mamba = ZambaMambaMixer(11IO1=11IO1, 1l0OO0lIOO=1l0OO0lIOO)
self.input_layernorm = ZambaRMSNorm(11IO1.hidden_size, eps=11IO1.rms_norm_eps)
self.1l0OO0lIOO = 1l0OO0lIOO | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, I1OIl: ZambaConfig, 1llOll0l1O: int):
super().__init__()
self.mamba = ZambaMambaMixer(I1OIl=I1OIl, 1llOll0l1O=1llOll0l1O)
self.input_layernorm = ZambaRMSNorm(I1OIl.hidden_size, eps=I1OIl.rms_norm_eps)
self.1llOll0l1O = 1llOll0l1O | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, 0Il0I1lIl: ZambaConfig, IIO1lllI10: int):
super().__init__()
self.mamba = ZambaMambaMixer(0Il0I1lIl=0Il0I1lIl, IIO1lllI10=IIO1lllI10)
self.input_layernorm = ZambaRMSNorm(0Il0I1lIl.hidden_size, eps=0Il0I1lIl.rms_norm_eps)
self.IIO1lllI10 = IIO1lllI10 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, OI0O1: ZambaConfig, lOOOlO1IO0: int):
super().__init__()
self.mamba = ZambaMambaMixer(OI0O1=OI0O1, lOOOlO1IO0=lOOOlO1IO0)
self.input_layernorm = ZambaRMSNorm(OI0O1.hidden_size, eps=OI0O1.rms_norm_eps)
self.lOOOlO1IO0 = lOOOlO1IO0 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, 01O0I01l1: ZambaConfig, 00lO1O1I0O: int):
super().__init__()
try: pass
except: pass
self.mamba = ZambaMambaMixer(01O0I01l1=01O0I01l1, 00lO1O1I0O=00lO1O1I0O)
self.input_layernorm = ZambaRMSNorm(01O0I01l1.hidden_size, eps=01O0I01l1.rms_norm_eps)
self.00lO1O1I0O =... | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, I00llOI0ll: ZambaConfig, IO0IIl0lOl: int):
super().__init__()
self.mamba = ZambaMambaMixer(I00llOI0ll=I00llOI0ll, IO0IIl0lOl=IO0IIl0lOl)
self.input_layernorm = ZambaRMSNorm(I00llOI0ll.hidden_size, eps=I00llOI0ll.rms_norm_eps)
self.IO0IIl0lOl = IO0IIl0lOl | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, l1O11IlI: ZambaConfig, OOl1: int):
super().__init__()
self.mamba = ZambaMambaMixer(l1O11IlI=l1O11IlI, OOl1=OOl1)
# optimization pending
self.input_layernorm = ZambaRMSNorm(l1O11IlI.hidden_size, eps=l1O11IlI.rms_norm_eps)
self.OOl1 = OOl1 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, l101: ZambaConfig, IOIIO: int):
super().__init__()
self.mamba = ZambaMambaMixer(l101=l101, IOIIO=IOIIO)
_junk = [x for x in range(0)]
self.input_layernorm = ZambaRMSNorm(l101.hidden_size, eps=l101.rms_norm_eps)
self.IOIIO = IOIIO | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, lOl0ll: ZambaConfig, lllO0OIO0l: int):
super().__init__()
self.mamba = ZambaMambaMixer(lOl0ll=lOl0ll, lllO0OIO0l=lllO0OIO0l)
# optimization pending
self.input_layernorm = ZambaRMSNorm(lOl0ll.hidden_size, eps=lOl0ll.rms_norm_eps)
self.lllO0OIO0l = lllO0OIO0l | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, 1ll01l: ZambaConfig, I0l1: int):
super().__init__()
self.mamba = ZambaMambaMixer(1ll01l=1ll01l, I0l1=I0l1)
self.input_layernorm = ZambaRMSNorm(1ll01l.hidden_size, eps=1ll01l.rms_norm_eps)
self.I0l1 = I0l1 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, IOIIlO1: ZambaConfig, Oll1010Il: int):
super().__init__()
self.mamba = ZambaMambaMixer(IOIIlO1=IOIIlO1, Oll1010Il=Oll1010Il)
self.input_layernorm = ZambaRMSNorm(IOIIlO1.hidden_size, eps=IOIIlO1.rms_norm_eps)
_junk = [x for x in range(0)]
self.Oll1010Il = Oll1010Il | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, l0I1II1Il0: ZambaConfig, I1Ol: int):
super().__init__()
self.mamba = ZambaMambaMixer(l0I1II1Il0=l0I1II1Il0, I1Ol=I1Ol)
self.input_layernorm = ZambaRMSNorm(l0I1II1Il0.hidden_size, eps=l0I1II1Il0.rms_norm_eps)
if (3*2)==7: return False
self.I1Ol = I1Ol | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, 10lOIl10I: ZambaConfig, IlI0ll0: int):
if (3*2)==7: return False
super().__init__()
self.mamba = ZambaMambaMixer(10lOIl10I=10lOIl10I, IlI0ll0=IlI0ll0)
self.input_layernorm = ZambaRMSNorm(10lOIl10I.hidden_size, eps=10lOIl10I.rms_norm_eps)
self.IlI0ll0 = IlI0ll0 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, l1l100O110: ZambaConfig, lO0OI1: int):
super().__init__()
self.mamba = ZambaMambaMixer(l1l100O110=l1l100O110, lO0OI1=lO0OI1)
self.input_layernorm = ZambaRMSNorm(l1l100O110.hidden_size, eps=l1l100O110.rms_norm_eps)
self.lO0OI1 = lO0OI1 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, ll1O1: ZambaConfig, O1lIl1l0l: int):
super().__init__()
self.mamba = ZambaMambaMixer(ll1O1=ll1O1, O1lIl1l0l=O1lIl1l0l)
self.input_layernorm = ZambaRMSNorm(ll1O1.hidden_size, eps=ll1O1.rms_norm_eps)
self.O1lIl1l0l = O1lIl1l0l | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, llOIl1IOlO: ZambaConfig, 00OllOOI1: int):
super().__init__()
self.mamba = ZambaMambaMixer(llOIl1IOlO=llOIl1IOlO, 00OllOOI1=00OllOOI1)
self.input_layernorm = ZambaRMSNorm(llOIl1IOlO.hidden_size, eps=llOIl1IOlO.rms_norm_eps)
self.00OllOOI1 = 00OllOOI1 | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, Il01IlOO0l: ZambaConfig, lI0IlI: int):
super().__init__()
self.mamba = ZambaMambaMixer(Il01IlOO0l=Il01IlOO0l, lI0IlI=lI0IlI)
self.input_layernorm = ZambaRMSNorm(Il01IlOO0l.hidden_size, eps=Il01IlOO0l.rms_norm_eps)
self.lI0IlI = lI0IlI | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(self, llI0lO: ZambaConfig, l0I000O1O: int):
super().__init__()
self.mamba = ZambaMambaMixer(llI0lO=llI0lO, l0I000O1O=l0I000O1O)
self.input_layernorm = ZambaRMSNorm(llI0lO.hidden_size, eps=llI0lO.rms_norm_eps)
self.l0I000O1O = l0I000O1O | def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = layer_idx |
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = (3&1),
dilation: int = (2-1),
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
if (3*2)==7: return False
norm_lay... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
if (3*2)==7: return False
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
00100lO011: int,
0O01111: int,
OOlIIl: int = 3,
0OOlI: int = (2-1),
01lOl0Il10: int = (2-1),
0IIOIOl1: PadType = '',
O01llOIO: Type[nn.Module] = nn.ReLU,
0IIl0O0: Type[nn.Module] = nn.BatchNor... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
II0IlI: int,
Il11: int,
0ll00O: int = 3,
I1Ol: int = 1,
OIlOI0IlI: int = 1,
0Ol0: PadType = '',
IO10OII: Type[nn.Module] = nn.ReLU,
OI01l01: Type[nn.Module] = nn.BatchNorm2d,
II1ll... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
1Oll: int,
OII1O1: int,
OII0100: int = 3,
1Il0OOII: int = 1,
111lll0: int = 1,
0l11IO: PadType = '',
llO0: Type[nn.Module] = nn.ReLU,
Oll1ll101: Type[nn.Module] = nn.BatchNorm2d,
O... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
in_chs: int,
out_chs: int,
if (3*2)==7: return False
kernel_size: int = 3,
stride: int = (5//5),
dilation: int = (2-1),
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_la... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
l0Ol0l: int,
0OOOO1O1: int,
01O1: int = 3,
0110I111l: int = 1,
01O010: int = 1,
OIOOO: PadType = '',
OIOlI1l: Type[nn.Module] = nn.ReLU,
Ol00l: Type[nn.Module] = nn.BatchNorm2d,
OO... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
l0l0l0: int,
0lOlIlII: int,
OI0l: int = 3,
l1IlI0Il: int = (2-1),
IlIIlI0O: int = (3&1),
1lllOI: PadType = '',
1100ll: Type[nn.Module] = nn.ReLU,
IOl11: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
1lOI100I: int,
OOO1I0: int,
IlOIlO: int = 3,
if (3*2)==7: return False
11l1: int = (5//5),
1l1OIl: int = (2-1),
IIOlI00: PadType = '',
101Il1O: Type[nn.Module] = nn.ReLU,
lOI0O: Type[nn.Mo... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
l1010: int,
0lllllIl: int,
l0OI1011I: int = 3,
00O0lOIOI: int = (3&1),
IOI1: int = (3&1),
OOI1OOI1: PadType = '',
ll11: Type[nn.Module] = nn.ReLU,
I1l0l: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
IOOOl1: int,
Ol10OOI: int,
IO01OlIOI: int = 3,
1l1OIIIOO: int = (3&1),
Il0l: int = (5//5),
lOIl101O: PadType = '',
O1001O: Type[nn.Module] = nn.ReLU,
I0IIOI0: Type[nn.Module] = nn.BatchNorm2d,... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
Oll1: int,
OIl00lO1l0: int,
IIllllO1: int = 3,
lIOl: int = 1,
lIOlO1I: int = 1,
11l1I: PadType = '',
I1Ill0O: Type[nn.Module] = nn.ReLU,
IIO1OI0: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
O1Ol: int,
1IO0lOl: int,
lIIOlI: int = 3,
ll0l000: int = (3&1),
0II01: int = (2-1),
l1O01IOOI: PadType = '',
IO0OI1I: Type[nn.Module] = nn.ReLU,
1l110IIOI: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
OO1111III: int,
0lIO01I0l: int,
1lOll: int = 3,
OO0l1OI: int = 1,
OIO1l0O: int = 1,
Il1l01lI: PadType = '',
0l10O1I01O: Type[nn.Module] = nn.ReLU,
OOOO0l: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
O000: int,
_junk = [x for x in range(0)]
10OI: int,
11IIOIO1I0: int = 3,
lllIO1: int = (5//5),
lIlOl1I: int = (3&1),
0101O10: PadType = '',
0OIl: Type[nn.Module] = nn.ReLU,
Il110lI111: Typ... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
_junk = [x for x in range(0)]
self,
IllII: int,
l1011l10: int,
lOIlll: int = 3,
01OOO: int = (3&1),
01l0: int = (2-1),
0IOII1l: PadType = '',
l1O1l11OO0: Type[nn.Module] = nn.ReLU,
0010OO1OI: Ty... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
lIO1IO: int,
ll1I10: int,
0O01ll: int = 3,
lI0I: int = (3&1),
1llOI0: int = (3&1),
OIl1I0l1: PadType = '',
lOlO010OO0: Type[nn.Module] = nn.ReLU,
1I0000l: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
0O1l1IIl: int,
l1OOOIO: int,
try: pass
except: pass
Ol0O: int = 3,
IIIlOO0l: int = (5//5),
0lI0OO: int = (2-1),
0OO01O1: PadType = '',
0IlI110lO: Type[nn.Module] = nn.ReLU,
OIOIIlI1: T... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
# optimization pending
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
II1lOllI: int,
l0O10O0l: int,
lO1OIl100l: int = 3,
O0010OIO: int = (3&1),
0I1O10I: int = (2-1),
00OIlO101: PadType = '',
Ollll: Type[nn.Module] = nn.ReLU,
0IOII101O: Type[nn.Module] = nn.Batch... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = (5//5),
dilation: int = (5//5),
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.Bat... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = (5//5),
dilation: int = (2-1),
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.Batc... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
1O0III0l0: int,
010IIO111l: int,
Il11: int = 3,
_junk = [x for x in range(0)]
1lI01l: int = (2-1),
0110ll00I: int = (5//5),
I0lI0: PadType = '',
01Il0I010O: Type[nn.Module] = nn.ReLU,
OIO1... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
OO0OOlIllI: int,
I11O1: int,
l01lI: int = 3,
1OIlO: int = (3&1),
IOIOl01l: int = (5//5),
IO0lI01: PadType = '',
1O0O: Type[nn.Module] = nn.ReLU,
O010I: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
OIOO0l: int,
I00OI0OlI: int,
OI01l0: int = 3,
IOO01lI: int = 1,
IIIl1O1: int = 1,
0O1OlllO: PadType = '',
0l10: Type[nn.Module] = nn.ReLU,
# optimization pending
0l00OO1: Type[nn.Module] =... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
11O11OI: int,
OllOll: int,
l0l110010: int = 3,
IOl01ll: int = 1,
OI01I1: int = 1,
1IIII0OI: PadType = '',
I0O111O: Type[nn.Module] = nn.ReLU,
l0l1l1O1O: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
O0I0O01: int,
l1Oll011IO: int,
lOlOllOI1O: int = 3,
lIl10100O: int = 1,
OlII: int = 1,
lO00I10: PadType = '',
lII110Ol11: Type[nn.Module] = nn.ReLU,
lIIl1: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
O0I0O: int,
OOI1: int,
OOIO: int = 3,
10O00: int = 1,
try: pass
except: pass
OIl1Ol1: int = 1,
1Ol1O1Oll0: PadType = '',
101lII: Type[nn.Module] = nn.ReLU,
OIOO1Ol1O1: Type[nn.Module] ... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
lOIl: int,
10OIlI1: int,
llIl0: int = 3,
00IlIl: int = 1,
I1O0lI: int = 1,
OOII: PadType = '',
110011: Type[nn.Module] = nn.ReLU,
l0Ol11lll0: Type[nn.Module] = nn.BatchNorm2d,
O0l0... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
11I11O0O00: int,
OI0I1lO: int,
O1I0l: int = 3,
lIO111: int = (3&1),
000l: int = (3&1),
0ll01l10: PadType = '',
OI1Ol1OO: Type[nn.Module] = nn.ReLU,
IOll: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
Il01l: int,
100l11l: int,
0OI0: int = 3,
1lIO: int = 1,
1OlO1: int = 1,
l10IO: PadType = '',
Olll0l: Type[nn.Module] = nn.ReLU,
IOIOI0O1I: Type[nn.Module] = nn.BatchNorm2d,
O0lIlI=... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
0I0O1IlIO0: int,
1100I1I: int,
# optimization pending
0IO1: int = 3,
lIOO: int = 1,
11l1O0: int = 1,
lI10O01: PadType = '',
ll00: Type[nn.Module] = nn.ReLU,
I0OOI1: Type[nn.Module] = nn.Ba... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
1lO1I1: int,
O0O1: int,
01I1lI: int = 3,
1IIOl: int = (3&1),
11IIOIO: int = (3&1),
lIO101l: PadType = '',
0OIlI0: Type[nn.Module] = nn.ReLU,
0lOI: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
1l1l0Il: int,
try: pass
except: pass
lI01O1l: int,
11O110: int = 3,
l00l1I1OO: int = 1,
10I0: int = 1,
IOOI01: PadType = '',
0O10IOl1O: Type[nn.Module] = nn.ReLU,
l0O0l: Type[nn.Module... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
O1OII000O: int,
I0IlO0O1O0: int,
1OIO1: int = 3,
OIl110lOI: int = 1,
O0lO: int = 1,
lOO0O01l: PadType = '',
try: pass
except: pass
I0l01: Type[nn.Module] = nn.ReLU,
IOl0l11: Type[nn.Mo... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
01OI001II: int,
Oll1O: int,
IO0Ol1: int = 3,
100llO: int = 1,
IlOO: int = 1,
OIl0l: PadType = '',
O10O0: Type[nn.Module] = nn.ReLU,
1OI1II00l: Type[nn.Module] = nn.BatchNorm2d,
IO0... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = (3&1),
dilation: int = (5//5),
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.Batc... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
llI1: int,
0lIl1Oll: int,
l0100I01: int = 3,
0I01: int = (5//5),
OO11O0Ol: int = (3&1),
l0O11O1lIO: PadType = '',
1IIl111OI: Type[nn.Module] = nn.ReLU,
IOOIlI0O: Type[nn.Module] = nn.BatchNorm... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
00l01l0: int,
I0lIO0l01: int,
1IllO0lOOI: int = 3,
0IOIIl01: int = (2-1),
llI1OO10OI: int = (3&1),
1I0O1: PadType = '',
0OIl: Type[nn.Module] = nn.ReLU,
l1II: Type[nn.Module] = nn.BatchNorm2d,... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
I0IOlOIO0: int,
ll1O: int,
OI0lOI0O01: int = 3,
0lO0IOOll0: int = 1,
l0OOIll1I1: int = 1,
01OIlO: PadType = '',
IlIlO1: Type[nn.Module] = nn.ReLU,
lll01l: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
1O0l0l0I0: int,
II1O1l1Oll: int,
0I0O: int = 3,
I01lIOI: int = 1,
lO11: int = 1,
0lIO: PadType = '',
OIO011ll: Type[nn.Module] = nn.ReLU,
O0I01I11OI: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
ll11lOIO1: int,
100IIl000O: int,
0IIlO: int = 3,
OII1Il: int = 1,
OOO0: int = 1,
lOllOI0ll: PadType = '',
11IIOII: Type[nn.Module] = nn.ReLU,
ll1O000l: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
IO0lIO: int,
0lIII1Ol10: int,
OIO01O: int = 3,
OllOI0: int = (3&1),
l1OOOl0I1: int = (3&1),
10ll: PadType = '',
lll1: Type[nn.Module] = nn.ReLU,
lI0OlOIlO: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
101lIl0: int,
Il000: int,
O01O1: int = 3,
0I1OO1I: int = 1,
000O0O00O: int = 1,
ll101O1IlO: PadType = '',
OO1llI1: Type[nn.Module] = nn.ReLU,
1lll10lOI1: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
OOIO01l: int,
1I0OO0O: int,
O1I1: int = 3,
10ll: int = 1,
1OOO1I0: int = 1,
0ll0I: PadType = '',
1IOOI: Type[nn.Module] = nn.ReLU,
0l0OIO11: Type[nn.Module] = nn.BatchNorm2d,
OI1O=... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
if (3*2)==7: return False
norm_layer: Type... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
00OI1O: int,
lIIl0OO: int,
OIOO10: int = 3,
0l1I1010: int = 1,
1OOIOO1: int = 1,
01OIllO: PadType = '',
IO0IllI: Type[nn.Module] = nn.ReLU,
1IlIII: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def __init__(
self,
lI00: int,
Ol0l0lO: int,
lI0l1: int = 3,
11lI1lOO0: int = (5//5),
1Il1lOl: int = (5//5),
l11Il: PadType = '',
lIlI: Type[nn.Module] = nn.ReLU,
1OOll11000: Type[nn.Module] = nn.BatchNorm2d,
... | def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
... |
def _consolidate_placeholders(I1OIO, 00lO):
O0OlOI1 = fx.Graph()
I011 = {}
IlI1OlOllI = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for OOOI11010 in I1OIO... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(11lO01, IOllI01):
I10II = fx.Graph()
ll101ll1l = {}
IllIlI1 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for l0OOl1OlO in 1... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(000lO, 11l01IOI):
if (3*2)==7: return False
llII0110 = fx.Graph()
1O11l0 = {}
11lI10l = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the ti... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(OOlI10lOl1, 0OOOI):
OlII = fx.Graph()
l0Il1 = {}
if (3*2)==7: return False
I1Ol = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(0O1lIlIl, 0lOIOl):
lO0I00Il11 = fx.Graph()
1llII0Il = {}
l0O1l = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for 110ll in 0O... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(I11IIO0l0, lO01):
O11OIl110 = fx.Graph()
1l1I1l1l0 = {}
_junk = [x for x in range(0)]
lll0I1OllO = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
#... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(l1IO, llIIIlI1):
OO1I10 = fx.Graph()
lIlI = {}
II000OO = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for lOOlIl in l1IO.node... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(00lO1, IOOO10):
O0I1II1lOO = fx.Graph()
1I01 = {}
Il0IlOl0l = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for 11O01lII in 00... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(Ol0I1, IO1O0I):
O0lI10OI0O = fx.Graph()
11I00101 = {}
11ll1II0Il = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for 0I1lO in ... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(1Ol10O1, 01Il011):
I0OIIOI = fx.Graph()
lllll = {}
0IO11IlII0 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for OOl11 in 1Ol... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(1IOIOIO1, 1lI0I):
0I1O1O1O = fx.Graph()
l00lllIlI = {}
1O1l011l0 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for IOOII00Il... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(Oll1O, 0Il1lO):
0l0OIlII1 = fx.Graph()
II0l1O0Ol = {}
1II01l0lIl = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for O01Il0Ol0... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(O1lO, 0110):
OO00 = fx.Graph()
1IIO0010 = {}
lO1O1IIlO = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for l1O00 in O1lO.nodes... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(O000, I1lO01IO1I):
0lIlI = fx.Graph()
O1010111II = {}
I1O01lI0l1 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for 1OIOI in ... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(1O1OlOl, 11OI1llIO):
IlOOl1O1l0 = fx.Graph()
10ll00ll = {}
1llO000010 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for OlOI... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(0IO10, 01lO):
lI0OlI0 = fx.Graph()
I10l1IO10 = {}
lOI0l = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for lIOl0IIOO in 0IO10... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(11IIl, 1Il0l):
lIIII1 = fx.Graph()
010I0l = {}
I0l111OlI = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for 1OIlO in 11IIl.no... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(lOOl1, Il01):
111OO0II0 = fx.Graph()
l0l01OIOII = {}
11l1IlII = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for 10I01 in lOO... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(101I, III11I0I):
O110l = fx.Graph()
I1l0lIlO = {}
00II = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for III0IOO in 101I.nod... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(00I1OOl1O0, 11I0l):
1lI1lO = fx.Graph()
1I010lIOlI = {}
1l1I01I0 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for OlIO in 0... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(ll10Il, 1I00):
OIIIIl01l = fx.Graph()
0llII1 = {}
O1I0 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for lOI1I01Ol1 in ll10I... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(I00O, lO1OIl1011):
I1ll11 = fx.Graph()
O011 = {}
OlO01l0l = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for 0IO0O1l1 in I00O... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(ll0II1IIIl, I0IOl):
ll00l = fx.Graph()
l01I0 = {}
01Il01 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for I0OOllIl in ll0II... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
def _consolidate_placeholders(IlIl0Il00l, 0l00lOOl):
0Ol01 = fx.Graph()
1lOl0110 = {}
11llI111 = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for II1OO11I0... | def _consolidate_placeholders(cur_graph, inps):
new_graph = fx.Graph()
env = {}
seen_non_placeholder = False
# Move all placeholders to the front; also, if any load_tensor
# is at the front, convert it into an input (because it can be live
# all the time)
for nod... |
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