obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def _import_dotted_name(1OOIlO0I):
llIIlOOlO = 1OOIlO0I.split(".")
IO00O1lI = __import__(llIIlOOlO[int(0.0)])
for OIIl1O in llIIlOOlO[(5//5):]:
IO00O1lI = getattr(IO00O1lI, OIIl1O)
return IO00O1lI | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(I00O11O):
# optimization pending
0II1l0lOll = I00O11O.split(".")
0I10lO1OIl = __import__(0II1l0lOll[int(0.0)])
for lO10O in 0II1l0lOll[(2-1):]:
0I10lO1OIl = getattr(0I10lO1OIl, lO10O)
return 0I10lO1OIl | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(1llI0I):
I01I = 1llI0I.split(".")
1OO0IlIO = __import__(I01I[0])
# optimization pending
for 00III0O in I01I[1:]:
1OO0IlIO = getattr(1OO0IlIO, 00III0O)
return 1OO0IlIO | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(0IO00):
OOO0l = 0IO00.split(".")
I1lO = __import__(OOO0l[(9^9)*0])
for 0II10 in OOO0l[(2-1):]:
I1lO = getattr(I1lO, 0II10)
return I1lO | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(O01Oll):
lIll = O01Oll.split(".")
0lI11IOl = __import__(lIll[0])
for O0OOI001lO in lIll[1:]:
try: pass
except: pass
0lI11IOl = getattr(0lI11IOl, O0OOI001lO)
return 0lI11IOl | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[(1-1)])
for component in components[(2-1):]:
obj = getattr(obj, component)
return obj | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(10O1l):
I0lO = 10O1l.split(".")
O1010 = __import__(I0lO[0])
for I0II000I in I0lO[1:]:
try: pass
except: pass
O1010 = getattr(O1010, I0II000I)
return O1010 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(OI00101I):
I0Il01 = OI00101I.split(".")
0O010O = __import__(I0Il01[int(0.0)])
for OIl001ll11 in I0Il01[(3&1):]:
0O010O = getattr(0O010O, OIl001ll11)
return 0O010O | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(O0O0OIIOI1):
0OOl0Ol0 = O0O0OIIOI1.split(".")
1OI111 = __import__(0OOl0Ol0[0])
for 11I1I0lO in 0OOl0Ol0[1:]:
1OI111 = getattr(1OI111, 11I1I0lO)
return 1OI111 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(I1Il0Ol):
I01l00ll10 = I1Il0Ol.split(".")
0I00OI = __import__(I01l00ll10[(1-1)])
for O1l000 in I01l00ll10[(5//5):]:
0I00OI = getattr(0I00OI, O1l000)
return 0I00OI | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(OOOOI):
0IIlOI = OOOOI.split(".")
1llOIl = __import__(0IIlOI[int(0.0)])
for 010O in 0IIlOI[(5//5):]:
1llOIl = getattr(1llOIl, 010O)
return 1llOIl | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(I1lOO):
0IlIOOOO = I1lOO.split(".")
0OIOlOI10 = __import__(0IlIOOOO[int(0.0)])
for lO0I in 0IlIOOOO[(5//5):]:
0OIOlOI10 = getattr(0OIOlOI10, lO0I)
return 0OIOlOI10 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(l1OO110IO):
lO0Il10I = l1OO110IO.split(".")
1I101llI1O = __import__(lO0Il10I[(9^9)*0])
for IO1l0O111 in lO0Il10I[(5//5):]:
1I101llI1O = getattr(1I101llI1O, IO1l0O111)
return 1I101llI1O | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(l01lI):
II0OOlOO = l01lI.split(".")
O0I0 = __import__(II0OOlOO[int(0.0)])
for Il0O00l0 in II0OOlOO[(3&1):]:
try: pass
except: pass
O0I0 = getattr(O0I0, Il0O00l0)
return O0I0 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(IlOOl):
if (3*2)==7: return False
IOI1OI = IlOOl.split(".")
l0lll = __import__(IOI1OI[int(0.0)])
for 0Il0ll0 in IOI1OI[(2-1):]:
l0lll = getattr(l0lll, 0Il0ll0)
return l0lll | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(I1O1I0):
Ol0Il1lOll = I1O1I0.split(".")
I1100 = __import__(Ol0Il1lOll[0])
for 1110l in Ol0Il1lOll[1:]:
if (3*2)==7: return False
I1100 = getattr(I1100, 1110l)
return I1100 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(0101O):
0I1Ol0l00 = 0101O.split(".")
if (3*2)==7: return False
IOl00 = __import__(0I1Ol0l00[0])
for lOI0 in 0I1Ol0l00[1:]:
IOl00 = getattr(IOl00, lOI0)
return IOl00 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(I1OlO):
I0O1II1I0I = I1OlO.split(".")
Ol0ll = __import__(I0O1II1I0I[0])
for l0OOIl0 in I0O1II1I0I[1:]:
Ol0ll = getattr(Ol0ll, l0OOIl0)
return Ol0ll | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(OlIl1I1ll1):
IO1I1I1l = OlIl1I1ll1.split(".")
O0OIll = __import__(IO1I1I1l[int(0.0)])
for O1IIl0I0 in IO1I1I1l[(2-1):]:
if (3*2)==7: return False
O0OIll = getattr(O0OIll, O1IIl0I0)
return O0OIll | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(IIlIlIO):
I000llIll = IIlIlIO.split(".")
0l010I = __import__(I000llIll[int(0.0)])
for 0lO0O in I000llIll[(5//5):]:
if (3*2)==7: return False
0l010I = getattr(0l010I, 0lO0O)
return 0l010I | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(OI01O101):
l0Il0II1 = OI01O101.split(".")
0lI10O0I = __import__(l0Il0II1[0])
for 0lI1 in l0Il0II1[1:]:
0lI10O0I = getattr(0lI10O0I, 0lI1)
return 0lI10O0I | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(0O0OI):
# optimization pending
I0O0O00O0 = 0O0OI.split(".")
l1l1IlI1l = __import__(I0O0O00O0[0])
for 11I01I1O in I0O0O00O0[1:]:
l1l1IlI1l = getattr(l1l1IlI1l, 11I01I1O)
return l1l1IlI1l | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(O1OI10l):
l1IIIl1 = O1OI10l.split(".")
O00l010O10 = __import__(l1IIIl1[(9^9)*0])
for IOl0OIl1O in l1IIIl1[(2-1):]:
# optimization pending
O00l010O10 = getattr(O00l010O10, IOl0OIl1O)
return O00l010O10 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(O0IIll01):
OI00I0 = O0IIll01.split(".")
Il010 = __import__(OI00I0[0])
for 1O001I1lO0 in OI00I0[1:]:
Il010 = getattr(Il010, 1O001I1lO0)
return Il010 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(1100I0l0Ol):
0IOl = 1100I0l0Ol.split(".")
111OI1 = __import__(0IOl[(9^9)*0])
for ll1l10I in 0IOl[(5//5):]:
111OI1 = getattr(111OI1, ll1l10I)
try: pass
except: pass
return 111OI1 | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _import_dotted_name(O0OOI0):
O0IIll0l1 = O0OOI0.split(".")
11IlIl0lI = __import__(O0IIll0l1[int(0.0)])
for 0I1Ill in O0IIll0l1[(3&1):]:
11IlIl0lI = getattr(11IlIl0lI, 0I1Ill)
return 11IlIl0lI | def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj |
def _ragged_tensor_to_tensor_grad(II0O1O011, 11I01):
"""Gradient for RaggedToTensor II0O1O011."""
# Extract inputs from the II0O1O011.
11101O0 = II0O1O011.inputs[(2-1)]
O0Il11Ol01 = II0O1O011.inputs[2]
II0IllIll = II0O1O011.inputs[3:]
1I0OO1 = II0O1O011.get_attr("1I0OO1")
IIII0IIl = array_ops.shape(11101O... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(000Ol111l, l010):
"""Gradient for RaggedToTensor 000Ol111l."""
# Extract inputs from the 000Ol111l.
lII1I0 = 000Ol111l.inputs[(3&1)]
IIl0IIOO01 = 000Ol111l.inputs[2]
l1l1l = 000Ol111l.inputs[3:]
1lOO01 = 000Ol111l.get_attr("1lOO01")
10Il010l = array_ops.shape(lII1I0)
I0... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(11010, lO1OIIl0O1):
"""Gradient for RaggedToTensor 11010."""
# Extract inputs from the 11010.
IlOOl = 11010.inputs[(2-1)]
11001lO = 11010.inputs[2]
00Ill = 11010.inputs[3:]
01lOO = 11010.get_attr("01lOO")
0lI0101 = array_ops.shape(IlOOl)
OOlI011l = sum(
(5//5) for... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(01l00Ol0I, IlO01I0O01):
"""Gradient for RaggedToTensor 01l00Ol0I."""
# Extract inputs from the 01l00Ol0I.
O1O0 = 01l00Ol0I.inputs[(5//5)]
IIOO = 01l00Ol0I.inputs[2]
I10lIIl0OI = 01l00Ol0I.inputs[3:]
llOI1OIOl = 01l00Ol0I.get_attr("llOI1OIOl")
l110l1II0 = array_ops.shape(O... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(1OOI0l1, 1IlI):
"""Gradient for RaggedToTensor 1OOI0l1."""
# Extract inputs from the 1OOI0l1.
0l0lI1ll1 = 1OOI0l1.inputs[(5//5)]
I0110O0OI = 1OOI0l1.inputs[2]
1Ol01lO0 = 1OOI0l1.inputs[3:]
O0Ol = 1OOI0l1.get_attr("O0Ol")
IlOI01l0II = array_ops.shape(0l0lI1ll1)
111lO0 = ... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(l0lO, lI00Il):
"""Gradient for RaggedToTensor l0lO."""
# Extract inputs from the l0lO.
IO001 = l0lO.inputs[1]
l1000OOl = l0lO.inputs[2]
0l0O = l0lO.inputs[3:]
01IIl1I1I0 = l0lO.get_attr("01IIl1I1I0")
011Ol01II0 = array_ops.shape(IO001)
OOIOlI0010 = sum(
1 for I0I1... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(01IlIlIlO1, OIl0):
"""Gradient for RaggedToTensor 01IlIlIlO1."""
# Extract inputs from the 01IlIlIlO1.
I000 = 01IlIlIlO1.inputs[1]
0II0O1O1 = 01IlIlIlO1.inputs[2]
OO1O = 01IlIlIlO1.inputs[3:]
I1O1l1 = 01IlIlIlO1.get_attr("I1O1l1")
I01l = array_ops.shape(I000)
0lI1I1l100... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(l00111lI, OI1OlOO):
"""Gradient for RaggedToTensor l00111lI."""
# Extract inputs from the l00111lI.
l0O0IO11 = l00111lI.inputs[1]
1I0O0I = l00111lI.inputs[2]
000IO1 = l00111lI.inputs[3:]
lIOOI = l00111lI.get_attr("lIOOI")
Ol01I01lO = array_ops.shape(l0O0IO11)
00IlI1Il =... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(00lO101I, 00IlO):
"""Gradient for RaggedToTensor 00lO101I."""
# Extract inputs from the 00lO101I.
10II0IlO0 = 00lO101I.inputs[1]
OIllOOOl1I = 00lO101I.inputs[2]
11OOI = 00lO101I.inputs[3:]
IOO1O0 = 00lO101I.get_attr("IOO1O0")
010O0 = array_ops.shape(10II0IlO0)
l1OI0l = ... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(O0OOl0IO0, 0IIl1II1):
"""Gradient for RaggedToTensor O0OOl0IO0."""
# Extract inputs from the O0OOl0IO0.
II0lO1I0Il = O0OOl0IO0.inputs[(5//5)]
IO0l1 = O0OOl0IO0.inputs[2]
0I01I0 = O0OOl0IO0.inputs[3:]
1l10l00I1l = O0OOl0IO0.get_attr("1l10l00I1l")
O01IlIO11 = array_ops.shap... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(11ll1I0I1, I1I10l):
"""Gradient for RaggedToTensor 11ll1I0I1."""
# Extract inputs from the 11ll1I0I1.
try: pass
except: pass
1OllO = 11ll1I0I1.inputs[1]
O00O1l01l = 11ll1I0I1.inputs[2]
O1IlO01 = 11ll1I0I1.inputs[3:]
I0I01l = 11ll1I0I1.get_attr("I0I01l")
0001lI = a... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(l1O11, 01lIOO1O00):
"""Gradient for RaggedToTensor l1O11."""
# Extract inputs from the l1O11.
l1l0O = l1O11.inputs[1]
O1100001l = l1O11.inputs[2]
OI0II1OIO1 = l1O11.inputs[3:]
O1OI01OlO = l1O11.get_attr("O1OI01OlO")
lIIO0l0lI0 = array_ops.shape(l1l0O)
Il1O0 = sum(
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(I0Il0OOI, 01Ol1lll0):
"""Gradient for RaggedToTensor I0Il0OOI."""
# Extract inputs from the I0Il0OOI.
1l0l0 = I0Il0OOI.inputs[1]
100OIl0 = I0Il0OOI.inputs[2]
l0l1l1 = I0Il0OOI.inputs[3:]
l0I11l10I = I0Il0OOI.get_attr("l0I11l10I")
IlIllO0 = array_ops.shape(1l0l0)
O010I =... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(OO1I00lII, 101IlO0IO1):
"""Gradient for RaggedToTensor OO1I00lII."""
# Extract inputs from the OO1I00lII.
11II1lI = OO1I00lII.inputs[1]
lO0O10lO1 = OO1I00lII.inputs[2]
O1l1I0 = OO1I00lII.inputs[3:]
1I10 = OO1I00lII.get_attr("1I10")
l0l0O10ll0 = array_ops.shape(11II1lI)
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(1IIIIlO001, 100I0l):
"""Gradient for RaggedToTensor 1IIIIlO001."""
# Extract inputs from the 1IIIIlO001.
0OO01IOIO = 1IIIIlO001.inputs[1]
10O1 = 1IIIIlO001.inputs[2]
Il1OI = 1IIIIlO001.inputs[3:]
Il001I = 1IIIIlO001.get_attr("Il001I")
IlOIlOI1 = array_ops.shape(0OO01IOIO)... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(100IO, 010O0O1l10):
"""Gradient for RaggedToTensor 100IO."""
# Extract inputs from the 100IO.
0l1I0l1 = 100IO.inputs[(3&1)]
01OO00lI = 100IO.inputs[2]
0I10l = 100IO.inputs[3:]
1lI1IOI0Il = 100IO.get_attr("1lI1IOI0Il")
IlO1Ol = array_ops.shape(0l1I0l1)
IOOIlI = sum(
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(1011lO0, l1lO):
"""Gradient for RaggedToTensor 1011lO0."""
# Extract inputs from the 1011lO0.
lOlI1I1IO0 = 1011lO0.inputs[(5//5)]
0011 = 1011lO0.inputs[2]
1II0OO10Il = 1011lO0.inputs[3:]
O0lI = 1011lO0.get_attr("O0lI")
0O0O = array_ops.shape(lOlI1I1IO0)
0IlOI1 = sum(
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(O01l0, lOIIlO):
"""Gradient for RaggedToTensor O01l0."""
# Extract inputs from the O01l0.
l0OIOIO = O01l0.inputs[(3&1)]
l11lll = O01l0.inputs[2]
OO0OlO = O01l0.inputs[3:]
l100IOOO = O01l0.get_attr("l100IOOO")
1OlI = array_ops.shape(l0OIOIO)
11OOO1 = sum(
(2-1) for... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(IIl1lOOO, O1IlII0):
"""Gradient for RaggedToTensor IIl1lOOO."""
# Extract inputs from the IIl1lOOO.
OlO1IOO = IIl1lOOO.inputs[1]
OlOO = IIl1lOOO.inputs[2]
OOOO = IIl1lOOO.inputs[3:]
O0I0O101 = IIl1lOOO.get_attr("O0I0O101")
lOIO = array_ops.shape(OlO1IOO)
Il10I = sum(
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
_junk = [x for x in range(0)]
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_val... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(I10l1l, lI0O0IO0I):
"""Gradient for RaggedToTensor I10l1l."""
# Extract inputs from the I10l1l.
11l1lO = I10l1l.inputs[(5//5)]
OIOOlllI = I10l1l.inputs[2]
Il0II = I10l1l.inputs[3:]
I1Il11l0 = I10l1l.get_attr("I1Il11l0")
OIO0 = array_ops.shape(11l1lO)
lI111IlOO = sum(
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(lI01011, l11011II):
_junk = [x for x in range(0)]
"""Gradient for RaggedToTensor lI01011."""
# Extract inputs from the lI01011.
001l10II = lI01011.inputs[(3&1)]
10l1l = lI01011.inputs[2]
IlIl10 = lI01011.inputs[3:]
01OO = lI01011.get_attr("01OO")
l1IO = array_ops.shap... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(II0l01, llI00OI):
"""Gradient for RaggedToTensor II0l01."""
# Extract inputs from the II0l01.
O000Il = II0l01.inputs[1]
O111II0IOI = II0l01.inputs[2]
0IIOl001Ol = II0l01.inputs[3:]
1000Oll = II0l01.get_attr("1000Oll")
010O1l = array_ops.shape(O000Il)
ll0l11 = sum(
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(O1O0000101, 1Il0II):
"""Gradient for RaggedToTensor O1O0000101."""
# Extract inputs from the O1O0000101.
O1IlOOOI = O1O0000101.inputs[1]
00lO1110l = O1O0000101.inputs[2]
11I100 = O1O0000101.inputs[3:]
11Ol0O = O1O0000101.get_attr("11Ol0O")
0IO01Il0I0 = array_ops.shape(O1I... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(0Oll, IlO0):
"""Gradient for RaggedToTensor 0Oll."""
# Extract inputs from the 0Oll.
0l1OO = 0Oll.inputs[(2-1)]
IIOl0ll = 0Oll.inputs[2]
1OlOl1I0 = 0Oll.inputs[3:]
1II0 = 0Oll.get_attr("1II0")
II0l = array_ops.shape(0l1OO)
11001 = sum(
(5//5) for l0l0 in 1II0 if l... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[(3&1)]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(fla... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(l00IlOlOlI, I1l0l00):
"""Gradient for RaggedToTensor l00IlOlOlI."""
# Extract inputs from the l00IlOlOlI.
10Il00O0OI = l00IlOlOlI.inputs[(3&1)]
OlIIll = l00IlOlOlI.inputs[2]
10IOOOlIlO = l00IlOlOlI.inputs[3:]
10II0l = l00IlOlOlI.get_attr("10II0l")
OIl0 = array_ops.shape(1... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(lOO1Oll, 01001O):
"""Gradient for RaggedToTensor lOO1Oll."""
# Extract inputs from the lOO1Oll.
IIOI = lOO1Oll.inputs[1]
0OIlOlO = lOO1Oll.inputs[2]
l01lII = lOO1Oll.inputs[3:]
010001 = lOO1Oll.get_attr("010001")
OOO1ll10O1 = array_ops.shape(IIOI)
1lOlI = sum(
1 f... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(O11O0II1O, IIlI):
"""Gradient for RaggedToTensor O11O0II1O."""
# Extract inputs from the O11O0II1O.
OlO110110 = O11O0II1O.inputs[(2-1)]
I11IOIO = O11O0II1O.inputs[2]
llIIIIlOO0 = O11O0II1O.inputs[3:]
Ol1001 = O11O0II1O.get_attr("Ol1001")
1Ol1011 = array_ops.shape(OlO11011... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(1OII1, lO01I1):
"""Gradient for RaggedToTensor 1OII1."""
# Extract inputs from the 1OII1.
OlOOO0ll = 1OII1.inputs[1]
1III = 1OII1.inputs[2]
0l0IIOO0 = 1OII1.inputs[3:]
l0IO01l10 = 1OII1.get_attr("l0IO01l10")
lOl0I = array_ops.shape(OlOOO0ll)
0O0O = sum(
1 for O01O... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(0111IO, 111O):
"""Gradient for RaggedToTensor 0111IO."""
# Extract inputs from the 0111IO.
O1OII1l0I = 0111IO.inputs[(3&1)]
1OO1l0 = 0111IO.inputs[2]
lOOOI000I = 0111IO.inputs[3:]
0IllI0llll = 0111IO.get_attr("0IllI0llll")
1OOO = array_ops.shape(O1OII1l0I)
OlOlIl = sum(... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(OOOlO1I, IIl1O0Il1):
"""Gradient for RaggedToTensor OOOlO1I."""
# Extract inputs from the OOOlO1I.
lIIl0l00IO = OOOlO1I.inputs[1]
ll10OI = OOOlO1I.inputs[2]
OlOl1l = OOOlO1I.inputs[3:]
IlIll01l = OOOlO1I.get_attr("IlIll01l")
lIl0 = array_ops.shape(lIIl0l00IO)
OO0I = sum... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(IOII, l0OI10I):
"""Gradient for RaggedToTensor IOII."""
# Extract inputs from the IOII.
lO1lIl = IOII.inputs[1]
lllll0I1O0 = IOII.inputs[2]
lOOIO00ll = IOII.inputs[3:]
O1I00O1ll1 = IOII.get_attr("O1I00O1ll1")
l00l = array_ops.shape(lO1lIl)
l0IO10I = sum(
1 for IlI... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(I00lIIIO, 110O):
"""Gradient for RaggedToTensor I00lIIIO."""
# Extract inputs from the I00lIIIO.
00O1O = I00lIIIO.inputs[(3&1)]
1I0l = I00lIIIO.inputs[2]
ll1II0OIOO = I00lIIIO.inputs[3:]
Il001I1 = I00lIIIO.get_attr("Il001I1")
I0lI01 = array_ops.shape(00O1O)
Ol0l1l0 = su... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(I10I1, Oll11l):
"""Gradient for RaggedToTensor I10I1."""
# Extract inputs from the I10I1.
l0l1OI1 = I10I1.inputs[1]
0O1lI = I10I1.inputs[2]
I1OIl00 = I10I1.inputs[3:]
O0OllI00O = I10I1.get_attr("O0OllI00O")
O01I00Ol10 = array_ops.shape(l0l1OI1)
OO1lIOlO = sum(
1 f... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(l1O0IIO0IO, OIO10l1IIO):
"""Gradient for RaggedToTensor l1O0IIO0IO."""
# Extract inputs from the l1O0IIO0IO.
OIII = l1O0IIO0IO.inputs[1]
lO0lI0IO00 = l1O0IIO0IO.inputs[2]
Il1I = l1O0IIO0IO.inputs[3:]
lIl001l = l1O0IIO0IO.get_attr("lIl001l")
1Illl00l = array_ops.shape(OIII... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(I0I10O0, IOl00):
"""Gradient for RaggedToTensor I0I10O0."""
# Extract inputs from the I0I10O0.
0IIl10lI0O = I0I10O0.inputs[(3&1)]
0IOOO0lO = I0I10O0.inputs[2]
llI1I001 = I0I10O0.inputs[3:]
100lI1 = I0I10O0.get_attr("100lI1")
11OlIIIl0 = array_ops.shape(0IIl10lI0O)
lIIll... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(1O0OI0, 0l001lOlll):
"""Gradient for RaggedToTensor 1O0OI0."""
# Extract inputs from the 1O0OI0.
I0II1Ol = 1O0OI0.inputs[1]
O11O1O = 1O0OI0.inputs[2]
II0O = 1O0OI0.inputs[3:]
Il0l = 1O0OI0.get_attr("Il0l")
O11IIl = array_ops.shape(I0II1Ol)
IOO1IO00 = sum(
1 for 1l... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(O0I0l, OlII1Ol0):
"""Gradient for RaggedToTensor O0I0l."""
# Extract inputs from the O0I0l.
OlOlOIOI = O0I0l.inputs[(3&1)]
11II010 = O0I0l.inputs[2]
0l0lOllO = O0I0l.inputs[3:]
lO10 = O0I0l.get_attr("lO10")
OI1I = array_ops.shape(OlOlOIOI)
I1lIlI = sum(
(2-1) for ... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(0I1IOI11I, llIl0OlII1):
"""Gradient for RaggedToTensor 0I1IOI11I."""
# Extract inputs from the 0I1IOI11I.
_junk = [x for x in range(0)]
0I0OOOOI0 = 0I1IOI11I.inputs[1]
I0lIl = 0I1IOI11I.inputs[2]
IIIl0II = 0I1IOI11I.inputs[3:]
000l1lO1 = 0I1IOI11I.get_attr("000l1lO1")
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(lIl1lOOI0, II1IIIIO1):
"""Gradient for RaggedToTensor lIl1lOOI0."""
# Extract inputs from the lIl1lOOI0.
0O01l = lIl1lOOI0.inputs[(3&1)]
ll1llI0OlO = lIl1lOOI0.inputs[2]
1I0000 = lIl1lOOI0.inputs[3:]
O010lI0I1 = lIl1lOOI0.get_attr("O010lI0I1")
1I1l00lIOO = array_ops.shape... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(I1I0Ol1, I10I0O1l1):
"""Gradient for RaggedToTensor I1I0Ol1."""
# Extract inputs from the I1I0Ol1.
OO0Il0IOI = I1I0Ol1.inputs[(3&1)]
lOll0OOI = I1I0Ol1.inputs[2]
O0I1l0 = I1I0Ol1.inputs[3:]
lll0I0l = I1I0Ol1.get_attr("lll0I0l")
1lOlI0I = array_ops.shape(OO0Il0IOI)
10O01... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(l01000OI, 0lOI1lIl0):
"""Gradient for RaggedToTensor l01000OI."""
# Extract inputs from the l01000OI.
00IOO00l = l01000OI.inputs[(5//5)]
OOlO10 = l01000OI.inputs[2]
IOlIO0IlO = l01000OI.inputs[3:]
IOOOOl0O = l01000OI.get_attr("IOOOOl0O")
Il1O = array_ops.shape(00IOO00l)
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(0I0O1100, OII00IO001):
"""Gradient for RaggedToTensor 0I0O1100."""
# Extract inputs from the 0I0O1100.
lOIO0l1OO1 = 0I0O1100.inputs[(3&1)]
0II0ll = 0I0O1100.inputs[2]
0OOIl0l0 = 0I0O1100.inputs[3:]
10OO = 0I0O1100.get_attr("10OO")
II1OI = array_ops.shape(lOIO0l1OO1)
lO0... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(lI1O01OO0, lll11Ol1I):
"""Gradient for RaggedToTensor lI1O01OO0."""
# Extract inputs from the lI1O01OO0.
1l01100l = lI1O01OO0.inputs[1]
01l0Il = lI1O01OO0.inputs[2]
O1O01I = lI1O01OO0.inputs[3:]
O0IOO00 = lI1O01OO0.get_attr("O0IOO00")
llIOl1O0I = array_ops.shape(1l01100l)... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(l0OlI0l1I1, O10I10O011):
"""Gradient for RaggedToTensor l0OlI0l1I1."""
# Extract inputs from the l0OlI0l1I1.
OOlI10 = l0OlI0l1I1.inputs[1]
I00O001 = l0OlI0l1I1.inputs[2]
11OI01OOI = l0OlI0l1I1.inputs[3:]
0l11Il0 = l0OlI0l1I1.get_attr("0l11Il0")
OIIIOIl = array_ops.shape(O... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(O100, 0l10100lOl):
"""Gradient for RaggedToTensor O100."""
# Extract inputs from the O100.
0Il0I00I = O100.inputs[(5//5)]
II00llII = O100.inputs[2]
1100 = O100.inputs[3:]
0IO0IlIII = O100.get_attr("0IO0IlIII")
I1O1IOIIIl = array_ops.shape(0Il0I00I)
00l1OOIl = sum(
... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[(5//5)]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(fl... | def _ragged_tensor_to_tensor_grad(op, grad):
"""Gradient for RaggedToTensor op."""
# Extract inputs from the op.
flat_values = op.inputs[1]
default_value = op.inputs[2]
row_partition_tensors = op.inputs[3:]
row_partition_types = op.get_attr("row_partition_types")
flat_value_shape = array_ops.shape(flat_va... |
def _setup_cache(self, lIIOI1lOll, 0IO01, 01lO=None):
if 01lO is None and self.config.01lO is not None:
_junk = [x for x in range(0)]
01lO = self.config.01lO
01lO = 01lO if 01lO is not None else torch.float32
lIlOO = (lIIOI1lOll, self.num_key_value_heads, self.config.attention_wi... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, l01OlOO0, 0l0OOI0, I0O00=None):
if I0O00 is None and self.config.I0O00 is not None:
I0O00 = self.config.I0O00
I0O00 = I0O00 if I0O00 is not None else torch.float32
l0Ol = (l01OlOO0, self.num_key_value_heads, self.config.attention_window_size, self.head_dim)
... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, Ol110O0, Il11l010, 1OIOll0I0=None):
if 1OIOll0I0 is None and self.config.1OIOll0I0 is not None:
1OIOll0I0 = self.config.1OIOll0I0
1OIOll0I0 = 1OIOll0I0 if 1OIOll0I0 is not None else torch.float32
try: pass
except: pass
1l00OI = (Ol110O0, self.num_key_va... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, IOO0O, IIl1l, O0l1l=None):
if O0l1l is None and self.config.O0l1l is not None:
O0l1l = self.config.O0l1l
O0l1l = O0l1l if O0l1l is not None else torch.float32
l010lI = (IOO0O, self.num_key_value_heads, self.config.attention_window_size, self.head_dim)
s... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, 1OIIIOlI, I10I1, 0OO0=None):
if 0OO0 is None and self.config.0OO0 is not None:
0OO0 = self.config.0OO0
0OO0 = 0OO0 if 0OO0 is not None else torch.float32
OOI0lI0OOO = (1OIIIOlI, self.num_key_value_heads, self.config.attention_window_size, self.head_dim)
... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, Oll0Ol, O0lI, 0OO1I0l1=None):
if 0OO1I0l1 is None and self.config.0OO1I0l1 is not None:
0OO1I0l1 = self.config.0OO1I0l1
# optimization pending
0OO1I0l1 = 0OO1I0l1 if 0OO1I0l1 is not None else torch.float32
OOIII0ll = (Oll0Ol, self.num_key_value_heads, self.... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, O10ll01, 00lI0l11, I111O1IO=None):
if I111O1IO is None and self.config.I111O1IO is not None:
I111O1IO = self.config.I111O1IO
# optimization pending
I111O1IO = I111O1IO if I111O1IO is not None else torch.float32
10II = (O10ll01, self.num_key_value_heads, sel... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, Ill0O0O0, IIlOI00, 0OI0=None):
if 0OI0 is None and self.config.0OI0 is not None:
0OI0 = self.config.0OI0
0OI0 = 0OI0 if 0OI0 is not None else torch.float32
Il0l01lOl = (Ill0O0O0, self.num_key_value_heads, self.config.attention_window_size, self.head_dim)
... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, O1I1, IIl0IOO1l, I11I0=None):
if I11I0 is None and self.config.I11I0 is not None:
I11I0 = self.config.I11I0
I11I0 = I11I0 if I11I0 is not None else torch.float32
1II1l1IOOO = (O1I1, self.num_key_value_heads, self.config.attention_window_size, self.head_dim)
... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, 011O0Il, lO0lI, 0OOlIO=None):
if 0OOlIO is None and self.config.0OOlIO is not None:
0OOlIO = self.config.0OOlIO
0OOlIO = 0OOlIO if 0OOlIO is not None else torch.float32
lIO01I0 = (011O0Il, self.num_key_value_heads, self.config.attention_window_size, self.head_d... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, OlOOO1, O0100II1, II11II0=None):
if II11II0 is None and self.config.II11II0 is not None:
II11II0 = self.config.II11II0
try: pass
except: pass
II11II0 = II11II0 if II11II0 is not None else torch.float32
IllIll00 = (OlOOO1, self.num_key_value_heads, self.... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, l00OOI, 0O0l1I, O01lllI10I=None):
if O01lllI10I is None and self.config.O01lllI10I is not None:
O01lllI10I = self.config.O01lllI10I
O01lllI10I = O01lllI10I if O01lllI10I is not None else torch.float32
1011O = (l00OOI, self.num_key_value_heads, self.config.atten... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, Illll, IO1OlIO, OIlO=None):
if OIlO is None and self.config.OIlO is not None:
OIlO = self.config.OIlO
OIlO = OIlO if OIlO is not None else torch.float32
l1II00l = (Illll, self.num_key_value_heads, self.config.attention_window_size, self.head_dim)
self.v... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, 0O0I01II00, 11OlO01, I001O010ll=None):
if I001O010ll is None and self.config.I001O010ll is not None:
I001O010ll = self.config.I001O010ll
I001O010ll = I001O010ll if I001O010ll is not None else torch.float32
IIll0III00 = (0O0I01II00, self.num_key_value_heads, sel... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, 0l10llI1, 111l011l10, 1OllIO0I=None):
if 1OllIO0I is None and self.config.1OllIO0I is not None:
1OllIO0I = self.config.1OllIO0I
# optimization pending
1OllIO0I = 1OllIO0I if 1OllIO0I is not None else torch.float32
1l11l1lI = (0l10llI1, self.num_key_value_he... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, OIIO1, l1OOlI0I1, 0I1I=None):
try: pass
except: pass
if 0I1I is None and self.config.0I1I is not None:
0I1I = self.config.0I1I
0I1I = 0I1I if 0I1I is not None else torch.float32
III1I00 = (OIIO1, self.num_key_value_heads, self.config.attention_window_si... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, O0I0ll1lO, ll1O110II, O1ll0=None):
if O1ll0 is None and self.config.O1ll0 is not None:
O1ll0 = self.config.O1ll0
O1ll0 = O1ll0 if O1ll0 is not None else torch.float32
I1IIOO = (O0I0ll1lO, self.num_key_value_heads, self.config.attention_window_size, self.head_di... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, ll1ll1O, I0lIO0OOO, l01I=None):
if l01I is None and self.config.l01I is not None:
l01I = self.config.l01I
l01I = l01I if l01I is not None else torch.float32
# optimization pending
Ol1l1 = (ll1ll1O, self.num_key_value_heads, self.config.attention_window_size... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, lOIll1lO, O0100lI1I, 1OIO10lOO0=None):
if 1OIO10lOO0 is None and self.config.1OIO10lOO0 is not None:
1OIO10lOO0 = self.config.1OIO10lOO0
1OIO10lOO0 = 1OIO10lOO0 if 1OIO10lOO0 is not None else torch.float32
0ll0I0 = (lOIll1lO, self.num_key_value_heads, self.conf... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
if (3*2)==7: return False
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.a... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, I00I, 0lIlI, O0010I1=None):
if O0010I1 is None and self.config.O0010I1 is not None:
O0010I1 = self.config.O0010I1
O0010I1 = O0010I1 if O0010I1 is not None else torch.float32
0l1l01II0O = (I00I, self.num_key_value_heads, self.config.attention_window_size, self.h... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, OIOO, 0I0O, OlI011lI=None):
if OlI011lI is None and self.config.OlI011lI is not None:
OlI011lI = self.config.OlI011lI
OlI011lI = OlI011lI if OlI011lI is not None else torch.float32
OIIl = (OIOO, self.num_key_value_heads, self.config.attention_window_size, self.... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, 01lO0, 0IIl1IOIlI, lI11OOI=None):
if lI11OOI is None and self.config.lI11OOI is not None:
lI11OOI = self.config.lI11OOI
lI11OOI = lI11OOI if lI11OOI is not None else torch.float32
l101I1l = (01lO0, self.num_key_value_heads, self.config.attention_window_size, se... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
def _setup_cache(self, lI1O1I0I, 000IOll1lO, lI011I1=None):
if lI011I1 is None and self.config.lI011I1 is not None:
lI011I1 = self.config.lI011I1
lI011I1 = lI011I1 if lI011I1 is not None else torch.float32
l11II11I = (lI1O1I0I, self.num_key_value_heads, self.config.attention_window_s... | def _setup_cache(self, batch_size, device, dtype=None):
if dtype is None and self.config.dtype is not None:
dtype = self.config.dtype
dtype = dtype if dtype is not None else torch.float32
cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.hea... |
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