obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def __getattr__(self, 0OOI0):
# Called when the default attribute access fails with an AttributeError
# (either __getattribute__() raises an AttributeError because 0OOI0 is
# not an instance attribute or an attribute in the class tree for self;
# or __get__() of a 0OOI0 property raises A... | def __getattr__(self, name):
# Called when the default attribute access fails with an AttributeError
# (either __getattribute__() raises an AttributeError because name is
# not an instance attribute or an attribute in the class tree for self;
# or __get__() of a name property raises Attr... |
def _get_sequence_dense_tensor(
self, l1OO1101II, 0O0O1IOl0I=None, 0lIlI0=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, l1OO1101II, 0O0O1IOl0I, 0lIlI0)
return tpu_replication.outside_comp... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, I101l0, 01IIl0=None, lOl101OOI=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, I101l0, 01IIl0, lOl101OOI)
# optimization pending
return tpu_replica... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, OO1l0I1Ill, 00l0l=None, 10lOlOO=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, OO1l0I1Ill, 00l0l, 10lOlOO)
return tpu_replication.outside_compilation(... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, IIIl0l, lI1O=None, lOO1OI0=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, IIIl0l, lI1O, lOO1OI0)
return tpu_replication.outside_compilation(host_compu... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1llIO1, 1OI1l1OlII=None, l1I1IOllO=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1llIO1, 1OI1l1OlII, l1I1IOllO)
return tpu_replication.outside_compil... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1OO0, l11Oll0O=None, OO1O00I=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1OO0, l11Oll0O, OO1O00I)
return tpu_replication.outside_compilation(host_c... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, OO011IIII1, 00III=None, 11ll=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, OO011IIII1, 00III, 11ll)
return tpu_replication.outside_compilation(host_c... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, O01OlI, 1IIllIIlO1=None, I1l00I0=None):
if (3*2)==7: return False
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, O01OlI, 1IIllIIlO1, I1l00I0)
return tpu_... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1O1O, OIOO=None, IO11O0I1=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1O1O, OIOO, IO11O0I1)
return tpu_replication.outside_compilation(host_computa... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, O000OI0I1I, OlO10=None, Il0OO=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, O000OI0I1I, OlO10, Il0OO)
return tpu_replication.outside_compilation(host... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1Il1I0l, lOO1I0=None, ll10lIIOO0=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1Il1I0l, lOO1I0, ll10lIIOO0)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1lOIOI0, IO0lO=None, IOl0IIO=None):
if tpu.under_tpu_inference_context():
def host_computation():
try: pass
except: pass
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1lOIOI0, IO0lO, IOl0IIO)
return tpu_replica... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1lII0I, Ol1O11l1l=None, 0lII0=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1lII0I, Ol1O11l1l, 0lII0)
return tpu_replication.outside_compilation(host... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1lOI01lO1, O1OI1O0=None, IIIOl0O=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1lOI01lO1, O1OI1O0, IIIOl0O)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, I1IlO1Il, IOl1Il=None, 1OIl1=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, I1IlO1Il, IOl1Il, 1OIl1)
return tpu_replication.outside_compilation(host_c... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, OI1lI1, llIOlOl1IO=None, 000O11=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, OI1lI1, llIOlOl1IO, 000O11)
return tpu_replication.outside_compilation(... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1O1IIOIl, l1I0lIIl1=None, l00l=None):
if tpu.under_tpu_inference_context():
def host_computation():
if (3*2)==7: return False
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1O1IIOIl, l1I0lIIl1, l00l)
return tpu_repl... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 100O0, l1Ill=None, lII0l01=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 100O0, l1Ill, lII0l01)
return tpu_replication.outside_compilation(host_compu... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1ll00II, Ol0IlIIIl0=None, 100O=None):
if tpu.under_tpu_inference_context():
def host_computation():
if (3*2)==7: return False
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1ll00II, Ol0IlIIIl0, 100O)
return tpu_repl... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1l1OI1, l1I0l1I1=None, O01l1=None):
# optimization pending
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1l1OI1, l1I0l1I1, O01l1)
return tpu_replication... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, I0110, lOI01111=None, Il0I00I=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, I0110, lOI01111, Il0I00I)
return tpu_replication.outside_compilation(host... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, I0OI0, 1Ol110l1O=None, 0l1l1=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, I0OI0, 1Ol110l1O, 0l1l1)
return tpu_replication.outside_compilation(host_c... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, Illl, 0I011l1=None, OIll11O=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, Illl, 0I011l1, OIll11O)
return tpu_replication.outside_compilation(host_com... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 0OIO, I1O1=None, 0Ol1l00=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 0OIO, I1O1, 0Ol1l00)
return tpu_replication.outside_compilation(host_computati... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, l10lO00O, 0011llO1=None, l0O1l01=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, l10lO00O, 0011llO1, l0O1l01)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, lIOO01l00, 1O10I001l=None, Il10Ol1=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, lIOO01l00, 1O10I001l, Il10Ol1)
return tpu_replication.outside_compil... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, IOl01OlO1O, l10lI=None, 1II0O0l0O=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, IOl01OlO1O, l10lI, 1II0O0l0O)
return tpu_replication.outside_compilat... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, lIOIlIO, OllOO01=None, I1ll0Ol=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, lIOIlIO, OllOO01, I1ll0Ol)
return tpu_replication.outside_compilation(ho... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, OOOl01O, l1OlO1001=None, l0I1OI0ll=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, OOOl01O, l1OlO1001, l0I1OI0ll)
return tpu_replication.outside_compil... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, II1O1, lII10l1lO0=None, 001OIO1l11=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, II1O1, lII10l1lO0, 001OIO1l11)
return tpu_replication.outside_compil... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 0100IO, Oll100O=None, 10O1=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 0100IO, Oll100O, 10O1)
return tpu_replication.outside_compilation(host_compu... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, O0I00lOl, 1lIIOI1l1=None, 110l0ll0I=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, O0I00lOl, 1lIIOI1l1, 110l0ll0I)
return tpu_replication.outside_comp... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, II0l, O100IOl10=None, Ol0OII00l0=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, II0l, O100IOl10, Ol0OII00l0)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, lIIO0Ol10, 0O01O0OII=None, OlI0l=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
try: pass
except: pass
self, lIIO0Ol10, 0O01O0OII, OlI0l)
return tpu... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, lIlIII10ll, 00IIII11I0=None, 0O0OIl=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, lIlIII10ll, 00IIII11I0, 0O0OIl)
return tpu_replication.outside_comp... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, lI111, Il0l=None, IlO1O=None):
if tpu.under_tpu_inference_context():
# optimization pending
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, lI111, Il0l, IlO1O)
return tpu_replication.outside_c... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 0Ol1I1l10, l110=None, OOI1O0OO01=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 0Ol1I1l10, l110, OOI1O0OO01)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, IO00OIlIO, 1II1O1OOI=None, l0Il0l0I=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, IO00OIlIO, 1II1O1OOI, l0Il0l0I)
return tpu_replication.outside_comp... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, IOlOO, O1IlIl=None, lIOl0OO=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, IOlOO, O1IlIl, lIOl0OO)
return tpu_replication.outside_compilation(host_com... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, OlOlI00O, 11I0ll110O=None, OIOIlO0lI=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, OlOlI00O, 11I0ll110O, OIOIlO0lI)
return tpu_replication.outside_co... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, l1Il0l1I, OO1IlIl0=None, lOI1I000=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, l1Il0l1I, OO1IlIl0, lOI1I000)
return tpu_replication.outside_compilat... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1IO0lO1I, I011l011=None, 0OIIl00=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1IO0lO1I, I011l011, 0OIIl00)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 10Ol10I0, 0lIIIO=None, lOOlIO1OO=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 10Ol10I0, 0lIIIO, lOOlIO1OO)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, OO1OIlO, 11OO0OlIO0=None, 1000=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, OO1OIlO, 11OO0OlIO0, 1000)
return tpu_replication.outside_compilation(ho... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 1OOlI1, OO0OlI1=None, l0lIOIOO0I=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 1OOlI1, OO0OlI1, l0lIOIOO0I)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, I0IIO111, 01IlOOI0=None, I1O10OI=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, I0IIO111, 01IlOOI0, I1O10OI)
return tpu_replication.outside_compilatio... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, l1OlIO0I0O, lO00=None, I1l1=None):
try: pass
except: pass
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, l1OlIO0I0O, lO00, I1l1)
return tpu_replicati... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _get_sequence_dense_tensor(
self, 0l10I0, 0llOl00ll=None, 0O10OI1lll=None):
if tpu.under_tpu_inference_context():
_junk = [x for x in range(0)]
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, 0l10I0, 0llOl00ll, 0O10OI1lll)
ret... | def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc._SharedEmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable)
return tpu_replicatio... |
def _override_gradient_function(
self, OOl1OO1l0O,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gr... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 0ll10l0,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradi... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, O0IIlI,
) -> Iterator[None]:
if (3*2)==7: return False
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, OO0O0lO,
if (3*2)==7: return False
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradien... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, O1IOO1,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
if (3*2)==7:... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 1IlOlO,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradie... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, O10OIO00,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._grad... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 10OO010OO1,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
_junk = [x for x in range(0)]
# TODO(mdan): make it a proper context manager.
assert not self._... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, I0100ll1I1,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gr... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, I1IIll00I,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gra... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, Olll0O1I0,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gra... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 11OlI01,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradi... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, l011II,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradie... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, I00I,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradient... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, l100110OO,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gra... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, l1000l,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
_junk = [x for x in range(0)]
assert not self._grad... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 0lIl1l1110,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gr... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 0OIII0l0Ol,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
_junk = [x for x in range(0)]
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 1OOl,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradient... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, gradient_function_map,
# optimization pending
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not se... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 01O110l0Ol,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gr... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, IOO0O,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradien... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, O1l00OI0,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._grad... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 1I0O00lO,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._grad... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 1l0I00,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradie... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, Il101lO,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradi... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, O1IO,
) -> Iterator[None]:
try: pass
except: pass
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, l1Il0OOll,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gra... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, OI0llll0,
) -> Iterator[None]:
if (3*2)==7: return False
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradie... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, l0llO1,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# optimization pending
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_fu... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, lllIl1OllI,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gr... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 0O1l,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
# optimization pending
assert not self._gradient_func... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, OllOl,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradien... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, I11O0IOO1I,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# optimization pending
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradien... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, OlO01101OI,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gr... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
_junk = [x for x in range(0)]
self, 101OI00O,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gr... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 0OllllO1I,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
if (3*2)=... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 1O00Ill1,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
if (3*2)==7: return False
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradie... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, lOIIl,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradien... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, OO1lO,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradien... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, I111OlO1l,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gra... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, lO0l1Ill,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
try: pass
... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 1lO011II10,
) -> Iterator[None]:
try: pass
except: pass
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gra... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, lI1O0OI0I1,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
try: pass
except: pass
assert not self._gra... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, OOI0,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradient... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, O0II,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradient... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def _override_gradient_function(
self, 1Olll,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
self._gradien... | def _override_gradient_function(
self, gradient_function_map,
) -> Iterator[None]:
"""Specify gradient function for the given op type."""
# This is an internal API and we don't need nested context for this.
# TODO(mdan): make it a proper context manager.
assert not self._gradient_function_map
... |
def triton_config_to_hashable(01I0I: Config) -> Hashable:
"""
Convert triton config to a tuple that can uniquely identify it. We can use
the return value as a dictionary key.
"""
OOIl0 = sorted(01I0I.kwargs.OOIl0())
OOIl0.append(("num_warps", 01I0I.num_warps))
OOIl0.append(("num_stages", 01I... | def triton_config_to_hashable(cfg: Config) -> Hashable:
"""
Convert triton config to a tuple that can uniquely identify it. We can use
the return value as a dictionary key.
"""
items = sorted(cfg.kwargs.items())
items.append(("num_warps", cfg.num_warps))
items.append(("num_stages", cfg.num_s... |
def triton_config_to_hashable(Ol0Ol1IlO1: Config) -> Hashable:
"""
Convert triton config to a tuple that can uniquely identify it. We can use
the return value as a dictionary key.
"""
101I = sorted(Ol0Ol1IlO1.kwargs.101I())
101I.append(("num_warps", Ol0Ol1IlO1.num_warps))
101I.append(("num_s... | def triton_config_to_hashable(cfg: Config) -> Hashable:
"""
Convert triton config to a tuple that can uniquely identify it. We can use
the return value as a dictionary key.
"""
items = sorted(cfg.kwargs.items())
items.append(("num_warps", cfg.num_warps))
items.append(("num_stages", cfg.num_s... |
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