code stringlengths 3 6.57k |
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makeindex(Command) |
makeglossary(Command) |
glossary(Command) |
index(Command) |
textContent(self) |
invoke(self, tex) |
Command.invoke(self, tex) |
iter(self.attributes['entry']) |
current.append(tok) |
key.append(current) |
len(sortkey) |
len(key) |
sortkey.append(current) |
sortkey.append(current) |
key.append(current) |
len(sortkey) |
len(key) |
sortkey.append(current) |
current.append(tok) |
current.append(tok) |
key.append(current) |
len(sortkey) |
len(key) |
sortkey.append(current) |
enumerate(sortkey) |
tex.expandTokens(item) |
enumerate(key) |
tex.expandTokens(item) |
macro.append(format.pop(0) |
join(macro) |
format.insert(0, EscapeSequence(macro) |
format.append(EscapeSequence('index-page-number') |
tex.expandTokens(format) |
append(IndexEntry(key, self, sortkey, format, type) |
IndexEntry(object) |
__init__(self, key, node, sortkey=None, format=None, type=0) |
enumerate(sortkey) |
self.sortkey.append(key[i].textContent) |
self.sortkey.append(sk) |
see(self) |
type(self) |
seealso(self) |
type(self) |
normal(self) |
not(self.see) |
not(self.seealso) |
__cmp__(self, other) |
cmp(zip([collator(x) |
isinstance(x, basestring) |
collator(x.textContent) |
zip([collator(x) |
isinstance(x, basestring) |
collator(x.textContent) |
len(self.key) |
len(other.key) |
cmp(len(self.key) |
len(other.key) |
__repr__(self) |
join(['@'.join(self.sortkey) |
join([x.source for x in self.key]) |
join(['@'.join(self.sortkey) |
join([x.source for x in self.key]) |
join([x.source for x in self.format]) |
__str__(self) |
repr(self) |
IndexPageNumber(Command) |
Copyright (c) |
delete_disconnected_nodes(gd) |
gd.items() |
len(gd[k].inputs) |
len(gd[k].outputs) |
len(gd[k].control_inputs) |
len(gd[k].control_outputs) |
empty_nodes.append(k) |
_main() |
np.arange(20, dtype=np.float64) |
np.zeros(len(x) |
np.zeros(len(x) |
range(len(avg) |
np.mean(x[i:i+n]) |
np.std(x[i:i+n]) |
print('AVG') |
print('\n'.join(str(x) |
print('STD:') |
print('\n'.join(str(x) |
np.square(x) |
np.convolve(squares, np.ones(n, dtype=int) |
np.square(avg) |
np.sqrt(var_fast) |
print('STD FAST:') |
print('\n'.join(str(x) |
_main() |
entropy(x) |
torch.exp(x) |
torch.sum(exp_x, dim=1) |
exp(x_i) |
torch.sum(x * exp_x, dim=1) |
exp(x_i) |
torch.log(A) |
DeeBertEncoder(nn.Module) |
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