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latex.add_package_to_preamble_if_available("tikz")
latex.add_to_mathjax_avoid_list("tikz")
latex.has_file("tikz.sty")
self.ground_set()
self._spring_layout()
enumerate(self.edge_coloring()
len(colors)
len(s)
list(s)
str(current_color)
str(pos[s[0]])
str(pos[s[1]])
str(current_color)
Set(s)
sorted(s, key = lambda x_y: arctan2(x_y[0] - cx, x_y[1] - cy)
str(x)
latex(v)
str(pos[v])
__doc__.format(METHODS_OF_IncidenceStructure=gen_rest_table_index(IncidenceStructure)
TestCompatPY2(object)
test_to_unicode_string(self)
to_unicode('test')
type(res)
test_to_unicode_unicode_encoded(self)
to_unicode('\xc3\xbf')
type(res)
test_to_unicode_unicode_double_decode(self)
decode('utf-8')
decode('utf-8')
to_unicode('\xc3\xbf'.decode('utf-8')
type(res)
test_to_unicode_unicode_string(self)
to_unicode(u'ÿ')
type(res)
test_to_unicode_bytearray(self)
to_unicode(bytearray('\xc3\xbf')
type(res)
test_to_unicode_bytearray_double_decode(self)
bytearray('\xc3\xbf')
decode('utf-8')
decode('utf-8')
to_unicode(bytearray('\xc3\xbf')
decode('utf-8')
type(res)
test_to_unicode_non_string(self)
to_unicode(1)
to_unicode(True)
to_unicode(None)
to_unicode(dict(key='value')
test_get_connection_response(self)
MockConn(object)
getresponse(self, *args, **kwargs)
MockConn()
get_connection_response(mock)
TestCompatPY3(object)
test_to_unicode_string(self)
to_unicode('test')
type(res)
test_to_unicode_unicode_encoded(self)
to_unicode('\xff')
type(res)
test_to_unicode_unicode_string(self)
to_unicode('ÿ')
type(res)
test_to_unicode_bytearray(self)
to_unicode(bytearray('\xff', 'utf-8')
type(res)
test_to_unicode_non_string(self)
to_unicode(1)
to_unicode(True)
to_unicode(None)
to_unicode(dict(key='value')
test_get_connection_response(self)
MockConn(object)
getresponse(self, *args, **kwargs)
MockConn()
get_connection_response(mock)
TestPy2Py3Compat(object)
test_reraise(self)
pytest.raises(Exception)
Exception('Ouch!')
raise (typ, val, tb)
sys.exc_info()
exc_info()
Exception('Obfuscate!')
reraise(typ, val, tb)
Copyright (c)
logging.getLogger(__name__)
split_paths(paths: str)
paths.split(os.pathsep)
paths.split("|")
load_ensemble_for_inference(filenames, task, model_arg_overrides=None)
apply_to_sample(f, sample)
hasattr(sample, '__len__')
len(sample)
_apply(x)
torch.is_tensor(x)
f(x)
isinstance(x, dict)